Modelling and Traceability of Composition Semantics in Requirements
一种基于加速度与表面肌电信息融合和统计语言模型的连续手语识别方法
ｓａ ｉｔ ａ ａ ｇ ａ ｅ ｍｏ ｅ ｓｃ ｎ ｔ ｔｄ ｔ ｅｅ ｔａ ｄ ｃ ｒｅ ｔｅｒ ｒｉ ｈ ｒ ｃ ｓ ｆｔ ｅｒ ｃ ｇ ｉ ｏ ． Ｆ ｒｔ ｅ ｔｔ ｉ ｌｌｎ ｕ ｇ ｄ ｌｗａ ｏ ｓｒ ｅ ｏ ｄ ｔｃ ｎ ｏ ｒｃ ｒｏ ｎ ｔｅ ｐ ｏ ｅ ｓｏ ｈ ｅ ｏ ｎｔ ｎ ｓｃ ｕｃ ｉ ｏ ｈ ｒ ｃ ｇ ｉｉｎ ｏ ０ Ｃ Ｌ ｓ ｂ ｒ ｓａ ｄ２ ０ ｓｎ ｅ ｃ ｓ ｈ ｖ ｒｇ ｅ ｏ ｎ ｔ ｎ ａ ｃ ｒ ｃｅ ｆｏ ｒｍｅｈ ｄ ｃ ｕｄ ｕ ｅ ｏ ｎ ｔ ｆ１ Ｓ ｕ ｗｏ ｄ ｎ ０ ｅ ｔｎ ｅ ，ｔｅ ａ ｅ ａ ｅ ｒｃ ｇ ｉ ｏ ｃ ｕ ａ ｉｓｏ ｕ ｔ ｏ ｏ ｌ ｐ ｏ ２ ｉ ｔ ％ ｏ９１ ａｄ８ ％ ｎ ４ ｒｓ ｅ ｔｖ ｌ ． Ｔ ｅ ｃ ｍｐ ｒ ｔ ｅ ａ ａｙ ｉ ｆ ｅ ｐ ｒｍｅ ｔｌ ｒｓ ｌ ｈ ｗｅ ｈ ｔ ｔｅ ｔｔｓｉａ ｅ ｐ ｃｉｅｙ ｈ ｏ ａａｉ ｎ ｌｓｓ ｘ ｅｉ ｎ ａ ｅ ｕ ｔ ｓ ｏ ｄ ｔ ａ ｈ ｓａｉｔｃ ｌ ｖ ｏ ｓ ａ ｄ ｈ ｒｃ ｇ ｉ ｏ ｎ ｔ ｅ ｅ ｏ ｎ ｔ ｎ ｉ
two-stage stochastic programming
two-stage stochastic programmingTwo-stage stochastic programming is a mathematical optimization approach used to solve decision-making problems under uncertainty. It is commonly applied in various fields such as operations research, finance, energy planning, and supply chain management. In this approach, decisions are made in two stages: the first stage involves decisions made before uncertainty is realized, and the second stage involves decisions made after observing the uncertain events.In two-stage stochastic programming, the decision-maker aims to optimize their decisions by considering both the expected value and the risk associated with different outcomes. The problem is typically formulated as a mathematical program with constraints and objective functions that capture the decision variables, uncertain parameters, and their probabilistic distributions.The first stage decisions are typically made with theknowledge of the uncertain parameters, but without knowing their actual realization. These decisions are usually strategic and long-term in nature, such as investment decisions, capacity planning, or resource allocation. The objective in the first stage is to minimize the expected cost or maximize the expected profit.The second stage decisions are made after observing the actual realization of the uncertain events. These decisions are typically tactical or operational in nature, such as production planning, inventory management, or scheduling. The objective in the second stage is to minimize the cost or maximize the profit given the realized values of the uncertain parameters.To solve two-stage stochastic programming problems, various solution methods can be employed. One common approach is to use scenario-based methods, where a set of scenarios representing different realizations of the uncertain events is generated. Each scenario is associated with a probability weight, and the problem is then transformed into a deterministic equivalent problem byreplacing the uncertain parameters with their corresponding scenario values. The deterministic problem can be solved using traditional optimization techniques such as linear programming or mixed-integer programming.Another approach is to use sample average approximation, where the expected value in the objective function is approximated by averaging the objective function valuesover a large number of randomly generated scenarios. This method can be computationally efficient but may introduce some approximation errors.Furthermore, there are also robust optimization techniques that aim to find solutions that are robust against the uncertainty, regardless of the actualrealization of the uncertain events. These methods focus on minimizing the worst-case cost or maximizing the worst-case profit.In summary, two-stage stochastic programming is a powerful approach for decision-making under uncertainty. It allows decision-makers to consider both the expected valueand the risk associated with uncertain events. By formulating the problem as a mathematical program and employing various solution methods, optimal or near-optimal solutions can be obtained to guide decision-making in a wide range of applications.。
ai 评估 模型
ai 评估模型
AI模型评估是一个重要的步骤，用于衡量模型工作结果的好与坏，进
一步找到不同模型之间的性能差异，优化模型。
评估通常涉及以下几
个方面：
1. 误差计算：使用损失函数来计算模型的误差，以评估模型的预测能力。
2. 误差分类：包括训练误差、测试误差和泛化误差。
其中，泛化误差（generalization error）是衡量模型在所有真实数据上的适用能力
的指标，通常通过在测试数据集上的误差来估计。
3. 过拟合与欠拟合：过拟合是指模型在训练数据上表现很好，但在测
试数据上表现较差；欠拟合是指模型在训练数据和测试数据上表现都
不好。
评估过程中需要识别和解决过拟合和欠拟合问题。
4. 性能度量：根据不同的模型类型，使用不同的性能度量标准。
例如，回归模型可以使用均方误差（MSE），分类模型可以使用交叉熵损失（cross-entropy loss）等。
5. 特征评估：评估模型的输入特征对预测结果的贡献度，以确定哪些
特征对模型最重要。
6. 模型复杂度：评估模型的复杂度，以确定模型是否过于复杂或过于
简单。
7. 稳定性评估：评估模型在不同数据集上的表现是否一致，以确定模
型的稳定性。
8. 可解释性评估：评估模型的可解释性，以确保模型不仅可预测，而且可理解。
AI模型评估的具体方法和技术会因应用领域、数据类型和模型复杂度等因素而异。
因此，在实践中，需要根据具体情况选择合适的评估方法和技术。
simulation modelling practice -回复
simulation modelling practice -回复Simulation Modelling Practice: A Step-by-Step GuideIntroduction:Simulation modelling is a valuable technique used to mimicreal-life systems and analyze their behavior. By creating virtual representations of complex systems, simulation modelling allows us to understand how different variables and factors interact and impact the overall system performance. In this article, we will provide a step-by-step guide on how to develop and execute a simulation model, ensuring accurate results and valuable insights.Step 1: Define the Scope and ObjectivesThe first and most crucial step in simulation modelling is to clearly define the scope and objectives of the study. This involves understanding the problem at hand, identifying the variables to be considered, and determining the specific goals to be achieved. For instance, if you are simulating a supply chain network, clarify whether you aim to optimize inventory levels, reduce lead time, or minimize costs.Step 2: Gather DataSimulating a system requires accurate and comprehensive data. Collecting data from reliable sources is essential to ensure the validity and reliability of the simulation model. This could include historical data, market trends, customer demand data, and any other relevant information. Data can be obtained through surveys, interviews, observations, or existing databases.Step 3: Develop the Conceptual ModelOnce the data is gathered, the next step is to develop the conceptual model. This involves identifying the components, relationships, and behaviors of the system to be simulated. Conceptual models can be represented using flowcharts, diagrams, or mathematical equations, depending on the complexity of the system.Step 4: Convert the Conceptual Model into a Computer ModelIn this step, the conceptual model is translated into a computer model using specialized simulation software. Multiple software options are available, such as AnyLogic, Simul8, or Arena. The choice of software depends on factors like complexity, desired output, and personal preference. The computer model includes allthe variables, parameters, and rules defined in the conceptual model.Step 5: Validate the ModelModel validation is crucial for ensuring the accuracy and reliability of simulation results. This involves comparing the model's output to real-life data or expert opinions. Validation can be done by running the simulation model on past data and evaluating how well it replicates the actual outcomes. If the model does not produce results that align with reality, adjustments are made until satisfactory validation is achieved.Step 6: Design Experiments and Run SimulationsBefore running simulations, it is important to design experiments that address the objectives defined in Step 1. Experiment design includes specifying the values for each variable, defining replication and randomization strategies, and determining the desired performance measures to be analyzed. Once the experiments are designed, simulations are executed using the computer model, and data is collected for subsequent analysis.Step 7: Analyze ResultsSimulation outputs provide valuable insights into system behavior. This step involves analyzing the simulation results to gain a deeper understanding of the system's performance. Statistical techniques like regression analysis, variance analysis, or Monte Carlo simulation can be used to explore the relationship between variables, identify performance bottlenecks, and optimize system performance.Step 8: Implement ImprovementsBased on the insights gained from the simulation analysis, improvements can be implemented to optimize the system. These improvements could involve adjusting parameters, redesigning processes, or reallocating resources. By simulating the effects of these changes, decision-makers can evaluate their impact on system performance and make informed decisions.Step 9: Communicate FindingsThe final step involves effectively communicating the findings and recommendations derived from the simulation study. Visualizations, such as charts, graphs, or interactive dashboards, can be used to present the results in a clear and concise format. This helps stakeholders understand the implications of the analysis andsupports informed decision-making.Conclusion:Simulation modelling is a powerful tool that allows us to study and optimize complex systems. By following the step-by-step guide outlined in this article, practitioners can develop reliable and insightful simulation models. Remember to define the scope and objectives, gather accurate data, design a conceptual model, convert it into a computer model, validate the model's outputs, run simulations, analyze the results, implement improvements, and effectively communicate the findings. By systematically going through these steps, you can unlock the potential of simulation modelling to tackle complex problems and drive informed decision-making.。
如何在马尔可夫决策过程中处理部分可观测性(四)
在强化学习中，马尔可夫决策过程（MDP）是一种常用的模型，用于描述智能体在环境中的决策过程。
然而，现实生活中许多情况下，智能体无法观测到完整的环境状态，而只能获得部分可观测的信息。
这种情况下，如何处理部分可观测性成为了一个重要而复杂的问题。
本文将从不同角度探讨如何在马尔可夫决策过程中处理部分可观测性。
首先，我们需要了解部分可观测马尔可夫决策过程（POMDP）的基本概念。
POMDP是对MDP的一种扩展，用于描述智能体在部分可观测环境中的决策过程。
在POMDP中，智能体无法直接观测到完整的环境状态，而只能通过观测到的部分信息来对环境状态进行推断。
因此，POMDP需要考虑观测、环境状态和动作之间的关系，并在此基础上进行决策。
处理POMDP的方法有很多种，其中一种常用的方法是基于置信度的方法。
在这种方法中，智能体会维护一个置信度分布，用来表示对环境状态的不确定性。
智能体会根据观测到的信息更新置信度分布，并基于置信度分布来做出决策。
这种方法能够有效地处理部分可观测性，但是需要对置信度分布进行精细的建模和更新，以确保对环境状态的推断是准确的。
另一种处理POMDP的方法是基于历史信息的方法。
在这种方法中，智能体会维护一个历史信息，用来记录之前的观测和动作序列。
智能体会根据历史信息来推断环境状态，并在此基础上做出决策。
这种方法能够充分利用之前的观测和动作信息，但是需要考虑历史信息的存储和更新，以确保对环境状态的推断是准确的。
除了以上两种方法，还有一种处理POMDP的方法是基于模型的方法。
在这种方法中，智能体会建立一个环境模型，用来表示观测、环境状态和动作之间的关系。
智能体会根据环境模型来推断环境状态，并在此基础上做出决策。
这种方法能够充分利用环境模型的信息，但是需要对环境模型进行准确的建模和更新，以确保对环境状态的推断是准确的。
综上所述，处理部分可观测性是一个重要而复杂的问题。
在POMDP中，需要考虑观测、环境状态和动作之间的关系，并在此基础上进行决策。
Example-based metonymy recognition for proper nouns
