Extracting semantic hierarchies from a large on-line dictionary

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Extracting significant patterns from musical strings

Extracting ’Significant’ Patterns from Musical Strings:Some Interesting Problems.Emilios CambouropoulosAustrian Research Institute for Artificial IntelligenceVienna, Austriaemilios@ai.univie.ac.atAbstractIn this paper a number of issues relating to the application of string processing techniques on musical sequences are discussed. Special attention is given to musical pattern extraction. Firstly, a number of general problems are presented in terms of musical representation and pattern processing methodologies. Then a number of interesting melodic pattern matching problems are presented. Finally, issues relating to pattern extraction are discussed, with special attention being drawn to defining musical pattern ‘significance’. This paper is not intended towards providing solutions to string processing problems but rather towards raising awareness of primarily music-related particularities that can cause problems in matching applications and also suggesting some interesting string processing problems that require efficient computational solutions.1. IntroductionIt is often hypothesised that a musical surface may be seen as a string of musical entities such as notes, chords etc. on which pattern recognition or induction techniques can be applied. In this text, the term pattern induction or extraction refers to techniques that enable the extraction of useful patterns from a string whereas pattern recognition refers to techniques that enable locating all the instances of a predefined pattern in a given string. Overviews of the application of pattern processing algorithms on musical strings can be found in (McGettrick, 1997; Crawford et al, 1998; Rolland et al, 1999).2. Issues of Musical Pattern Representation2.1. Pattern Matching vs Pattern Extraction (Problem of Significance)One of the differences between pattern matching and pattern induction techniques is that the latter requires a notion of pattern ‘significance’. Pattern matching techniques do not encounter this problem because the search query is given; the user has decided a priori that a certain pattern is important and then all the matches in a string or set of strings are located. In pattern extraction, however, one has to decide what types of patterns the algorithm should look for - finding all the patterns is often not very useful. Selecting ‘significant’ patterns can be done either after all the patterns have been found (which is not usually the most efficient approach) or before by forcing algorithms to stop when the specific types of patterns are found (e.g. periods or covers). The former approach is briefly discussed in section 4.2 whereas the latter in section 4.3.Presented at the London String Days 2000 workshop, 3-4 April 2000, King’s College London and City University.2.2 Musical Notes vs Musical Relations between NotesExpressive MIDI files are adequate for searching pitch patterns but are problematic in terms of rhythm patterns. The reason is that MIDI data are not quantised, i.e. onsets, durations and interonset intervals are not categorically organised so they can not be represented by the usual symbolic nominal musical values (e.g. quarter notes etc). MIDI files require preprocessing so that they can be converted to a score-like format - one computational system for score extraction from MIDI files is presented in (Cambouropoulos, 2000). However, the algorithms discussed in section 3 can be used for approximate matching on melodies in the time domain, in which case quantisation may not be necessary.A melodic sequence is commonly represented as a set of independent strings of elementary musical parameters, e.g. pitch and duration, or alternatively as strings of relations between adjacent notes, e.g. pitch intervals and duration ratios.In the pitch domain, the main problem with applying a pattern-processing algorithm on an absolute pitch string is that transpositions are not accounted for. There is plenty of evidence, both theoretical and experimental, that transposition is paramount in the understanding of musical patterns. The obvious solution to this problem is the use of relative pitch, mainly through the derivation of pitch intervals from the absolute pitch surface. It is herein maintained that pattern-matching and pattern-induction algorithms should be developed primarily for sequences of pitch intervals. As will be shown in section 4.3, pattern induction algorithms that can be applied on absolute pitch sequences may not be meaningful for pitch interval sequences. An extended discussion on pitch representation for pattern matching can be found in (Cambouropoulos et al, 2000).In terms of the rhythmic component of musical strings, string-processing algorithms are most commonly applied to strings of durations or inter-onset intervals. This type of matching can be very effective, but one should also consider encoding rhythm strings as strings of duration relations such as duration ratios or shorter/longer/equal strings. Duration ratios encapsulate the observation that listeners usually remember a rhythmic pattern as a relative sequence of durations that is independent of an absolute tempo. Duration ratios can reveal, for instance, augmentations or diminutions of a rhythmic pattern.2.3 1-D vs 2-D MatchingA polyphonic musical work can be represented either as a 2-dimensional graph (pitch against time) or as a collection of 1-dimensional strings. In the former case, special algorithms have to be used for finding patterns in a two dimensional space. Such algorithms are very useful because most commonly musical databases contain simple unstructured MIDI files. Additionally they enable the retrieval of polyphonic structures rather than just melodic patterns (see Dovey 1999). One potential problem is that, if a (melodic) search query is not long enough and also contains large pitch leaps, any algorithm is likely to return a large number of instances that are musically and/or perceptually implausible.The second representation requires sophisticated streaming algorithms, i.e. algorithms that can split the polyphonic work into ‘meaningful’ independent streams (or voice parts). This is not a trivial task. The development however of such algorithms can be very useful for preparing the musical data for pattern processing tasks.A preliminary version of such an algorithm is presented in (Cambouropoulos, 2000). The streaming algorithm is based on the Gestalt principle of proximity and simply tries to find the shortest streams that connect all the onsets within a beat (figure 1). Crossing of streams is not allowed. The number of streams is always equal to the number of notes in the largest chord. The solution to this problem is not trivial and appropriate searching techniques are required for developing an efficient algorithm. The current elementary version of the algorithm makes mistakes (see figure 2) but can be improved if other principles like ‘goodness of continuation’ are taken into account. Streaming is a large research topic in its own right (see Bregman 1990).Sonata KV282. Dots in the graphFigure 2 The streaming algorithm fails locally on this excerpt from Mozart’s Sonata KV282 (see captionof figure 1 for explanation of graph).3. Pattern MatchingIn this section a number of interesting pattern matching problems for strings consisting of integers will be presented. These involve primarily matching problems in the pitch domain but some could also be extended in the time domain.3.1 Patterns with Similar IntervalsMost computer-aided musical applications adopt an absolute numeric pitch representation - most commonly MIDI pitch and pitch intervals in semitones; duration is also encoded in a numeric form. In all the examples below melodic strings are represented as strings of pitch intervals in semitones .One way to account for similarity between closely related but non-identical musical strings is to use what will be referred to as δ-approximate matching . In δ-approximate matching , equal-length patterns consisting of integers match if each pair of corresponding integers differ by not more than δ - e.g. an ascending major chord arpeggio [+4, +3, +5] and a minor arpeggio [+3, +4, +5] sequence can be matched if a tolerance δ=1 is allowed in the matching process (the total sum of δ tolerance allowed for a pattern match can be constrained by a further γ tolerance parameter resulting in δ-γ approximate matching ).Efficient algorithms for solving these problems are presented in (Cambouropoulos et al, 1999).3.2 Filling and Thinning of PatternsThe above algorithm for δ-approximate matching accounts only for equal length patterns. A common technique of musical composition is filling and thinning of musical motivic and thematic