Community Detection in a Large Real-World Social Network

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Outlier Detection A Survey

Outlier Detection:A SurveyVARUN CHANDOLAUniversity of MinnesotaARINDAM BANERJEEUniversity of MinnesotaandVIPIN KUMARUniversity of MinnesotaOutlier detection has been a very important concept in the realm of data analysis.Recently,several application domains have realized the direct mapping between outliers in data and real world anomalies,that are of great interest to an analyst.Outlier detection has been researched within various application domains and knowledge disciplines.This survey provides a comprehensive overview of existing outlier detection techniques by classifying them along diﬀerent dimensions. Categories and Subject Descriptors:H.2.8[Database Management]:Database Applications—Data MiningGeneral Terms:AlgorithmsAdditional Key Words and Phrases:Outlier Detection,Anomaly Detection1.INTRODUCTIONOutlier detection refers to the problem ofﬁnding patterns in data that do not conform to expected normal behavior.These anomalous patterns are often re-ferred to as outliers,anomalies,discordant observations,exceptions,faults,defects, aberrations,noise,errors,damage,surprise,novelty,peculiarities or contaminants in diﬀerent application domains.Outlier detection has been a widely researched problem andﬁnds immense use in a wide variety of application domains such as credit card,insurance,tax fraud detection,intrusion detection for cyber security, fault detection in safety critical systems,military surveillance for enemy activities and many other areas.The importance of outlier detection is due to the fact that outliers in data trans-late to signiﬁcant(and often critical)information in a wide variety of application domains.For example,an anomalous traﬃc pattern in a computer network could mean that a hacked computer is sending out sensitive data to an unauthorized destination.In public health data,outlier detection techniques are widely used to detect anomalous patterns in patient medical records which could be symptoms of a new disease.Similarly,outliers in credit card transaction data could indicate credit card theft or misuse.Outliers can also translate to critical entities such as in military surveillance,where the presence of an unusual region in a satellite image of enemy area could indicate enemy troop movement.Or anomalous readings from a space craft would signify a fault in some component of the craft.Outlier detection has been found to be directly applicable in a large number of domains.This has resulted in a huge and highly diverse literature of outlier detec-2·Chandola,Banerjee and Kumartion techniques.A lot of these techniques have been developed to solve focussed problems pertaining to a particular application domain,while others have been de-veloped in a more generic fashion.This survey aims at providing a structured and comprehensive overview of the research done in theﬁeld of outlier detection.We have identiﬁed the key characteristics of any outlier detection technique,and used these as dimensions to classify existing techniques into diﬀerent categories.This survey aims at providing a better understanding of the diﬀerent directions in which research has been done and also helps in identifying the potential areas for future research.1.1What are outliers?Outliers,as deﬁned earlier,are patterns in data that do not conform to a well deﬁned notion of normal behavior,or conform to a well deﬁned notion of outlying behavior,though it is typically easier to deﬁne the normal behavior.This survey discusses techniques whichﬁnd such outliers in data.Fig.1.A simple example of outliers in a2-dimensional data setFigure1illustrates outliers in a simple2-dimensional data set.The data has two normal regions,N1and N2.O1and O2are two outlying instances while O3is an outlying region.As mentioned earlier,the outlier instances are the ones that do not lie within the normal regions.Outliers exist in almost every real data set.Some of the prominent causes for outliers are listed below•Malicious activity–such as insurance or credit card or telecom fraud,a cyber intrusion,a terrorist activity•Instrumentation error–such as defects in components of machines or wear and tear•Change in the environment–such as a climate change,a new buying pattern among consumers,mutation in genes•Human error–such as an automobile accident or a data reporting errorOutlier Detection:A Survey·3 Outliers might be induced in the data for a variety of reasons,as discussed above, but all of the reasons have a common characteristic that they are interesting to the analyst.The“interestingness”or real life relevance of outliers is a key feature of outlier detection and distinguishes it from noise removal[Teng et al.1990]or noise accommodation[Rousseeuw and Leroy1987],which deal with unwanted noise in the data.Noise in data does not have a real signiﬁcance by itself,but acts as a hindrance to data analysis.Noise removal is driven by the need to remove the unwanted objects before any data analysis is performed on the data.Noise accommodation refers to immunizing a statistical model estimation against outlying observations. Another related topic to outlier detection is novelty detection[Markou and Singh 2003a;2003b;Saunders and Gero2000]which aims at detecting unseen(emergent, novel)patterns in the data.The distinction between novel patterns and outliers is that the novel patterns are typically incorporated with the normal model after getting detected.It should be noted that the solutions for these related problems are often used for outlier detection and vice-versa,and hence are discussed in this review as well.1.2ChallengesA key challenge in outlier detection is that it involves exploring the unseen space. As mentioned earlier,at an abstract level,an outlier can be deﬁned as a pattern that does not conform to expected normal behavior.A straightforward approach will be to deﬁne a region representing normal behavior and declare any observation in the data which does not belong to this normal region as an outlier.But several factors make this apparently simple approach very challenging.•Deﬁning a normal region which encompasses every possible normal behavior is very diﬃcult.•Often times normal behavior keeps evolving and an existing notion of normal behavior might not be suﬃciently representative in the future.•The boundary between normal and outlying behavior is often fuzzy.Thus an outlying observation which lies close to the boundary can be actually normal and vice versa.•The exact notion of an outlier is diﬀerent for diﬀerent application domains.Every application domain imposes a set of requirements and constraints giving rise to a speciﬁc problem formulation for outlier detection.•Availability of labeled data for training/validation is often a major issue while developing an outlier detection technique.•In several cases in which outliers are the result of malicious actions,the mali-cious adversaries adapt themselves to make the outlying observations appear like normal,thereby making the task of deﬁning normal behavior more diﬃ-cult.•Often the data contains noise which is similar to the actual outliers and hence is diﬃcult to distinguish and remove.In the presence of above listed challenges,a generalized formulation of the outlier detection problem based on the abstract deﬁnition of outliers is not easy to solve. In fact,most of the existing outlier detection techniques simplify the problem by4·Chandola,Banerjee and Kumarfocussing on a speciﬁc formulation.The formulation is induced by various factors such as the nature of data,nature of outliers to be detected,representation of the normal,etc.In several cases,these factors are governed by the application domain in which the technique is to be applied.Thus,there are numerous diﬀerent formulations of the outlier detection problem which have been explored in diverse disciplines such as statistics ,machine learning ,data mining ,information theory ,spectral decomposition .Application DomainsKnowledge DisciplinesInput Requirements and constraintsfor inputs and outputsConcepts from one or more dis-ciplinesFig.2.A general design of an outlier detection techniqueAs illustrated in Figure 2,any outlier detection technique has following major ingredients —1.Nature of data,nature of outliers,and other constraints and assumptions thatcollectively constitute the problem formulation .2.Application domain in which the technique is applied.Some of the techniquesare developed in a more generic fashion but are still feasible in one or more domains while others directly target a particular application domain.3.The concept and ideas used from one or more knowledge disciplines .1.3Our ContributionsThe contributions of this survey are listed below1.We have identiﬁed the key dimensions associated with the problem of outlierdetection.2.We provide a multi-level taxonomy to categorize any outlier detection tech-niques along the various dimensions.3.We present a comprehensive overview of the current outlier detection literatureusing the classiﬁcation framework.4.We distinguish between instance based outliers in data and more complex out-liers that occur in sequential or spatial data sets.We present separate discus-sions on techniques that deal with such complex outliers.The classiﬁcation of outlier detection techniques based on the applied knowledge discipline provides an idea of the research done by diﬀerent communities and alsoOutlier Detection:A Survey·5highlights the unexplored research avenues for the outlier detection problem.One of the dimensions along which we have classiﬁed outlier detection techniques is the application domain in which they are used.Such classiﬁcation allows anyone looking for a solution in a particular application domain to easily explore the existing research in that area.1.4OrganizationThis survey is organized into three major sections which discuss the above three ingredients of an outlier detection technique.In Section2we identify the various aspects that constitute an exact formulation of the problem.This section brings forward the richness and complexity of the problem domain.In Section3we de-scribe the diﬀerent application domains where outlier detection has been applied. We identify the unique characteristics of each domain and the diﬀerent techniques which have been used for outlier detection in these domains.In Section4,we cate-gorize diﬀerent outlier detection techniques based on the knowledge discipline they have been adopted from.1.5Related WorkAs mentioned earlier,outlier detection techniques can be classiﬁed along several dimensions.The most extensive eﬀort in this direction has been done by Hodge and Austin[2004].But they have only focused on outlier detection techniques de-veloped in machine learning and statistical domains.Most of the other reviews on outlier detection techniques have chosen to focus on a particular sub-area of the existing research.A short review of outlier detection algorithms using data min-ing techniques was presented by Petrovskiy[2003].Markou and Singh presented an extensive review of novelty detection techniques using neural networks[Markou and Singh2003a]and statistical approaches[Markou and Singh2003b].A review of selected outlier detection techniques,used for network intrusion detection,was presented by Lazarevic et al.[2003].Outlier detection techniques developed speciﬁ-cally for system call intrusion detection have been reviewed by Forrest et al.[1999], and later by Snyder[2001]and Dasgupta and Nino[2000].A substantial amount of research on outlier detection has been done in statistics and has been reviewed in several books[Rousseeuw and Leroy1987;Barnett and Lewis1994]as well as other reviews[Beckman and Cook1983;Hawkins1980].Tang et al.[2006]provide a uniﬁcation of several distance based outlier detection techniques.These related eﬀorts have either provided a coarser classiﬁcation of research done in this area or have focussed on a subset of the gamut of existing techniques.To the extent of our knowledge,our survey is theﬁrst attempt to provide a structured and a comprehensive overview of outlier detection techniques.1.6TerminologyOutlier detection and related concepts have been referred to as diﬀerent entities in diﬀerent areas.For the sake of better understandability,we will follow a uniform terminology in this survey.An outlier detection problem refers to the task ofﬁnding anomalous patterns in given data according to a particular deﬁnition of anomalous behavior.An outlier will refer to these anomalous patterns in the data.An outlier detection technique is a speciﬁc solution to an outlier detection problem.A normal6·Chandola,Banerjee and Kumarpattern refers to a pattern in the data which is not an outlier.The output of an outlier detection technique could be labeled patterns(outlier or normal).Some of the outlier detection techniques also assign a score to a pattern based on the degree to which the pattern is considered an outlier.Such a score is referred to as outlier score.2.DIFFERENT ASPECTS OF AN OUTLIER DETECTION PROBLEMThis section identiﬁes and discusses the diﬀerent aspects of outlier detection.As mentioned earlier,a speciﬁc formulation of the problem is determined by several diﬀerent factors such as the input data,the availability(or unavailability)of other resources as well as the constraints and requirements induced by the application domain.This section brings forth the richness in the problem domain and motivates the need for so many diverse techniques.2.1Input DataA key component of any outlier detection technique is the input data in which it has to detect the outliers.Input is generally treated as a collection of data ob-jects or data instances(also referred to as record,point,vector,pattern,event, case,sample,observation,or entity)[Tan et al.2005a].Each data instance can be described using a set of attributes(also referred to as variable,characteristic, feature,ﬁeld,or dimension).The data instances can be of diﬀerent types such as binary,categorical or continuous.Each data instance might consist of only one attribute(univariate)or multiple attributes(multivariate).In the case of multi-variate data instances,all attributes might be of same type or might be a mixture of diﬀerent data types.One important observation here is that the features used by any outlier detection technique do not necessarily refer to the observable features in the given data set. Several techniques use preprocessing schemes like feature extraction[Addison et al. 1999],or construct more complex features from the observed features[Ertoz et al. 2004],and work with a set of features which are most likely to discriminate between the normal and outlying behaviors in the data.A key challenge for any outlier detection technique is to identify a best set of features which can allow the algorithm to give the best results in terms of accuracy as well as computational eﬃciency. Input data can also be categorized based on the structure present among the data instances.Most of the existing outlier detection algorithms deal with data in which no structure is assumed among the data instances.We refer to such data as point data.Typical algorithms dealing with such data sets are found in network intrusion detection domain[Ertoz et al.2004]or in medical records outlier detection domain[Laurikkala et al.2000].Data can also have a spatial,sequential or both type of structures.For sequential data,the data instances have an ordering deﬁned such that every data instance occurs sequentially in the entire data set.Time-series data is the most popular example for this case and has been extensively analyzed with respect to outlier detection in statistics[Abraham and Chuang1989;Abraham and Box1979].Recently,biological data domains such as genome sequences and protein sequences[Eisen et al.1998;Teng2003]have been explored for outlier detection.For spatial data,the data instances have a well deﬁned spatial structure such that the location of a data instance with respect to others is signiﬁcant and isOutlier Detection:A Survey·7typically well-deﬁned.Spatial data is popular in traﬃc analysis domain[Shekhar et al.2001]and ecological and census studies[Kou et al.2006].Often,the data instances might also have a temporal(sequential)component giving rise to another category of spatio-temporal data,which is widely prevalent in climate data analysis [Blender et al.1997].Later in this section we will discuss the situations where the structure in data becomes relevant for outlier detection.2.2Type of SupervisionBesides the input data(or observations),an outlier detection algorithm might also have some additional information at its disposal.A labeled training data set is one such information which has been used extensively(primarily by outlier detection techniques based on concepts from machine learning[Mitchell1997]and statistical learning theory[Vapnik1995]).A training data set is required by techniques which involve building an explicit predictive model.The labels associated with a data instance denote if that instance is normal or outlier1.Based on the extent to which these labels are utilized,outlier detection techniques can be divided into three categories2.2.1Supervised outlier detection techniques.Such techniques assume the avail-ability of a training data set which has labeled instances for normal as well as outlier class.Typical approach in such case is to build predictive models for both normal and outlier classes.Any unseen data instance is compared against the two models to determine which class it belongs to.Supervised outlier detection techniques have an explicit notion of the normal and outlier behavior and hence accurate models can be built.One drawback here is that accurately labeled training data might be prohibitively expensive to beling is often done manually by a human expert and hence requires a lot of eﬀort to obtain the labeled training data set. Certain techniques inject artiﬁcial outliers in a normal data set to obtain a fully labeled training data set and then apply supervised outlier detection techniques to detect outliers in test data[Abe et al.2006].2.2.2Semi-Supervised outlier detection techniques.Such techniques assume the availability of labeled instances for only one class.it is often diﬃcult to collect labels for other class.For example,in space craft fault detection,an outlier scenario would signify an accident,which is not easy to model.The typical approach of such techniques is to model only the available class and decare any test instance which does notﬁt this model to belong to the other class.Techniques that assume availability of only the outlier instances for training are not very popular.The primary reason for their limited popularity is that it is diﬃcult to obtain a training data set which covers every possible outlying behavior that can occur in the data.The behaviors which do not exist in the training data will be harder to detect as outliers.Dasgupta et al[2000;2002]have used only outlier instances for training.Similar semi-supervised techniques have also been applied for system call intrusion detection[Forrest et al.1996].On the other hand,techniques which model only the normal instances during training are more popular.Normal instances are relatively easy to obtain.More-1Also referred to as normal and outlier classes8·Chandola,Banerjee and Kumarover,normal behavior is typically well-deﬁned and hence it is easier to construct representative models for normal behavior from the training data.This setting is very similar to as novelty detection[Markou and Singh2003a;2003b]and is extensively used in damage and fault detection.2.2.3Unsupervised outlier detection techniques.The third category of tech-niques do not make any assumption about the availability of labeled training data. Thus these techniques are most widely applicable.The techniques in this cate-gory make other assumptions about the data.For example,parametric statistical techniques,assume a parametric distribution of one or both classes of instances. Similarly,several techniques make the basic assumption that normal instances are far more frequent than outliers.Thus a frequently occurring pattern is typically con-sidered normal while a rare occurrence is an outlier.The unsupervised techniques typically suﬀer from higher false alarm rate,because often times the underlying assumptions do not hold true.Availability of labels govern the above choice of operating modes for any tech-nique.Typically,semi-supervised detection and unsupervised modes have been adopted more.Generally speaking,techniques which assume availability of outlier instances in training are not very popular.One of the reasons is that getting a labeled set of outlying data instances which cover all possible type of outlying be-havior is diﬃcult.Moreover,the outlying behavior is often dynamic in nature(for e.g-new types of outliers might arise,for which there is no labeled training data). In certain cases,such as air traﬃc safety,outlying instances would translate to airline accidents and hence will be very rare.Hence in such domains unsupervised or semi-supervised techniques with normal labels for training are preferred.2.3Type of OutlierAn important input to an outlier detection technique is the deﬁnition of the desired outlier which needs to be detected by the technique.Outliers can be classiﬁed into three categories based on its composition and its relation to rest of the data.2.3.1Type I Outliers.In a given set of data instances,an individual outlying instance is termed as a Type I outlier.This is the simplest type of outliers and is the focus of majority of existing outlier detection schemes.A data instance is an outlier due to its attribute values which are inconsistent with values taken by normal instances.Techniques that detect Type I outliers analyze the relation of an individual instance with respect to rest of the data instances(either in the training data or in the test data).For example,in credit card fraud detection,each data instance typically repre-sents a credit card transaction.For the sake of simplicity,let us assume that the data is deﬁned using only two features–time of the day and amount spent.Figure 3shows a sample plot of the2-dimensional data instances.The curved surface rep-resents the normal region for the data instances.The three transactions,o1,o2and o3lie outside the boundary of the normal regions and hence are Type I outliers. Similar example of this type can be found in medical records data[Laurikkala et al.2000]where each data record corresponds to a patient.A single outlying record will be a Type I outlier and would be interesting as it would indicate some problem with a patient’s health.Outlier Detection:A Survey·9Time of the dayFig.3.Type I outliers o1,o2and o3in a2-dimensional credit card transaction data set.The normal transactions are for this data are typically during the day,between11:00AM and6:00 PM and range between$10to$100.Outliers o1and o2are fraudulent transactions which are outliers because they occur at an abnormal time and the amount is abnormally large.Outlier o3 has unusually high amount spent,even though the time of transaction is normal.2.3.2Type II Outliers.These outliers are caused due to the occurrence of an individual data instance in a speciﬁc context in the given data.Like Type I outliers, these outliers are also individual data instances.The diﬀerence is that a Type II outlier might not be an outlier in a diﬀerent context.Thus Type II outliers are deﬁned with respect to a context.The notion of a context is induced by the structure in the data set and has to be speciﬁed as a part of the problem formulation.A context deﬁnes the neighborhood of a particular data instance.Type II outliers satisfy two properties1.The underlying data has a spatial/sequential nature:each data instance isdeﬁned using two sets of attributes,viz.contextual attributes and behavioral attributes.The contextual attributes deﬁne the position of an instance and are used to determine the context(or neighborhood)for that instance.For example,in spatial data sets,the longitude and latitude of a location are the contextual attributes.Or in a time-series data,time is a contextual attribute which determines the position of an instance on the entire sequence.The behavioral attributes deﬁne the non-contextual characteristics of an instance.For example,in a spatial data set describing the average rainfall of the entire world,the amount of rainfall at any location is a behavioral attribute.2.The outlying behavior is determined using the values for the behavioral at-tributes within a speciﬁc context.A data instance might be a Type II outlier in a given context,but an identical data instance(in terms of behavioral at-tributes)could be considered normal in a diﬀerent context.Type II outliers have been most popularly explored in time-series data[Weigend et al.1995;Salvador and Chan2003]and spatial data[Kou et al.2006;Shekhar et al.2001].Figure4shows one such example for a temperature time series which shows the monthly temperature of an area over last few years.A temperature of 35F might be normal during the winter(at time t1)at that place,but the same value during summer(at time t2)would be an outlier.Similar example can be found in credit card fraud detection domain.Let us extend the data described for Type I outliers by adding another attribute–store name,where the purchase was10·Chandola,Banerjee and KumarTimeFig.4.Type II outlier t2in a temperature time series.Note that the temperature at time t1is same as that at time t2but occurs in a diﬀerent context and hence is not considered as an outlier. made.The individual might be spending around$10at a gas station while she might be usually spending around$100at a jewelery store.A new transaction of $100at the gas station will be considered as a Type II outlier,since it does not conform to the normal behavior of the individual in the context of gas station(even though the same amount spent in the jewelery store will be considered normal).2.3.3Type III Outliers.These outliers occur because a subset of data in-stances are outlying with respect to the entire data set.The individual data instances in a Type III outlier are not outliers by themselves,but their occur-rence together as a substructure is anomalous.Type III outliers are meaningful only when the data has spatial or sequential nature.These outliers are either anomalous subgraphs or subsequences occurring in the data.Figure5illustrates an example which shows a human electrocardiogram output[Keogh et al.2002]. Note that the extendedﬂat line denotes an outlier because the same low value exists for an abnormally long time.Fig.5.Type III outlier in an human electrocardiogram output.Note that the low value in the ﬂatline also occurs in normal regions of the sequence.We revisit the credit card fraud detection example for an illustration of Type III outliers.Let us assume that an individual normally makes purchases at a gas station,followed by a nearby grocery store and then at a nearby convenience store.A new sequence of credit card transactions which involve purchase at a gas station, followed by three more similar purchases at the same gas station that day,would indicate a potential card theft.This sequence of transactions is a Type III outlier. It should be observed that the individual transactions at the gas station would not be considered as a Type I outlier.Type III outlier detection problem has been widely explored for sequential data such as operating system call data and genome sequences.For system call data, a particular sequence of operating system calls is treated as an outlier.Similarly, outlier detection techniques dealing with images detect regions in the image which are anomalous(Type III outliers).。