Example-Based Metonymy Recognition for Proper NounsYves PeirsmanQuantitative Lexicology and Variational LinguisticsUniversity of Leuven,Belgiumyves.peirsman@arts.kuleuven.beAbstractMetonymy recognition is generally ap-proached with complex algorithms thatrely heavily on the manual annotation oftraining and test data.This paper will re-lieve this complexity in two ways.First,it will show that the results of the cur-rent learning algorithms can be replicatedby the‘lazy’algorithm of Memory-BasedLearning.This approach simply stores alltraining instances to its memory and clas-sifies a test instance by comparing it to alltraining examples.Second,this paper willargue that the number of labelled trainingexamples that is currently used in the lit-erature can be reduced drastically.Thisfinding can help relieve the knowledge ac-quisition bottleneck in metonymy recog-nition,and allow the algorithms to be ap-plied on a wider scale.1IntroductionMetonymy is afigure of speech that uses“one en-tity to refer to another that is related to it”(Lakoff and Johnson,1980,p.35).In example(1),for in-stance,China and Taiwan stand for the govern-ments of the respective countries:(1)China has always threatened to use forceif Taiwan declared independence.(BNC) Metonymy resolution is the task of automatically recognizing these words and determining their ref-erent.It is therefore generally split up into two phases:metonymy recognition and metonymy in-terpretation(Fass,1997).The earliest approaches to metonymy recogni-tion identify a word as metonymical when it vio-lates selectional restrictions(Pustejovsky,1995).Indeed,in example(1),China and Taiwan both violate the restriction that threaten and declare require an animate subject,and thus have to be interpreted metonymically.However,it is clear that many metonymies escape this characteriza-tion.Nixon in example(2)does not violate the se-lectional restrictions of the verb to bomb,and yet, it metonymically refers to the army under Nixon’s command.(2)Nixon bombed Hanoi.This example shows that metonymy recognition should not be based on rigid rules,but rather on statistical information about the semantic and grammatical context in which the target word oc-curs.This statistical dependency between the read-ing of a word and its grammatical and seman-tic context was investigated by Markert and Nis-sim(2002a)and Nissim and Markert(2003; 2005).The key to their approach was the in-sight that metonymy recognition is basically a sub-problem of Word Sense Disambiguation(WSD). Possibly metonymical words are polysemous,and they generally belong to one of a number of pre-defined metonymical categories.Hence,like WSD, metonymy recognition boils down to the auto-matic assignment of a sense label to a polysemous word.This insight thus implied that all machine learning approaches to WSD can also be applied to metonymy recognition.There are,however,two differences between metonymy recognition and WSD.First,theo-retically speaking,the set of possible readings of a metonymical word is open-ended(Nunberg, 1978).In practice,however,metonymies tend to stick to a small number of patterns,and their la-bels can thus be defined a priori.Second,classic 71WSD algorithms take training instances of one par-ticular word as their input and then disambiguate test instances of the same word.By contrast,since all words of the same semantic class may undergo the same metonymical shifts,metonymy recogni-tion systems can be built for an entire semantic class instead of one particular word(Markert and Nissim,2002a).To this goal,Markert and Nissim extracted from the BNC a corpus of possibly metonymical words from two categories:country names (Markert and Nissim,2002b)and organization names(Nissim and Markert,2005).All these words were annotated with a semantic label —either literal or the metonymical cate-gory they belonged to.For the country names, Markert and Nissim distinguished between place-for-people,place-for-event and place-for-product.For the organi-zation names,the most frequent metonymies are organization-for-members and organization-for-product.In addition, Markert and Nissim used a label mixed for examples that had two readings,and othermet for examples that did not belong to any of the pre-defined metonymical patterns.For both categories,the results were promis-ing.The best algorithms returned an accuracy of 87%for the countries and of76%for the orga-nizations.Grammatical features,which gave the function of a possibly metonymical word and its head,proved indispensable for the accurate recog-nition of metonymies,but led to extremely low recall values,due to data sparseness.Therefore Nissim and Markert(2003)developed an algo-rithm that also relied on semantic information,and tested it on the mixed country data.This algo-rithm used Dekang Lin’s(1998)thesaurus of se-mantically similar words in order to search the training data for instances whose head was sim-ilar,and not just identical,to the test instances. Nissim and Markert(2003)showed that a combi-nation of semantic and grammatical information gave the most promising results(87%). However,Nissim and Markert’s(2003)ap-proach has two major disadvantages.Thefirst of these is its complexity:the best-performing al-gorithm requires smoothing,backing-off to gram-matical roles,iterative searches through clusters of semantically similar words,etc.In section2,I will therefore investigate if a metonymy recognition al-gorithm needs to be that computationally demand-ing.In particular,I will try and replicate Nissim and Markert’s results with the‘lazy’algorithm of Memory-Based Learning.The second disadvantage of Nissim and Mark-ert’s(2003)algorithms is their supervised nature. Because they rely so heavily on the manual an-notation of training and test data,an extension of the classifiers to more metonymical patterns is ex-tremely problematic.Yet,such an extension is es-sential for many tasks throughout thefield of Nat-ural Language Processing,particularly Machine Translation.This knowledge acquisition bottle-neck is a well-known problem in NLP,and many approaches have been developed to address it.One of these is active learning,or sample selection,a strategy that makes it possible to selectively an-notate those examples that are most helpful to the classifier.It has previously been applied to NLP tasks such as parsing(Hwa,2002;Osborne and Baldridge,2004)and Word Sense Disambiguation (Fujii et al.,1998).In section3,I will introduce active learning into thefield of metonymy recog-nition.2Example-based metonymy recognition As I have argued,Nissim and Markert’s(2003) approach to metonymy recognition is quite com-plex.I therefore wanted to see if this complexity can be dispensed with,and if it can be replaced with the much more simple algorithm of Memory-Based Learning.The advantages of Memory-Based Learning(MBL),which is implemented in the T i MBL classifier(Daelemans et al.,2004)1,are twofold.First,it is based on a plausible psycho-logical hypothesis of human learning.It holds that people interpret new examples of a phenom-enon by comparing them to“stored representa-tions of earlier experiences”(Daelemans et al., 2004,p.19).This contrasts to many other classi-fication algorithms,such as Naive Bayes,whose psychological validity is an object of heavy de-bate.Second,as a result of this learning hypothe-sis,an MBL classifier such as T i MBL eschews the formulation of complex rules or the computation of probabilities during its training phase.Instead it stores all training vectors to its memory,together with their labels.In the test phase,it computes the distance between the test vector and all these train-ing vectors,and simply returns the most frequentlabel of the most similar training examples.One of the most important challenges inMemory-Based Learning is adapting the algorithmto one’s data.This includesfinding a represen-tative seed set as well as determining the rightdistance measures.For my purposes,however, T i MBL’s default settings proved more than satis-factory.T i MBL implements the IB1and IB2algo-rithms that were presented in Aha et al.(1991),butadds a broad choice of distance measures.Its de-fault implementation of the IB1algorithm,whichis called IB1-IG in full(Daelemans and Van denBosch,1992),proved most successful in my ex-periments.It computes the distance between twovectors X and Y by adding up the weighted dis-tancesδbetween their corresponding feature val-ues x i and y i:∆(X,Y)=ni=1w iδ(x i,y i)(3)The most important element in this equation is theweight that is given to each feature.In IB1-IG,features are weighted by their Gain Ratio(equa-tion4),the division of the feature’s InformationGain by its split rmation Gain,the nu-merator in equation(4),“measures how much in-formation it[feature i]contributes to our knowl-edge of the correct class label[...]by comput-ing the difference in uncertainty(i.e.entropy)be-tween the situations without and with knowledgeof the value of that feature”(Daelemans et al.,2004,p.20).In order not“to overestimate the rel-evance of features with large numbers of values”(Daelemans et al.,2004,p.21),this InformationGain is then divided by the split info,the entropyof the feature values(equation5).In the followingequations,C is the set of class labels,H(C)is theentropy of that set,and V i is the set of values forfeature i.w i=H(C)− v∈V i P(v)×H(C|v)2This data is publicly available and can be downloadedfrom /mnissim/mascara.73P F86.6%49.5%N&M81.4%62.7%Table1:Results for the mixed country data.T i MBL:my T i MBL resultsN&M:Nissim and Markert’s(2003)results simple learning phase,T i MBL is able to replicate the results from Nissim and Markert(2003;2005). As table1shows,accuracy for the mixed coun-try data is almost identical to Nissim and Mark-ert’sfigure,and precision,recall and F-score for the metonymical class lie only slightly lower.3 T i MBL’s results for the Hungary data were simi-lar,and equally comparable to Markert and Nis-sim’s(Katja Markert,personal communication). Note,moreover,that these results were reached with grammatical information only,whereas Nis-sim and Markert’s(2003)algorithm relied on se-mantics as well.Next,table2indicates that T i MBL’s accuracy for the mixed organization data lies about1.5%be-low Nissim and Markert’s(2005)figure.This re-sult should be treated with caution,however.First, Nissim and Markert’s available organization data had not yet been annotated for grammatical fea-tures,and my annotation may slightly differ from theirs.Second,Nissim and Markert used several feature vectors for instances with more than one grammatical role andfiltered all mixed instances from the training set.A test instance was treated as mixed only when its several feature vectors were classified differently.My experiments,in contrast, were similar to those for the location data,in that each instance corresponded to one vector.Hence, the slightly lower performance of T i MBL is prob-ably due to differences between the two experi-ments.Thesefirst experiments thus demonstrate that Memory-Based Learning can give state-of-the-art performance