material. That is,extra notes are added in a musical pattern (filling) or taken away (thinning). Approximate matching algorithms that can account for this phenomenon rely usually on dynamic programming techniques. In this section we will merely try to describe in more detail this problem. The melodic examples presented in this section are taken from the classical study on thematic processes by Reti (1951).Adding a note between two notes essentially can be interpreted as splitting the initial pitch interval into two successive intervals the sum of which is equal to the initial interval - e.g. initial sequence 60, 62(interval: +2); sequence with added note: 60, 67, 62 (intervals: +7, -5); the sum of the two resulting intervals is equal to the initial interval. This property can be used for matching different length sequences by allowing one interval of one string to be matched against two or more successive intervals of the other string whose sum is equal (or δ-approximate) to the initial interval. See Figures 3-5.A-2-2-1-2-11 | | | | |B-2 2-2-2-1-2-11A-2 -2 -1 -2 -1 1C-2 2 -4 +4-5-5-77-88 -7B-2 2-2-2-1-2-11 | |C -22-4+4-5-5-77 -8 8 -7Figure 3 Beginning of Toccata (B) and theme of Fugue (C) from Bach’s D-minor Toccata and Fugue ABCA 4 3-2-1-2-2B 22120-3-40Figure 4 First Allegro theme (A) and first Finale theme (B) from Beethoven’s First Symphony (pattern B is also the retrograde of pattern A).A-11 -11-1||B-1 01 -1 01-1 0A-1 1-1 1-1 |C3-24-2-3 1 4-2-31B-1 0 1-10 1-1 0 | C 3-2 4-2-3 14-2 -3 1Figure 5 Opening theme (B) and part of Finale theme (C) of Mozart’s Symphony in G minor3.3 Retrogrades and InversionsInversions of patterns can be matched if the absolute of the sum of the corresponding intervals of the original and the inversion is not more than δ. See figures 6 and 7.ABABCABA 8 3 -1 1-8-1 3-1 4-1 3-4B-8 -3 1 -1 8 1 -3 1 -4 1 -3 4|Sum| 0 0 0 0 0 0 0 0 0 0 0 0Figure 6 Original and Inversion of 12-tone series in Webern’s Cantata No.1, Op.29ABA 2 2 1-3 2-3B-2 -1 -2 3 -1 3|Sum| 0 1 1 0 1 0Figure 7 Two instances of a motive from Bach’s Two Part Inventions, No.1 (BWV 772) – δ=1.It would be very useful to have one algorithm that can do all the above types of matching presented in these sections (3.1, 3.2 and 3.3) by allowing control of different parameters.4. Pattern Extraction4.1 Finding All PatternsAn efficient algorithm that computes all the exact repetitions in a given string is described in (Crochemore, 1981; Iliopoulos et al., 1996). For a given string of symbols (e.g. string of pitch intervals), the matching process starts with the smallest pattern length and ends when the largest pattern match is found. This algorithm takes O(n·logn) time where n is the length of the string. Dynamic programming algorithms can be used for finding all the approximate repetitions in a string.It is apparent that such a procedure for the discovery of all identical melodic patterns (even more so for approximate matching) will produce an extremely large number of possible patterns most of which would be considered counter-intuitive and non-pertinent by a human musician/analyst. So the problem of pattern ‘significance’ arises.4.2 Pattern Significance (a posteriori)Firstly, pattern significance can be determined after all the patterns have been found. According to one such procedure proposed in (Cambouropoulos 1998) a prominence value is attached to each of the discovered patterns based on the following factors: a) prefer longer patterns, b) prefer most frequentlyoccurring patterns, c) avoid overlapping. A selection function that calculates a numerical strength value for a single pattern according to the these principles can be devised, for instance:ƒ(L,F,DOL)=F a·L b/10c·DOLwhere: L: pattern length; F: frequency of occurrence for one pattern; DOL: degree of overlapping; a, b, c: constants that give different prominence to the above principles. For every pattern discovered by the above exact pattern induction algorithm a value is calculated by the selection function. The patterns that score the highest should be the most significant ones.4.3 Pattern Significance (a priori)An alternative approach, is determining types of significant patterns in advance so as to enable algorithms to stop as soon as the appropriate significance criteria are met. ‘Significant’ types of patterns are, for instance, squares, periods and covers; for example, abc is a period of abcabcabca, and abca is a cover of abcabcaabca (these specific types of patterns are important in biological string processing applications). What types of patterns are ‘significant’ for musical extraction tasks? One possibly interesting type of musical pattern may relate to immediate repetitions (2 or more consecutive repetitions). The obvious type of pattern that would seem appropriate for finding such consecutive repetitions is the period. This is true for the absolute pitch domain (which is not very interesting) and for the inter-onset interval domain (which is very useful). For the pitch interval domain (and interonset interval ratio domain), however, some other type of pattern is necessary for finding immediate repetitions. We will call this type of pattern a disjunct period which is essentially a repeating pattern separated by single symbols. For example, abc is a disjunct period of abcdabcaabcbabc. These separating symbols (intervals) are necessary if consecutive pitch patterns are expected not to overlap. See figures 8 & 9.9 -4 4 -9 7 -3 3 -7 7 -3 3 -7 7 -3 3 -7 7 -3 3 -8 8 -5 5local disjunct period9 -4 4 -9 7 -3 3 -7 7 -3 3 -7 7 -3 3 -7 7 -3 3 -8 8 -5 5local period9 -4 4 -9 7 -3 3 -7 7 -3 3 -7 7 -3 3 -7 7 -3 3 -8 8 -5 5local coverFigure 8 Section from Alberti Basspci4 12-75 22-972120002-2-202-2-101-1-202-2-2 nci2 11-53 11-65111000 1 -1 -10 1 -1 -10 1 -1 -10 1 -1 -1OR nci2 11-53 11-65111000 1 -1-10 1 -1-10 1 -1-10 1 -1-1 sl l s s-l l s s-l l s s s000s -s -s0s -s -s0s -s -s0s -s -sOR sl l s s-l l s s-l l s s s000 s -s -s0 s -s -s0 s -s-s0 s -s-s Figure 9 The opening melody of Chopin’s Valse, Op. 18(pci: pitch-class interval, nci: name-class interval, sl: step-leap)5. ConclusionsIn this paper a number of general problems were presented regarding musical representation and pattern processing methodologies. A number of interesting integer pattern-matching problems were presented. Musical pattern ‘significance’ was also discussed and an attempt was made to formalise some interesting types of patterns for which pattern extraction algorithms can be developed. It is hoped that the problems discussed herein may contribute towards a better understanding of the distinctive qualities of musical pattern processing tasks and give rise to new useful and efficient pattern processing algorithms.AcknowledgementsThis research is part of the project Y99-INF, sponsored by the Austrian Federal Ministry of Science and Transport in the form of a START Research Prize.ReferencesBregman, A. S. (1990) Auditory Scene Analysis.The MIT Press, Cambridge (Ma). Cambouropoulos, E. (2000) From MIDI to Traditional Musical Notation. In Proceedings of the AAAI Workshop on Artificial Intelligence and Music, Austin, Texas (forthcoming).Cambouropoulos, E., Crochemore, M., Iliopoulos, C.S., Mouchard, L. and Pinzon, Y.J. (1999) Algorithms for Computing Approximate Repetitions in Musical Sequences. In Proceedings of the AWOCA’99 Workshop (Australasian Workshop on Combinatorial Algorithms), Perth. Cambouropoulos, E., Crawford, T. and Iliopoulos, C.S. (2000) Pattern Processing in Melodic Sequences: Challenges, Caveats and Prospects. Computers and the Humanities, 34:4 (forthcoming). Cambouropoulos, E. (1998b) Musical Parallelism and Melodic Segmentation. In Proceedings of the XII Colloquium of Musical Informatics, Gorizia, Italy.Crawford, T., Iliopoulos, C.S. and Raman, R. (1998) String Matching Techniques for Musical Similarity and Melodic Recognition. Computing in Musicology, 11:71-100.Cope, D. (1990) Pattern-Matching as an Engine for the Computer Simulation of Musical Style. In Proceedings of the International Computer Music Conference, Glasgow.Crochemore, M. (1981) An Optimal Algorithm for Computing the Repetitions in a Word. Information Processing Letters, 12(5):244-250.Dovey, M.J. (1999) An Algorithm for Locating Polyphonic Phrases within a Polyphonic Musical Piece. In Proceedings of the AISB99 Convention(Artificial Intelligence and Simulation of Behaviour),Edinburgh, U.K.Iliopoulos, C.S., Moore, D.W.G. and Park, K. (1996) Covering a String. Algorithmica, 16:288-297. McGettrick, P. (1997) MIDIMatch: Musical Pattern Matching in Real Time. MSc Dissertation, York University, U.K.Reti, R. (1951) The thematic Processes in Music, The Macmillan Company, New York.Rolland, P.Y., Ganascia, J.G. (1999) Musical Pattern Extraction and Similarity Assessment. In Readings in Music and Artificial Intelligence. E. Miranda. (ed.). Harwood Academic Publishers (forthcoming).。