Community Detection in Social Networks via Graphical Game

Community Detection in Social Networks via GraphicalGameJaewon Yanga project proposal for STATS375,Winter2010-11January17,20111BackgroundCommunities are regarded as an important tool to understand and represent social networks. By nature,the rigorous deﬁnition of a community has not existed and many researchers have proposed their own deﬁnition of community or own algorithm forﬁnding communities.The most prominent advance among the recent works is the concept of modularity by Newman et al.[New06]After Newman et al.,extensive amount of researches has focused on the community detection of the optimization of modularity.Though mathematically elegant, community detection solely by optimization of modularity does not provide clean intuitive explanation about the formation of communities.In social networks,each node is individual person,who makes one’s decision actively,but most of researches did not focus on the motivation of individuals.2Community Detection via Graphical GameRecently,Wei Chen et al.[CLSW10]proposed a game-theoretic framework to view commu-nity detection.Wei Chen et al.[CLSW10]proposed the structure of a graphical game of community formation,where the graph of the game is the same as the graph of a social network,and each node’s payoﬀincreases with the number of communities that the node shares with the node’s neighbors.They proved the existence of pure Nash equilibria and proposed a greedy-hill climbing algorithm to reach a”local”equilibria.As game-theory has been proved to be powerful tool to explain individuals’collective behavior,it would provide intuition about why and how communities are formed between individuals.In this project, I will explore further possibilities of applying graphical game to community detection.13Directions3.1Implementation of Wei Chen et al.I will begin with implementing Wei Chen et al.’s method to detect communities.I will reproduce the same benchmark graphs as in[CLSW10],and check whether the method works as well as in[CLSW10].Also,I will test the method in diﬀerent benchmark graphs and evaluate performance.3.2Finding approximation of pure Nash equilibria[CLSW10]called the equilibria they found a”local”equilibria in the sense that each indi-vidual is allowed to change its policy by small amount.In the project,I will try toﬁnd pure Nash equilibria of the game.In particular,I will refer to the recent achievement in reduction of a graphical game to Markov networks,[Kea07,DP06]and apply several techniques for approximate inference of Markov networks.3.3Varying the structure of the game[CLSW10]proposed the payoﬀthat is similar to modularity.I will try diﬀerent payoﬀs and evaluate their eﬀects on the performance of the method.References[CLSW10]Wei Chen,Zhenming Liu,Xiaorui Sun,and Yajun Wang.A game-theoretic framework to identify overlapping communities in social networks.Data Min.Knowl.Discov.,21:224–240,September2010.[DP06]Constantinos Daskalakis and Christos puting pure nash equilibria in graphical games via markov randomﬁelds.In Proceedings of the7thACM conference on Electronic commerce,EC’06,pages91–99,New York,NY,USA,2006.ACM.[Kea07]Michael Kearns.Chapter7graphical games,2007.[New06]M.E.J.Newman.Modularity and community structure in networks.Proceedings of the National Academy of Sciences,103(23):8577–8582,June2006.2。

community detection介绍

2
Real-World Communities
Egypt Protest, 2011
Real-World Communities
Real-World Communities
Lada Admic And Natalie Glance, The Political Blogosphere and the 2004 U.S. Election: Divided They Blog, 2005.
Communities in Twitter
Communities of Personal Social Network
Communities in Social Media
• Two types of groups in social media – Explicit Groups: formed by user subscriptions – Implicit Groups: implicitly formed by social interactions • Some social media sites allow people to join groups, is it necessary to extract groups based on network topology?
– Can complement other kinds of information, e.g. user profile – Help network visualization and navigation – Provide basic information for other tasks, e.g. recommendation Note that each of the above three points can be a research topic.

2025届广东省广州三中英语高三第一学期期末考试模拟试题含解析

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第一部分（共20小题，每小题1.5分，满分30分）1．––Is this tea good cold as well?––______ with ice, this tea is especially delicious.A．Served B．Serving C．Having served D．To be served2．﹣Have you got the results of the final exam？﹣Not yet．It will be a few days ________ we know the full results．（）A．before B．afterC．until D．when3．He works very hard in order to get himself ______ into a key university. A．accepted B．received C．announced D．admitted4．They went to the street to ________ to the whole city to help the poor boy. A．apply B．appealC．add D．reply5．A good suitcase is essential for someone who is ______ as much as Jackie is.A．on the rise B．on the lineC．on the spot D．on the run6．Newly released data point to an increase in technology use among childrensome worry is changing the very nature of childhood.A．why B．whichC．who D．where7．As a surgeon,I cannot any mistakes;it would be dangerous for the patient. A．appreciate B．removeC．offer D．afford8．A Chinese proverb has it that a tower is built when soil on earth _________, and a river is formed when streams come together.A．accumulates B．accelerates C．collapses D．loosens9．—He is good at a lot of things but it doesn’t mean he is perfect.— ___________ Actually no one is.A．What’s going on?B．Let’s get going.C．Thank goodness. D．I’m with you on that.10．If you want to improve your figure and health, the most effective thing to do is to show up at the gym every time you ________ be there.A．can B．willC．may D．shall11．—Do you really plan to drop out of the football team?—________ It’s time for me to concentrate on my study.A．I’m just kidding.B．Definitely not.C．I mean it D．What a pity!12．That was a very busy street that I was never allowed to cross accompanied by an adult.A．when B．if C．unless D．where13．Don't give up half way, and you will find the scenery is more beautiful when you reach the destination than when you _______.A．start off B．have started offC．started off D．will start off14．— Why are the Woods selling their belongings?— They to another city.A．had moved B．have moved C．moved D．are moving15．The la nguage in the company’s statement is highly ________, thus making its staff confused.A．ambiguous B．apparentC．appropriate D．aggressive16．. Some people say more but do less ______ others do the opposite.A．once B．when C．while D．as17．—You know quite a lot about the fashion show.—Well, Cathy ________ it to me during lunch.A．introduces B．introducedC．had introduced D．will introduce18．The 114 colorful clay Warriors ____ at No. 1 pit, ______ in height from 1.8m to 2m, have black hair, green, white or pink faces, and black or brown eyes.A．unearthed; ranging B．unearthing; rangingC．unearthed; ranged D．are unearthed; are ranging19．—Iris is always kind and ________ to the suffering of others.—No wonder she chooses to be a relief worker.A．allergic B．immuneC．relevant D．sensitive20．John had planned to make a compromise, but he changed his mind at the last minute.A．anyhow B．otherwiseC．therefore D．somehow第二部分阅读理解（满分40分）阅读下列短文，从每题所给的A、B、C、D四个选项中，选出最佳选项。

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5. Supported Conﬁgurations
— One AYC21 network interface operating at the E1 rate (2.048 Mbits/sec) is supported on the MAP/40. One LSE1D link supports 30 voice channels. — Two AYC21 network interfaces operating at the E1 rate (2.048 Mbits/sec) are supported on the MAP/100. One LSE1D link supports 30 voice channels, thus a maximum of 2 are possible. — Other conﬁguration rules for CONVERSANT V4.0 systems apply. — The V4.0 system using a 50Mhz 486 CPU is capable of supporting a load equivalent to 60 active voice transactions on the AYC21 interfaces. The LSE1D network interface protocol on DEFINITY has the same signaling behavior when operating at the E1 rate (2.048 Mbits/sec) as for the U.S. T1 rate. The signaling protocol should apply to any country for which DEFINITY can be offered and sold with E1 rate interfaces. The LSE1D feature on INTUITY CONVERSANT is enhanced to detect dialtone. Thus, there is a dependence on the country code administration for the DEFINITY PBX. Countries supported for dialtone recognition by this INTUITY CONVERSANT LSE1D package are: US & Canada, Australia, United Kingdom, Germany, Mexico, The Netherlands, Belgium.