in metonymy recognition.In this re-spect,it is important to stress that the results for the country data were reached without any se-mantic information,whereas Nissim and Mark-ert’s(2003)algorithm used Dekang Lin’s(1998) clusters of semantically similar words in order to deal with data sparseness.This fact,togetherAcc RT i MBL78.65%65.10%76.0%—Figure1:Accuracy learning curves for the mixed country data with and without semantic informa-tion.in more detail.4Asfigure1indicates,with re-spect to overall accuracy,semantic features have a negative influence:the learning curve with both features climbs much more slowly than that with only grammatical features.Hence,contrary to my expectations,grammatical features seem to allow a better generalization from a limited number of training instances.With respect to the F-score on the metonymical category infigure2,the differ-ences are much less outspoken.Both features give similar learning curves,but semantic features lead to a higherfinal F-score.In particular,the use of semantic features results in a lower precisionfig-ure,but a higher recall score.Semantic features thus cause the classifier to slightly overgeneralize from the metonymic training examples.There are two possible reasons for this inabil-ity of semantic information to improve the clas-sifier’s performance.First,WordNet’s synsets do not always map well to one of our semantic la-bels:many are rather broad and allow for several readings of the target word,while others are too specific to make generalization possible.Second, there is the predominance of prepositional phrases in our data.With their closed set of heads,the number of examples that benefits from semantic information about its head is actually rather small. Nevertheless,myfirst round of experiments has indicated that Memory-Based Learning is a sim-ple but robust approach to metonymy recogni-tion.It is able to replace current approaches that need smoothing or iterative searches through a the-saurus,with a simple,distance-based algorithm.Figure3:Accuracy learning curves for the coun-try data with random and maximum-distance se-lection of training examples.over all possible labels.The algorithm then picks those instances with the lowest confidence,since these will contain valuable information about the training set(and hopefully also the test set)that is still unknown to the system.One problem with Memory-Based Learning al-gorithms is that they do not directly output prob-abilities.Since they are example-based,they can only give the distances between the unlabelled in-stance and all labelled training instances.Never-theless,these distances can be used as a measure of certainty,too:we can assume that the system is most certain about the classification of test in-stances that lie very close to one or more of its training instances,and less certain about those that are further away.Therefore the selection function that minimizes the probability of the most likely label can intuitively be replaced by one that max-imizes the distance from the labelled training in-stances.However,figure3shows that for the mixed country instances,this function is not an option. Both learning curves give the results of an algo-rithm that starts withfifty random instances,and then iteratively adds ten new training instances to this initial seed set.The algorithm behind the solid curve chooses these instances randomly,whereas the one behind the dotted line selects those that are most distant from the labelled training exam-ples.In thefirst half of the learning process,both functions are equally successful;in the second the distance-based function performs better,but only slightly so.There are two reasons for this bad initial per-formance of the active learning function.First,it is not able to distinguish between informativeandFigure4:Accuracy learning curves for the coun-try data with random and maximum/minimum-distance selection of training examples. unusual training instances.This is because a large distance from the seed set simply means that the particular instance’s feature values are relatively unknown.This does not necessarily imply that the instance is informative to the classifier,how-ever.After all,it may be so unusual and so badly representative of the training(and test)set that the algorithm had better exclude it—something that is impossible on the basis of distances only.This bias towards outliers is a well-known disadvantage of many simple active learning algorithms.A sec-ond type of bias is due to the fact that the data has been annotated with a few features only.More par-ticularly,the present algorithm will keep adding instances whose head is not yet represented in the training set.This entails that it will put off adding instances whose function is pp,simply because other functions(subj,gen,...)have a wider variety in heads.Again,the result is a labelled set that is not very representative of the entire training set.There are,however,a few easy ways to increase the number of prototypical examples in the train-ing set.In a second run of experiments,I used an active learning function that added not only those instances that were most distant from the labelled training set,but also those that were closest to it. After a few test runs,I decided to add six distant and four close instances on each iteration.Figure4 shows that such a function is indeed fairly success-ful.Because it builds a labelled training set that is more representative of the test set,this algorithm clearly reduces the number of annotated instances that is needed to reach a given performance.Despite its success,this function is obviously not yet a sophisticated way of selecting good train-76Figure5:Accuracy learning curves for the organi-zation data with random and distance-based(AL) selection of training examples with a random seed set.ing examples.The selection of the initial seed set in particular can be improved upon:ideally,this seed set should take into account the overall dis-tribution of the training examples.Currently,the seeds are chosen randomly.Thisflaw in the al-gorithm becomes clear if it is applied to another data set:figure5shows that it does not outper-form random selection on the organization data, for instance.As I suggested,the selection of prototypical or representative instances as seeds can be used to make the present algorithm more robust.Again,it is possible to use distance measures to do this:be-fore the selection of seed instances,the algorithm can calculate for each unlabelled instance its dis-tance from each of the other unlabelled instances. In this way,it can build a prototypical seed set by selecting those instances with the smallest dis-tance on average.Figure6indicates that such an algorithm indeed outperforms random sample se-lection on the mixed organization data.For the calculation of the initial distances,each feature re-ceived the same weight.The algorithm then se-lected50random samples from the‘most proto-typical’half of the training set.5The other settings were the same as above.With the present small number of features,how-ever,such a prototypical seed set is not yet always as advantageous as it could be.A few experiments indicated that it did not lead to better performance on the mixed country data,for instance.However, as soon as a wider variety of features is taken into account(as with the organization data),the advan-pling can help choose those instances that are most helpful to the classifier.A few distance-based al-gorithms were able to drastically reduce the num-ber of training instances that is needed for a given accuracy,both for the country and the organization names.If current metonymy recognition algorithms are to be used in a system that can recognize all pos-sible metonymical patterns across a broad variety of semantic classes,it is crucial that the required number of labelled training examples be reduced. This paper has taken thefirst steps along this path and has set out some interesting questions for fu-ture research.This research should include the investigation of new features that can make clas-sifiers more robust and allow us to measure their confidence more reliably.This confidence mea-surement can then also be used in semi-supervised learning algorithms,for instance,where the clas-sifier itself labels the majority of training exam-ples.Only with techniques such as selective sam-pling and semi-supervised learning can the knowl-edge acquisition bottleneck in metonymy recogni-tion be addressed.AcknowledgementsI would like to thank Mirella Lapata,Dirk Geer-aerts and Dirk Speelman for their feedback on this project.I am also very grateful to Katja Markert and Malvina Nissim for their helpful information about their research.ReferencesD.W.Aha, D.Kibler,and M.K.Albert.1991.Instance-based learning algorithms.Machine Learning,6:37–66.W.Daelemans and A.Van den Bosch.1992.Generali-sation performance of backpropagation learning on a syllabification task.In M.F.J.Drossaers and A.Ni-jholt,editors,Proceedings of TWLT3:Connection-ism and Natural Language Processing,pages27–37, Enschede,The Netherlands.W.Daelemans,J.Zavrel,K.Van der Sloot,andA.Van den Bosch.2004.TiMBL:Tilburg Memory-Based Learner.Technical report,Induction of Linguistic Knowledge,Computational Linguistics, Tilburg University.D.Fass.1997.Processing Metaphor and Metonymy.Stanford,CA:Ablex.A.Fujii,K.Inui,T.Tokunaga,and H.Tanaka.1998.Selective sampling for example-based wordsense putational Linguistics, 24(4):573–597.R.Hwa.2002.Sample selection for statistical parsing.Computational Linguistics,30(3):253–276.koff and M.Johnson.1980.Metaphors We LiveBy.London:The University of Chicago Press.D.Lin.1998.An information-theoretic definition ofsimilarity.In Proceedings of the International Con-ference on Machine Learning,Madison,USA.K.Markert and M.Nissim.2002a.Metonymy res-olution as a classification task.In Proceedings of the Conference on Empirical Methods in Natural Language Processing(EMNLP2002),Philadelphia, USA.K.Markert and M.Nissim.2002b.Towards a cor-pus annotated for metonymies:the case of location names.In Proceedings of the Third International Conference on Language Resources and Evaluation (LREC2002),Las Palmas,Spain.M.Nissim and K.Markert.2003.Syntactic features and word similarity for supervised metonymy res-olution.In Proceedings of the41st Annual Meet-ing of the Association for Computational Linguistics (ACL-03),Sapporo,Japan.M.Nissim and K.Markert.2005.Learning to buy a Renault and talk to BMW:A supervised approach to conventional metonymy.In H.Bunt,editor,Pro-ceedings of the6th International Workshop on Com-putational Semantics,Tilburg,The Netherlands. G.Nunberg.1978.The Pragmatics of Reference.Ph.D.thesis,City University of New York.M.Osborne and J.Baldridge.2004.Ensemble-based active learning for parse selection.In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics(HLT-NAACL).Boston, USA.J.Pustejovsky.1995.The Generative Lexicon.Cam-bridge,MA:MIT Press.78。