词汇学课本练习答案

Chapter I1.主观题2.How did the Norman Conquest and the Renaissance influence the English vocabulary ?The transitional period（转型时期）from Old English to Modern English is known as Middle English(ME 1100----1500), which is characterized by the strong influence of French following the Norman Conquest in 1066, French was used for all state affairs and for most social and culture matters, which influenced English in daily life.The English language from 1500 to the present is called Modern English. In the early stage of this period the Renaissance（文艺复兴）brought great change to the vocabulary. The renewed（复兴的）study of Greek in the Renaissance not only led to the borrowing of Greek words indirectly through the medium（媒介）of Latin, but also led to the introduction of some Greek words directly into English vocabulary. Greek borrowings were mostly literary, technical and scientific words,(page 4~5)3.Enumerate the causes for the rapid growth of neologisms（新词，旧词新意，新词的创造者/使用者）after World WarⅡ. Give four examples for each cause.①marked progress of science and technology. Example: to blast off(炸掉，炸毁) ,to countdown ,capsule, launching pad②socio-economic（社会经济）, political and cultural changes. Example: roller-hockey ,surf-riding, skydiving（跳伞运动）, designated hitter③the influence from other cultures and languages（page6~7）Example: cosmonaut ,discotheque（小舞厅，迪斯科舞厅）,ombudsman（调查官员舞弊情况的政府官员）, apartheid（种族隔离）.4.What are the fundamental features of the basic word stock（词库）of the English vocabulary ?(1). National character(全民通用性)：Words of the basic word stock belong to the people as a whole, not to a limited group.(2). Stability（稳定性）:As words in the basic word stock denote the commonest things necessary to life, they are likely to remain unchanged. However, a certain number of Old English words have dropped out of the basic word stock, while new words have joined the rank of basic words, following social and technological changes.(3). Word-forming ability(构词)：Basic words are very active in forming new words.(4). Ability to form collocations（搭配能力）：Basic words combine readily with other words to form habitual expressions and phrases.Since the great majority of the basic word stock are native words, they are naturally the ones used most frequently in everyday speech and writing.(Page 10 paragraph 4 , 5 ,7 , 8 and Page 11 paragraph 2)5.What are the characteristics of the English vocabulary as a result of its historical development ?The historical development of English language shows that English is a heavy borrower; it has adopted words from almost every known language, especially from Latin, French and Greek.（page 18.）6.Why do we say that native words are the core of the English vocabulary?First, because the native words form the great majority of the basic word stock of the English language. And the basic word stock is the foundation of the vocabulary accumulated over a number of epochs.Second, they make up the most familiar, most useful part of the English vocabulary. So we say that native words are the core of the English vocabulary for its importance. （Page 10 paragraph 2, and Page 19 paragraph 2）7.What do we mean by literary and common words ?(1) Common or popular words are words connected with the ordinary things or activities necessary to everyday life. The great majority of English words are common words . The core of the common words is the basic word stock. They are stylistically (在文体上) neutral , and hence they are appropriate in both formal and informal writing and speech. (Page 11 paragraph 6)(2) Literary words are chiefly used in writing, especially in books written in a more elevated（升高的，提高的，崇高的）style, in official documents, or in formal speeches. They are comparatively seldom used in ordinary conversation.(Page 12 paragraph 1)Chapter 2Q1:Explain the following terms and provide example:a.Morphemic 形位b.Allomorph 形位变体c.free and bound morphemicd.hybrid 混合词Morphemic: the smallest meaningful linguistic unit of language, not divisible or analyzable into smaller forms. Example: nation (page21 ,paragraph2, line 1) Allomorph:any of the variant forms of a morphemic as conditioned by position oradjoining sounds. Example: books, pigs.( page22 , paragraph 3, line 4)Free morphemic: one that can be uttered alone with meaning. Example: man，read，faith (page23 , paragraph2, line 1 To2 )Bound morphemic: cannot stand by itself as a complete utterance表达; it must appear with at least one other morphemic. Example: unkind (page23 , paragraph2, line4) Hybrid: a word made up of elements form two or more different language. Example: goddess, rewrite.( page27 , paragraph2, line 4)Q2. What are the differences between inflectional and derivational affixes? P26页第4段开头P29页第4自然段末尾Inflectional affixes （屈折词缀）are related to grammar only. Derivational affixes （派生词缀）are subdivided into prefixes and suffixes, which are related to the formation of new words. Roots, prefixes前缀and suffixes后缀. are the building blocks with which words are formed.The number of derivational affixes, although limited, is much larger than that of inflectional affixes.Q3:In what two ways are derivational affixes 派生词缀classified? p26 Derivational affixes are classified in prefixes 前缀and suffixes后缀.Q4:How are words classified on the morphemic（语素的）level? P29 paragraph 5 On the morphemic level, words can be classified into simple, complex and compound words（复合词）.Chapter IIIⅠExplain1、(p32)Word-formation rules:The rules of word-formation define the scope and methods whereby speakers of a language may create new words2、Root, stem and base. Analyze the word denationalized into root, base and stem. Denationalized①Root：nation②stem：denationalize③base：nationalizedⅡCompounding1、What are the relative criteria of a compound?(p35-p36)①Orthographic criterion②Phonological criterion③Semantic criterionⅢDerivation1、What is derivation?(p42-p43)Derivation is a word- formation process by which new words are created by adding a prefix, or suffix, or both to an already existing word.2、What is the difference between prefixation and suffixation?Prefixation is the addition of a prefix to the base. Prefixes modify the meaning of the base, but they do not generally alter its word-class. Every prefix has a specific meaning of its own; prefixes are therefore classified according to their meanings.Suffixation refers to the addition of a suffix to the base. Suffixes frequently alter theword-class of the base. Therefore, suffixes are classified according to the class of word they form into noun-forming suffixes, verb-forming suffixes, etc(p66)3、How are the major living prefixes classified? Give a few examples to illustrate each kind.（P44）The major living prefixes are classified into the following eight categories by their meaning :1) negative prefixes (un- , non- , in- , dis- , a- ). eg , unhappy ,nonhero , injustice ,disadvantage , atypical ）2) reversative or privative prefixes （un - , de - , dis -）. eg , unwrap , decentralize ,disunite3) pejorative prefixes ( mis - , mal - , pseudo - ) .eg. mistrust , maltreat, pseudo-science4) prefixes of degree or size ( arch - , super - , out - , sub - , over - , under - , hyper - , ultra - , mini - ) eg, archbishop，supercurrent hyperactive, outlive , ultra-conservative 5) prefixes of attitude ( co - , counter - , antic - , pro - ) eg, cooperation, anti-nuclear ,pro-student , counterpart6) locative prefixes ( super-, sub- ,inter- , trans- ) eg. Subarctic , superacid, transcode7) prefixes of time and order ( fore - ,pre - , post - , ex - , re - ) forehead , reconsider ,prereading , post-war8) number prefixes ( uni - / mono - , bi - / di - , multi - / poly -) multi-purpose , monocle , bi-media4、How can you form deverbal nouns, denominal nouns, deadjective verbs, and denominal adjectives by suffixation?（P50）answer：1）deverbal noun suffixes: verb-noun suffixes , such as –er in writer , -eein employee, -ation in exploitation and –ment in development .2) denominal noun suffixes : noun –noun suffixes , such as –hood in boyhood , - ship in scholarship , - let in booklet , and –dom in stardom .3) deadjective verb suffixes : adjective – verb suffixes , such as –ify in simplify , - ize in modernize , and –en in quicken4) denominal adjective suffixes: noun – adjective suffixes, such as –full in helpful, -less in limitless, -y in silky and –ish in foolish.5、Give the meaning of the following words and analyze the structure of each word:（P51）answer: 1) a driver means a person who drives2) a lighter means a machine used for lightering3) a gardener means a person who garden4) a New Yorker means a person from New York5) a villager means inhabitant of village6) a diner is‘a dining carriage on a train‘7) a lifer is‘slang. A person sentenced to imprisonment for life8) a dresser meansAnalyze : as for 1、2、3 ,affixed to a verb ,the suffix forms agent nouns with the meaning of ‗ one who performs an action ‘as for 4、5 ，this affix may also be joined to the means of cities , countries , and to other place names . as for 6、7、8 colloquial and slangy .ⅣConversion1、what is the difference between conversion（此类转化法）and suffixation（加后缀）?(P55 介绍conversion的第一段)：Conversion is a word-formation process whereby a word of a certain word-class is shifted into a word of another word-class without the addition of an affix. It is also called zero-derivation.e.g. bottle (n. ) ---- bottle ( v. ), buy (v. ) ---- buy ( n.), tutor ( n. ) ---- tutor ( v. )（例子也可以举其他的如attack）(P49 介绍Suffixation的第一段)：Suffixation: It's the formation of a new word by adding a suffix or a combining form to the base, and usually changing the word-class of the base.e.g.boy n. + -ish -- boyish adj. boy n. +hood -- boyhood n.2、In a conversion pair, how can you determine which of the two is the base and which the derived word(派生词)?（P56 中间三个例子）•The base is derivation by zero suffix.Spy –a deverbal noun without suffix, meaning one who spies.•The derived word is derivation by suffixWirter---a deverbal noun with "-er" suffix，meaning one who writes3、Illustrate the axiom（原理）,"The actual grammatical classification of any word is pendent upon its use."（P57最后一段）Notice how the word-class of round varies in accordance with its use in the following sentence:i.e. The second round(n)（回合）was exciting. Any round(adj)（圆的）plate will do.Some drivers round(v)（绕行）coners too rapidly.The sound goes round and round(phrase). (旋转)The above examples tell us a very important fact: because word order（词序）is more fixed in Modern English than ever before, the function shifts within sentence structures are possible without causing any confusion in intelligibility(可懂度，可理解性).『这一段可不要』4、Why is the conversion from noun to verb the most productive process of conversion?(58—59页)First in contemporary English, there is a tendency o f ―a preponderance of nouns aver verb‖.Second, there are only a few verb-forming affixes in English. They are be-, en-, -ify, -ize and –en.5、What are the major semantic types under noun to verb conversion?（a）“to put in/on N”（b）“to give N, to provide N”（c）“to deprive of N; or to remove the object denoted by the noun from something”（d）“To….with N”（e）“To｛be/ act as｝N with respect to…”(1)verbs from human nouns(2)verbs from animal nouns(3)verbs from inanimate nouns（f）“To ｛make/change｝…into N”（g）“To ｛send/go｝by N”(1)mail(2)bicycle（h）“To spend the period of time denoted by N”6、Why is the poor an example of partial conversion?（62页）It is used as noun when preceded by the definite article; yet the converted noun takes on only some of the features of the noun; i.e. It does not take plural and genitive inflection, nor can it be preceded by determiners like a, this, my, etc.8、Pick out the converted words in the sentences below and state(1)the word-classof the converted words and their meanings; (2)to what word-class the base of each of the converted words belongs:（1）They are going to summer in Guilin.the converted word: summer（v.）the word-class of it: conversionmeaning:避暑；过夏天the base of the word of the word-class belongs: summer(n.)(2)They hurrahed his wonderful performance.the converted word: hurrah(v.)the word-class of it: conversionmeaning:欢呼，叫好，为----喝彩the base of the word of the word-class belongs: hurrah(n.)(3)You have to round your lips in order to make the sound/u:/.the converted word: round(v.)the word-class of it: conversionmeaning:弄圆，使---成圆形the base of the word of the word-class belongs: round(n.)(4)They are great sillies.the converted word: silly(n.)the word-class of it: conversionmeaning:傻瓜the base of the word of the word-class belongs: silly(adj.)(5)She dusted the furniture every morning.the converted word: dust(v.)the word-class of it: conversionmeaning: 拂去灰尘the base of the word of the word-class belongs: dust(n.)(6) It is a good buy.the converted word: buy(n.)the word-class of it: conversionmeaning:购买，买卖；所购的物品the base of the word of the word-class belongs: buy(v.)Chapter4I. Explain the following terms and provide examples.1. Initialism:Initialism is a type of shortening, using the first letters of words to form a proper name, atechnical term, or a phrase；an initialism is pronounced letter by letter.2. Acronym:Acronyms are words formed from the initial letters of the name of an organization or a scientific term, etc.3. Blend:Blending is a process of word-formation in which a new word is formed by combining the meanings and sounds of two words, one of which is not in its full form or both of which are not in their full forms.4. Front and back clipping:The process of clipping involves the deletion of one or more syllables from a word (usually a noun), which is also available in its full form.Back clipping may occur at the end of the word. This is the most common type of clipping.Front clipping occurs at the beginning of the word.5. back-formation:Back-formation is a term used to refer to a type of word-formation by which a shorter word is coined by the deletion of a supposed affix from a longer form already present in the language.6. Reduplication:Reduplication is a minor type of word-formation by which a compound word is createdby the repetition(1)of one word like go-go; (2)of two almost identical words with a change in the vowel‘s such as ping-pong; (3)of two almost identical words with a change in the initial consonants, as in teenyweeny.Chapter V1.How are the sound and meaning of most words related? Give examples toillustrate your point. (P93)Most English words are conventional（常规的）, arbitrary symbols; consequently, there is no intrinsic（内在的，固有的）relation between the sound-symbol and its sense.e.g. house ( English)maison ( French)fangzi ( Chinese)dom ( Russian)casa ( Spanish)A more convincing evidence of the conventional and arbitrary nature of the connection between sound-symbol（声音符号）and meaning can also be illustrated by a set of homophones(同音异义词): write, right, and rite(仪式，礼拜式）. They are pronounced the same but convey different meanings.2.What do we mean by phonetic motivation? （P94和PPT）Words motivated phonetically are called echoic words（拟声词）or onomatopoeic words, whose pronunciation suggests the meaning. They show a close relation of name to sense whereas non-echoic words don‘t show any such relationship.Onomatopoeic words（拟声词）can be divided into primary Onomatopoeia（直接拟声）and secondary Onomatopoeia（间接拟声）.Primary Onomatopoeia means the imitation of sound by sound. Secondary Onomatopoeia means that certain sounds and sound-sequences are associated with certain senses in an expressive relationship.3.Quote a short poem or passage that shows the literary effect of onomatopoeic words. (P94倒数第二行)“The ice was here, the ice was there,The ice was all around;It cracked and growled, and roared and howled,Like noises in a swound!‖5.What is meant by grammatical meaning?（P96~97）Grammatical meaning(词法意义) consists of word-class（词类）and inflectional paradigm（词形变化）。