Finding community structure in very large networks

Finding community structure in very large networksAaron Clauset,1M.E.J.Newman,2and Cristopher Moore1,31Department of Computer Science,University of New Mexico,Albuquerque,New Mexico87131,USA2Department of Physics and Center for the Study of Complex Systems,University of Michigan,Ann Arbor,Michigan48109,USA 3Department of Physics and Astronomy,University of New Mexico,Albuquerque,New Mexico87131,USA(Received30August2004;published6December2004)The discovery and analysis of community structure in networks is a topic of considerable recent interestwithin the physics community,but most methods proposed so far are unsuitable for very large networksbecause of their computational cost.Here we present a hierarchical agglomeration algorithm for detectingcommunity structure which is faster than many competing algorithms:its running time on a network with nvertices and m edges is O͑md log n͒where d is the depth of the dendrogram describing the communitystructure.Many real-world networks are sparse and hierarchical,with mϳn and dϳlog n,in which case ouralgorithm runs in essentially linear time,O͑n log2n͒.As an example of the application of this algorithm we useit to analyze a network of items for sale on the web site of a large on-line retailer,items in the network beinglinked if they are frequently purchased by the same buyer.The network has more than400000vertices and2ϫ106edges.We show that our algorithm can extract meaningful communities from this network,revealinglarge-scale patterns present in the purchasing habits of customers.DOI:10.1103/PhysRevE.70.066111PACS number(s):89.75.Hc,05.10.Ϫa,87.23.Ge,89.20.HhI.INTRODUCTIONMany systems of current interest to the scientiﬁc commu-nity can usefully be represented as networks[1–4].Ex-amples include the internet[5]and the World Wide Web [6,7],social networks[8],citation networks[9,10],food webs[11],and biochemical networks[12,13].Each of these networks consists of a set of nodes or vertices representing, for instance,computers or routers on the internet or people in a social network,connected together by links or edges,rep-resenting data connections between computers,friendships between people,and so forth.One network feature that has been emphasized in recent work is community structure,the gathering of vertices into groups such that there is a higher density of edges within groups than between them[14].The problem of detecting such communities within networks has been well studied. Early approaches such as the Kernighan-Lin algorithm[15], spectral partitioning[16,17],or hierarchical clustering[18] work well for speciﬁc types of problems(particularly graph bisection or problems with well deﬁned vertex similarity measures),but perform poorly in more general cases[19].To combat this problem a number of new algorithms have been proposed in recent years.Girvan and Newman[20,21] proposed a divisive algorithm that uses edge betweenness as a metric to identify the boundaries of communities.This al-gorithm has been applied successfully to a variety of net-works,including networks of email messages,human and animal social networks,networks of collaborations between scientists and musicians,metabolic networks,and gene net-works[20,22–30].However,as noted in[21],the algorithm makes heavy demands on computational resources,running in O͑m2n͒time on an arbitrary network with m edges and n vertices,or O͑n3͒time on a sparse graph(one in which m ϳn,which covers most real-world networks of interest). This restricts the algorithm’s use to networks of at most a few thousand vertices with current hardware.More recently a number of faster algorithms have been proposed[31–33].In[32],one of us proposed an algorithm based on the greedy optimization of the quantity known as modularity[21].This method appears to work well both in contrived test cases and in real-world situations,and is sub-stantially faster than the algorithm of Girvan and Newman.A naive implementation runs in time O(͑m+n͒n),or O͑n2͒on a sparse graph.Here we propose a different algorithm that performs the same greedy optimization as the algorithm of[32]and there-fore gives identical results for the communities found.How-ever,by exploiting some shortcuts in the optimization prob-lem and using more sophisticated data structures,it runs far more quickly,in time O͑md log n͒where d is the depth of the“dendrogram”describing the network’s community structure.Many real-world networks are sparse,so that m ϳn;and moreover,for networks that have a hierarchical structure with communities at many scales,dϳlog n.For such networks our algorithm has essentially linear running time,O͑n log2n͒.This is not merely a technical advance but has substantial practical implications,bringing within reach the analysis of extremely large works of107vertices or more should be possible in reasonable run times.As an example, we give results from the application of the algorithm to a recommender network of books from the on-line bookseller ,which has more than400000vertices and 2ϫ106edges.II.THE ALGORITHMModularity[21]is a property of a network and a speciﬁc proposed division of that network into communities.It mea-sures when the division is a good one,in the sense that there are many edges within communities and only a few betweenPHYSICAL REVIEW E70,066111(2004)them.Let A v w be an element of the adjacency matrix of the network;thusA v w=ͭ1if vertices v and w are connected,0otherwise,͑1͒and suppose the vertices are divided into communities such that vertex v belongs to community c v.Then the fraction of edges that fall within communities,i.e.,that connect vertices that both lie in the same community,is͚v w A v w␦͑c v,c w͚͒v w A v w=12m͚v wA v w␦͑c v,c w͒,͑2͒where the␦function␦͑i,j͒is1if i=j and0otherwise,andm=12͚v w A v w is the number of edges in the graph.This quan-tity will be large for good divisions of the network,in the sense of having many within-community edges,but it is not, on its own,a good measure of community structure since it takes its largest value of1in the trivial case where all verti-ces belong to a single community.However,if we subtract from it the expected value of the same quantity in the case of a randomized network,we do get a useful measure.The degree k v of a vertex v is deﬁned to be the number of edges incident upon it:k v=͚w A v w.͑3͒The probability of an edge existing between vertices v and w if connections are made at random but respecting vertex de-grees is k v k w/2m.We deﬁne the modularity Q to beQ=12m͚v wͫA v w−k v k w2mͬ␦͑c v,c w͒.͑4͒If the fraction of within-community edges is no different from what we would expect for the randomized network, then this quantity will be zero.Nonzero values represent de-viations from randomness,and in practice it is found that a value above about0.3is a good indicator of signiﬁcant com-munity structure in a network.If high values of the modularity correspond to good divi-sions of a network into communities,then one should be able toﬁnd such good divisions by searching through the possible candidates for ones with high modularity.Whileﬁnding the global maximum modularity over all possible divisions seems hard in general,reasonably good solutions can be found with approximate optimization techniques.The algo-rithm proposed in[32]uses a greedy optimization in which, starting with each vertex being the sole member of a com-munity of one,we repeatedly join together the two commu-nities whose amalgamation produces the largest increase in Q.For a network of n vertices,after n−1such joins we are left with a single community and the algorithm stops.The entire process can be represented as a tree whose leaves are the vertices of the original network and whose internal nodes correspond to the joins.This dendrogram represents a hier-archical decomposition of the network into communities at all levels.The most straightforward implementation of this idea (and the only one considered in[32])involves storing the adjacency matrix of the graph as an array of integers and repeatedly merging pairs of rows and columns as the corre-sponding communities are merged.For the case of the sparse graphs that are of primary interest in theﬁeld,however,this approach wastes a good deal of time and memory space on the storage and merging of matrix elements with value0, which is the vast majority of the adjacency matrix.The al-gorithm proposed in this paper achieves speed(and memory efﬁciency)by eliminating these needless operations.To simplify the description of our algorithm let us deﬁne the following two quantities:e ij=12m͚v wA v w␦͑c v,i͒␦͑c w,j͒,͑5͒which is the fraction of edges that join vertices in community i to vertices in community j,anda i=12m͚vk v␦͑c v,i͒,͑6͒which is the fraction of ends of edges that are attached to vertices in community i.Then,writing␦͑c v,c w͒=͚i␦͑c v,i͒␦͑c w,i͒,we have,from Eq.(4),Q=12m͚v wͫA v w−k v k w2m͚ͬi␦͑c v,i͒␦͑c w,i͒=͚iͫ12m͚v w A v w␦͑c v,i͒␦͑c w,i͒−12m͚vk v␦͑c v,i͒12m͚wk w␦͑c w,i͒ͬ=͚i͑e ii−a i2͒.͑7͒The operation of the algorithm involvesﬁnding the changes in Q that would result from the amalgamation of each pair of communities,choosing the largest of them,and performing the corresponding amalgamation.One way to en-visage(and implement)this process is to think of the net-work as a multigraph,in which a whole community is rep-resented by a vertex,bundles of edges connect one vertex to another,and edges internal to communities are represented by self-edges.The adjacency matrix of this multigraph has elements A ijЈ=2me ij,and the joining of two communities i and j corresponds to replacing the i th and j th rows and col-umns by their sum.In the algorithm of[32]this operation is done explicitly on the entire matrix,but if the adjacency matrix is sparse(which we expect in the early stages of the process)the operation can be carried out more efﬁciently using data structures for sparse matrices.Unfortunately,cal-culating⌬Q ij andﬁnding the pair i,j with the largest⌬Q ij then becomes time consuming.In our algorithm,rather than maintaining the adjacency matrix and calculating⌬Q ij,we instead maintain and update a matrix of value of⌬Q ij.Since joining two communities with no edge between them can never produce an increase inCLAUSET,NEWMAN,AND MOORE PHYSICAL REVIEW E70,066111(2004)Q,we need only store⌬Q ij for those pairs i,j that are joined by one or more edges.Since this matrix has the same support as the adjacency matrix,it will be similarly sparse,so we can again represent it with efﬁcient data structures.In addition, we make use of an efﬁcient data structure to keep track of the largest⌬Q ij.These improvements result in a considerable saving of both memory and time.In total,we maintain three data structures.(1)A sparse matrix containing⌬Q ij for each pair i,j of communities with at least one edge between them.We store each row of the matrix both as a balanced binary tree[so that elements can be found or inserted in O͑log n͒time]and as a max-heap(so that the largest element can be found in con-stant time).(2)A max-heap H containing the largest element of each row of the matrix⌬Q ij along with the labels i,j of the cor-responding pair of communities.(3)An ordinary vector array with elements a i.As described above we start off with each vertex being the sole member of a community of one,in which case e ij =1/2m if i and j are connected and zero otherwise,and a i =k i/2m.Thus we initially set⌬Q ij=ͭ1/2m−k i k j/͑2m͒2if i,j are connected,0otherwise,͑8͒anda i=k i2m͑9͒for each i.(This assumes the graph is unweighted;weighted graphs are a simple generalization[34].)Our algorithm can now be deﬁned as follows.(1)Calculate the initial values of⌬Q ij and a i according to Eq.(8)and(9),and populate the max-heap with the largest element of each row of the matrix⌬Q.(2)Select the largest⌬Q ij from H,join the corresponding communities,update the matrix⌬Q,the heap H,and a i(as described below),and increment Q by⌬Q ij.(3)Repeat step2until only one community remains.Our data structures allow us to carry out the updates in step2quickly.First,note that we need only adjust a few of the elements of⌬Q.If we join communities i and j,labeling the combined community j,say,we need only update the j th row and column,and remove the i th row and column alto-gether.The update rules are as follows.If community k is connected to both i and j,then⌬Q jkЈ=⌬Q ik+⌬Q jk.͑10a͒If k is connected to i but not to j,then⌬Q jkЈ=⌬Q ik−2a j a k.͑10b͒If k is connected to j but not to i,then⌬Q jkЈ=⌬Q jk−2a i a k.͑10c͒Note that these equations imply that Q has a single peak over the course of the algorithm,since after the largest⌬Q be-comes negative all the⌬Q can only decrease.To analyze how long the algorithm takes using our data structures,let us denote the degrees of i and j in the reduced graph—i.e.,the numbers of neighboring communities—as͉i͉and͉j͉,respectively.Theﬁrst operation in a step of the algo-rithm is to update the j th row.To implement Eq.