大语言模型增强因果推断
大语言模型（LLM）是一种强大的自然语言处理技术，它可以理解和生成自然语言文本，并具有广泛的应用场景。
然而，虽然LLM能够生成流畅、自然的文本，但在因果推断方面，它仍存在一些限制。
通过增强LLM的因果推断能力，我们可以更好地理解和解释人工智能系统的行为，从而提高其可信度和可靠性。
首先，我们可以通过将LLM与额外的上下文信息结合，来增强其因果推断能力。
上下文信息包括时间、地点、背景、情感等各个方面，它们可以为LLM提供更全面的信息，使其能够更好地理解事件之间的因果关系。
通过这种方式，LLM可以更好地预测未来的结果，并解释其预测的依据。
其次，我们可以通过引入可解释性建模技术，来增强LLM的因果推断能力。
这些技术包括决策树、规则归纳、贝叶斯网络等，它们可以帮助我们更好地理解LLM的决策过程，从而更准确地预测其结果。
此外，这些技术还可以帮助我们识别因果关系的路径，从而更深入地了解因果关系。
最后，我们可以通过将LLM与其他领域的知识结合，来增强其因果推断能力。
例如，我们可以将经济学、心理学、社会学等领域的知识融入LLM中，以帮助其更好地理解和解释因果关系。
通过这种方式，LLM可以更全面地考虑各种因素，从而更准确地预测和解释因果关系。
在应用方面，增强因果推断能力的LLM可以为许多领域提供更准确、更可靠的决策支持。
例如，在医疗领域，它可以辅助医生制定更有效的治疗方案；在金融领域，它可以辅助投资者做出更明智的投资决策；在政策制定领域，它可以为政策制定者提供更全面、更准确的政策建议。
总之，通过增强大语言模型（LLM）的因果推断能力，我们可以更好地理解和解释人工智能系统的行为，从而提高其可信度和可靠性。
这将有助于推动人工智能技术的广泛应用和发展，为社会带来更多的便利和价值。
同时，我们也需要关注和解决相关伦理和社会问题，以确保人工智能技术的发展符合人类的价值观和利益。
CMMI中英文术语对照表
CMMI中英文术语对照表1标准名词术语1 AT Assessment Team 评审小组2 ATM Assessment Team Member 评审小组成员3 BA Baseline Assessment 基线评审4 CAR Causal Analysis and Resolution 原因分析与决策5 CBA CMM-Based Appraisal 基于CMM的评价6 CBA-IPI CMM-Based Appraisal for Internal Process Improvement 为内部过程改进而进行的基于CMM的评价（通常称为CMM评审）7 CC Configuration Controller 配置管理员8 CF Common Feature 公共特性9 CFPS Certified Function Point Specialist 注册功能点专家10 CI Configuration Item 配置项11 CM Configuration Management 配置管理12 CMM Capability Maturity Model 能力成熟度模型13 CMMI Capability Maturity Model Integration 能力成熟度集成模型14 COTS Commerce off the shelf 商业现货供应15 DAR Decision Analysis and Resolution 决策分析与制定16 DBD Database Design 数据库设计17 DD Detailed Design 详细设计18 DP Data Provider 数据提供者19 DR Derived Requirement 派生需求20 EPG Engineering Process Group 工程过程小组21 FP Function Point 功能点22 FPA Function Point Analysis 功能点分析23 FR Functional Requirement 功能性需求24 GA Gap Analysis 差距分析25 ID Interface Design 接口设计26 IFPUG International Function Point Users Group 国际功能点用户组织27 IPM Integrated Project Management 集成项目管理28 IR Interface Requirement 接口需求29 KPA Key Process Area 关键过程域30 KR Key Requirements 关键需求31 LA Lead Assessor 主任评审员32 MA Measurement and Analysis 测量与分析33 MAT Metrics Advisory Team 度量咨询组34 MCA Metrics Coordinator and Analyst 度量专员35 ML matreraty library 度量数据库36 NFR Non-functional Requirement 非功能性需求37 OC Operational Concept 操作概念38 OID Organizational Innovation and Deployment 组织革新与部署39 OPD Organizational Process definition 组织过程定义40 OPF Organizational Process focus 组织过程焦点41 OPL Organizational Process Assets 组织过程财富42 OPP Organaizational Process Perormance 组织过程性能43 OSSP Organization’s Set of Standard Process 组织标准过程集合44 OT Organizational Training 组织级培训45 PA Process Areas 过程域46 PAT Process Action Team 过程行动小组47 PB Process Assets Library 过程财富库48 PD Preliminary Design 概要设计49 PDSP Project Defined Standard Processes 项目定义标准过程50 PI Produce Integration 产品集成51 PLC Product Life Cycle 产品生命周期52 PMC Project Monitoring and Control 项目监控53 PP Project Planning 项目策划54 PPQA Process and Product Quality Assurance 过程与产品质量保证55 PPR Price Performance Ratio 性能价格比56 QA Software Quality Assurance 软件质量保证57 QA Quality Assurance 质量保证58 QAP Software Quality Assurance Plan 质量保证计划59 QPM Quantitative Project Management 量化项目管理60 RD Requirements Development 需求开发61 RM/ReqM Requirements Management 需求管理62 RSKM Risk Management 风险管理63 RTM Requirement Traceability Matrix 需求跟踪矩阵64 SAM Supplier Agreement Management. 供应协议管理65 SC Steering Committee 指导委员会66 SCAMPI Standard CMMI Assessment Method for Process Improvement 过程改进CMMI标准评审方法67 SCCB Software Configuration Control Board 软件配置管理控制委员会68 SCM Software Configuration Management 软件配置管理69 SDP Software Development Plan 软件开发计划70 SEI Software Engineering Institute (美国)软件工程学院71 SEPG Software Engineering Process Group 软件工程过程组72 SPI Software Process Improvement 软件过程改进73 SPP Software Project Planning 软件项目策划74 SPTO Software Project Tracking and Oversight 软件项目跟踪与监控75 SR System Requirements 系统需求76 SRS Software Requirement Specification 软件需求规格77 SSM Software Subcontract Management 软件分包管理78 SSR Software System Requirement 软件系统需求79 TS Technical Solution 技术解决方案80 UC Use Case 用例81 UID User Interface Design 用户界面设计82 V AL Validation 确认83 VER Verification 验证84 WBS Work Breakdown Structure 工作分解结构85 WP Work Products 工作产品86 Pre-assessment 预评审87 Baseline 基线88 Quality Attribute 质量属性89 Scenario 场景2以字母检索单词Aability to perform执行的能力: （参见公共特性/common feature）acceptance criteria接受标准：为让用户、客户或其他授权组织接受，一个系统或组件所必须满足的条件。
分辨力的英语
分辨力的英语Developing discernment is crucial in navigating the complexities of modern life. Discernment, often defined as the ability to judge well, is essential for making informed decisions, understanding situations accurately, and avoiding pitfalls. It encompasses various aspects of critical thinking, emotional intelligence, and wisdom. In this article, we will explore the importance of discernment, its components, and how to cultivate it in daily life.At its core, discernment involves the capacity to differentiate between different options, ideas, or courses of action. It requires the ability to evaluate information objectively, identify underlying motives or biases, and anticipate potential consequences. Discernment is not merely about making quick judgments but rather about exercising thoughtful consideration and sound judgment.One aspect of discernment is critical thinking, which involves analyzing and evaluating information to form well-founded judgments or decisions. Critical thinkers question assumptions, seek evidence, and consider alternative perspectives before drawing conclusions. They are adept at recognizing logical fallacies, distinguishing between fact and opinion, and detecting misinformation or propaganda.Another component of discernment is emotional intelligence, which involves understanding and managing emotions, both in oneself and others. Emotionally intelligent individuals are attuned to their own feelings and motivations, enabling them to make decisions that align with their values and goals. They also possess empathy, allowing them to perceive and respond to the emotions of others effectively.Wisdom is also integral to discernment, encompassing a deep understanding of human nature, ethics, and the broader implications of actions or decisions. Wise individuals draw upon their knowledge and experience to navigate complex situations with clarity and insight. They recognize the interconnectedness of phenomena and consider long-term consequences when making choices.Cultivating discernment requires intentional effort and practice. One way to enhance discernment is by honing critical thinking skills through activities such as reading diverse perspectives, engaging in reasoned debate, and solving complex problems. Reflecting on past decisions and analyzing their outcomes can also deepen one's understanding of effective judgment.Developing emotional intelligence involves increasing self-awareness through practices such as mindfulness meditation, journaling, or therapy. Learning to recognize and regulate emotions can help individuals make more rational decisions and navigate interpersonal relationships more effectively. Additionally, cultivating empathy through acts of kindness and perspective-taking can foster deeper connections with others and enhance understanding.Wisdom is often cultivated through a combination of life experience, reflection, and learning from mentors or role models. Seeking out diverse sources of knowledge, such as literature, philosophy, or spiritual teachings, can broaden one's perspective and deepen insight into human behavior and the nature of reality. Integrating lessons learned from past experiences and applying them to future situations is essential for developing wisdom.In conclusion, discernment is a vital skill for navigating the complexities of the modern world. It involves critical thinking, emotional intelligence, and wisdom, enabling individuals to make informed decisions, understand situations accurately, and navigate ethical dilemmas effectively. By honing these skills through intentional practice and reflection, one can cultivate greater discernment and enhance their ability to thrive in an increasingly complex and interconnected world.。
成考学位英语考试
1、What is the primary purpose of writing a business email?A. To express personal emotions.B. To provide detailed instructions on a hobby.C. To communicate professionally and efficiently in a work setting.D. To discuss current events with friends. (答案：C)2、Which of the following is NOT a common feature of academic writing?A. Use of formal language.B. Inclusion of personal anecdotes.C. Clear organization and structure.D. Citation of sources. (答案：B)3、In which situation would you use a SWOT analysis?A. When creating a personal journal entry.B. When evaluating the strengths, weaknesses, opportunities, and threats of a business or project.C. When writing a fictional short story.D. When planning a casual social gathering. (答案：B)4、What does "ROI" stand for in the context of business and finance?A. Return On InvestmentB. Random Order InputC. Rapid Online InquiryD. Resource Optimization Initiative (答案：A)5、Which of these is an example of active listening in a business meeting?A. Interrupting the speaker to share your own opinion.B. Checking your phone while the other person is talking.C. Nodding and providing verbal cues to show understanding.D. Thinking about your next response without paying attention to the speaker. (答案：C)6、What is the main goal of a marketing strategy?A. To increase production costs.B. To decrease the quality of products.C. To identify and satisfy customer needs and wants.D. To limit competition in the market. (答案：C)7、Which of the following is a key element of effective time management?A. Procrastinating tasks until the last minute.B. Prioritizing tasks based on urgency and importance.C. Multitasking constantly without focus.D. Avoiding planning and spontaneously tackling tasks as they arise. (答案：B)8、In project management, what does the acronym "SMART" stand for when setting goals?A. Specific, Measurable, Achievable, Relevant, Time-boundB. Simple, Modern, Accessible, Reliable, TimelyC. Strategic, Minimal, Attractive, Responsive, TechnologicalD. Swift, Meticulous, Ambitious, Resourceful, Tactical (答案：A)9、Which of the following best describes the concept of "supply and demand" in economics?A. The relationship between the quantity of a product available and the desire for that product.B. The process of producing goods and services.C. The study of how money is created and managed.D. The analysis of government spending and taxation. (答案：A)10、What is the purpose of a feasibility study in starting a new business?A. To determine the company's annual revenue.B. To assess the legal requirements for operating the business.C. To evaluate the practicality and potential success of the business idea.D. To design the company's logo and branding. (答案：C)。