编译原理英文版课后答案

编译原理英文版课后答案Chapter 1: Introduction to Compilation1. What is compilation?Compilation is the process of translating a high-level programming language code into a low-level machine language code that can be directly executed by a computer.2. What are the main components of a compiler?The main components of a compiler are:•Lexer: also known as a tokenizer, it breaks the source code into a sequence of tokens.•Parser: it verifies the syntax of the source code and builds an intermediate representation such as an abstract syntax tree (AST).•Semantic Analyzer: it checks for semantic correctness and assigns meaning to the program.•Intermediate Code Generator: it generates a representation of the program that can be easily translated into machine code.•Optimizer: it improves the efficiency of the program by performing various optimizations.•Code Generator: it translates the intermediate code into the target machine code.3. What are the advantages of compilation over interpretation?•Performance: Compiled code runs faster than interpreted code as the compilation process optimizes the code for a specific target machine.•Portability: Once a program is compiled, it can be executed on any machine that supports the target machine code, eliminating the need for aruntime environment.•Security: The source code is not distributed with the compiled program, making it harder for others to access and modify the code.4. What are the disadvantages of compilation?•Longer development cycle: Compilation requires additional time and effort compared to interpretation, as it involves multiple stages such as code generation and optimization.•Platform dependency: Compiled code is specific to the target machine, so it may not run on different architectures or operating systems withoutrecompilation.•Lack of flexibility: Changes made to the source code may require recompilation of the entire program.5. What is the difference between a compiler and an interpreter?A compiler translates the entire source code into machine code before execution, while an interpreter translates and executes the source code line by line.Chapter 2: Lexical Analysis1. What is lexical analysis?Lexical analysis, also known as tokenization, is the process of dividing the source code into a sequence of tokens.2. What are tokens?Tokens represent the basic building blocks of a programming language. They can be keywords, identifiers, constants, operators, or punctuation symbols.3. What is a regular expression?A regular expression is a sequence of characters that defines a search pattern. It is used in lexical analysis to describe the patterns of tokens.4. What are regular languages?Regular languages are a class of formal languages that can be described by regular expressions. They can be recognized by finite automata.5. What is a finite automaton?A finite automaton is a mathematical model of a computation process. It consists of a finite set of states and transitions between those states based on input.Chapter 3: Parsing1. What is parsing?Parsing is the process of analyzing the structure of a program according to the rules of a formal grammar. It involves constructing an abstract syntax tree (AST) from the given source code.2. What is an abstract syntax tree (AST)?An abstract syntax tree is a hierarchical representation of the syntactic structure of a program. It captures the relationships between different elements of the code, such as expressions and statements.3. What is a context-free grammar (CFG)?A context-free grammar is a formal way to describe the syntax of a programming language. It consists of a set of production rules that define how valid program statements can be constructed.4. What is the difference between a parse tree and an abstract syntax tree (AST)?A parse tree represents the complete syntactic structure of a program, including all the intermediate steps taken during parsing. An abstract syntax tree (AST) is a simplified version of the parse tree, where redundant information is removed, and only the essential structure of the program is retained.5. What is ambiguous grammar?An ambiguous grammar is a grammar that allows for multiple parse trees for a single input string. It can lead to parsing conflicts and difficulties in determining the correct interpretation of a program.Chapter 4: Semantic Analysis1. What is semantic analysis?Semantic analysis is the phase of the compilation process that checks the semantic correctness of a program. It assigns meaning to the code and ensures that it adheres to the rules and constraints of the programming language.2. What are static semantics?Static semantics are the properties of a program that can be determined at compile-time. These include type checking, scope rules, and variable declarations.3. What are dynamic semantics?Dynamic semantics are the properties of a program that can only be determined at runtime. These include program behavior, control flow, and runtime errors.4. What is type checking?Type checking is the process of verifying that the types of expressions and variables in a program are compatible according to the rules of the programming language. It prevents type-related errors during execution.5. What is symbol table?A symbol table is a data structure used by the compiler to store information about variables, functions, and other symbols in a program. It enables efficient semantic analysis and name resolution.Note: These answers are for reference purposes only and may vary depending on the specific context and requirements of the course or textbook.。