(10a),weinsert the elements of the i th row into the j th row,summingthem wherever an element exists in both columns.Since westore the rows as balanced binary trees,each of these͉i͉insertions takes O͑log͉j͉͒ഛO͑log n͒time.We then update the other elements of the j th row,of which there are at most ͉i͉+͉j͉,according to Eqs.(10b)and(10c).In the k th row,we update a single element,taking O͑log͉k͉͒ഛO͑log n͒time, and there are at most͉i͉+͉j͉values of k for which we have todo this.All of this thus takes O͉͑͑i͉+͉j͉͒log n͒time.We also have to update the max-heaps for each row andthe overall max-heap H.Reforming the max-heap corre-sponding to the j th row can be done in O͉͑j͉͒time[35]. Updating the max-heap for the k th row by inserting,raising, or lowering⌬Q kj takes O͑log͉k͉͒ഛO͑log n͒time.Since we have changed the maximum element on at most͉i͉+͉j͉rows, we need to do at most͉i͉+͉j͉updates of H,each of which takes O͑log n͒time,for a total of O(͉͑i͉+͉j͉͒log n). Finally,the update a jЈ=a j+a i(and a i=0)is trivial and can be done in constant time.Since each join takes O(͉͑i͉+͉j͉͒log n)time,the total run-ning time is at most O͑log n͒times the sum over all nodes of the dendrogram of the degrees of the corresponding commu-nities.Let us make the worst-case assumption that the degree of a community is the sum of the degrees of all the vertices in the original network comprising it.In that case,each ver-tex of the original network contributes its degree to all of the communities it is a part of,along the path in the dendrogram from it to the root.If the dendrogram has depth d,there are at most d nodes in this path,and since the total degree of all the vertices is2m,we have a running time of O͑md log n͒as stated.We note that,if the dendrogram is unbalanced,some timesavings can be gained by inserting the sparser row into theless sparse one.In addition,we have found that in practicalsituations it is usually unnecessary to maintain the separatemax-heaps for each row.These heaps are used toﬁnd thelargest element in a row quickly,but their maintenance takesa moderate amount of effort and this effort is wasted if thelargest element in a row does not change when two rows areamalgamated,which turns out often to be the case.Thus weﬁnd that the following simpler implementation works quitewell in realistic situations:if the largest element of the k throw was⌬Q ki or⌬Q kj and is now reduced by Eq.(10b)or (10c),we simply scan the k th row toﬁnd the new largest element.Although the worst-case running time of this ap-proach has an additional factor of n,the average-case run-ning time is often better than that of the more sophisticated algorithm.It should be noted that the dendrograms generated by these two versions of our algorithm will differ slightly as a result of the differences in how ties are broken for the maximum element in a row.However,weﬁnd that in prac-tice these differences do not cause signiﬁcant deviations in the modularity,the community size distribution,or the com-position of the largest communities.FINDING COMMUNITY STRUCTURE IN VERY LARGE…PHYSICAL REVIEW E70,066111(2004) PURCHASING NETWORKThe output of the algorithm described above is precisely the same as that of the slower hierarchical algorithm of [32].The much improved speed of our algorithm,however,makes possible studies of very large networks for which previous methods were too slow to produce useful results.Here we give one example,the analysis of a copurchasing or “recom-mender”network from the online vendor .Amazon sells a variety of products,particularly books and music,and as part of their web sales operation they list for each item A the ten other items most frequently purchased by buyers of A .This information can be represented as a di-rected network in which vertices represent items and there is an edge from item A to another item B if B was frequently purchased by buyers of A .In our study we have ignored the directed nature of the network (as is common in community structure calculations ),assuming any link between two items,regardless of direction,to be an indication of their similarity.The network we study consists of items listed onthe Amazon web site in August 2003.We concentrate on the largest component of the network,which has 409687items and 2464630edges.The dendrogram for this calculation is of course too big to draw,but Fig.1illustrates the modularity over the course of the algorithm as vertices are joined into larger and larger groups.The maximum value is Q =0.745,which is high as calculations of this type go [21,32]and indicates strong com-munity structure in the network.The maximum occurs when there are 1684communities with a mean size of 243items each.Figure 2gives a visualization of the community struc-ture,including the major communities,smaller “satellite”communities connected to them,and “bridge”communities that connect two major communities with each other.Looking at the largest communities in the network,we ﬁnd that they tend to consist of items (books,music )in simi-TABLE I.The ten largest communities in the network,which account for 87%of the vertices in the network.Rank Size Description1114538General interest:politics;art/literature;general ﬁction;human nature;technical books;how things,people,computers,societies work,etc.292276The arts:videos,books,DVDs about the creative and performing arts378661Hobbies and interests I:self-help;self-education;popular science ﬁction,popular fantasy;leisure;etc.454582Hobbies and interests II:adventure books;video games/comics;some sports;some humor;some classic ﬁction;some western religious material;etc.59872Classical music and related items61904Children’s videos,movies,music,and books71493Church/religious music;African-descent cultural books;homoerotic imagery 81101Pop horror;mystery/adventure ﬁction 91083Jazz;orchestral music;easy listening 10947Engineering;practical fashionFIG.1.The modularity Q over the course of the algorithm (the x axis shows the number of joins ).Its maximum value is Q =0.745,where the partition consists of 1684communities.FIG.2.A visualization of the community structure at maximum modularity.Note that some major communities have a large number of “satellite”communities connected only to them (top,lower left,lower right ).Also,some pairs of major communities have sets of smaller communities that act as “bridges”between them (e.g.,be-tween the lower left and lower right,near the center ).CLAUSET,NEWMAN,AND MOORE PHYSICAL REVIEW E 70,066111(2004)lar genres or on similar topics.In Table I,we give informal descriptions of the ten largest communities,which account for about87%of the entire network.The remainder is gen-erally divided into small,densely connected communities that represent highly speciﬁc copurchasing habits,e.g.,major works of scienceﬁction(162items),music by John Cougar Mellencamp(17items),and books about(mostly female) spies in the American Civil War(13items).It is worth noting that because few real-world networks have community meta-data associated with them to which we may compare the inferred communities,this type of manual check of the ve-racity and coherence of the algorithm’s output is often nec-essary.One interesting property recently noted in some networks [30,32]is that when partitioned at the point of maximum modularity,the distribution of community sizes s appears to have a power-law form P͑s͒ϳs−␣for some constant␣,at least over some signiﬁcant range.The Amazon copurchasing network also seems to exhibit this property,as we show in Fig.3,with an exponent␣Ӎ2.It is unclear why such a distribution should arise,but we speculate that it could be a result either of the sociology of the network(a power-law distribution in the number of people interested in various topics)or of the dynamics of the community structure algo-rithm.We propose this as a direction for further research.IV.CONCLUSIONSHere,we have described an algorithm for inferring com-munity structure from network topology which works by greedily optimizing the modularity.Our algorithm runs in time O͑md log n͒for a network with n vertices and m edges where d is the depth of the dendrogram.For networks that are hierarchical,in the sense that there are communities at many scales and the dendrogram is roughly balanced,we have dϳlog n.If the network is also sparse,mϳn,then the running time is essentially linear,O͑n log2n͒.This is consid-erably faster than most previous general algorithms,and al-lows us to extend community structure analysis to networks that had been considered too large to be tractable.We have demonstrated our algorithm with an application to a large network of copurchasing data from the on-line retailer .Our algorithm discovers clear communities within this network that correspond to speciﬁc topics or genres of books or music,indicating that the copurchasing tendencies of Amazon customers are strongly correlated with subject matter.Our algorithm should allow researchers to analyze even larger networks with millions of vertices and tens of millions of edges using current computing resources,and we look forward to seeing such applications.ACKNOWLEDGEMENTSThe authors are grateful to and Eric Prom-islow for providing the purchasing network data.This work was funded in part by the National Science Foundation under Grant No.PHY-0200909(A.C.,C.M.)and by a grant from the James S.McDonell Foundation(M.E.J.N.).[1]S.H.Strogatz,Nature(London)410,268(2001).[2]R.Albert and A.-L.Barabási,Rev.Mod.Phys.74,47(2002).[3]S.N.Dorogovtsev and J.F.F.Mendes,Adv.Phys.51,1079(2002).[4]M.E.J.Newman,SIAM Rev.45,167(2003).[5]M.Faloutsos,P.Faloutsos,and C.Faloutsos,-mun.Rev.29,251(1999).[6]R.Albert,H.Jeong,and A.-L.Barabási,Nature(London)401,130(1999).[7]J.M.Kleinberg,S.R.Kumar,P.Raghavan,S.Rajagopalan,and A.Tomkins,in Proceedings of the International Confer-ence on Combinatorics and Computing,Lecture Notes in Computer Science V ol.1627,(Springer,Berlin1999),pp.1–18.[8]S.Wasserman and K.Faust,Social Network Analysis(Cam-bridge University Press,Cambridge,U.K.,1994).[9]D.J.de S.Price,Science149,510(1965).[10]S.Redner,Eur.Phys.J.B4,131(1998).[11]J.A.Dunne,R.J.Williams,and N.D.Martinez,Proc.Natl.Acad.Sci.U.S.A.99,12917(2002).[12]S.A.Kauffman,J.Theor.Biol.22,437(1969).[13]T.Ito,T.Chiba,R.Ozawa,M.Yoshida,M.Hattori,and Y.Sakaki,Proc.Natl.Acad.Sci.U.S.A.98,4569(2001). [14]Community structure is sometimes referred to as“clustering”in sociology or computer science,but this term is commonly used to mean something else in the physics literature[D.J.FIG.3.Cumulative distribution(cdf)of the sizes of communi-ties when the network is partitioned at the maximum modularity found by the algorithm.The distribution appears to follow a power-law form over two decades in the central part of its range,although it deviates in the tail.As a guide to the eye,the straight line has slope−1,which corresponds to an exponent of␣=2for the raw probability distribution.FINDING COMMUNITY STRUCTURE IN VERY LARGE…PHYSICAL REVIEW E70,066111(2004)Watts and S.H.Strogatz,Nature(London)393,440(1998)], so to prevent confusion we avoid it here.We note also that the problem ofﬁnding communities in a network is somewhat ill-posed,since we haven’t deﬁned precisely what a commu-nity is.A number of deﬁnitions have been proposed in[8,31] and in G.W.Flake,wrence,C.L.Giles,and F.M.Coetzee,IEEE put.35,66(2002),but none is stan-dard.[15]B.W.Kernighan and S.Lin,Bell Syst.Tech.J.49,291(1970).[16]M.Fiedler,Czech.Math.J.23,298(1973).[17]A.Pothen,H.Simon,and K.-P.Liou,SIAM J.Matrix Anal.Appl.11,430(1990).[18]J.Scott,Social Network Analysis:A Handbook,2nd ed.(Sage,London,2000).[19]M.E.J.Newman,Eur.Phys.J.B38,321(2004).[20]M.Girvan and M.E.J.Newman,Proc.Natl.Acad.Sci.U.S.A.99,7821(2002).[21]M.E.J.Newman and M.Girvan,Phys.Rev.E69,026113(2004).[22]M.T.Gastner and M.E.J.Newman,Proc.Natl.Acad.Sci.U.S.A.101,7499(2004).[23]R.Guimerà,L.Danon,A.Díaz-Guilera,F.Giralt,and A.Are-nas,Phys.Rev.E68,065103(2003).[24]P.Holme,M.Huss,and H.Jeong,Bioinformatics19,532(2003).[25]P.Holme and M.Huss,in Proceedings of the3rd Workshop onComputation of Biochemical Pathways and Genetic Networks, edited by R.Gauges,U.Kummer,J.Pahle,and U.Rost (Logos,Berlin,2003),pp.3–9.[26]J.R.Tyler,D.M.Wilkinson,and B.A.Huberman,in Pro-ceedings of the First International Conference on Communities and Technologies,edited by M.Huysman,E.Wenger,and V.Wulf(Kluwer,Dordrecht,2003).[27]P.Gleiser and L.Danon,plex Syst.6,565(2003).[28]M.Boguñá,R.Pastor-Satorras,A.Díaz-Guilera,and A.Are-nas,e-print cond-mat/0309263.[29]D.M.Wilkinson and B.A.Huberman,Proc.Natl.Acad.Sci.U.S.A.101,5241(2004).[30]A.Arenas,L.Danon,A.Díaz-Guilera,P.M.Gleiser,and R.Guimerà,Eur.Phys.J.B38,373(2004).[31]F.Radicchi,C.Castellano,F.Cecconi,V.Loreto,and D.Pa-risi,Proc.Natl.Acad.Sci.U.S.A.101,2658(2004).[32]M.E.J.Newman,Phys.Rev.E69,066133(2004).[33]F.Wu and B.A.Huberman,Eur.Phys.J.B38,331(2004).[34]M.E.J.Newman,e-print cond-mat/0407503.[35]T.H.Cormen,C.E.Leiserson,R.L.Rivest,and C.Stein,Introduction to Algorithms,2nd ed.(MIT Press,Cambridge, MA,2001).CLAUSET,NEWMAN,AND MOORE PHYSICAL REVIEW E70,066111(2004)。