simulation modelling practice
simulation modelling practiceSimulation modelling is a crucial tool in the field of science and engineering. It allows us to investigate complex systems and predict their behaviour in response to various inputs and conditions. This article will guide you through the process of simulation modelling, from its basicprinciples to practical applications.1. Introduction to Simulation ModellingSimulation modelling is the process of representing real-world systems using mathematical models. These models allow us to investigate systems that are too complex or expensiveto be fully studied using traditional methods. Simulation models are created using mathematical equations, functions, and algorithms that represent the interactions and relationships between the system's components.2. Building a Basic Simulation ModelTo begin, you will need to identify the key elements that make up your system and define their interactions. Next, you will need to create mathematical equations that represent these interactions. These equations should be as simple as possible while still capturing the essential aspects of the system's behaviour.Once you have your equations, you can use simulation software to create a model. Popular simulation softwareincludes MATLAB, Simulink, and Arena. These software packages allow you to input your equations and see how the system will respond to different inputs and conditions.3. Choosing a Simulation Software PackageWhen choosing a simulation software package, consider your specific needs and resources. Each package has its own strengths and limitations, so it's important to select one that best fits your project. Some packages are more suitable for simulating large-scale systems, while others may bebetter for quickly prototyping small-scale systems.4. Practical Applications of Simulation ModellingSimulation modelling is used in a wide range of fields, including engineering, finance, healthcare, and more. Here are some practical applications:* Engineering: Simulation modelling is commonly used in the automotive, aerospace, and manufacturing industries to design and test systems such as engines, vehicles, and manufacturing processes.* Finance: Simulation modelling is used by financial institutions to assess the impact of market conditions on investment portfolios and interest rates.* Healthcare: Simulation modelling is used to plan and manage healthcare resources, predict disease trends, and evaluate the effectiveness of treatment methods.* Education: Simulation modelling is an excellent toolfor teaching students about complex systems and how they interact with each other. It helps students develop critical thinking skills and problem-solving techniques.5. Case Studies and ExamplesTo illustrate the practical use of simulation modelling, we will take a look at two case studies: an aircraft engine simulation and a healthcare resource management simulation.Aircraft Engine Simulation: In this scenario, a simulation model is used to assess the performance ofdifferent engine designs under various flight conditions. The model helps engineers identify design flaws and improve efficiency.Healthcare Resource Management Simulation: This simulation model helps healthcare providers plan their resources based on anticipated patient demand. The model can also be used to evaluate different treatment methods and identify optimal resource allocation strategies.6. ConclusionSimulation modelling is a powerful tool that allows us to investigate complex systems and make informed decisions about how to best manage them. By following these steps, you can create your own simulation models and apply them to real-world problems. Remember, it's always important to keep anopen mind and be willing to adapt your approach based on the specific needs of your project.。
双语词汇识别中的联结主义模型
收稿日期:2008-05-31作者简介:敖 锋(1980-),男,四川泸州人,国防科技大学人文与社科学院外语系讲师,博士,研究方向为心理语言学及自然语言处理;胡卫星(1975-),女,湖南益阳人,国防科技大学人文与社科学院外语系副教授,博士研究生,研究方向为心理语言学及二语习得。
双语词汇识别中的联结主义模型敖 锋,胡卫星(国防科技大学人文与社科学院,湖南长沙410073)摘 要:本文探讨了计算模型在双语词汇识别研究中的作用并介绍了两种得到应用的联结主义模型:局域式网络模型和分布式网络模型。
这两种模型在结构和功能上既有相同之处,又存在差异。
尽管这两种模型对双语研究的理论发展有积极作用,但目前研究者们仍倾向于利用不同的模型来研究不同的语言现象,因此该领域还存在很大的研究空间尚待开发。
关键词:双语词汇识别;联结主义;计算模型中图分类号:H 087 文献标识码:A 文章编号:10022722X (2010)04200122050.引言从上世纪80年代开始,联结主义(Connecti o n 2is m)¹(即人工神经网络)模型被广泛用于研究母语的理解与产出,其机制在于模拟成年人的语言系统以及该系统的发展过程。
在双语词汇识别领域也有一部分研究采用了联结主义模型的方法。
本文主要介绍其中的两种模型,即局域式网络模型和分布式网络模型。
1.双语词汇识别中的计算模型计算模型在双语词汇识别研究中具有一定优势。
首先,建立计算模型必须使理论本身足够清晰以满足计算机应用的需要;其次,当某种理论的预测结果很难确定时(比如理论中各因素之间的相互影响非常复杂时),计算模型通过训练后能够生成可检验的结果,将模型的计算结果和实验数据进行对比即可分析理论的解释力水平;最后,计算模型可以模拟一些语言研究中的特殊情况,比如脑损伤造成的语言缺失,而这很难通过实验的方法来研究。
但是,在双语词汇识别研究中应用计算模型仍有一些值得考虑的问题。
model evaluate的含义
model evaluate的含义
模型评估的含义
模型评估是指对机器学习模型在特定数据集上的性能进行定量和定性的衡量和分析。
它是机器学习算法开发和优化过程中的重要一环，旨在评估模型的预测准确性和可靠性。
在模型评估中，一般会使用一些评估指标来衡量模型的性能。
常见的评估指标包括准确率（Accuracy）、精确率（Precision）、召回率（Recall）、F1值（F1 Score）、ROC曲线（Receiver Operating Characteristic Curve）等。
这些指标能够提供模型在不同方面的性能信息，帮助我们了解模型的优劣。
评估模型时，我们会使用一部分已知结果的数据，将其分为训练集和测试集。
训练集用来训练模型，测试集用来评估模型的性能。
这样的做法可以在一定程度上模拟模型在现实环境中的表现情况。
除了常规的评估方法，还有一些交叉验证和混淆矩阵等技术可供选择。
交叉验证可以更充分地利用数据，减少因数据划分不合理而引起的偏差，同时还能评估模型的稳定性和一致性。
混淆矩阵则可以展示模型的分类性能，清晰地呈现真阳性、真阴性、假阳性和假阴性等不同类别的预测结果。
特别需要注意的是，在进行模型评估时，需要避免过度拟合（Overfitting）和欠拟合（Underfitting）的问题。
过度拟合指的是模型过于复杂，对于训练数据过度拟合，而在未知数据上表现较差。
欠拟合则是指模型过于简单，未能捕捉到数据中的复杂关系，导致无法很好地进行预测。
综上所述，模型评估是评估机器学习模型性能的重要过程，通过选择合适的评估指标和方法，我们可以更加客观地评估模型的优劣，从而更好地优化和改进模型的表现。
人工智能(AI)中英文术语对照表
人工智能（AI）中英文术语对照表目录人工智能（AI）中英文术语对照表 (1)Letter A (1)Letter B (2)Letter C (3)Letter D (4)Letter E (5)Letter F (6)Letter G (6)Letter H (7)Letter I (7)Letter K (8)Letter L (8)Letter M (9)Letter N (10)Letter O (10)Letter P (11)Letter Q (12)Letter R (12)Letter S (13)Letter T (14)Letter U (14)Letter V (15)Letter W (15)Letter AAccumulated error backpropagation 累积误差逆传播Activation Function 激活函数Adaptive Resonance Theory/ART 自适应谐振理论Addictive model 加性学习Adversarial Networks 对抗网络Affine Layer 仿射层Affinity matrix 亲和矩阵Agent 代理/ 智能体Algorithm 算法Alpha-beta pruning α-β剪枝Anomaly detection 异常检测Approximation 近似Area Under ROC Curve／AUC Roc 曲线下面积Artificial General Intelligence/AGI 通用人工智能Artificial Intelligence/AI 人工智能Association analysis 关联分析Attention mechanism注意力机制Attribute conditional independence assumption 属性条件独立性假设Attribute space 属性空间Attribute value 属性值Autoencoder 自编码器Automatic speech recognition 自动语音识别Automatic summarization自动摘要Average gradient 平均梯度Average-Pooling 平均池化Action 动作AI language 人工智能语言AND node 与节点AND/OR graph 与或图AND/OR tree 与或树Answer statement 回答语句Artificial intelligence,AI 人工智能Automatic theorem proving自动定理证明Letter BBreak-Event Point／BEP 平衡点Backpropagation Through Time 通过时间的反向传播Backpropagation/BP 反向传播Base learner 基学习器Base learning algorithm 基学习算法Batch Normalization/BN 批量归一化Bayes decision rule 贝叶斯判定准则Bayes Model Averaging／BMA 贝叶斯模型平均Bayes optimal classifier 贝叶斯最优分类器Bayesian decision theory 贝叶斯决策论Bayesian network 贝叶斯网络Between-class scatter matrix 类间散度矩阵Bias 偏置/ 偏差Bias-variance decomposition 偏差-方差分解Bias-Variance Dilemma 偏差–方差困境Bi-directional Long-Short Term Memory/Bi-LSTM 双向长短期记忆Binary classification 二分类Binomial test 二项检验Bi-partition 二分法Boltzmann machine 玻尔兹曼机Bootstrap sampling 自助采样法／可重复采样／有放回采样Bootstrapping 自助法Letter CCalibration 校准Cascade-Correlation 级联相关Categorical attribute 离散属性Class-conditional probability 类条件概率Classification and regression tree/CART 分类与回归树Classifier 分类器Class-imbalance 类别不平衡Closed -form 闭式Cluster 簇/类/集群Cluster analysis 聚类分析Clustering 聚类Clustering ensemble 聚类集成Co-adapting 共适应Coding matrix 编码矩阵COLT 国际学习理论会议Committee-based learning 基于委员会的学习Competitive learning 竞争型学习Component learner 组件学习器Comprehensibility 可解释性Computation Cost 计算成本Computational Linguistics 计算语言学Computer vision 计算机视觉Concept drift 概念漂移Concept Learning System /CLS概念学习系统Conditional entropy 条件熵Conditional mutual information 条件互信息Conditional Probability Table／CPT 条件概率表Conditional random field/CRF 条件随机场Conditional risk 条件风险Confidence 置信度Confusion matrix 混淆矩阵Connection weight 连接权Connectionism 连结主义Consistency 一致性／相合性Contingency table 列联表Continuous attribute 连续属性Convergence收敛Conversational agent 会话智能体Convex quadratic programming 凸二次规划Convexity 凸性Convolutional neural network/CNN 卷积神经网络Co-occurrence 同现Correlation coefficient 相关系数Cosine similarity 余弦相似度Cost curve 成本曲线Cost Function 成本函数Cost matrix 成本矩阵Cost-sensitive 成本敏感Cross entropy 交叉熵Cross validation 交叉验证Crowdsourcing 众包Curse of dimensionality 维数灾难Cut point 截断点Cutting plane algorithm 割平面法Letter DData mining 数据挖掘Data set 数据集Decision Boundary 决策边界Decision stump 决策树桩Decision tree 决策树／判定树Deduction 演绎Deep Belief Network 深度信念网络Deep Convolutional Generative Adversarial Network/DCGAN 深度卷积生成对抗网络Deep learning 深度学习Deep neural network/DNN 深度神经网络Deep Q-Learning 深度Q 学习Deep Q-Network 深度Q 网络Density estimation 密度估计Density-based clustering 密度聚类Differentiable neural computer 可微分神经计算机Dimensionality reduction algorithm 降维算法Directed edge 有向边Disagreement measure 不合度量Discriminative model 判别模型Discriminator 判别器Distance measure 距离度量Distance metric learning 距离度量学习Distribution 分布Divergence 散度Diversity measure 多样性度量／差异性度量Domain adaption 领域自适应Downsampling 下采样D-separation （Directed separation）有向分离Dual problem 对偶问题Dummy node 哑结点Dynamic Fusion 动态融合Dynamic programming 动态规划Letter EEigenvalue decomposition 特征值分解Embedding 嵌入Emotional analysis 情绪分析Empirical conditional entropy 经验条件熵Empirical entropy 经验熵Empirical error 经验误差Empirical risk 经验风险End-to-End 端到端Energy-based model 基于能量的模型Ensemble learning 集成学习Ensemble pruning 集成修剪Error Correcting Output Codes／ECOC 纠错输出码Error rate 错误率Error-ambiguity decomposition 误差-分歧分解Euclidean distance 欧氏距离Evolutionary computation 演化计算Expectation-Maximization 期望最大化Expected loss 期望损失Exploding Gradient Problem 梯度爆炸问题Exponential loss function 指数损失函数Extreme Learning Machine/ELM 超限学习机Letter FExpert system 专家系统Factorization因子分解False negative 假负类False positive 假正类False Positive Rate/FPR 假正例率Feature engineering 特征工程Feature selection特征选择Feature vector 特征向量Featured Learning 特征学习Feedforward Neural Networks/FNN 前馈神经网络Fine-tuning 微调Flipping output 翻转法Fluctuation 震荡Forward stagewise algorithm 前向分步算法Frequentist 频率主义学派Full-rank matrix 满秩矩阵Functional neuron 功能神经元Letter GGain ratio 增益率Game theory 博弈论Gaussian kernel function 高斯核函数Gaussian Mixture Model 高斯混合模型General Problem Solving 通用问题求解Generalization 泛化Generalization error 泛化误差Generalization error bound 泛化误差上界Generalized Lagrange function 广义拉格朗日函数Generalized linear model 广义线性模型Generalized Rayleigh quotient 广义瑞利商Generative Adversarial Networks/GAN 生成对抗网络Generative Model 生成模型Generator 生成器Genetic Algorithm/GA 遗传算法Gibbs sampling 吉布斯采样Gini index 基尼指数Global minimum 全局最小Global Optimization 全局优化Gradient boosting 梯度提升Gradient Descent 梯度下降Graph theory 图论Ground-truth 真相／真实Letter HHard margin 硬间隔Hard voting 硬投票Harmonic mean 调和平均Hesse matrix海塞矩阵Hidden dynamic model 隐动态模型Hidden layer 隐藏层Hidden Markov Model/HMM 隐马尔可夫模型Hierarchical clustering 层次聚类Hilbert space 希尔伯特空间Hinge loss function 合页损失函数Hold-out 留出法Homogeneous 同质Hybrid computing 混合计算Hyperparameter 超参数Hypothesis 假设Hypothesis test 假设验证Letter IICML 国际机器学习会议Improved iterative scaling/IIS 改进的迭代尺度法Incremental learning 增量学习Independent and identically distributed/i.i.d. 