Mining Usama Fayyad,

T HE SCIENTIST AT THE OTHEREND OFtoday’s data collection machinery—whether a satellite collecting data from a remote sensing platform, a tele-scope scanning the skies, or a micro-scope probing the minute details of a cell—is typically faced with the prob-lem: What do I do with all the data? Scientific instruments can easily gen-erate terabytes and petabytes of data at rates as high as gigabytes per hour. There is a rapidly widening gap between data collection capabilities and the ability to analyze the data. The traditional approach of a lone investi-gator staring at raw data in pursuit of (often hypothesized) phenomena or underlying structure is quickly becom-ing infeasible. The root of the prob-lem is that data size and dimensionali-ty are too large. A scientist can work effectively with a few thousand obser-vations, each having a small number of measurements, say five. Effectively digesting millions of data points, eachwith tens or hundreds of measure-ments, is another matter.When a problem is fully understoodand the scientist knows what to lookfor in the data through well-defined procedures, data volume can be han-dled effectively through data reduc-tion.1By reducing data, a scientist is U s a m a F a y y a d,D a v i d H a u s s l e r,a n dP a u l S t o l o r zTERRYWIDENERMining Scientific DataDigesting millions of data points, each with tens or hun-dreds of measurements—generally beyond a scientist'shuman capability—can be turned over to data miningtechniques for data reduction, which functions as aninterface between the scientist and large datasets.1Data reduction is a term used in science data analysisto refer to the extraction of essential variables of inter-est from raw observations. Particularly appropriatewhen dealing with image datasets, it involves transfor-mation, selection, and normalization operations.COMMUNICATIONS OF THE ACM November 1996/Vol. 39, No. 1151effectively bringing data size down to a range that is analyzable.In scientific investigation, because we are often interested in new knowledge, effective data manipulation and exploratory data analysis looms as one of the biggest hurdles in the way of exploiting the data. In this article, we give an overview of the main issues in the exploitation of scientific datasets through automated methods, pre-sent five case studies in which knowledge discovery in databases (KDD) tools play important and enabling roles, and conclude with future challenges for data mining and KDD techniques in science data analysis.Data Reduction and Data Types D ATA mining and KDD techniques for automated data analysis can and do play an importantrole as an interface between sci-entists and large datasets. Machines are still far from approaching human abilities in the areas of synthesis of new knowledge, hypothesis formation, and creative modeling. The processes of drawing insight and conducting investigative analyses are still clearly in the realm of tasks best left to humans. Howev-er, automating the data reduction procedure is a significant niche suitable for computers. Data reduction involves cataloging, classification, segmentation, parti-tioning of data, and more. It is the most tedious stage of analysis, typ-ically involving manipulation of enormous amounts of data. Once a dataset is reduced (say to a cata-log or other appropriate form), the scientist can proceed to ana-lyze it using more traditional (manual), statistical, or visualiza-tion techniques. The higher lev-els of analysis include theoryformation, hypothesis of newlaws and phenomena, filteringwhat is useful from background,and searching for hypothesesthat require a large amount ofhighly specialized domainknowledge.Data comes in many forms—from measurements in flat files tomixed (e.g., multispectral/multi-modal) data including time series(e.g., sonar signatures and DNAsequences), images, and struc-tured attributes. Most data min-ing algorithms in statistics andKDD [3] (see also Glymour's arti-cle in this special section) aredesigned to work with data in flatfiles of feature vectors.Data types include:Image mon in scienceapplications, image data offersunique advantages in that it isrelatively easy for humans toexplore and digest. On theother hand, image data posesserious challenges on the datamining side. Feature extractionis the dominant problem; usingindividual pixels as features istypically problematic, since asmall portion of an image easilyturns into a high-dimensionalvector.2Time-series and sequence data.Challenges here include extract-ing stationary characteristics ofan entire series, whether or not itis stationary; if it is not stationary(e.g., in the case of DNAsequences), segmentation isneeded to identify and extractnonstationary behavior and tran-sitions between quantitatively Machines arestill far fromapproachinghuman abilitiesin the areas ofsynthesis of newknowledge,hypothesisformation, andcreativemodeling.52November 1996/Vol. 39, No. 11 COMMUNICATIONS OF THE ACMand qualitatively different regimes in the series. An effective means for dealing with sequence data is to infer transition probabilities between process state variables from the observed data. A particularly suc-cessful class of techniques used in this type of mining is hidden Markov models (HMMs) [8], which have been extensively developed in the context of speech recognition. An HMM describes a series of observa-tions by a “hidden’’ stochastic process—a Markov process.In the case of speech, the observations are sounds forming words, and a model represents a hidden ran-dom process that generates certain sequences of sounds, constituting variant pronunciations of a sin-gle word, with high probability. In modeling proteins, a word corresponds to a protein sequence, and a fam-ily of proteins with similar structure or function can be viewed as a set of variant pronunciations of a word. This observation allows a large amount of mathemat-ical and algorithmic HMM machinery developed in the context of speech processing to be adapted and applied to protein modeling, greatly reducing imple-mentation and development time and allowing impressive results to be obtained quickly [5]. Numerical measurements vs. categorical values. While a majority of measurements (e.g., pixels and sensors) are numeric, some notable examples (e.g., protein sequences) consist of categorical measure-ments. The advantage of dealing with numerical data is that the notion of distance between any two data points (feature vectors) is easier than defining dis-tance metrics over categorical-value variables. Many classification and clustering algorithms rely funda-mentally on the existence of a metric distance and the ability to define means and centroids.Structured and sparse data.In some problems, vari-ables may have some structure to them (e.g., hierar-chical attributes or conditional variables that have different meanings under different circumstances). In other cases, different variables are measured for different observations, rendering flat-file representa-tion inappropriate.Reliability of data (sensor vs. model data). Raw sen-sor-derived data is often assimilated to provide a smooth homogeneous data product. For example, regular gridded data is often required in climate stud-ies, even when data points are collected haphazardly, raising the question of data reliability; some data points need to be dealt with especially carefully, as they may not correspond to direct sensor-derived information.Case StudiesFive case studies illustrate the contribution and potential of KDD for science data analysis. For each case, our focus is primarily the application's impact, the reasons why KDD systems succeeded, the limita-tions of techniques, and future challenges.Sky Survey CatalogingThe 2nd Palomar Observatory Sky Survey (POSS-II) took more than six years to complete. The survey con-sisted of 3TB of image data containing an estimated 2 billion sky objects. The 3,000 photographic images are scanned into 16-bit/pixel-resolution digital images at 23,040ϫ23,040 pixels per image. The basic problem is to generate a survey catalog recording the attributes of each object along with its class (e.g., star or galaxy). The attributes are defined by the astronomers.Once basic image segmentation is performed, 40 attributes per object are measured. The problem is identifying the class of each object. Once the class is known, astronomers can conduct all sorts of scientif-ic analyses, like probing galactic structure from star and galaxy counts, modeling evolution of galaxies, and studying the formation of large structure in the universe [13]. To achieve these goals, we developed the Sky Image Cataloging and Analysis Tool (SKI-CAT) system [12].D ETERMINING the classes for faint objectsin the survey is a difficult problem. Themajority of objects in each image arefaint objects whose class cannot bedetermined by visual inspection or clas-sical computational approaches in astronomy. Our goal was to classify objects at least one isophotal mag-nitude fainter than objects classified in previous com-parable surveys. We tackled the problem using decision-tree learning algorithms (see chapter 19 in [3]) to accurately predict the classes of objects. The accuracy of the procedure was verified through a very limited set of high-resolution charged-couple device (CCD) images as ground truth.By extracting rules via statistical optimization over multiple trees (see chapter 19 in [3]), we achieved 94% accuracy in predicting sky object classes. Reliable classification of faint objects increased the number of objects classified (usable for analysis) by 300%. Hence, astronomers could extract much more out of the data in terms of new scientific results [12].SKICAT's classification scheme recently helped aCOMMUNICATIONS OF THE ACM November 1996/Vol. 39, No. 1153team of astronomers discover 16new high red-shift quasars in at least one order of magnitude less observation time [4]. These objects are extremely difficult to find and are some of the farthest (hence oldest) objects in the uni-verse. They provide valuable and rare clues about the early history of the universe.SKICAT was successful for sev-eral reasons:• The astronomers solved the fea-ture extraction problem—the proper transformation from pixel space to feature space.This transformation implicitly encodes a significant amount of prior knowledge.• Within the 40-dimensional fea-ture space, at least eight dimen-sions are needed for accurate classification. Hence, it was dif-ficult for humans to discover which eight of the 40 to use, let alone how to use them in classi-fication. Data mining methods contributed by solving the clas-sification problem.• Manual approaches to classifica-tion were simply not feasible.Astronomers needed an auto-mated classifier to make the most of the data.• Decision-tree methods,although involving blind greedy search (see Fayyad's overview article on the KDD process in this special section) proved to be an effective tool for finding the important dimensions for this problem.Directions being pursued now involve clustering the data.Unusual or unexpected clusters in the data might be indicative of new phenomena, perhaps even a new discovery. A difficulty here is that new classes are likely to be rare in the data (one per millionobservations), so algorithms need to be tuned to looking for small interesting clusters rather than ignoring them as noise or out-liers.Finding Volcanoes on VenusThe Magellan spacecraft orbited the planet Venus for more than five years and used synthetic aperture radar (SAR) to map the surface of the planet, penetrating the gas and cloud cover that per-manently obscures the surface in the optical range. The resulting dataset is a unique high-resolu-tion global map of an entire planet. We have more of the planet Venus mapped at the 75-m/pixel resolution than we do of the Earth’s surface (since most of the Earth’s surface is covered by water). This dataset is uniquely valuable because of its complete-ness and because Venus is the most similar planet to Earth in size. Learning about the geologi-cal evolution of Venus could offer valuable lessons about Earth.The sheer size of the dataset prevents planetary geologists from effectively exploiting its con-tent. The first pass of Venus using the left-looking radar yielded more than 30,000 images at 1,000ϫ1,000 pixels each. To help a group of geologists at Brown University analyze this dataset, the Jet Propulsion Laboratory devel-oped the Adaptive Recognition Tool (JARtool) [1]. The system seeks to automate the search for an important feature on the plan-et—small volcanoes—by training the system via examples. The geol-ogists would label volcanoes on a few (say 30 to 40) images, and the system would then automatically construct a classifier that would proceed to scan the rest of the image database and attempt to54November 1996/Vol. 39, No. 11 COMMUNICATIONS OF THE ACMKDD applications in science may generally be easier than applications in business, finance,or other areas—mainly because science users typically know their data in intimatedetail.locate and measure the planet's estimated 1 million small volcanoes. Note the wide gap between the raw collected data (pixels) and the level at which scien-tists operate (catalogs of objects). In this case, unlike the case with SKICAT, the mapping from pixels to features would have to be done by the system. Hence, little prior knowledge is provided to the data mining system.JARtool uses an approach based on matched filter-ing for focus of attention (triggering on candidates that vaguely resemble volcanoes and having a high false detection rate) followed by feature extraction based on projecting the data onto the dominant eigen-vectors in the training data, and then by classification learning to distinguish true detections from false alarms. The tool matches scientist performance for certain classes of volcanoes (e.g., high-probability vol-canoes vs. those scientists are not sure about) [1]. Lim-itations include sensitivity to variances in illumination, scale, and rotation. This approach does not, however, generalize well to a wider variety of volcanoes.The use of data mining methods here was motivat-ed by several factors:• Scientists did not know much about image process-ing or about the SAR properties. Hence, they could easily label images but could not design rec-ognizers.