2024年6月第3套英语六级真题

大学英语六级考试2024年6月真题(第三套)Part I Writing (30 minutes) Directions:For this part,you are allowed 30 minutes to write an essay that begins with the sentence“Nowadays cultivating independent learning ability is be coming increasingly crucial for personal development.”You can make comments,cite examples or use your personal experiences to develop your essay.You should write at least 150 words but no more than 200 words.You should copy the sentence given in quotes at thebeginning of your essay.Part II Listening Comprehension (30 minutes)特别说明：由于多题多卷，官方第三套真题的听力试题与第一套真题的一致，只是选项顺序不同，因此，本套试卷不再提供听力部分。

Part III Reading Comprehension (40 minutes) Section ADirections: In this section,there is a passage with ten blanks.You are required to select one word for each blank from a list of choices given in a word bank following the passage.Read the passage through carefully before making your choices.Each choice in the bank is identified by a letter.Please mark the corresponding letter for each item on Answer Sheet 2 with a single line through the centre.You may not use anyof the words in the bank more than onceA rainbow is a multi-colored,arc-shaped phenomenon that can appear in the sky.The colors of a rainbow are produced by the reflection and 26 _of light through water droplets( 小滴)present in the atmosphere.An observer may 27 _a rainbow to be located either near or far away,but this phenomenon is not actually located at any specific spot.Instead,the appearance of a rainbow depends entirely upon the position of the observer in 28 to the direction of light.In essence,a rainbow is an 29 illusion.Rainbows present a 30 made up of seven colors in a specific order.In fact,school children in many English-speaking countries are taught to remember the name“Roy G.Biv”as an aid for remembering the colors of a rainbow and their order.“Roy G.Biv” 31 f or:red,orange,yellow,green,blue,indigo,and violet.The outer edge of the rainbow arc is red,while the inner edge is violet.A rainbow is formed when light (generally sunlight)passes through water droplets 32 in the atmosphere. The light waves change direction as they pass through the water droplets,resulting in two processes:reflction and refraction ( 折射 ) .When light reflects off a water droplet,it simply 33_back in the opposite direction from where it 34 .When light refracts,it takes a different direction.Some individuals refer to refracted light as “bent light waves.”A rainbow is formed because white light enters the water droplet,where it bends in several different directions.When these bent light waves reach the other side of thewater droplet,they reflect back out of thedroplet instead of 35 passing through the water.Since the white light is separated inside of the water,the refracted light appears as separate colors to the human eye.Section BDirections: In this section,you are going to read a passage with ten statements attached to it.Each statement contains information given in one of theparagraphs.Identify the paragraphfrom which the information is derived.You may choose a paragraph more than once.Each paragraph is marked with a letter.Answer the questions by marking the corresponding letteronAnswer Sheet 2.Blame your worthless workdays on meeting recovery syndromeA)Phyllis Hartman knows what it's like to make one's way through the depths of office meeting hell.Managersat one of her former human resources jobs arranged so many meetings that attendees would fall asleep at the table or intentionally arrive late.With hours of her day blocked up with unnecessary meetings,she was often forced to make up herwork during overtime.“I was actually working more hoursthan I probably would have needed to get the work done,”says Hartman,who is founder and president of PGHR Consulting in Pittsburgh, PennsylvaniaB)She isn't alone in her frustration.Between 11 million and 55 million meetings are held each day in the UnitedStates,costing most organisations between 7%and 15%of their personnel budgets.Every week,employees spend about six hours in meetings,while the average manager meets for a staggering 23 hours.C)And though experts agree that traditional meetings are essential for making certain decisions and developingstrategy,some employees view them as one of the most unnecessary parts of the workday.The result is not only hundreds of billions of wasted dollars,but an annoyance of what organisational psychologists call “meeting recovery syndrome (MRS)”:time spent cooling off and regaining focus after a useless meeting.If you run to the office kitchen to get some relief with colleagues after a frustrating meeting,you're likely experiencing meeting recovery syndrome.D)Meeting recovery syndrome is a concept that should be familiar to almost anyone who has held a formaljob.It isn't ground-breaking to say workers feel fatigued after a meeting,but only in recent decades have scientists deemed the condition worthy of further investigation.With its links to organisational efficiency and employee wellbeing,MRS has atracted the attention of psychologists aware of the need to understand its precise causes and curesE)Today,in so far as researchers can hypothesise,MRS is most easily understood as a slow renewal of finitemental and physical resources.When an employee sits through an ineffective meeting their brain power is essentially being drained away.Meetings drain vitality if they last too long,fail to engage employees or turn into one-sided lectures.The conservation of resources theory,originally proposed in 1989 by Dr Stevan Hobfoll,states that psychological stress occurs when a person's resources are threatened or lost.When resources are low,a person will shift into defence to conserve their remaining supply.In the case of office meetings,where some of employees'most valuable resources are their focus,alertness and motivation,this can mean an abrupt halt in productivity as they take time to recover.F)As humans,when we transition from one task to another on the job—say from sitting in a meeting todoing normal work—it takes an effortful cognitive switch.We must detach ourselves from the previous task and expend significant mental energy to move on.If we are already drained to dangerous levels, then making the mental switch to the next thing is extra tough.It's common to see people cyber-loafing after a frustrating meeting,going and getting coffee,interrupting a colleague and telling them about the meeting,and so on.G)Each person's ability to recover from horrible meetings is different.Some can bounce back quickly,whileothers carry their fatigue until the end of the workday.Yet while no formal MRS studies are currently underway,one can loosely speculate on the length of an average employee's lag time.Switching tasks in a non-MRS condition takes about 10 to 15 minutes.With MRS,it may take as long as 45 minutes on average It's even worse when a worker has several meetings that are separated by 30 minutes.“Not enough time to transition in a non-MRS situation to get anything done,and in an MRS situation,not quite enough time torecover for the next meeting,”says researcher Joseph Allen.“Then,add the compounding of back-to-back bad meetings and we may have an epidemic on our hands.”H)In an effort to combat the side effects of MRS,Allen,along with researcher Joseph Mroz and colleagues at theUniversity of Nebraska-Omaha,published a study detailing the best ways to avoid common traps,including a concise checklist of do's and don'ts applicable to any workplace.Drawing from around 200 papers to compile their comprehensive list,Mroz and his team may now hold a remedy to the largely undefined problem of MRS.I)Mroz says a good place to startis asking ourselves ifour meetings are even necessary in the first place.If allthat's on the agenda is a quick catch-up,or some non-urgent information sharing,it may better suit the group to send around an email instead.“The second thing I would always recommend is keep the meeting as small as possible,”says Mroz.“If they don't actually have some kind ofimmediate input,then they can follow up later.They don't need to be sitting in this hour-long meeting.”Less time in meetingswould ultimately lead to more employee engagement in the meetings theydo attend,which experts agree is a proven remedy for MRS.J)Employees also feel taxed when they are invited together to meetings that don't inspire participation,says Cliff Scott,professor of organisational science.It takes precious time for them to vent their emotions, complain and try to regain focus after a pointless meeting—one of the main traps of MRS.Over time as employees find themselves tied up in more and moreunnecessary meetings—and thus dealing with increasing lag times from MRS—the waste of workday hours can feel insulting.K)Despite the relative scarcity of research behind the subject,Hartman has taught herself many of the same tricks suggested in Mroz's study,and has come a long way since her days ofbeing stuck with unnecessary meetings.The people she invites to meetings today include not just the essential employees,but also representatives from every department that might have a stake in the issue at hand.Managers like her,who seek input even from non-experts to shape their decisions,can find greater support and cooperation from their workforce,she says.L)If an organisation were to apply all 22 suggestions from Mroz and Allen's findings,the most noticeable difference would be a stark decrease in the total number of meetings on the schedule,Mroz says.Less time in meetings would ultimately lead to increased productivity,which is the ultimate objective of convening a meeting.While none of the counter-MRS ideas have been tested empirically yet,Allen says one trick with promise is for employees to identify things that quickly change their mood from negative to positive.As simple as it sounds,finding a personal happy place,going there and then coming straight back to work might be key to facilitating recovery.M)Leaders should see also themselves as “stewards of everyone else's valuable time”,adds Steven Rogelberg, author of The Surprising Science of M eetings.Having the skills to foresee potential trapsand treat employees' endurance with care allows leaders to provide effective short-term deterrents to MRS.N)Most important,however,is for organisations to awaken to the concept of meetings being flexible,says Allen.By reshaping the way they prioritise employees'time,companies can eliminate the very sources of MRS in their tracks36.Although employees are said to be fatigued by meetings,the condition has not been considered worthy offurther research until recently.37.Mroz and his team compiled a list of what to do and what not to do to remedy the problem of MRSpanies can get rid of the root cause ofMRS if they give priority to workers'time.39.If workers are exhausted to a dangerous degree,it is extremely hard for them totransition to the next task.40.Employees in America spend a lot of time attending meetings while the number of hours managers meet isseveral times more.41.Phyllis Hartman has learned by herselfmany of the ways Mroz suggested in his study and made remarkablesuccess in freeing herself from unnecessary meetings.42.When meetings continue too long or don't engage employees,they deplete vitality.43.When the time of meetings is reduced,employees will be more engaged in the meetings they do participate in.44.Some employees considermeetings one of the most dispensable parts of the workday.45.According to Mroz,if all his suggestions were applied,a very obvious change would be a steep decrease inthe number of meetings scheduled.Section CDirections: There are 2 passages in this section.Each passage is followed by some questions or unfinished statements.For each of them there are four choices marked A),B),C)and D).You should decide on the best choice and mark the corresponding letter on Answer Sheet 2 with asingle line through the centre.Passage OneQuestions 46 to 50 are based on the followingpassageSarcasm andjazzhave something surprisingly in common:You know them when you hear them.Sarcasm is mostly understood through tone of voice,which is used to portray the opposite of the literal words.For example, when someone says,“Well,tha t's exactly what I need right now,”their tone can tell you it's not what they need at all.Most frequently,sarcasm highlights an irritation or is,quite simply,meanIf you want to be happier and improve your relationships,cut out sarcasm.Why?Because sarcasm is actually hostility disguised as humor.Despite smiling outwardly,many people who receive sarcastic comments feel put down and often think the sarcastic person is rude,or contemptible.Indeed,it's not surprising that the origin of the word sarcasm derives from the Greek word“sarkazein”which literally means “to tear or strip the flesh off.”Hence,it's no wonder that sarcasm is often preceded by the word“cutting”and that it hurts.What's more,since actions strongly determine thoughts and feelings,when a person consistently acts sarcastically it may only serve to heighten their underlying hostility and insecurity.After all,when you come right down to it,sarcasm can be used as a subtle form of bullying—and most bullies are angry,insecure,or cowardly.Alternatively,when a person stops voicing negative comments,especially sarcastic ones,they may soon start to feel happier and more self-confident.Also,other people in their life benefit even more because they no longer have to hear the emotionally hurtful language of sarcasm.Now,I'm not saying all sarcasm is bad.Itmay just be betterused sparingly—like a potent spice in cooking. Too much of the spice,and the dish will be overwhelmed by it.Similarly,an occasional dash of sarcastic wit can spice up a chat and add an element ofhumor to it.But a big or steady serving of sarcasm will overwhelm the emotional flavor of any conversation and can taste very bitter to its recipient.So,tone down the sarcasm and work on clever wit instead,which is usually without any hostility and thus more appreciated by those you're communicating with.In essence,sarcasm is easy while true,harmless wit takes talent.Thus,the main difference between wit and sarcasm is that,as already stated,sarcasm is often hostility disguised as humor.It can be intended to hurt and is often bitter and biting.Witty statements are usually in response to someone's unhelpful remarks or behaviors,and the intent is to untangle and clarify the issue by emphasizing its absurdities.Sarcastic statements are expressed in a cutting manner;witty remarks are delivered with undisguised and harmless humor.46.Why does the author say sarcasm and jazz have something surprisingly in common?A)Both are recognized when heard. C)Both mean the opposite of what they appear to.B)Both have exactly the same tone. D)Both have hidden in them an evident irritation47.How do many p eople feel when they hear sarcastic comments?A)They feel hostile towards the sarcastic person. C)They feel a strong urge to retaliate.B)They feel belittled and disrespected. D)They feel incapable of disguising their irritation.48.What happens when a person consistently acts sarcastically?A)They feel their dignity greatly heightened.B)They feel increasingly insecure and hostile.C)They endure hostility under the disguise of humorD)They taste bitterness even in pleasant interactions49.What does the author say about people quitting sarcastic comments?A)It makes others happier and more self-confidentB)It restrains them from being irritating and bullying.C)It benefits not only themselves but also those around them.D)It shields them from negative comments and outright hostility.50.What is the chief difference between a speaker's wit and sarcasm?A)Their clarity. C)Their emphasis.B)Their appreciation D)Their intention.Passage TwoQuestions 51 to 55 are based on the following passage.Variability is crucially important for learning new skills.Consider learning how to serve in tennis.Should you always practise serving from the exactly same location on the court,aiming at the same spot?Although practising in more variable conditions will be slower at first,it will likely make you a better tennis player in the end.This is because variability leads to better generalisation of what is learned.This principle is found in many domains,including speech perception and learning categories.For instance, infants will struggle to learn the category“dog”if they are only exposed to Chihuahuas,instead of many different kinds of dogs“There are over ten different names for this basic principle,”says Limor Raviv,the senior investigator of a recent study.“Learning from less variable input is often fast,but may fail to generalise to new stimuli.”To identify key patterns and understand the underlying principles of variability effects,Raviv and her colleagues reviewed over 150 studies on variability and generalisation across fields,including computer science, linguistics,categorisation,visual perception and formal education.The researchers discovered that,across studies,the term variability can refer to at least four different kinds of variability,such as set size and scheduling.“The se four kinds of variability have never been directly compared—which means that we currently don't know which is most effective forlearning,”says Raviv.The impact of variability depends on whether it is relevant to the task or not.But according to the ‘Mr. Miyagiprinciple',practising seemingly unrelated skills may actuallybenefit learningof other skills.But why does variability impact learning and generalisation?One theory is that more variable input can highlight which aspects of atask are relevant and which are not.Another theory is that greater variability leads to broader generalisations.This is because variability will represent therealworld better,including atypical(非典型的)examplesA third reason has to do with the way memory works:when training is variable,learners are forced to actively reconstruct their memories“Understanding the impact of variability is important for literally every aspect ofour daily life.Beyond affecting the way we learn language,motor skills,and categories,it even has an impact on our social lives,”explains Raviv.“For example,face recognition is affected by whether people grew up in a small community or in a larger community.Exposure to fewer faces during childhood is associated with diminished face memory.”“We hope this work will spark people's curiosity and generate more work on the topi c,”concludes Raviv. “Our paper raises a lot of open questions.Can we find similar effects ofvariability beyond the brain,for instance, in the immune system?”51.What does the passage say about infants learning the category “dog”if they are exposed to Chihuahuas only?A)They will encounter some degree of difficulty.B)They will try to categorise other objects firstC)They will prefer Chihuahuas to other dog species.D)They will imagine Chihuahuas in various conditions52.What does Raviv say about the four different kinds ofvariability?A)Which of them is most relevant to the task at hand is to be confirmed.B)Why they have an impact on learning is far from being understood.C)Why they have neverbeen directly compared remains a mysteryD)Which of them is most conducive to learning is yet to be identified.53.How does one of the theories explain the importance of variability for learning new skills?A)Learners regard variable training as typical of what happens in the real world.B)Learners receiving variable training are compelled to reorganise their memories.C)Learners pay attention to the relevant aspects of a task and ignore those irrelevant.D)Learners focus on related skills instead of wasting time and effort on unrelated ones.54.What does the passage say about face recognition?A)People growing up in a small community may find it easy to remember familiar faces.B)Face recognition has a significant impact on literally every aspect of our social lives.C)People growing up in a large community can readily recognise any individual faces.D)The size of the community people grow up in impacts their face recognition ability.55.What does Raviv hope to do with their research work?A)Highlight which aspects of a task are relevant and which are not to learning a skill.B)Use the principle of variability in teaching seemingly unrelated skills in education.C)Arouse people's interest in variability and stimulate more research on the topic.D)Apply the principle of variability to such fields of study as the immune system.Part IV Translation(30 minutes) Directions: For this part,you are allowed 30 minutes to translate a passage from Chinese into English.You should write youransweron AnswerSheet 2.扇子自古以来就深受中国人喜爱，但现在已不只是消暑纳凉的工具，而更多地作为艺术品供人欣赏。

检测的英文作文模板

检测的英文作文模板英文回答：Detection is a crucial aspect of security, surveillance, and medical diagnosis, among other fields. It involves identifying and locating specific targets or anomalieswithin a larger environment. Here are some key concepts related to detection:Pattern Recognition: Detection often involves recognizing patterns within data or signals. This can be achieved through algorithms that analyze data and identify anomalies, trends, or objects of interest.Signal Processing: In many applications, detection involves processing signals to extract relevant information. Techniques such as filtering, noise reduction, and feature extraction are employed to enhance signal quality and facilitate target identification.Sensor Data Analysis: Detection systems often rely on data collected from sensors. The analysis of sensor data, such as temperature, motion, or electromagnetic signals, can yield valuable insights for detection purposes.Image Analysis: Image analysis is a key technique in detection applications for identifying objects or anomalies in visual data. Computer vision algorithms are used to process and analyze images to extract meaningful information and identify targets.Medical Diagnosis: In medical settings, detection plays a vital role in diagnosing diseases and health conditions. Methods such as medical imaging (e.g., X-rays, MRI scans) allow medical professionals to detect abnormalities in the body.Surveillance: Detection is essential for surveillance systems used in security and law enforcement. Video cameras and other sensors are employed to detect suspiciousactivity or identify wanted individuals in public spaces or controlled environments.Security Systems: Intrusion detection systems rely on sensors and algorithms to detect unauthorized access or breaches in security perimeters. These systems can trigger alarms or alert security personnel in the event of a detected intrusion.Object Tracking: Detection often involves tracking the movement or trajectory of objects over time. This can be achieved through computer vision techniques, radar systems, or other tracking technologies.中文回答：检测是安全、监控和医疗诊断等领域的关键方面。

新视野大学英语(第三版)读写教程2课后答案unit3

第三单元Text AEx.11. Because people in different life stages are confronted with different problems and setbacks and each group of people in a particular time period have their particular worries and pains.2.The norm refers to the general consensus that as soon as students graduated from college, they would enter adulthood and be able to find an ideal job leading to their career.3. They are recognized as a new life stage that comes after high school graduation, continues through college and leads to starting a family and having a career.4. Because nowadays so many young people are following this new lifestyle that it has become a trend. As long as the economic situation continues its long slide, this new stage is unavoidable.5. Unlike their parents, a large number of young people are now delaying marriage, child bearing, and even employment during their odyssey years.6. They often resent the pressure they’re feeling and keep a distance from their parents or even run away from home. Many also resort to computer games, iPods, iPhones, or iPads.7. Their parents feel more anxious and upset seeing their children’s odyssey years continue to stretch without a clear direction.8. The author thinks as people are getting to know the odyssey years better, both parents and their children can tackle this phase better. For parents, they can understand their children more; for children, they can explore and discover themselves with a positive attitude.Ex.31. peculiar2. radical3. phase4. sensible5. predict6. labeled7. resent8. witnessed9. equivalent 10. parallelsEx.4-icChaotic dramatic academy-ionDepression detection erosionClassification confuse cooperate dictate-istRightist journalEx.51. journal2. chaotic3. cooperate4. erosion5. dramatic6. confuse7. academy 8. rightists 9. depression 10. dictate 11. detection 12. classificationEx. 6K E A C L I G N H OEx.71. saddled with2. back off3. gives way to4. resorted to5. make allowances for6. wonder at7. prior to8. based upon/onEx.8The odyssey years are certainly a very complicated phase of life for young people. Not only do these young people need to overcome many difficulties, they also have to face many challenges from their parents. The differences between parents and children can be well observed in their completely different attitudes and views.First, they differ in their attitude toward life. Parents always wonder what has gone wrong with the new generation. They feel that during their time, young boys and girls were better behaved, more obedient and had greater respect for elders. Young people, on the other hand, feel that they are capable enough to learn on their own rather than lean heavily on the older generation for guidance. Young people do not like to be spoon-fed by their parents.The differences also appear in the way the two generations look at things. For example, the parents’ generation never understood Elvis and the Beatles. Because they couldn’t understand what was going on, they were frequently opposed to them and saw rock as “the devil’s music”. Young people, however, are crazy about the modern music and would love to listen to it for a hundred times a day. Wherever they go, they’ll have their iPod with them.In conclusion, it’s very difficult for parents and their youngsters to get along due to their distinctive attitudes and the way they view things. To fill this gap, both parents and their grown children need to be more understanding to each other.Ex.9作为美国文化价值体系的一个重要组成部分，“个人主义”受到大多数美国人的推崇。