独立同分布Independent Component Analysis/ICA 独立成分分析Indicator function 指示函数Individual learner 个体学习器Induction 归纳Inductive bias 归纳偏好Inductive learning 归纳学习Inductive Logic Programming／ILP 归纳逻辑程序设计Information entropy 信息熵Information gain 信息增益Input layer 输入层Insensitive loss 不敏感损失Inter-cluster similarity 簇间相似度International Conference for Machine Learning/ICML 国际机器学习大会Intra-cluster similarity 簇内相似度Intrinsic value 固有值Isometric Mapping/Isomap 等度量映射Isotonic regression 等分回归Iterative Dichotomiser 迭代二分器Letter KKernel method 核方法Kernel trick 核技巧Kernelized Linear Discriminant Analysis／KLDA 核线性判别分析K-fold cross validation k 折交叉验证／k 倍交叉验证K-Means Clustering K –均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base 知识库Knowledge Representation 知识表征Letter LLabel space 标记空间Lagrange duality 拉格朗日对偶性Lagrange multiplier 拉格朗日乘子Laplace smoothing 拉普拉斯平滑Laplacian correction 拉普拉斯修正Latent Dirichlet Allocation 隐狄利克雷分布Latent semantic analysis 潜在语义分析Latent variable 隐变量Lazy learning 懒惰学习Learner 学习器Learning by analogy 类比学习Learning rate 学习率Learning Vector Quantization/LVQ 学习向量量化Least squares regression tree 最小二乘回归树Leave-One-Out/LOO 留一法linear chain conditional random field 线性链条件随机场Linear Discriminant Analysis／LDA 线性判别分析Linear model 线性模型Linear Regression 线性回归Link function 联系函数Local Markov property 局部马尔可夫性Local minimum 局部最小Log likelihood 对数似然Log odds／logit 对数几率Logistic Regression Logistic 回归Log-likelihood 对数似然Log-linear regression 对数线性回归Long-Short Term Memory/LSTM 长短期记忆Loss function 损失函数Letter MMachine translation/MT 机器翻译Macron-P 宏查准率Macron-R 宏查全率Majority voting 绝对多数投票法Manifold assumption 流形假设Manifold learning 流形学习Margin theory 间隔理论Marginal distribution 边际分布Marginal independence 边际独立性Marginalization 边际化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field 马尔可夫随机场Maximal clique 最大团Maximum Likelihood Estimation/MLE 极大似然估计／极大似然法Maximum margin 最大间隔Maximum weighted spanning tree 最大带权生成树Max-Pooling 最大池化Mean squared error 均方误差Meta-learner 元学习器Metric learning 度量学习Micro-P 微查准率Micro-R 微查全率Minimal Description Length/MDL 最小描述长度Minimax game 极小极大博弈Misclassification cost 误分类成本Mixture of experts 混合专家Momentum 动量Moral graph 道德图／端正图Multi-class classification 多分类Multi-document summarization 多文档摘要Multi-layer feedforward neural networks 多层前馈神经网络Multilayer Perceptron/MLP 多层感知器Multimodal learning 多模态学习Multiple Dimensional Scaling 多维缩放Multiple linear regression 多元线性回归Multi-response Linear Regression ／MLR 多响应线性回归Mutual information 互信息Letter NNaive bayes 朴素贝叶斯Naive Bayes Classifier 朴素贝叶斯分类器Named entity recognition 命名实体识别Nash equilibrium 纳什均衡Natural language generation/NLG 自然语言生成Natural language processing 自然语言处理Negative class 负类Negative correlation 负相关法Negative Log Likelihood 负对数似然Neighbourhood Component Analysis/NCA 近邻成分分析Neural Machine Translation 神经机器翻译Neural Turing Machine 神经图灵机Newton method 牛顿法NIPS 国际神经信息处理系统会议No Free Lunch Theorem／NFL 没有免费的午餐定理Noise-contrastive estimation 噪音对比估计Nominal attribute 列名属性Non-convex optimization 非凸优化Nonlinear model 非线性模型Non-metric distance 非度量距离Non-negative matrix factorization 非负矩阵分解Non-ordinal attribute 无序属性Non-Saturating Game 非饱和博弈Norm 范数Normalization 归一化Nuclear norm 核范数Numerical attribute 数值属性Letter OObjective function 目标函数Oblique decision tree 斜决策树Occam’s razor 奥卡姆剃刀Odds 几率Off-Policy 离策略One shot learning 一次性学习One-Dependent Estimator／ODE 独依赖估计On-Policy 在策略Ordinal attribute 有序属性Out-of-bag estimate 包外估计Output layer 输出层Output smearing 输出调制法Overfitting 过拟合／过配Oversampling 过采样Letter PPaired t-test 成对t 检验Pairwise 成对型Pairwise Markov property成对马尔可夫性Parameter 参数Parameter estimation 参数估计Parameter tuning 调参Parse tree 解析树Particle Swarm Optimization/PSO粒子群优化算法Part-of-speech tagging 词性标注Perceptron 感知机Performance measure 性能度量Plug and Play Generative Network 即插即用生成网络Plurality voting 相对多数投票法Polarity detection 极性检测Polynomial kernel function 多项式核函数Pooling 池化Positive class 正类Positive definite matrix 正定矩阵Post-hoc test 后续检验Post-pruning 后剪枝potential function 势函数Precision 查准率／准确率Prepruning 预剪枝Principal component analysis/PCA 主成分分析Principle of multiple explanations 多释原则Prior 先验Probability Graphical Model 概率图模型Proximal Gradient Descent/PGD 近端梯度下降Pruning 剪枝Pseudo-label伪标记Letter QQuantized Neural Network 量子化神经网络Quantum computer 量子计算机Quantum Computing 量子计算Quasi Newton method 拟牛顿法Letter RRadial Basis Function／RBF 径向基函数Random Forest Algorithm 随机森林算法Random walk 随机漫步Recall 查全率／召回率Receiver Operating Characteristic/ROC 受试者工作特征Rectified Linear Unit/ReLU 线性修正单元Recurrent Neural Network 循环神经网络Recursive neural network 递归神经网络Reference model 参考模型Regression 回归Regularization 正则化Reinforcement learning/RL 强化学习Representation learning 表征学习Representer theorem 表示定理reproducing kernel Hilbert space/RKHS 再生核希尔伯特空间Re-sampling 重采样法Rescaling 再缩放Residual Mapping 残差映射Residual Network 残差网络Restricted Boltzmann Machine/RBM 受限玻尔兹曼机Restricted Isometry Property/RIP 限定等距性Re-weighting 重赋权法Robustness 稳健性/鲁棒性Root node 根结点Rule Engine 规则引擎Rule learning 规则学习Letter SSaddle point 鞍点Sample space 样本空间Sampling 采样Score function 评分函数Self-Driving 自动驾驶Self-Organizing Map／SOM 自组织映射Semi-naive Bayes classifiers 半朴素贝叶斯分类器Semi-Supervised Learning半监督学习semi-Supervised Support Vector Machine 半监督支持向量机Sentiment analysis 情感分析Separating hyperplane 分离超平面Searching algorithm 搜索算法Sigmoid function Sigmoid 函数Similarity measure 相似度度量Simulated annealing 模拟退火Simultaneous localization and mapping同步定位与地图构建Singular Value Decomposition 奇异值分解Slack variables 松弛变量Smoothing 平滑Soft margin 软间隔Soft margin maximization 软间隔最大化Soft voting 软投票Sparse representation 稀疏表征Sparsity 稀疏性Specialization 特化Spectral Clustering 谱聚类Speech Recognition 语音识别Splitting variable 切分变量Squashing function 挤压函数Stability-plasticity dilemma 可塑性-稳定性困境Statistical learning 统计学习Status feature function 状态特征函Stochastic gradient descent 随机梯度下降Stratified sampling 分层采样Structural risk 结构风险Structural risk minimization/SRM 结构风险最小化Subspace 子空间Supervised learning 监督学习／有导师学习support vector expansion 支持向量展式Support Vector Machine/SVM 支持向量机Surrogat loss 替代损失Surrogate function 替代函数Symbolic learning 符号学习Symbolism 符号主义Synset 同义词集Letter TT-Distribution Stochastic Neighbour Embedding/t-SNE T –分布随机近邻嵌入Tensor 张量Tensor Processing Units/TPU 张量处理单元The least square method 最小二乘法Threshold 阈值Threshold logic unit 阈值逻辑单元Threshold-moving 阈值移动Time Step 时间步骤Tokenization 标记化Training error 训练误差Training instance 训练示例／训练例Transductive learning 直推学习Transfer learning 迁移学习Treebank 树库Tria-by-error 试错法True negative 真负类True positive 真正类True Positive Rate/TPR 真正例率Turing Machine 图灵机Twice-learning 二次学习Letter UUnderfitting 欠拟合／欠配Undersampling 欠采样Understandability 可理解性Unequal cost 非均等代价Unit-step function 单位阶跃函数Univariate decision tree 单变量决策树Unsupervised learning 无监督学习／无导师学习Unsupervised layer-wise training 无监督逐层训练Upsampling 上采样Letter VVanishing Gradient Problem 梯度消失问题Variational inference 变分推断VC Theory VC维理论Version space 版本空间Viterbi algorithm 维特比算法Von Neumann architecture 冯·诺伊曼架构Letter WWasserstein GAN/WGAN Wasserstein生成对抗网络Weak learner 弱学习器Weight 权重Weight sharing 权共享Weighted voting 加权投票法Within-class scatter matrix 类内散度矩阵Word embedding 词嵌入Word sense disambiguation 词义消歧。
第三章蛋白质的分子设计修改
• Java-based on-line biomolecular modeling package –B /~nwhite/Biomer
■蛋白质设计目前存在的问题
设计的蛋白质与天然蛋白质比较，缺乏结构的独特性及明显的功能优 越性。所有设计的蛋白质有正确的形貌、显著的二级结构及合理的热 力学稳定性，但一般说来它们三级结构的确定性较差
第二节 基于天然蛋白质结构的分子设计
■一、概述
即使蛋白质的三维结构已知，选择 一个合适的突变体依然困难，这说明 蛋白质设计任务的艰巨性，它涉及多 种学科的配合，如计算机模拟专家、 X 射线晶体学家、蛋白质化学家、生 物技术专家等的合作与配合。
蛋白质分子设计
第一节 分子设计概况 第二节 基于天然蛋白质结
构的分子设计 第三节 全新蛋白质设计 第四节 计算蛋白质设计 第五节 基于结构的药物分
子设计
第一节 分子设计概况
随着理论化学方法六十年来不断发展，加上近年来计算 机技术突飞猛进，分子设计已经从炼金术士的梦想走上实 际的研究和应用。世界最大的二十家药厂无一例外地运用 分子设计的方法把药物筛选的范围缩小到原先的1/5到1/10 。从电子结构出发，设计具有特殊性质的新材料、新化合 物也开始走向现实。 分子设计也称为分子建模(Molecular Modeling)，目前已 经成为有的外国大学化学系的课程。它包括理论化学方法 和计算机化学方法。理论化学方法包括量子化学、统计热 力学和非平衡统计力学等。
…………………………
■ 小结
第一节 分子设计概况
分子设计历史 – 计算化学 (量子化学, 分子力学等) – 结构化学 (晶体学，谱学等) – 计算机技术 (计算数学，软硬件，数据库，图形学等)
催化基础知识普及、探讨帖之五：催化期刊及投稿
催化基础知识普及、探讨帖之五：催化期刊及投稿催化基础知识普及、探讨帖之五：催化期刊及投稿催化知识普及、探讨系列帖第 5 帖——催化期刊及投稿此帖主题相信大家平时了解的比较多，恐怕也是大家最为关心的问题之一。
小木虫论文投稿专版关于此方面的介绍比较多也比较详细，且我们催化专版也有几个帖子专门进行了探讨和讨论，而我对这方面了解比较少（主要是没发过什么文章，哈哈），此帖内容主要是对网络上的一些投稿知识进行汇总（加入了少的可怜的自己对催化期刊的认识及投稿经验）。
目的还是办此系列帖的主旨：介绍催化相关基础知识、抛砖引玉、相互学习、分享经验及教训。
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协作机器人关节摩擦特性辨识与补偿技术
第4期2019年4月组合机床与自动化加工技术Modular Machine Tool & Automatic Manufacturing TechnitjueNo. 4Apr. 2019文章编号：1001 -2265 (2019)04 -0028 -04 D01：10.13462/ki.m m tam t.2019. 04. 007协作机器人关节摩擦特性辨识与补偿技术！陶岳1，赵飞2’.，曹巨江1(1.陕西科技大学机电工程学院，西安=10021 % 2.西安交通大学机械工程学院，西安了扣"々'；3.陕西智能机器人重点实验室，西安710049)摘要：为提高机器人的运动精度和换向过程的运动平稳性，降低换向过程中因摩擦引起的位置误差和速度波动，文章提出了一种基于改进库伦+黏性摩擦模型的补偿方法。
该改进模型引入sigmoid函数克服了传统库伦+黏性摩擦模型在换向过程中的不连续问题；并采用恒速辨识方法实现了摩擦模型参数的精确辨识；在此基础上，利用前馈补偿技术实现了机器人关节摩擦特性的补偿。
实验结果表明’文中提出的辨识方法能够有效的辨识出摩擦模型参数和基于模型的补偿算法明显降低了换向过程中的摩擦误差。
关键词：机器人关节；摩擦;辨识；补偿中图分类号:TH165;TG659 文献标识码:AResearch on Friction Characteristics Identification and Compensation of Cooperative Robots JointsTAO Yue1，ZHA0Fei2,3，CA0Ju-jiang1(1.College of Mechanical &Electrical Engineering, Shaanxi University of Science&Technology, Xiran710021，China；2. School of Mechanical Engineering，Xir an Jiaotong University，Xiran710049，China)Abstract ：I n order to improve the motion accuracy of tlie robot and the motion stability in the commutationprocesses，and to reduce the position eror and velocity fluctuation caused by friction duri tion process，a compensation metliod based on the improved coulomb and viscous friction model is proposed in this paper.The sig^m oid function is introduced to overcome the discontinuity of th ie traditional coulomb and viscous friction model in the commutation process.And the identification of constant sj?eed m ethi-od is used to realize the accurate identification of friction model parameters.On this bas friction compensation of robot joints by using friction feedforward compensation technology.The experimental results show that the proposed identification method can effectively identify the ton model and the model-based compensation algorithm can significantly reduce the frict commutation proce&.Key words ：robot joints；friction；identification；compensation〇引言随着机器人技术的不断发展，使用机器人代替人的繁复劳动已成为目前各行业降低成本、提高制造质量的主要手段。
BPM2Modelling
Model is a simplified imagination of a modeling object, which adequately reflects it’s properties, most significant in terms of modeling
A reference process model generally describes best practices in a given industry.