• As is often the case with cataloging tasks, there is little variation in illumination and orientation of objects of interest, making mapping from pixels to features an easier problem.• The geologists were motivated to work with us; they lacked other easy means for finding small vol-canoes.• The result is to extract valuable data from an extensive dataset. Also, the adaptive approach (training by example) is flexible and would in principle lends itself to reuse in other tasks. D UE to the proliferation of image data-bases and digital libraries, data min-ing systems capable of searching forcontent are becoming a necessity. Indealing with images, the train-by-example approach, or querying for “things that look like this,” is a natural interface, since humans can visually recognize items of interest, but trans-lating those visual intuitions into pixel-level algo-rithmic constraints is difficult to do. Work is proceeding to extend JARtool to other applica-tions, like classification and cataloging of sunspots.Biosequence DatabasesIn its simplest computer form, the human genome is a string of about 3 billion letters containing instances of four letters—A, C, G, and T, representing the four nucleic acids, the constituents of DNA, strung togeth-er to make the chromosomes in our cells. These chro-mosomes contain our genetic heritage, a blueprint for a human being. A large international effort is under way to obtain this string, but obtaining it is not enough; the string has to be interpreted. DNA is first transcribed into RNA and then trans-lated in turn from RNA into pro-teins to form the actualbuilding blocks (chromo-somes) of our makeup. Theproteins do most of the workwithin the cell, and each ofthe approximately 100,000 dif-ferent kinds of protein in ahuman cell has a unique struc-ture and function. Elucidating thestructures and functions of proteins and structural RNA molecules (for humans and for other organ-isms) is the central task of molecular biology.In biosequence databases, there are several press-ing data mining tasks, including:• Find the genes in the DNA sequences of various organisms from among DNA devoted in part to other functions as well. Gene-finding programs, such as GRAIL, GeneID, GeneParser, GenLang, FGENEH, Genie, and EcoParse (see e.g., [6, 7, 9]), use neural nets and other artificial intelli-gence or statistical methods to locate genes in DNA sequences.3Looking for ways to improve the accuracy of these methods is a major thrust of cur-rent research in this area.• Develop methods to search the database for sequences that have higher-order structure or function similar to that of the query sequence, rather than doing a more naive string matching on the sequences themselves. The unique folded structure of each biomolecule (e.g., protein and RNA) is crucial to its function.Two popular systems for modeling proteins, based on the HMM ideas mentioned earlier, are HMMerand SAM. HMMs and their variants have also beenCOMMUNICATIONS OF THE ACM November 1996/Vol. 39, No. 1155applied to the gene-finding problem [6, 7] and to the problem of modeling structural RNA.4The gene-finding methods GeneParser, Genie, and EcoParse,mentioned earlier, are examples of this. RNA analy-sis uses an extension of HMMs called stochastic con-text-free grammars. This extension permits modeling certain types of interactions among letters of a sequence that are distant in the primary structure but adjacent in the folded RNA structure, a function simple HMMs cannot perform.COMPUTER -BASEDanalysis of biose-quences increasingly affects the field of biology. Computational biose-quence analysis and database search-ing tools are now an integrated andessential part of the field, leading to numerous important scientific discoveries in the last few years.Most have resulted from database searches revealing unexpected similarities between molecules previ-ously not known to be related. However, these meth-ods are increasingly important in the direct determination of structure and function of biomol-ecules as well.HMMs and related models have been successful in helping scientists with this task because they provide a solid statistical model flexible enough to incorpo-rate important biological knowledge. The key chal-lenge is to build computer methods that can interpret biosequences using a still more complete integration of biological knowledge and statistical methods at the outset, allowing biologists to operate at a higher level in the interpretation process, where their creativity and insight is of maximum value.Geosciences: Quakefinder and CONQUESTA major problem facing scientists in such domains as remote sensing is the fact that important signals about temporal processes are often buried within noisy image streams, requiring the application of sys-tematic statistical inference concepts in order for raw image data to be transformed into scientific under-standing.One class of problems that exploit inference in this way is the measurement of subtle changes in images. Consider, for example, the case of two images, taken before and after an earthquake. If the earthquake fault motions are much smaller in mag-nitude than the pixel resolution (a relatively com-mon scenario), it is essentially impossible to describe and measure the fault motion by simply comparing the two images manually (or even by naive differenc-ing by computer). However, by repeatedly register-ing different local regions of the two images (a task known to be doable to subpixel precision), it is pos-sible to infer the direction and magnitude of ground motion due to the earthquake. This fundamental concept is broadly applicable to many data mining situations in the geosciences and other fields, includ-ing earthquake detection, continuous monitoring of crustal dynamics and natural hazards, target identifi-cation in noisy images, and more.One example of such a geoscientific data mining system is Quakefinder [10], which automatically detects and measures tectonic activity in the Earth’s crust by examining satellite data. Quakefinder has been used to automatically map the direction and magnitude of ground displacements due to the 1992Landers earthquake in Southern California over a spatial region of several hundred square kilometers at a resolution of 10 m to a (sub-pixel) precision of 1 m. It is implemented on a 256-node Cray T3D par-allel supercomputer to ensure rapid turnaround of scientific results. The issues of developing scalable algorithms and their implementation on scalable platforms addressed here are in fact quite general and are likely to influence the great majority of future data mining efforts geared to the analysis of genuinely massive datasets.In addition to automatically measuring known faults, the system permits a form of automatic knowledge discovery by indicating novel unex-plained tectonic activity away from the primary Landers faults—activity never before observed.Future work will focus on the measurement of con-tinuous processes over many images, instead of simply abrupt behavior seen during earthquakes,and to related image-understanding problems.Analysis of atmospheric data is another classic area in which processing and data collection power has far outstripped our ability to interpret the results. The mismatch is huge between pixel-level data and scientific language that understands such spatiotemporal patterns as cyclones and tornadoes.Cross-disciplinary collaborations attempt to bridge this gap, as exemplified by the team formed by JPL and UCLA to develop COncurrent QUErying Space and Time (CONQUEST) [11].Parallel supercomputers were used in CON-QUEST to implement queries concerning the pres-56November 1996/Vol. 39, No. 11 COMMUNICATIONSOF THE ACMence, duration, and strength of extratropical cyclones and distinctive blocking features in the atmosphere, scanning through this dataset in minutes. Upon extraction, the features are stored in a relational database. This content-based indexing dramatically reduces the time required to search the raw datasets of atmospheric variables when further queries are formulated. The system also features parallel imple-mentations of singular value decomposition and neural network pattern recognition algorithms in order to identify spatiotemporal features as a whole. The long-term hope is that a common set of flexible, extensible, and seamless tools can be applied across a number of scientific domains.Conclusions and ChallengesSeveral issues need to be considered when contem-plating a KDD application in science datasets. Some are common with many other data mining applica-tions (e.g., feature extraction, choice of data mining tasks and methods, and understandability of derived models and patterns) [3]. Some considerations are more important in science applications than in financial or business KDD applications, including: • Ability to use prior knowledge during mining (more documented knowledge is typically avail-able in science applications);• More stringent requirements for accuracy (e.g., better than 90% accuracy was required for SKI-CAT);• Issues of scalability of machines and algorithms (e.g., parallel supercomputers used in scientific applications); and• Ability to deal with minority (low-probability) classes, whose occurrence in the data is rare, asin SKICAT clustering.In conclusion, we point out that KDD applica-tions in science may generally be easier than appli-cations in business, finance, or other areas—mainly because science users typically know their data in intimate detail. This knowledge allows them to intu-itively guess the important transformations. Scien-tists are trained to formalize intuitions into procedures and equations, making migration to computers easier. Background knowledge is usually available in well-documented form (papers and books), providing backup resources when the initial data mining attempts fail. This luxury (sometimes a burden) is not usually available in nonscientific fields.References1.Burl, M.C., Fayyad, U., Perona, P., Smyth, P., and Burl, M.P.Automating the hunt for volcanoes on Venus. In Proceedings of Computer Vision and Pattern Recognition Conference (CVPR-94) (Seattle 1994). IEEE Computer Science Press, Los Alamitos, Calif., 1994, pp. 302–308.2.Chothia, C. One thousand families for the molecular biologist.Nature 357 (1992), 543–544.3.Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy,R. Advances in Knowledge Discovery in Databases. MIT Press, Cam-bridge, Mass., 1996.4.Kennefick, J.D., DeCarvalho, R.R., Djorgovski, S.G., Wilber,M.M., Dickinson, E.S., Weir, N., Fayyad, U., and Roden, J.Astron. J. 110, 1 (1995), 78–86.5.Krogh, A., Brown, M., Mian, I.S., Sjolander, K., and Haussler,D. Hidden Markov models in computational biology: Applica-tions to protein modeling. J. Mol. Biol. 235(1994), 1501–1531.6.Krogh, A., Mian, I.S., and Haussler, D. A hidden Markov modelthat finds genes in E. coli DNA. Nucleic Acids Res. 22(1994), 4768–4778.7.Kulp, D., Haussler, D., Reese, M., and Eeckman, F. A general-ized hidden Markov model for the recognition of human genes in DNA. In Proceedings of the Conference on Intelligent Systems in Molecular Biology(1996). AAAI Press, Menlo Park, Calif., 1996.8.Rabiner, L.R. A tutorial on hidden Markov models and select-ed applications in speech recognition. Proc. IEEE 77 (1989), 257–286.9.Snyder, E.E., and Stormo, G.D. Identification of coding regionsin genomic DNA sequences: An application of dynamic pro-gramming and neural networks. Nucleic Acids Res. 21 (1993), 607–613.10.Stolorz, P., and Dean, C. Quakefinder: A scalable data miningsystem for detecting earthquakes from space. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (Portland, Oreg., 1996), AAAI Press, Menlo Park, Calif., 1996.11.Stolorz, P., Nakamura, H. Mesrobian, E., Muntz, R.R., Shek,E.C., Mechoso, C.R., Farrara, J.D. Fast spatiotemporal datamining of large. geophysical datasets. In Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (Montréal, Aug. 1995), AAAI Press, Menlo Park, Calif. 1995, pp. 300–305.12. Weir, N., Fayyad, U.M., and Djorgovski, S.G. Automatedstar/galaxy classification for digitized POSS-II. Astron. J. 109, 6 (1995), 2401–2412.13.Weir, N., Djorgovski, S.G., and Fayyad, U.M. Initial galaxycounts from digitized POSS-II. Astron. J. 110, 1 (1995), 1–20.Additional references for this article can be found at /research/datamine/CACM-DM-refs/.USA MA FA YYA D is senior researcher at Microsoft and a Distin-guished Visiting Scientist at the Jet Propulsion Laboratory, Califor-nia Institute of Technology. He can be reached at fayyad@.DAVID HAUSSLER is a professor of computer science at the Uni-versity of California, Santa Cruz. He can be reached at haussler@.PAUL STOLORZ is technical group supervisor at the Jet Propul-sion Laboratory, California Institute of Technology. He can be reached at pauls@.Permission to make digital/hard copy of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication and its date appear, and notice is given that copying is by permission of ACM, Inc. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior specific permission and/or a fee.© ACM 0002-0782/96/1100 $3.50COMMUNICATIONS OF THE ACM November 1996/Vol. 39, No. 1157。