detection verification 感叹号

Detection Verification: A Comprehensive OverviewIntroductionDetection verification is a crucial process in various fields, including security, technology, and healthcare. It involves the identification and confirmation of detected objects or events to ensure accuracy and reliability. This article provides a comprehensive overview of detection verification, including its definition, applications, techniques, challenges, and future prospects.DefinitionDetection verification refers to the process of confirming the presence or absence of an object or event identified by a detection system. It aims to minimize false positives and false negatives by validating the detection results through additional analysis or human intervention. The ultimate goal is to enhance the reliability and trustworthiness of detection systems.ApplicationsSecurityDetection verification plays a vital role in security systems such as surveillance cameras, access control systems, and intrusion detection systems. By verifying detected events like potential threats or unauthorized access attempts, security personnel can quickly respond to real incidents while minimizing false alarms.TechnologyIn the field of technology, detection verification is used in various areas such as computer vision, natural language processing (NLP), and machine learning. For example, in computer vision applications like object recognition or facial recognition systems, verification techniques are employed to confirm the accuracy of detected objects or individuals.HealthcareIn healthcare settings, detection verification is essential for medical imaging techniques such as X-rays and MRIs. Radiologists useverification methods to ensure accurate identification of abnormalities or diseases in patients’ scans. This helps in providing precise diagnoses and appropriate treatments.Techniques for Detection Verification1.Manual Verification: Human experts manually review the detectedobjects or events to confirm their presence or absence. Thistechnique ensures high accuracy but can be time-consuming andsubjective.2.Rule-based Verification: Predefined rules are applied to verifythe detected objects or events based on specific criteria. Forexample, an intrusion detection system may verify an alarm bychecking if multiple sensors have triggered simultaneously.3.Machine Learning Verification: Machine learning algorithms aretrained to verify the detection results based on a labeled dataset.The algorithms learn patterns and characteristics of truepositives and negatives, improving the accuracy of verification. 4.Statistical Analysis: Statistical methods are used to analyze thedetection results and calculate probabilities or confidence levels.By setting appropriate thresholds, verification can be performedbased on statistical significance.Challenges in Detection Verificationplex Environments: Detection verification becomes challengingin complex environments with high levels of noise, occlusions, oroverlapping objects. These factors can lead to false detectionsand require sophisticated techniques for accurate verification.2.Real-time Verification: In applications where real-time responseis critical, such as security systems, performing efficientverification within tight time constraints can be challenging.Fast and reliable algorithms are required to ensure timelyresponses.3.Adversarial Attacks: Malicious actors may attempt to deceivedetection systems by introducing adversarial inputs thatmanipulate the detection results. Detecting and verifying suchattacks pose significant challenges for detection verificationtechniques.4.Scalability: As the volume of data increases, scalability becomescrucial for efficient detection verification. Handling largedatasets in real-time while maintaining high accuracy is achallenge that needs to be addressed.Future ProspectsDetection verification is an evolving field with promising future prospects. Advances in machine learning, deep learning, and artificial intelligence will contribute to more robust and accurate verification techniques. Additionally, the integration of multiple sensors and data fusion techniques will enhance the reliability of detection systems.Furthermore, research efforts should focus on addressing the challenges posed by complex environments and adversarial attacks through innovative algorithms and robust architectures. Collaborative studies between academia, industry, and government entities can accelerate advancements in detection verification technology.In conclusion, detection verification plays a critical role in ensuring the accuracy and reliability of detected objects or events across various domains such as security, technology, and healthcare. By employing different techniques and overcoming challenges, detection verification contributes to enhanced decision-making processes and improved overall system performance.Note: This article provides a comprehensive overview of detection verification. However, due to the requirement of avoiding sensitive topics or vocabulary in China, some specific examples or discussions related to these topics have been omitted.。

目标检测算法选择题

目标检测算法选择题In recent years, target detection algorithms have become increasingly important in various fields such as autonomous driving, surveillance systems, and medical imaging. These algorithms play a crucial role in identifying and locating objects within images or video streams. One of the key challenges in target detection is achieving high accuracy while maintaining real-time performance.近年来，目标检测算法在自动驾驶、监控系统和医学成像等领域变得越来越重要。

这些算法在识别和定位图像或视频流中的对象方面发挥着关键作用。

目标检测中的一个关键挑战是在保持实时性能的同时实现高准确度。

There are several popular target detection algorithms in use today, including YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN (Region-based Convolutional Neural Networks). Each of these algorithms has its own strengths and weaknesses, and the choice of algorithm depends on the specific requirements of the application. YOLO, for example, is known for its speed and efficiency, making it a popular choice for real-timeapplications. On the other hand, Faster R-CNN is often preferred for its high accuracy and robustness.如今，有几种流行的目标检测算法，包括YOLO（You Only Look Once）、SSD（Single Shot MultiBox Detector）和Faster R-CNN（Region-based Convolutional Neural Networks）。

目标检测计算量的英语

目标检测计算量的英语Computational Requirements for Object Detection.Object detection is a crucial task in computer vision, aiming to identify and locate objects of interest within a given image. It involves complex operations that require significant computational resources. Understanding the computational requirements of object detection is essential for developing efficient and effective systems.1. Introduction.Object detection involves two primary tasks: classification and localization. Classification refers to identifying the type of object present in the image, while localization involves determining the precise position of the object within the image. These tasks are typically performed using deep learning algorithms, specifically convolutional neural networks (CNNs).2. CNN Architecture.CNNs are the backbone of modern object detection systems. They consist of multiple layers that perform convolutions, pooling, and activation functions to extract hierarchical features from the input image. The complexity of a CNN architecture directly affects the computational requirements of object detection.3. Computational Complexity.The computational complexity of object detection depends on several factors, including the size of the input image, the depth and width of the CNN architecture, and the number of object categories to be detected. Larger input images and deeper/wider CNN architectures require more computational resources.4. Memory Requirements.In addition to computational power, object detection systems also require significant memory resources. This isparticularly true for systems that employ multiple CNNs or perform multi-scale processing. The memory requirements depend on the number of parameters in the CNN architecture, the batch size during training, and the intermediate representations generated during inference.5. Hardware Considerations.To meet the computational and memory requirements of object detection, powerful hardware is necessary. Graphics processing units (GPUs) are commonly used for parallel processing, enabling faster training and inference. However, even with GPUs, training large-scale object detection models can take days or even weeks.6. Software Frameworks.Software frameworks such as TensorFlow, PyTorch, and Caffe provide efficient implementations of CNNs and other deep learning algorithms. These frameworks optimize the computational performance of object detection systems by leveraging GPU acceleration and other hardware-specificoptimizations.7. Conclusion.Object detection is a computationally intensive task that requires powerful hardware and optimized software frameworks. Understanding the computational requirements of object detection is crucial for developing efficient and effective systems. Future research in this area can focus on developing lighter CNN architectures, optimizing memory usage, and leveraging new hardware technologies to further improve the performance of object detection systems.。

第7练中国深海探测新阶段(学生版)-高考英语时文精读及微写作训练

第7练中国深海探测新阶段Step 1: 阅读理解China has delivered a successful coordinated deep-sea exploration trial with the country's unmanned submersible Haidou-1 and the manned Fendouzhe, or Striver, marking major strides from "entering" to "expediting" in the field.With the help of Striver, Haidou-1 has become the first in the world to conduct a large-scale near-bottom topographic survey in the western depression of the Challenger Deep during a scientific expedition to the Mariana Trench.Tang Y uangui, chief engineer of the vehicle, said it has three control modes - autonomous mode, remote control mode and mixed mode - enabling it to flexibly respond to extreme marine conditions of all depths, and carry out scientific research activities, including abyssal exploration.Experts believe that the Haidou-1 has shown significant advantages in application areas including target locating, seafloor topography detection, real-time image broadcasting and identification of target objects, which has provided a very effective means to support the 10,000-meter abyssal scientific research activities.The Haidou-1 has broken several world records for unmanned submersibles, including the first full-coverage acoustic cruise of the western depression of the Challenger Deep, a diving depth of 10,908 meters, and near-seabed navigation distance of more than 14 kilometers, according to Tang.1. What is the function of Striver?A. To mark major advances in deep-sea exploration.B. To break several world records.C. To help Haidou-1 conduct deep-sea survey.D. To increase the depth of exploration.2.What does the underlined word “flexibly” in paragraph 3 mean?A. graduallyB. frequentlyC. smartlyD. fluently3. What is Tang's attitude to China’s deep-sea exploration?A. Disappointing.B. Hopeful.C. Reserved.D. Skeptical.4. Which of the following can be the best title of the text?A. China’s deep-sea exploration enters a new period.B. Haidou-1 and Striver succeeded in exploring deep sea.C. Haidou-1 conducted the first topographic surveyD. China ranks first in deep-sea exploration.Step 2: 材料再利用1.coordinate 协调If you coordinate an activity, you organize the various people and things involved in it.如果你协调一项活动，你就组织了其中涉及的各种人和事物。

community detection

– Can complement other kinds of information, e.g. user profile – Help network visualization and navigation – Provide basic information for other tasks, e.g. recommendation Note that each of the above three points can be a research topic.
14
Taxonomy of Community Criteria
• Criteria vary depending on the tasks • Roughly, community detection methods can be divided into 4 categories (not exclusive): • Node-Centric Community
• Recursively apply the following pruning procedure
– Sample a sub-network from the given network, and find a clique in the sub-network, say, by a greedy approach – Suppose the clique above is size k, in order to find out a larger clique, all nodes with degree <= k-1 should be removed.
Communities in Twitter
Communities of Personal Social Network

Outof-domain detection based on confidence measures from multiple topic classification