5
Business process identification
Top-down BP identification approach 6
Attribute is a specification that defines necessary, essential, integral property of an object, element
10
Business process modeling essentials
What business process modeling is used for:
13
Business process modeling essentials Business process modeling princiles:
Reliability (semantic and syntax)
Significance of objects and relationships in a model
Business process identification
Business processes should be decomposed according to the significant segmentation criteria, such as:
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Modelling and Traceability of Composition Semantics in Requirements Ruzanna Chitchyan, Safoora Shakil Khan, Awais RashidComputing Department, Lancaster University, Lancaster{rouza;shakilkh;marash}@AbstractRequirements composition is one of the cornerstones of Aspect-Oriented Requirements Engineering [1] . In this paper we discuss how such compositions can be represented in our Requirements Description Language (RDL). The RDL draws a number of concepts from natural language syntax and semantics; its composition semantics are entirely based on semantics of requirements interdependencies1. We also present a visual notationfor illustrating such compositions. While our RDL is dedicated to preservation of semantics of the requirements and their interdependencies, the visualisation further facilitates linking the composition semantics with their contributor requirements. The visual notation also serves as a simple means for validation of compositions with the stakeholders as well as for recording traceability of requirements participating in a composition to their sources.1. IntroductionIn surveying the contemporary works on Requirements Engineering [1], we have identified composition as one of the primary contributions of Aspect-Oriented Requirements Engineering (AORE). We have also pointed out that though several AORE [2, 3] and other [4] approaches provide some supportfor composition, more research is needed on the semantics of composition, the mechanisms for traceably capturing such semantics, and validating them with the stakeholders [1].1In a related paper [5] (also submitted to this workshop) we discuss in detail how a relatively complete set of requirements interdependencies is derived from the semantics of natural language words used in requirements themselves. We also discuss tracing these semantics into the composition-based requirements analysis.Yet composition semantics are inherent to the natural language description of requirements1, as these semantics reflect the influences of some requirements on the others [5]. For instance, security requirements could be expected to broadly influence other user requirements; response time requirements may conflict with security requirements, etc. Thus, it is essential to capture such mutual influences of requirements in order to be able to carry out an informed and adequate requirements analysis. If such influences are not identified and addressed in a timely fashion, the resultant requirements specification is likely to contain contradictions and incompatibilities (i.e. conflicts).In this paper we focus in particular on representation of requirements and their interdependencies – a necessary step before any (non- trivial) requirements analysis can commence2. Our representation is mainly centred on an XML-based Requirements Description Language (RDL). The RDL draws a number of concepts from natural language syntax and semantics; its composition semantics are entirely based on semantics of requirements interdependencies [5]1. The RDL is complemented bya simple yet intuitive visual representation to enable the requirements analyst to take the compositions backto the stakeholders for validation, hence providing validation and traceability link to the original information sources.The main concepts of our RDL and its supported composition are outlined in section 2 of this paper. Section 3 presents the visualisation of the composition, and section 4 discusses the traceability support delivered by our visualisation approach. In section 5 we discuss the related work, and finally draw the conclusions and point to the future directions of this work in section 6.2We leave discussion on conflict resolution to a future paper. The issue of dependency identification is presented in detail in [5].2. Composition Semantics in RequirementsInitial (natural language) requirements artefacts already contain the semantics which cause their interdependencies and interaction. These are the very semantics that we need to reveal and use in composition, as composition, in essence, is nothing else than an explicit revelation of such requirement interdependencies and interactions.Our approach to dependency identification is based on principles of work in Natural Language Processing (NLP). In particular, we use Dixon’s [6] verb classification to ground our own verb classification and to derive a set of possible requirement interdependency types1 [5].Presently we use six main verb categories (Affect, Move, Rest, Communicate, Mental Action, and General Action), each of which has a number of sub-categories1. Our RDL, outlined in sub-section 2.1 below, also uses the concepts based on natural language syntax and semantics.In order to illustrate the concepts of the RDL we use an extract of requirements from a requirements statement document for an online auction system [7]: “All potential users of the system must first enrol with the system…. The seller has the right to cancel the auction as long as the auction’s start date has not already passed.... An auction is activated when the auction start date provided by the seller arrives...”2.1. RDLThe most basic concepts of the RDL are that of concern, requirement, subject, object, relationship, composition, and sequencing. (The composition element and its sub-elements are discussed in the following sub-section.)Concern is a module for encapsulating requirements. A concern can be simple (containing only requirements), or composite (containing other concerns as well as requirements). Depending on the requirements structuring approach selected, concern can be instantiated to represent the requirement encapsulation module of that approach (e.g. viewpoint, theme, use case, etc.). Each concern is identified by its name.From our example requirements extract we can identify three concerns: Enrol, Seller, and Auction Management, each of which has only one requirement. These are depicted in Figure 1.Requirements are defined in line with accepted Requirements Engineering terminology. They are descriptions of what services the stakeholders expect of the system, the system behaviour, and constraints and standards that it should meet [8].Each of the sentences provided in the example extract are requirement sentences (Figure 1). Of course, it is not always necessary for each sentence to be a new requirement by itself: some may be providing details of a more general requirement, etc. This is why we have adopted a nested structure for requirements – each requirement can have any number of sub-requirements, as has initially been presented in [2].Similar to the subject in natural language grammar, a subject in our RDL is the entity that undertakes actions described within the requirement sentence.In the example extract from the auction system requirements the following subjects are identified (Figure 1): users, seller, auction, start date. These are subjects since they undertake the action of a requirement: users by enrolling, sellers by canceling the auction, auction by activating, and start date by passing. It is worth noting that though we have only three requirement sentences, we have identified four subjects. This is because the last sentence has a complement clause (‘when the start date provided by the user arrives’) which in turn has its own subject – start date.<Concern name="Enrol"><Requirement id="1"> All potential<Subject>users</Subject> of the system must<Sequencing type="temporal" semantics="prior">first</Sequencing><Relationship type="Move" semantics="Group">enrol</Relationship>with the <Object>system</Object>.</Requirement></Concern><Concern name=“Seller”><Requirement id=“1"> The<Subject>seller</Subject> has the right to<Relationship type="Move" semantics="Set_in_Motion">cancel</Relationship> the<Object>auction</Object><Sequencing type="conditional" semantics="if">as long as </Sequencing>the auction's<Subject>start date</Subject>has not been<Relationship type="Move" semantics="Set_in_Motion">passed</Relationship>, i.e., the<Subject>auction</Subject>has not<Sequencing type="temporal" semantics="prior">already</Sequencing><Relationship type="Move" semantics="Set_in_Motion">started</Relationship></Requirement></Concern><Concern name="Auction Management"><Requirement id="1">An<Object>Auction</Object>is<Relationship type="Move" semantics="Set_in_Motion">activated</Relationship><Sequencing type="temporal" semantics="following"> when</Sequencing>the auction<Subject>start date</Subject>provided by the seller<Relationship type="Move" semantics="Set_in_Motion">arrives</Relationship>.</Requirement></Concern>Figure 1: Demonstration of RDL concepts: concern, requirement, subject, object, relationship.An object in our RDL is also in line with the natural language grammar object in that it is the entity which is being affected by the actions undertaken by thesubject of the requirement sentence, or in respect with which the actions are undertaken.The objects in our example extract are (Figure 1): system (as users need to register with the system), and auction (as it can be cancelled). Again, it is worth noting that there are only two objects for the three requirement sentences. Thus, objects can often be omitted or implied either due to the common use of a verb/expression, or due to context, etc.Relationship depicts the action performed by the subject on or in respect with its object(s). This does not need to be any specific type of action, but any action denoted by a verb or verb expression.In the given examples the relationships are: enrol, since this is what the user must do with respect to the system; cancel, as this is what the seller may do in respect with the auction; passed, as the start date may pass; activate, as this is what is done to the auction; and, finally, arrive, as this is what the start date may do.As mentioned above, we also use our own classification system to group the verbs into semantic categories each of which reflects a certain general meaning1. The (simplified) RDL representation of the example requirements along with their verb classifications is presented in Figure 1, where type attribute reflects the top-level category of the verb, and semantics category reflects its more precise sub-category.Sequencing reflects the ordering of stakeholder/ system requirements. This element is applicable, for instance, when the stakeholders want to stipulate some temporal, conditional, or unconditional ordering of certain requirements.In our example (Figure 1) the words first, as long as, already, and when provide examples of the temporal and conditional dependencies. The attribute structure of sequencing is similar to the relationship element, in that this element too has the type and semantics attributes which reflect the general type (temporal, conditional, unconditional) of the given word, and its more presence semantics (e.g., prior, following, if).2.2. Composition2Composition is the process of integrating requirements into a coherent set by exposing their interdependencies and ordering. Having revealed such interdependencies we can detect conflicts and 2 Several other features of composition, e.g. the outcome element and its attributes are not discussed in this paper. inconsistencies between requirements and take an early corrective action.The Composition element of the RDL consists of two main sub-elements: constraint and base (c.f. Figure 3).The constraint element selects a (set of) requirements or concerns and defines how this (set of) requirements influences a second (set of) requirements or concerns. This second (set of) requirements/ concerns is selected by the base element. Base also defines the order and conditions of the constraint elements’ influence (or application) onto the base.Composition utilises the above presented elements of the RDL by both directly referencing the elements (e.g., referring by name to concerns, subjects, etc., referring by type or semantics to relationships or sequencing elements, etc.) as well as by using a set of operators derived from the verb categories1 and from the sequencing element types. In the present paper we will not be discussing operator derivation in detail, but some examples of such operators are presented inRelationship and (b) Composition elements.The operators are used in the constraint and base sub-elements of the composition element. The operators derived from the Relationship categories are used to specify the how of the constraint element. The operators derived from the Sequencing are used to define the order/condition in the base element.In Figure 3 we demonstrate the composition specification in our RDL. We illustrate two compositions:•the first (named EnrolComposition) states that “a concern named Enrol must be affiliatedbefore all other concerns”;•the second (named CancelAuction-Composition) states that “a seller may cancel(end) an auction only if an auction has notbegun”.