黑猩猩交流方式英文版

黑猩猩交流方式英文版Chimpanzee Communication: An OverviewIntroduction:Chimpanzees, known for their intelligence and social behavior, have a complex system of communication. This article aims to explore the various ways in which chimpanzees communicate with each other and express their needs and emotions.Body:1. Vocalizations:Chimpanzees communicate through a wide range of vocalizations. These include screams, barks, hoots, and grunts, each serving different purposes. Screams are typically used to express fear or alarm, while barks are often heard during aggressive encounters. Hoots and grunts, on the other hand, are used for a range of social purposes, such as greeting or indicating the presence of food.2. Body Language:Body language plays a crucial role in chimpanzee communication. Gestures like hand clapping, stomping, and foot-dragging are used as part of their displays to convey dominance or submission. Additionally, facial expressions, such as bared teeth to show aggression or relaxed lips to indicate submission, also contribute to their communication system.3. Gestures:Chimpanzees rely heavily on gestures to communicate complex messages. They use a variety of distinct hand movements, such as reaching out for an object or pointing towards a direction of interest. These gestures help in coordinating group activities, sharing information about food sources, and signaling intentions within their social groups.4. Tool Use:Tool use is an important aspect of chimpanzee communication. By observing and mimicking others, young chimpanzees learn how to use tools for various purposes, such as cracking nuts or extracting termites from mounds. This form of communication not only aids in acquiring food but also allows for the transmission of knowledge and skills among group members.5. Postures:Chimpanzees possess a rich repertoire of postures that convey different meanings. For instance, a crouched posture with piloerection signifies fearor threat, while an upright and relaxed posture indicates a calm and non-aggressive state. These postures, combined with other forms of communication, facilitate social interactions and establish hierarchies within the group.6. Facial Expressions:Facial expressions in chimpanzees often mirror their emotional state. Raised eyebrows can indicate surprise or curiosity, while narrowed eyes convey aggression or anger. By closely observing these subtle changes infacial expressions, chimpanzees can accurately interpret and respond to the emotions of their peers.Conclusion:Chimpanzees possess a sophisticated system of communication, utilizing a combination of vocalizations, body language, gestures, tool use, postures, and facial expressions. Their ability to convey complex messages and emotions mirrors human communication to a significant extent. Understanding chimpanzee communication not only sheds light on our evolutionary past but also raises questions about the origins of language and social interaction in humans.。