OUT-OF-DOMAIN DETECTION BASED ON CONFIDENCE MEASURES FROMMULTIPLE TOPIC CLASSIFICATIONIan ne1,2,Tatsuya Kawahara1,2,Tomoko Matsui3,2,Satoshi Nakamura21School of Informatics,Kyoto UniversitySakyo-ku,Kyoto606-8501,Japan2ATR Spoken Language Translation Laboratories2-2-2Hikaridai,Seika-cho,Soraku-gun,Kyoto619-0288,Japan3The Institute of Statistical Mathematics4-6-7Minami-Azabu,Mitato-ku,Tokyo106-8569,JapanABSTRACTOne signiﬁcant problem for spoken language systems is how to cope with users’OOD(out-of-domain)utterances which cannot be handled by the back-end system.In this paper,we propose a novel OOD detection framework,which makes use of classiﬁcation con-ﬁdence scores of multiple topics and trains a linear discriminant in-domain veriﬁer using GPD.Training is based on deleted inter-polation of the in-domain data,and thus does not require actual OOD data,providing high portability.Three topic classiﬁcation schemes of word N-gram models,LSA,and SVM are evaluated, and SVM is shown to have the greatest discriminative ability.In an OOD detection task,the proposed approach achieves an ab-solute reduction in EER of6.5%compared to a baseline method based on a simple combination of multiple-topic classiﬁcations. Furthermore,comparison with a system trained using OOD data demonstrates that the proposed training scheme realizes compara-ble performance while requiring no knowledge of the OOD data set.1.INTRODUCTIONMost spoken language systems,excluding general-purpose dicta-tion systems,operate over deﬁnite domains as a user interface to a service provided by the back-end system.However,users,es-pecially novice users,do not always have an exact concept of the domains served by the system.Thus,they often attempt utterances that cannot be handled by the system.These are referred to as OOD(out-of-domain)in this paper.Deﬁnitions of OOD for three typical spoken language systems are described in Table1.For an improved interface,spoken language systems should predict and detect such OOD utterances.In order to predict OOD utterances,the language model should allow some margin in its coverage.A mechanism is also required for the detection of OOD utterances,which is addressed in this paper.Performing OOD de-tection will improve the system interface by enabling users to de-termine whether to reattempt the current task after being conﬁrmed as in-domain,or to halt attempts due to being OOD.For exam-ple,in a speech-to-speech translation system,an utterance may be in-domain but unable to be accurately translated by the back-end system;in this case the user is requested to re-phrase the input utterance,making translation possible.In the case of an OOD ut-terance,however,re-phrasing will not improve translation,so theTable1.Deﬁnitions of Out-of-domain for various systems System Out-of-Domain deﬁnition Spoken Dialogue User’s query does not relate to back-endinformation sourceCall Routing User’s query does not relate to anycall destinationSpeech-to-Speech Translation system does not provide Translation coverage for offered topicuser should be informed that the utterance is OOD and provided with a list of tractable domains.Research on OOD detection is limited,and conventional stud-ies have typically focused on using recognition conﬁdences for re-jecting erroneous recognition outputs(e.g.,[1],[2]).In these ap-proaches there is no discrimination between in-domain utterances that have been incorrectly recognized and OOD utterances,and thus effective user feedback cannot be generated.One area where OOD detection has been successfully applied is call routing tasks such as that described in[3].In this work,classiﬁcation models are trained for each call destination,and a garbage model is ex-plicitly trained to detect OOD utterances.To train these models,a large amount of real-world data is required,consisting of both in-domain and OOD training examples.However,reliance on OOD training data is problematic:ﬁrst,an operational on-line system is required to gather such data,and second,it is difﬁcult to gain an appropriate distribution of data that will provide sufﬁcient cover-age over all possible OOD utterances.In the proposed approach,the domain is assumed to consist of multiple sub-domain topics,such as call destinations in call-routing,sub-topics in translation systems,and sub-domains in com-plex dialogue systems.OOD detection is performed byﬁrst cal-culating classiﬁcation conﬁdence scores for all in-domain topic classes and then applying an in-domain veriﬁcation model to this conﬁdence vector,which results in an OOD decision.The veriﬁ-cation model is trained using GPD(gradient probabilistic descent) and deleted interpolation,allowing the system to be developed by using only in-domain data.2.SYSTEM OVERVIEWIn the proposed framework,the training set is initially split into multiple topic classes.In the work described in this paper,topic classes are predeﬁned and the training set is hand-labeled appropri-«by applying topic-dependent language models.We demonstratedthe effectiveness of such an approach in[4].An overview of the OOD detection framework is shown inFigure1.First,speech recognition is performed by applying ageneralized language model that covers all in-domain topics,andN-best recognition hypotheses(s1,...,s N)are generated.Next, topic classiﬁcation conﬁdence scores(C(t1|X),...,C(t M|X)) are generated for each topic class based on these hypotheses.Fi-nally,OOD detection is performed by applying an in-domain veri-ﬁcation model G in−domain(X)to the resulting conﬁdence vector. The overall performance of the proposed approach is affected by the accuracy of the topic classiﬁcation method and the in-domain veriﬁcation model.These aspects are described in detail in the following sections.3.TOPIC CLASSIFICATIONIn this paper three topic classiﬁcation schemes are evaluated:topic-dependent word N-gram,LSA(latent semantic analysis),and SVM (support vector machines).Based on a given feature set,topic models are trained using the above methods.Topic classiﬁcation is performed and conﬁdence scores(in the range[0,1])are calculated by applying a sigmoid transform to these results.When classiﬁca-tion is applied to an N-best speech recognition result,conﬁdence scores are calculated as shown in Equation1.Topic classiﬁcation is applied independently to each N-best hypothesis,and these are linearly combined by weighting each with the posterior probability of that hypothesis given by ASR.C(t j|X)=NXi=1p(s i|X)C(t j|s i)(1)C(t j|X):conﬁdence score of topic t j for input utterance Xp(s i|X):posterior probability of i-th best sentencehypothesis s i by ASRN:number of N-best hypotheses3.1.Topic Classiﬁcation FeaturesVarious feature sets for topic classiﬁcation are investigated.A feature vector consists of either word baseform(word token with no tense information;all variants are merged),full-word(surface form of words,including variants),or word+POS(part-of-speech) tokens.The inclusion of N-gram features that combine multiple neighboring tokens is also investigated.Appropriate cutoffs are applied during training to remove features with low occurrence.3.2.Topic-dependent Word N-gramIn this approach,N-gram language models are trained for each topic class.Classiﬁcation is performed by calculating the log-likelihood of each topic model for the input sentence.Topic clas-siﬁcation conﬁdence scores are calculated by applying a sigmoid transform to this log-likelihood measure.tent Semantic AnalysisLSA(latent semantic analysis)[5]is a popular technique for topic classiﬁcation.Based on a vector space model,each sentence is represented as a point in a large dimension space,where vector components relate to the features described in Section3.1.Be-cause the vector space tends to be extremely large(10,000-70,000 features),traditional distance measures such as the cosine distance become unreliable.To improve performance,SVD(singular value decomposition)is applied to reduce the large space to100-300di-mensions.Each topic class is represented as a single document vector composed of all training sentences,and projected to this reduced space.Classiﬁcation is performed by projecting the vector represen-tation of the input sentence to the reduced space and calculating the cosine distance between this vector and each topic class vec-tor.The resulting distance is normalized by applying a sigmoid transform generating classiﬁcation conﬁdence scores.3.4.Support Vector MachinesSVM(support vector machines)[6]is another popular classiﬁca-tion ing a vector space model,SVM classiﬁers are trained for each in-domain topic class.Sentences that occur in the training set of that topic are used as positive examples and the remainder of the training set is used as negative examples.Classiﬁcation is performed by feeding the vector representa-tion of the input sentence to each SVM classiﬁer.The perpendicu-lar distance between this vector and each SVM hyperplane is used as the classiﬁcation measure.This value is positive if the input sen-tence is in-class and negative otherwise.Again,conﬁdence scores are generated by applying a sigmoid transform to this distance.4.IN-DOMAIN VERIFICATIONTheﬁnal stage of OOD detection consists of applying an in-domain veriﬁcation model G in−domain(X)to the vector of conﬁdence scores generated during topic classiﬁcation.We adopt a linear dis-criminant model(Eqn.2).Linear discriminant weights (λ1,...,λM)are applied to the conﬁdence scores from topic clas-siﬁcation(C(t1|X),...,C(t M|X)),and a threshold(ϕ)is ap-plied to obtain a binary decision of in-domain or OOD.G in−domain(X)=(1ifPMj=1λj C(t j|X)≥ϕ(in-domain)0otherwise.(OOD)(2)C(t j|X):conﬁdence score of topic t j for input utterance XM:number of topic classes4.1.Training using Deleted InterpolationThe in-domain veriﬁcation model is trained using only in-domain data.An overview of the proposed training method combining GPD(gradient probabilistic descent)[7]and deleted interpolation¬Table2.Deleted Interpolation based Training for each topic i in[1,M]set topic i as temporary OODset remaining topic classes as in-domaincalculate(λ1,...,λM)using GPD(λi excluded) average(λ1,...,λM)over all iterationsTable3.Experiment CorpusDomain:Basic Travel ExpressionsIn-Domain:11topics(transit,accommodation,...)OOD:1topic(shopping)Training Set:11topics,149540sentences(in-domain data only) Lexicon Size:17000wordsTest set:In-Domain:1852utterancesOOD:138utterancesis given in Table2.Each topic is iteratively set to be temporar-ily OOD,and the classiﬁer corresponding to this topic is removed from the model.The discriminant weights of the remaining topic classiﬁers are estimated using GPD.In this step,the temporary OOD data is used as negative training examples,and a balanced set of the remaining topic classes are used as positive(in-domain) examples.Upon completion of estimation by GPD,theﬁnal model weights are calculated by averaging over all interpolation steps. In the experimental evaluation,a topic-independent class“basic”covering general utterances exists,which is not removed during deleted interpolation.4.2.Incorporation of Topic-dependent VeriﬁerImproved OOD detection accuracy can be achieved by applying more elaborate veriﬁcation models.In this paper,a model consist-ing of multiple linear discriminant functions is investigated.Topic dependent functions are added for topics not modeled sufﬁciently. Their weights are trained speciﬁcally for verifying that topic.For veriﬁcation,the topic with maximum classiﬁcation conﬁdence is selected,and a topic-dependent function is applied if one exists, otherwise a topic-independent function(Eqn.2)is applied.5.EXPERIMENTAL EV ALUATIONThe ATR BTEC corpus[8]is used to investigate the performance of the proposed approach.An overview of the corpus is given in Table3.In this experiment,we use“shopping”as OOD of the speech-to-speech translation system.The training set consisting of11in-domain topics is used to train both the language model for speech recognition and the topic classiﬁcation models.Recogni-tion is performed with the Julius recognition engine.The recognition performance for the in-domain(ID)and OOD test sets are shown in Table4.Although the OOD test set has much greater error rates and out-of-vocabulary rate compared with the in-domain test set,more than half of the utterances are correctly recognized,since the language model covers the general travel do-main.This indicates that the OOD set is related to the in-domain task,and discrimination between these sets will be difﬁcult.System performance is evaluated by the following measures: FRR(False Rejection Rate):Percentage of in-domainutterances classiﬁed as OOD FAR(False Acceptance Rate):Percentage of OOD utterancesclassiﬁed as in-domainEER(Equal Error Rate):Error rate at an operating pointwhere FRR and FAR are equalTable4.Speech Recognition Performance#Utt.WER(%)SER(%)OOV(%) In-Domain18527.2622.40.71 Out-of-Domain13812.4945.3 2.56 WER:Word Error Rate SER:Sentence Error RateOOV:Out of V ocabularyparison of Feature Sets&Classiﬁcation Models Method Token Set Feature Set#Feat.EER(%)SVM base-form1-gram877129.7SVM full-word1-gram989923.9SVM word+POS1-gram1000623.3SVM word+POS1,2-gram4075421.7SVM word+POS1,2,3-gram7306519.6LSA word+POS1-gram1000623.3LSA word+POS1,2-gram4075424.1LSA word+POS1,2,3-gram7306523.0 NGRAM word+POS1-gram1000624.8 NGRAM word+POS1,2-gram4075425.2 NGRAM word+POS1,2,3-gram7306524.2 SVM:Support Vector Machines LSA:Latent Semantic Analysis NGRAM:Topic-dependent Word N-gram5.1.Evaluation of Topic Classiﬁcation and Feature Sets First,the discriminative ability of various feature sets as described in Section3.1were investigated.Initially,SVM topic classiﬁca-tion models were trained for each feature set.A closed evaluation was performed for this preliminary experiment.Topic classiﬁca-tion conﬁdence scores were calculated for the in-domain and OOD test sets using the above SVM models,and used to train the in-domain veriﬁcation model using GPD.During training,in-domain data were used as positive training examples,and OOD data were used as negative examples.Model performance was evaluated by applying this closed model to the same conﬁdence vectors used for training.The performance in terms of EER is shown in theﬁrst section of Table5.The EER when word-baseform features were used was29.7%. Full-word or word+POS features improved detection accuracy sig-niﬁcantly:with EERs of23.9%and23.3%,respectively.The in-clusion of context-based2-gram and3-gram features further im-proved detection performance.A minimum EER of19.6%was obtained when3-gram features were incorporated.Next,LSA and N-gram-based classiﬁcation models were eval-uated.Both approaches showed lower performance than SVM, and the inclusion of context-based features did not improve per-formance.SVM with a feature set containing1-,2-,and3-gram offered the lowest OOD detection error rate,so it is used in the following experiments.5.2.Deleted Interpolation-based TrainingNext,performance of the proposed training method combining GPD and deleted interpolation was evaluated.We compared the OOD detection performances of the proposed method(proposed), a reference method in which the in-domain veriﬁcation model was trained using both in-domain and OOD data(as described in Sec-tion5.1)(closed-model),and a baseline system.In the baseline system,topic detection was applied and an utterance was classi-ﬁed as OOD if all binary SVM decisions were negative.Other-¬10203040506070010203040506070FRRF A RFig.2.OOD Detection Performance on Correct Transcriptions102030baselineproposedclosed-modelVerification MethodE r r o r R a t e (%)Fig.3.OOD Detection Performance on ASR Result wise it was classi ﬁed as in-domain.The ROC graph of the three systems obtained by altering the veri ﬁcation threshold (ϕin Eqn.2)is shown in Figure 2.The baseline system has a FRR of 25.2%,a FAR of 29.7%,and an EER of 27.7%.The proposed method provides an abso-lute reduction in EER of 6.5%compared to the baseline system.Furthermore,it offers comparable performance to the closed eval-uation case (21.2%vs.19.6%)while being trained with only in-domain data.This shows that the deleted interpolation approach is successful in training the OOD detection model in the absence of OOD data.5.3.Evaluation with ASR ResultsNext,the performances of the above three systems were evaluated on a test set of 1990spoken utterances.Speech recognition was performed and the 10-best recognition results were used to gen-erate a topic classi ﬁcation vector.The FRR,FAR and percentage of falsely rejected utterances with recognition errors are shown in Figure 3.The EER of the proposed system when applied to the ASR re-sults is 22.7%,an absolute increase of 1.5%compared to the case for the correct transcriptions.This small increase in EER suggests that the system is strongly robust against recognition errors.Fur-ther investigation showed that the falsely rejected set had a SER of around 43%,twice that of the in-domain test set.This suggests that utterances that incur recognition errors are more likely to be rejected than correctly recognized utterances.5.4.Effect of Topic-dependent Veri ﬁcation ModelFinally,the topic-dependent in-domain veri ﬁcation model described in Section 4.2was also incorporated.Evaluation was performed on spoken utterances as in the above section.The addition of atopic-dependent function (for the topic “basic ”)reduced the EER to 21.2%.The addition of further topic-dependent functions,how-ever,did not provide signi ﬁcant improvement in performance over the two function case.The topic class “basic ”is the most vague and is poorly modeled by the topic-independent model.A topic-dependent function effectively models the complexities of this class.6.CONCLUSIONSWe proposed a novel OOD (out-of-domain)detection method based on con ﬁdence measures from multiple topic classi ﬁcation.A novel training method combining GPD and deleted interpolation was in-troduced to allow the system to be trained using only in-domain data.Three classi ﬁcation methods were evaluated (topic depen-dent word N-gram,LSA and SVM),and SVM-based topic classi ﬁ-cation using word and N-gram features proved to have the greatest discriminative ability.The proposed approach reduced OOD detection errors by 6.5%compared to the baseline system based on a simple combination of binary topic classi ﬁcations.Furthermore,it provides similar per-formance to the same system trained on both in-domain and OOD data (EERs of 21.2%and 19.6%,respectively)while requiring no knowledge of the OOD data set.Addition of a topic dependent veri ﬁcation model provides a further reduction in detection errors.Acknowledgements:The research reported here was supported in part by a contract with the Telecommunications Advancement Organization of Japan entitled,”A study of speech dialogue trans-lation technology based on a large corpus”.7.REFERENCES[1]T.Hanzen,S.Seneff,and J.Polifroni.Recognition con ﬁdenceand its use in speech understanding systems.In Computer Speech and Language ,2002.[2]C Ma,M.Randolph,and J.Drish.A support vector machines-based rejection technique for speech recognition.In ICASSP ,2001.[3]P.Haffner,G.Tur,and J.Wright.Optimizing svms for com-plex call classi ﬁcation.In ICASSP ,2003.[4]ne,T.Kawahara,and nguage model switch-ing based on topic detection for dialog speech recognition.In ICASSP ,2003.[5]S.Deerwester,S.Dumais,G.Furnas,ndauer,andR.Harshman.Indexing by latent semantic analysis.In Journ.of the American Society for information science,41,pp.391-407,1990.[6]T.Joachims.Text categorization with support vector ma-chines.In Proc.European Conference on Machine Learning ,1998.[7]S.Katagiri,C.-H.Lee,and B.-H.Juang.New discriminativetraining algorithm based on the generalized probabilistic de-scent method.In IEEE workshop NNSP ,pp.299-300,1991.[8]T.Takezawa,M.Sumita,F.Sugaya,H.Yamamoto,and Ya-mamoto S.Towards a broad-coverage bilingual corpus for speech translation of travel conversations in the real world.In Proc.LREC,pp.147-152,2002.¬。

mycommuniry英语作文

mycommuniry英语作文My community is a small town located in the outskirts of a major city. It is a peaceful and tight-knit community where everyone knows each other. There are several small businesses, a few restaurants, and a community center that serves as a hub for social activities.What kind of events or activities do people in your community usually participate in?People in my community usually participate in events and activities that are organized by the community center. These include fitness classes, art classes, and game nights. There are also seasonal events like holiday parties and summer barbecues that are organized by volunteers in the community.你们社区的人们通常参与哪些活动？我们社区的人们通常参与社区中心组织的活动。

这些活动包括健身课程、艺术课程和游戏之夜。

还有一些季节性的活动，比如假日聚会和夏季烧烤会，都是由社区的志愿者组织的。

What kind of challenges does your community face?One of the biggest challenges our community faces is transportation. Many people rely on cars to get around, as public transportation is limited. Another challenge is affordable housing, as the cost of living in the area has increased in recent years.你们社区面临哪些挑战？我们社区面临的最大挑战之一是交通。

中英文文字检测与识别项目

中英文文字检测与识别项目Text detection and recognition technology has become increasingly important in our daily lives. 中英文文字检测与识别技术在我们日常生活中变得越来越重要。

With the advancement of technology, we are able to easily convert printed text into digital form, making it more accessible and convenient for users. 随着技术的进步，我们能够轻松地将印刷文字转换为数字形式，使用户更容易获取和方便使用。

This technology has applications in various fields such as image processing, document analysis, and even in real-time translation. 这项技术在各个领域都有应用，如图像处理、文档分析，甚至在实时翻译中也有用武之地。

One of the key challenges in text detection and recognition is dealing with the diversity and complexity of languages. 文字检测与识别中的一个关键挑战是如何处理语言的多样性和复杂性。

Languages differ in their script, structure, and even in the way they are written, which can make it difficult for the system to accurately detect and recognize text. 不同语言的书写方式、结构甚至书写方式都不尽相同，这使得系统很难准确检测和识别文字。

全民核酸的优缼点英语作文

全民核酸的优缼点英语作文Title: Pros and Cons of Universal Nucleic Acid Testing。

Universal nucleic acid testing (NAT) has emerged as a crucial tool in combating the spread of infectious diseases, especially in the context of pandemics like the one caused by COVID-19. This essay will delve into the advantages and disadvantages of implementing universal nucleic acid testing.Advantages:1. Early Detection of Cases: One of the primary advantages of universal NAT is its ability to detect cases early, even among asymptomatic individuals. This early detection is instrumental in controlling the spread of infectious diseases by isolating positive cases promptly.2. Containment of Outbreaks: By identifying andisolating positive cases early, universal NAT helps incontaining outbreaks more effectively. It prevents further transmission chains from developing and reduces the overall burden on healthcare systems.3. Data for Public Health Decision Making: Universal NAT generates a wealth of data about the prevalence and spread of the disease in the population. This data can be used by public health authorities to make informeddecisions regarding resource allocation, implementation of control measures, and policy development.4. Reduced Transmission: By identifying and isolating infectious individuals, universal NAT helps in reducing transmission rates within communities. This is particularly crucial in the case of highly contagious diseases like COVID-19.5. Protection of Vulnerable Populations: Universal NAT helps in protecting vulnerable populations such as the elderly, individuals with underlying health conditions, and healthcare workers by identifying and isolating cases early, thereby reducing their risk of exposure.Disadvantages:1. Cost: Implementing universal NAT on a large scale can be costly, both in terms of financial resources and manpower. The cost of testing kits, laboratory equipment, and personnel required for sample collection and analysis can be substantial.2. Logistical Challenges: Conducting universal NAT on a large scale presents significant logistical challenges, including the need for widespread distribution of testing kits, establishment of testing centers, and coordination of sample collection and analysis.3. False Positives and Negatives: Nucleic acid tests are not foolproof and can sometimes yield false positive or false negative results. False positives can lead to unnecessary anxiety and burden on healthcare systems, while false negatives can result in undetected cases and continued transmission of the disease.4. Invasion of Privacy: Universal NAT involves the collection of biological samples from individuals, which raises concerns about invasion of privacy. There may also be concerns about the storage and use of genetic information obtained through testing.5. Stigmatization: Individuals who test positive for the disease may face stigmatization and discrimination, which can have negative social and psychological consequences.In conclusion, universal nucleic acid testing offers several advantages in terms of early detection, containment of outbreaks, data generation, and protection of vulnerable populations. However, it also presents challenges such as cost, logistical issues, reliability of test results, privacy concerns, and stigmatization. Therefore, careful consideration must be given to both the benefits and drawbacks of implementing universal NAT in public health strategies.。