<Composition name="Enrol Composition"><Constraint operator="affiliate">concern="Enrol"</Constraint><Base operator="before">all concerns</Base></Composition><Composition name="CancelAuctionComposition"><Constraint operator="begin/end">subject="seller" andrelationship="end" and object="auction"</Constraint><Base operator="ifNot">subject="auction" and[relationship="begin“]</Base></Composition>Figure 3: Example of composition in RDLHence, the first example in Figure 3 shows that there is an affiliate relationship between Enrol concern and all other concerns of the system. The second example shows that there is a conditional begin/end relationship between any requirement sentence with subject seller that has relationship end to an object auction and any other requirement sentence with subject action and relationship begin - the constraint element is only applicable when the bracketed part of the base element (Figure 3) is not satisfied.Thus, we have used the semantics of the requirements themselves, in particular their verbs and condition/order specifications in making requirement interdependencies explicit in the composition. The most powerful feature of our RDL is its use of the semantics of the natural language. These are used not only in operator derivation, but also in referencing the subjects, objects and relationships: thus, when we referto subject of seller kind, we imply semantic, and not simply syntactic matching. For instance with subject=”seller” all cases where subject is a seller, salesman or any other synonym of seller will be matched. Synonym matching can be supported eitherby general synonym dictionaries, or by building case-specific dictionaries for each system.3. Visual Representation of CompositionAs discussed in the section above, the RDL annotates requirements with an XML-based language. While it is expressive and powerful and serves as an effective communication medium between requirements analysts, architects and designers, the RDL may not be easy to understand for stakeholders who are not technically-minded. In particular, the compositions in the RDL may be confusing for the typical end-users. To overcome this “understandability barrier” we have developed a simple visual representation of requirements and their compositions (i.e. mutual influences). These visual representations can then be presented to the stakeholders for validation.Within AORE some previous research has been devoted to visual representation of requirements that facilitates modelling, assuring correct understanding of the requirements before design or implementation. For instance, the Unified Modelling Language (UML) has been widely furnished with stereotypes and metamodel extensions [9, 10]. However, such approaches lack generality as they are restricted to the models with specific extensions/stereotypes. On the other hand, approaches such as the Theme/Doc [3] defer composition to design. Thus, there is a gap in visual representation of composition in a generic (model-independent) and scalable fashion at the requirements level.Our visual representation of composition (initially inspired by Theme/Doc [3]) aims to provide the first step towards filling this gap. It proposes pictorial notations to support the rich composition semantics of our generic RDL (described in section 2 of the paper) and facilitates compact representation of compositionin a traceable way.3.1 Visual Representation for RDL compositionOur visual representation approach aims to give a pictorial representation of the RDL composition. Thus,it must provide corresponding notations for the RDL composition operators (a sample of which is depictedin Figure 2) and the complete composition specifications, as shown in Figure 3. In accordance with the material presented about the RDL, in this section we discuss the visual notations for compositions with constraint and base operators3.Figure 4 presents the visual notation counterparts for the temporal base operator between,the RDL description of which was presented in Figure 2(b). Between operator is bi-directional (i.e. it can be presented from the perspective of each of the two participating requirements separately) and it is further classified into before, after, meets, and overlap sub-operators. The visualization of between sub-operators3 Discussion of outcome operators is not included in this paper.are represented with triangular shapes which intuitively depict the sequencing of time similar to the less-than “<” and greater-than “>” comparison operators. If the time of event A is less than (<) time ofFigure 4: Temporal between operator and its further classificationsIn our visual notation the constraint operators are visually represented by enclosing them in double curly bracket {{constraint operator}} (e.g., {{move}}, etc.). All constraint operators depicted in Figure 2(a) would appear in this way on the composition visualization.The RDL compositions are represented in a double rectangular shaped box. Each composition box has a name that corresponds to the name of the composition specification given in the RDL. Each box also encloses constraint and base operators which establish links between concerns. The concerns, containing subjects (denoted by S:|), relationships (denoted by R:|), and objects (denoted by O:|), are represented as rectangles with their RDL-given names in the top left corner (e.g., Figure 6). In cases where the roles of subject, object, or relationship are not significant for a given composition, they can be omitted from the diagram (e.g., Figure 5). The last element within a concern rectangle is the list of sources (e.g., stakeholders, user manuals, standards, etc.), that relate to the given concern, enclosed into square brackets.Figure 5 shows the composition between ‘enrol’ concern and all of the other auction system concerns (composition specified in the RDL in Figure 3). The ‘Enrol’ concern originates from the owner of the auction system (referred to as Auctioneer), and all other concerns are restricted by this concern which is specified by ‘[*]’. The composition for EnrolComposition is visually represented by {{affiliating}} the enrol concern before (as pictorially shown by ‘before’ sub-operator of between operator) all auction system concerns (pictorially shown by concern having name ‘all concern ’).{{affiliate}}The visual representation is further elaborated in Figure 6. As discussed in Figure 3 and section 2.2 earlier, any requirement that the seller ends/cancels the auction (shown in Figure 6 with any requirement that has subject seller [S:| seller], object auction [O:| auction], and relationship end [R:| end] between them will be realized only if the auction has not begun ([S:| seller] [S:| auction] [R:| begin]). Here the constraint operator is {{begin/end}}. We have also used the visual representation of the ifnot RDL conditional base operator which has not been previously presented in section 2.As shown in Figure 6, concerns and their subjects, object, and relationships identified by RDL can be represented in our notation in a compact and easy to perceive manner. This also highlights the scalability support of this visual representation. Moreover, this notation facilitates reusability by delimiting preceding subjects and objects with a ‘|’ sign. This enables the subject and object to be replaced by any other systems subjects and objects. Thus, the composition can be reused for any other system where subjects/objects differ but the rest of the composition semantics are unchanged.4. TraceabilityWe have discussed before that our RDL is used to semi-formally represent any textual requirements, highlighting the syntactic and semantic elements ofparticular significance within them. However, the RDL representation of these requirements and, in particular, their compositions is not very intuitive to an average end-user. Consequently we have developed a simple visual notation so that this visual representation of requirements and their compositions (i.e. mutual influences) can then be taken back to the stakeholdersWe have also discussed that our visual notation provides corresponding elements for each composition element of the RDL and lists the sources of each of the composition participants (e.g., Figures 5 and 6). Thus, simply by establishing this process (presented in Figure 7) we have established a clear backward traceability link from the textual requirements to the RDL, visual representation and back to the source.The dependency trace from the early requirements provided in the textual format to both their independent and composed semi-structured RDL specification helps the architects and designers in understanding the consequences of requirements dependencies, but it is not necessarily useable for communication with the stakeholders. Instead, the stakeholders use the visual representations of the compositions to verify whether the software engineer has correctly structured the requirements and composition between these requirements.In case a question arises about a given composition, the backward traceability links are used to identify the sources of the composed concerns, which can then help resolve the questions. For instance, in case a modification needs to be incorporated into a software system, a set of impact analysis scenarios derived from the existing compositions can be prepared. These scenarios can then be presented for selection and validation to the relevant end-users the list of which is obtained from the visual notations.Thus, through the visualisation and backward traceability links our approach promotes better understanding of requirements influences by both requirements analysts and end-users, as well as supports validation of compositions by the stakeholders. 5. Related WorkThere has been very little previous work on requirements composition visualisation. However there has been some work carried out on requirements composition, and significant research on traceability and visualisation of requirements. Thus, we discuss the related work for each of these areas.As mentioned before, work on requirement composition is particularly relevant for the AORE approaches [2, 11], and our work has evolved from addressing the shortcomings of these. Both [2, 11] have a relatively simple set of composition operators (where operators in base are called actions and in constraint operators) which have been identified from completed case studies. We provide a methodical approach to such operator derivation. Besides, our composition specification has much richer semantics based on the semantics of the requirements themselves and their dependencies. In addition, the need for composition specification in [2, 11] is rooted only in the judgement of the requirements engineer, whereas in our approach the need for compositions is identified from the semantics of requirements dependencies.ThemeDoc [3] also has some elements of composition: by looking at the layers of themes on the clipped action view of the theme graph, we can determine some of the expected sequence of composition. The actual composition is then carried out at the design stage using the semantics of ThemeUML [3]. In our approach a more complete setof sequencing and conditional dependencies is identified, with composition being performed as part of RE analysis.Some work in also currently underway in defining a composition mechanism for problem frames [12] and goal-based [13] approaches. Though these approaches themselves are not new (and well predate AORE), the issue of composition has not been vigorously addressed in them previously. Here again, it is the rooting of our work in semantics of requirements (i.e. the internal view) and use of NLP mechanisms that distinguishes our approach.Some similarity to our work in modelling concerns along with their relationships can also be found with the approaches proposed in [14, 15]. In this work Sutton et. al. put forward a schema for concern classification where a section for relationships is also included. However, the issue of composition is not addressed.Requirements traceability has long been acknowledged as an important issue in RE. It has also been known that different tasks within RE (e.g., release panning, change management, tracing todesign, etc.) have different perspectives and needs for tractability. Moreover, the needs of each individual company also widely vary with the kinds of traceability information required. Thus, most research in this area is fragmented per tasks. For instance, [16] looks at traceability links for software release planning; [17] looks at linking the requirements to their sources early on at the time of requirements elicitation task; [18] considers use of goal derivation graphs for traceably recording requirements derivation while the GOPCSD tool [19] generates goal models that are symbolically executed to trace the aspects from the root to terminal, etc. More recently some researchers have considered ways of integrating the fragmented task-based works on traceability into generic models [20, 21]. However, all these approaches focus on traceability links largely defined by development activities (e.g., release planning, etc.) investigating such links as cost-related, or added value links from one requirement to the other and refinement and change tracking links, etc. We, on the other hand, focus specifically on the (internal) semantics of the requirements interdependencies themselves, without addressing other (external process-related) links. We believe that it is necessary to understand the internal links first – these will form the foundation of the traceability framework, and can then be augmented with additional external links (if necessary). We also look at the traceability of compositions of requirements, not only individual requirements one by one, which is more usual for the other discussed approaches.While there is a large body of work on visualization of UML-based AO design artefacts (e.g., [3, 22, 23]), the corresponding area for requirements visualisation is rather sparsely populated. A notable exception here is the Theme/Doc approach [3] that visually represents the requirements, which leads to the identification of crosscutting functionality. Yet, Theme/Doc does not cater for the visual representation of composition rules, which is the focus of our visualization approach.[24] proposes an approach based on Interaction Pattern Specification (IPS) for visual representation of requirements and composition between the identified aspectual and non-aspectual requirements by composition operators (AND, OR, IN). The composed aspectual and non-aspectual requirements are visually represented in a composed sequence diagram. In comparison, our RDL-based visual notation has richer composition semantics, represented in a simple notation that is understandable and verifiable by stakeholder.[10] has provided a visual representation for very simple composition of functional and crosscutting requirements applying use case diagrams and three (<<overlap>>, <<override>>, <<wrapby>>) composition operator stereotypes. This approach is only applicable for use case based AORE models and a very limited set of composition semantics is addressed.The FRIDA model [23] provides traceability from requirements to design phase, also, visually modellingthe aspects, pointcut, and advices by stereotypes. Butthis visual representation is closer to UML based design which is not easily understandable by end-usersand not easily verifiable.In summary, compared to the other composition visualization approaches, our work has markedly richer composition semantics, based on the requirements dependency semantics themselves [5]. Our work also focuses on such semantics in building traceability links and compositions.6. Summary and ConclusionsThe main contribution of this paper is presentationof an RDL whose development has been motivated and grounded in the semantics and grammar of natural language requirements. Due to its roots, the RDL is as generic and flexible as the grammar of the natural language itself.We have also discussed how this RDL can be usedto reveal the mutual influences of requirements, usingthe composition operators derived from the very semantics of the requirements dependencies.In the future we will continue the work on our RDL and, in particular, will validate its features through larger case studies.We have also discussed how the RDL has been complemented with a simple visual notation for composition representation. This is particularly usefulfor validation of the requirements composition with non-technically minded stakeholders who may find understanding RDL-specified composition difficult.Our future visualization work will focus on establishing automated traceability links (via a visualisation tool) between composition points of concerns picked out via semantics-based pointcuts; visual verification of the correctness of such pointcuts (with user participation), as well as a experiments to verify the scalability and reusability features of the composition representation.7. AcknowledgementThis work is supported by European Commission Grant IST-2-004349: European Network of Excellenceon AOSD (AOSD-Europe) and EPSRC Grant EP/C003330/1: Multidimensional Analysis of Requirements Level Trade-Offs (MULDRE).。