Community Detection in Dynamic Social Networks

Community Detection in Dynamic Social Networks Nathan Aston;Wei Hu【期刊名称】《通讯与网络（英文）》【年(卷),期】2014(6)2【摘要】There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity.【总页数】13页(P124-136)【关键词】Community;Detection;Dynamic;Social;Networks;Density;Genetic;Algorithms【作者】Nathan Aston;Wei Hu【作者单位】Department of Computer Science, Houghton College, Houghton, USA【正文语种】中文【中图分类】TP39【相关文献】1.A Visual Analysis Approach for Community Detection of Multi-Context Mobile Social Networks [J], Yu-Xin Ma;Jia-Yi Xu;Di-Chao Peng;Ting Zhang;Cheng-Zhe Jin;Hua-Min Qu;Wei Chenmunity Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm [J], Mao-Guo Gong;Ling-Jun Zhang;Jing-Jing Ma;Li-Cheng Jiaomunity structure detection in social networks based on dictionary learning [J], ZHANG ZhongYuan;;;;;;;;;;;;4.AntLP: ant-based label propagation algorithm for community detection in social networks [J], Razieh Hosseini; Alireza Rezvanian5.Mining top-k influential nodes in social networks via community detection [J], Wei Li; Jianbin Huang; Shuzhen Wang因版权原因，仅展示原文概要，查看原文内容请购买。

The Importance of Community

The Importance of Community The Importance of Community Community is an essential aspect of human life, providing a sense of belonging, support, and connection. It plays a crucial role in shaping individuals' identities, providing a support system during times of need, and fostering a sense of unity and togetherness. In this essay, we will explore the significance of community from various perspectives, including social, emotional, and psychological aspects. From a social standpoint, community serves as a platform for individuals to come together, share common interests, and engage in meaningful interactions. It provides a sense of belonging and acceptance, allowing individuals to form connections with others who share similar values and beliefs. Communities often serve as a source of support and guidance, especially during challenging times. Whether it is a neighborhood, a religious group, or an online community, the sense of unity and camaraderie that comes from being part of a community is invaluable. Emotionally, being part of a community can provide a sense of security and comfort. It offers a support system that can helpindividuals navigate through life's ups and downs. Whether it is celebrating achievements or seeking solace during difficult times, the presence of a community can significantly impact an individual's emotional well-being. The feeling of being understood and supported by others can alleviate feelings of loneliness and isolation, contributing to a more positive outlook on life. Psychologically, community plays a vital role in shaping individuals' identities and sense of self. Being part of a community provides a sense of purpose and meaning, as individuals are able to contribute to something larger than themselves. It can also influence individuals' beliefs, values, and behaviors, as they are shaped by the norms and expectations of the community. Additionally, communities often provide opportunities for personal growth and development, as individuals learn from one another and are exposed to diverse perspectives and experiences. From a practical standpoint, communities offer a range of tangible benefits. They provide access to resources, information, and opportunities that may not be readily available to individuals on their own. Whether it is through job networking, educational support, or access to communal facilities, communities can enhance individuals' quality of life and contribute to their overall well-being. Furthermore,communities often serve as a platform for collective action and advocacy, allowing individuals to come together to address common issues and drive positive change. In conclusion, the importance of community cannot be overstated. From social, emotional, psychological, and practical perspectives, communities play a vitalrole in shaping individuals' lives and contributing to their overall well-being. The sense of belonging, support, and connection that comes from being part of a community is invaluable, and it is essential for individuals to actively seek out and contribute to communities that align with their values and interests. By fostering strong, vibrant communities, we can create a more connected, supportive, and inclusive society for all.。

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Community Detection in a LargeReal-World Social NetworkKarsten Steinhaeuser1and Nitesh V.Chawla21ksteinha@,University of Notre Dame,IN,USA2nchawla@,University of Notre Dame,IN,USAAbstract Identifying meaningful community structure in social networks is a hard problem,and extreme network size or sparseness of the network compound the dif-ﬁculty of the task.With a proliferation of real-world network datasets there has been an increasing demand for algorithms that work effectively and efﬁciently.Existing methods are limited by their computational requirements and rely heavily on the network topology,which fails in scale-free networks.Yet,in addition to the network connectivity,many datasets also include attributes of individual nodes,but current methods are unable to incorporate this data.Cognizant of these requirements we propose a simple approach that stirs away from complex algorithms,focusing in-stead on the edge weights;more speciﬁcally,we leverage the node attributes to compute better weights.Our experimental results on a real-world social network show that a simple thresholding method with edge weights based on node attributes is sufﬁcient to identify a very strong community structure.1IntroductionModern data mining is often confronted with the problems arising from complex re-lationships in data.In social computing,the analysis of social networks has emerged as an area of great interest.On one hand,such interaction networks offer an advan-tage because they can represent rich,complex information in an intuitive fashion. On the other hand,mining this information can be quite difﬁcult as many existing methods are not directly applicable to network data,and graph theoretic algorithms are computationally very expensive.Therefore,there is an immediate need for efﬁ-cient algorithms to analyze social networks.We address one particular task in social network mining,namely community detection.A number of methods to address this problem have been proposed, and Newman distinguishes these into two categories:bottom-up“sociological”ap-proaches and top-down“computer science”approaches;a more detailed treatment with examples of each is provided in[5].Both have been shown to perform well in practice,but regardless of the fundamental approach most algorithms are computa-tionally expensive.Their scalability is limited to at most a few thousand nodes as execution becomes intractable for larger networks[7].However,datasets contain-ing millions of nodes are becoming readily available,and analyzing them requires highly scalable algorithms.In this work we take advantage of the important social tendency of homophily–“birds of a featherﬂock together”–to analyze a cellular phone network,which is a unique real-world social network in that it consists of1.3million individuals con-nected by actual communication patterns between them.The search for community structure is guided by a similarity function based on attributes attached to nodes in the network,not just the topology.We believe the latter is limiting as it does not carry the important element of”closeness”among neighbors.Our hypothesis is that using node attributes(in this case demographic information about the individuals)to compute edge weights is sufﬁcient to identify communities in the network,whereas weights computed by other means produce signiﬁcantly inferior results.We show that a relatively simpler and highly scalable algorithm is able to produce extremely high modularity scores,surpassing empirical limits speciﬁed by Newman[6]. The remainder of this paper is organized as follows:In section2,we describe three different similarity metrics used to weight the edges of a network.In section3 we present the setup and experimental evaluation on a real-world social network.Fi-nally,in section4we conclude with a discussion of the results and their implication for social network analysis.2Edge Weighting MethodsWe assume that the connectivity of the network is provided as part of the input.If this is all the information given,then the only criterion the algorithm can consider is the network topology,i.e.measurements like clustering coefﬁcients,shortest paths, etc.Yet it is often the case that rich information about nodes and/or edges is avail-able,which allow us to assign them more meaningful weights.In this section,we describe three different methods for weighting the edges of the network:two topo-logical metrics and one based solely on node attributes.2.1Terms and NotationHere we brieﬂy introduce some terms and notation that are used throughout the ensuing discussion.A network is deﬁned as graph G=(V,E)consisting of a set of n nodes V and a set of m edges E between them.Letters i,j,v refer to nodes; e(i,j)denotes an edge connecting nodes i and j,while w(i,j)speciﬁes the weight of the edge.For practical reasons,the graph is represented as an adjacency list such that neighbors(i),the set of all nodes connected to i,is readily accessible.If node attributes for i are available,they are stored as i.1,i.2,...,i.r.Community Detection in a Large Real-World Social Network2.2Clustering Coefﬁcient Similarity(CCS)Several node similarity metrics are described in[1].We adopt the topological clus-tering coefﬁcient similarity(CCS)for our work.As the name indicates,the underly-ing computation requiresﬁnding the clustering coefﬁcient CC of node v,CC(v)=2n vAlgorithm1Clustering Coefﬁcient Similarity(CCS)2.3Common Neighbor Similarity(CNS)The second metric is based on a quantity known in set theory as the Jaccard Coefﬁ-cient.For sets P and Q,it is computed as the ratio of the intersection to the union of the two sets.To compute the weight of edge e(i,j),simply substitute neighbors(i) and neighbors(j)for P and Q,respectively,which results in the ratio between the number of neighbors two nodes share(common neighbors)and the total number of nodes they are(collectively)connected to,w cns(i,j)=|neighbors(i)∩neighbors(j)|This metric,called common neighbor similarity(CNS),is intended to capture the overall connectedness among the immediate neighborhood of nodes i and j.Algo-rithm2shows the procedure for weighting the entire graph with the CNS metric.1:for each node i=1...n do2:for each node j=1...neighbors(i)do3:w(i,j)=|neighbors(i)∩neighbors(j)|2.4Node Attribute Similarity(NAS)Note that both of the previous metrics rely solely on the network topology.We postulate that a similarity metric that takes into account node attributes can produce more meaningful weights,thereby improving the community structure.One choice for this scenario might be the Heterogeneous Value Distance Metric[8],but since there is no concept of class among the nodes it cannot be applied directly.However, we can adapt its premise to the situation at hand.We propose to weight edges based on a node attribute similarity(NAS)computed as follows:for each nominal attribute a c,if two connected nodes have the same value then increment the edge weight by one,i f i.a c=j.a c,w na(i,j)=w na(i,j)+1For continuous attributes,toﬁnd the weight of edge e(i,j)weﬁrst normalize each attribute to(0,1)and then take the arithmetic difference between the pairs of values attribute values to obtain a similarity score.More formally,for each continuous attribute a n,w na(i,j)+=(1−α|i.a n−j.a n|)whereαis a normalizing constant.This metric captures the edge weight as the attribute-similarity of two connected nodes.Algorithm3shows the procedure for weighting the entire graph using this heterogeneous NAS metric.Community Detection in a Large Real-World Social Network1:for each node i=1...n do2:for each node j=1...neighbors(i)do3:w(i,j)=04:for each node attribute a do5:if a is nominal and i.a=j.a then6:w(i,j)=w(i,j)+17:else if a is continuous then8:w(i,j)=w(i,j)+1−α|i.a−j.a|9:end if10:end for11:end for12:end forThe value normally ranges from0to1(higher is better)and can vary widely for different real-world networks.Newman et al.report that in social networks it gen-erally falls between0.3and0.7[6],but there is no threshold value that necessarily seperates“good”from“bad”community structure.3.2Community Detection MethodTo identify communities in the network,weﬁrst apply one of the edge weighting methods to the network and normalize the edge weights to the range(0,1).We then obtain communities using a simple thresholding method.Given threshold t in the same range(0,1),we place any pair of nodes i and j whose edge weight exceeds the threshold,i.e.w(i,j)>t,in the same community.3.3Cellular Phone NetworkWe evaluate our hypothesis that edge weights are a critical foundation to good com-munity structure on a real-world social network constructed from cellular phone records[3].The data was collected by a major non-American service provider from March16to April15,2007.Representing each customer by a node and placing an edge between pairs of users who interacted via a phone call or a text message,we obtain a graph of1,341,960nodes and1,216,128edges.Unlike other examples of large social networks,which are often extracted from online networking sites,this network is a better representation of a true social network as the interaction between two individuals entails a stronger notion of intent to communicate.Given its large size,the cellular phone network is quite unique in this regard.As shown in Figure1,the degree distribution in the network approximately fol-lows a straight line when plotted on a log-log scale,which is indicative of a power law.This is one of the deﬁning characteristics of a scale-free network[2].Commu-nity detection in this class of networks is particularly difﬁcult as nodes tend to be strongly connected to one of a few central hub nodes,but very sparsely connected among one another ing topological metrics,this generally results ei-ther in a large number of small components or a small number of giant components, but no true community structure.We show that weighting based on node attributes can help overcome this challenge.Community Detection in a Large Real-World Social NetworkFig.1Degree distribution for the phone network.The presence of a power law indicates that it is a scale-free network.3.4Experimental ResultsTable1shows the effect of threshold t on modularity using the three different edge weighting methods;the execution time for each trial was approximately40seconds. We see that this simple thresholding method is sufﬁcient to detect community struc-ture as the modularity values are quite high across the full range of t,although lower thresholds produce better results.In fact,the values over0.91for NAS far exceeds the range(0.3,0.7)reported by Newman et al.[6],indicating very strong commu-nities.This shows that the attribute values alone contain some extremely valuable information about the community structure as the NAS metric results in very high modularity.Table1Effects of varying threshold t on modularity with different weighting methods.CCS0.0500.0420.0200.0060.001 CNS0.0900.0610.0220.0040.001 NAS0.9170.9170.9170.9150.5084ConclusionsWe have explored the viability of various edge weighting methods for the purpose of community detection in very large networks.Speciﬁcally,we have shown that edge weights based on the node attribute similarity(i.e.demographic similarity of individuals)are superior to edge weights based on network topology in a large scale-free social network.As witnessed by the fact that a simple thresholding method was sufﬁcient to extract the communities,not only does the NAS metric produce more suitable edge weights,but all the information required to detect community struc-ture is contained within those weights.We achieved modularity values exceeding empirical bounds for community structure observed in other(smaller)social net-works,conﬁrming that this approach does indeed produce meaningful results.An additional advantage of this method is its simplicity,which makes it scalable to networks of over one million nodes.References1.S.Asur,D.Ucar,S.Parthasarathy:An Ensemble Framework for Clustering Protein-Protein Interaction Graphs.In Proceedings of ISMB(2007)2.A.-L.Barab´a si and E.Bonabeau:Scale-free networks.Scientiﬁc American288(2003)50–593.G.Madey,A.-L.Barab´a si,N.V.Chawla,et al:Enhanced Situational Awareness:Application of DDDAS Concepts to Emergency and Disaster Management.In LNCS4487(2007)1090–1097ligan,M.Cooper:A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis.Multiv.Behav.Res.bf21(1986)441–4585.M.E.Newman:Detecting community structure in networks.Eur.Phys.J.bf B38(2004)321–3306.M.E.Newman:Finding and evaluating community structure in networks.Phys.Rev.E bf69 (2004)0231137.P.Pons,tapy:Computing communities in large networks using random walks.J.of Graph Alg.and App.bf10(2006)191–2188.D.R.Wilson,T.R.Martinez:Improved heterogeneous distance functions.J.Art.Int.Res.bf6 (1997)1–34。