Extraction Patterns for Information Extraction Tasks A Survey

Extraction Patterns for Information Extraction Tasks:A SurveyIon MusleaInformation Sciences Institute and Department of Computer ScienceUniversity of Southern California4676Admiralty WayMarina del Rey,CA90230,USAmuslea,minton,knoblock@AbstractInformation Extraction systems rely on a set of extractionpatterns that they use in order to retrieve from each docu-ment the relevant information.In this paper we survey thevarious types of extraction patterns that are generated by ma-chine learning algorithms.We identify three main categoriesof patterns,which cover a variety of application domains,andwe compare and contrast the patterns from each category.IntroductionInformation Extraction(IE)is concerned with extracting therelevant data from a collection of documents.For instance,a typical IE task might be toﬁnd management changes re-ported in the Wall Street Journal or to identify the targets ofterrorist attacks reported in the newspapers.A key compo-nent of any IE system is its set of extraction patterns(or ex-traction rules)that is used to extract from each document theinformation relevant to a particular extraction task.As writ-ing useful extraction patterns is a difﬁcult,time-consumingtask,several research efforts have focused on learning theextraction rules from training examples provided by the user.In this paper,we review several types of extraction patternsthat are generated by machine learning algorithms.We be-gin by analyzing the extraction rules used for free text docu-ments,and we continue with the rules that can be applied tomore structured types of online documents.IE From Free TextIn this section we review extraction patterns that are usedonly to process documents that contain grammatical,plaintext.Such extraction rules are based on syntactic and se-mantic constraints that help identify the relevant informationwithin a document.Consequently,in order to apply the ex-traction patterns below,one has to pre-process the originaltext with a syntactic analyzer and a semantic tagger.AutoSlogAutoSlog(Riloff1993)builds a dictionary of extraction pat-terns that are called concepts or concept nodes.Each Au-toSlog concept has a conceptual anchor that activates it andCONCEPT NODE:Name:target-subject-passive-verb-bombed Trigger:bombedVariable Slots:(target(*S*1))Constraints:(class phys-target*S*)Constant Slots:(type bombing)Enabling Conditions:((passive))Figure2:A LIEP extraction pattern.the subject of a sentence that also contains a verb group followed by a prepositional phrase)and the semantic con-straints(e.g.,TRGT is a“physical-target”,the verb“bomb”is used in its passive form,and the prepositional phrase starts with“by”).PALKAThe PALKA system(Kim&Moldovan1995)learns extrac-tion patterns that are expressed as frame-phrasal pattern structures(for short,FP-structures).As shown in Figure3, an FP-structure consists of a meaning frame and a phrasal pattern.Each slot in the meaning frame deﬁnes an item-to-be-extracted together with the semantic constraints associ-ated to it(e.g.,the target of the bombing event must be a physical object).The phrasal pattern represents an ordered sequence of lexical entries and/or semantic categories taken from a predeﬁned concept hierarchy.The FP-structure combines the meaning frame and the phrasal pattern by linking the slots of the former to the ele-ments of the latter.Applying an FP-structure to a sentence represents a straightforward process:if the phrasal pattern matches the sentence,the FP-structure is activated,and then the corresponding meaning frame is used to actually extract the data.As opposed to AutoSlog,FP-structures can be activated both by exact match and via the isFigure3:Example of FP-structure. Concept type:BUILDING BOMBINGSUBJECT:Classes include:PhysicalTargetTerms include:BUILDINGExtract:targetVERB:Root:BOMBMode:passive PREPOS-PHRASE:Preposition:BYClasses include:PersonNameExtract:perpetrator nameSEGMENTED DOCUMENT:segmﬁeld1:HEAD LA Forecast/HEAD/segm segmﬁeld1:.MONDAY...ﬁeld2:CLOUDY/segm segmﬁeld1:.TUESDAY...ﬁeld2:SUNNY/segm Concept type:FORECASTConstraints:FIELD:Classes include:DayTerms include:“.”,“...”Extract:dayFIELD:Classes include:Weather ConditionExtract:conditionsFigure6:Example of Egraph used by HASTEN.for CRYSTAL,all other systems allow semantic class con-straints only on the slots to be extracted(for the other sen-tence elements they allow exact word and verb root con-straints).Third,PALKA,CRYSTAL,and HASTEN can gener-ate both single-and multi-slot rules,while AutoSlog learns only single-slot rules,and LIEP can not induce single-slot st but not least,AutoSlog,PALKA,and CRYSTAL were designed to always use the syntactic context;that is,if a relevant item can appear either in the subject or in a prepo-sitional phrase,they must create two distinct extraction pat-terns.LIEP and HASTEN do not suffer this limitation,and, consequently,they can create a single rule that covers both cases.IE from online documentsWith the expansion of the Web,users can access collections of documents that consist of a mixture of grammatical,tele-graphic,and/or ungrammatical text.Performing IE tasks on corpora of job postings,bus schedules,or apartment rentals has immediate practical applications,but the IE techniques for free text are notﬁt for online documents.In order to han-dle such documents,the three types of extraction rules pre-sented in this section combine syntactic/semantic constraints with delimiters that“bound”the text to be extracted. WHISKWHISK(Soderland1998)is a learning system that gener-ates extraction rules for a wide variety of documents ranging from rigidly formatted to free text.The WHISK extraction patterns are a special type of regular expressions that have two components:one that describes the context that makes a phrase relevant,and one that speciﬁes the exact delimiters of the phrase to be extracted.Depending of the structure of the text,WHISK generates patterns that rely on either of the components(i.e.,context-based patterns for free text,and delimiter-based patterns for structured text)or on both of them(i.e.,for documents that lay in between structured and free text).In Figure7we show a sample WHISK extraction task from online texts.The sample document is taken from an apartment rental domain that consists of ungrammati-cal constructs,which,without being rigidly formatted,obey some structuring rules that make them human understand-able.The sample pattern in Figure7has the following mean-ing:ignore all the characters in the text until youﬁnd a digit followed by the“br”string;extract that digit andﬁll theﬁrst extraction slot with it(i.e.,“Bedrooms”).Then ignore again all the remaining characters until you rich a dollar sign im-mediately followed by a number.Extract the number andﬁll the“Price”slot with it.DOCUMENT:EXTRACTED DATA:Capitol Hill-1br twnhme.Bedrooms:1D/W W/D.Pkg incl$675.Price:6753BR upperﬂr no gar.$995.Bedrooms:3(206)999-9999br Price:995Extraction rule:*(Digit)’BR’*’$’(Nmb)Output:Rental Bedrooms@1Price@2A RAPIER extraction task.gDOCUMENT-1:...to purchase4.5mln Trilogy shares at...DOCUMENT-2:...acquire another2.4mln Roach shares...Acquisition:-length(2),some(?A[]capitalized true),some(?A[next-token]all-lower-case true),some(?A[right-AN]wn-word‘stock’).D1: 1.Joe’s:(313)323-55452.Li’s:(406)545-2020D2: 1.KFC:818-224-40002.Rome:(656)987-1212WIEN rule:*’.’(*)’:’*’(’(*)’)’SoftMeaky rule:*’.’(*)EITHER’:’()’-’OR’:’*’(’()’)’Output:Restaurant Name@1AreaCode@2A STALKER extraction domain.gfully applied to document D1,but it fails on D2because ofthe different phone number formatting.The WIEN rule above is an instance of LR class,which thesimplest type of WIEN rules.The classes HLRT,OCLR,andHOCLRT are extensions of LR that use document head andtail delimiters,tuple delimiters,and both of them,respec-tively.WIEN deﬁnes two other classes,N-LR and N-HLRT,but their induction turned out to be impractical.SoftMealy(Hsu&Dung1998)is a wrapper inductionalgorithm that generates extraction rules expressed asﬁnite-state transducers.It allows both the use of semantic classesand disjunctions,which are especially useful when the doc-uments contain several formatting conventions or variousorderings of the items of interest.Figure10also shows aSoftMealy extraction rule1that can deal with the differentformatting of the area code.The SoftMealy rule reads asfollows:ignore all tokens until youﬁnd a’.’;then extractthe restaurant name,which is the string that ends before theﬁrst’:’.If’:’is immediately followed by a number,extractit as the area code;otherwise ignore all characters until youﬁnd a’(’immediately followed by a number,which rep-resents the area code.SoftMealy’s extraction patterns areobviously more expressive than the WIEN ones;their mainlimitation consists of their inability to use delimiters that donot immediately precede and follow the relevant items.STALKER(Muslea,Minton,&Knoblock1999)is a wrap-per induction system that performs hierarchical informationextraction.In Figure11,we have a sample document thatrefers to a restaurant-chain that has restaurants located inseveral cities.In each city,the restaurant may have severaladdresses,and at each address it may have several phonenumbers.It is easy to see that the multi-slot output schemais not appropriate for extraction tasks that are performed onsuch documents with multiple levels of embedded data.Inorder to cope with this problem,STALKER introduces theEmbedded Catalog Tree(ECT)formalism to describe the hi-erarchical organization of the documents.The ECT speciﬁes the output schema for the extraction task,and it is also used to guide the hierarchical information extraction process. For a given ECT,STALKER generates one extraction rule for each node in the tree,together with an additional iter-ation rule for each LIST node.The extraction process is performed in a hierarchical manner.For instance,in order to extract all CityName s in a document,STALKER begins by applying to the whole document the extraction rule for the LIST(City),which skips to the second br in the page and extracts everything until it encounters a hr; then in order to extract each individual City,it applies the LIST(City)iteration rule to the content of the list.Finally, STALKER applies to the content of each extracted City the CityName extraction rule.There are two main differences between the rules gener-ated by STALKER and WHISK.First,even though STALKER uses semantic constraints,it does not enforce any linguis-tic constraints.Second,the STALKER rules are single-slot. However,unlike RAPIER and SRV,the single-slot nature of the STALKER rules does not represent a limitation because STALKER uses the ECT to group together the individual items that were extracted from the same multi-slot template (i.e.,from the same ECT parent).Using single-slot rules in conjunction with the ECT has two major advantages.First, to our knowledge,STALKER is the only IE inductive system that can extract data from documents that contain arbitrarily complex combinations of embedded lists and items.2Sec-ond,as each item is extracted independently of its siblings in the ECT,the various orderings of the items does not re-quire one rule for each existing permutation of the items to be extracted.ConclusionsWith the growth of the amount of online information,the availability of robust,ﬂexible IE systems will become a stringent necessity.Depending on the characteristics of their application domains,today’s IE systems use extraction pat-terns based on one of the following approaches:syntac-tic/semantic constraints,delimiter-based,or a combination of both.WHISK is the only system that is currently capa-ble of generating multi-slot rules for the whole spectrum of document types.On the other hand,by using RAPIER-or SRV-like rules in conjunction with the Embedded Catalog Tree,one could obtain rules that are more expressive than the ones of WHISK.ReferencesBrill,E.1994.Some advances in rule-based part of speech tagging.Proceedings of the12th Annual Conference on Artiﬁcial Intelligence(AAAI-94)722–727.Califf,M.,and Mooney,R.1997.Relational learning of pattern-match rules for information extraction.Work-。

英语作文描述表格类型模板

英语作文描述表格类型模板英文回答：Table types are fundamental building blocks of spreadsheet design. These structures enable users to organize, analyze, and present data effectively. There are various types of tables, each with its unique purpose and functionality.Standard Table。

The standard table is the most basic and common type. It comprises a grid of cells arranged in rows and columns, where each cell contains a single piece of data. Standard tables are suitable for storing and organizing simple datasets, such as a list of names, prices, or dates.Pivot Table。

A pivot table is a powerful tool for data summarizationand analysis. It allows users to create dynamic summariesof large datasets by rearranging data into different rows, columns, and values. Pivot tables are ideal for extracting insights from multi-dimensional data and generating reports.Calculated Table。

模拟ai英文面试题目及答案

模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

摘要
在竞争激烈的工业自动化生产过程中，机器视觉对产品质量的把关起着举足轻重的作用，机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检测技术相比，自动化的视觉检测系统更加经济、快捷、高效与安全。纹理物体在工业生产中广泛存在，像用于半导体装配和封装底板和发光二极管，现代化电子系统中的印制电路板，以及纺织行业中的布匹和织物等都可认为是含有纹理特征的物体。本论文主要致力于纹理物体的缺陷检测技术研究，为纹理物体的自动化检测提供高效而可靠的检测算法。纹理是描述图像内容的重要特征，纹理分析也已经被成功的应用与纹理分割和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检测算法。这种算法能容忍物体变形引起的图像配准误差，对纹理的影响也具有鲁棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义，如缺陷区域的大小、形状、亮度对比度及空间分布等。同时，在参考图像可行的情况下，本算法可用于同质纹理物体和非同质纹理物体的检测，对非纹理物体的检测也可取得不错的效果。在整个检测过程中，我们采用了可调控金字塔的纹理分析和重构技术。与传统的小波纹理分析技术不同，我们在小波域中加入处理物体变形和纹理影响的容忍度控制算法，来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段，我们检测了一系列具有实际应用价值的图像。实验结果表明本文提出的纹理物体缺陷检测算法具有高效性和易于实现性。关键字: 缺陷检测；纹理；物体变形；可调控金字塔；重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II

intriguing properties of neural networks 精读

intriguing properties of neural networks 精读Intriguing Properties of Neural NetworksIntroduction:Neural networks are a type of machine learning model inspired by the human brain's functioning. They are composed of interconnected nodes known as neurons that work together to process and analyze complex data. Neural networks have gained immense popularity due to their ability to learn, adapt, and make accurate predictions. In this article, we will delve into some of the intriguing properties of neural networks and explore how they contribute to their success in various fields.1. Non-linearity:One of the key properties of neural networks is their ability to model nonlinear relationships in data. Traditional linear models assume a linear relationship between input variables and the output. However, neural networks introduce non-linear activation functions that allow them to capture complex patterns and correlations. This property enables neural networks to excel in tasks such as image recognition, natural language processing, and voice recognition.2. Parallel Processing:Neural networks possess the remarkable ability to perform parallel processing. Unlike traditional algorithms that follow a sequential execution path, neural networks operate by simultaneously processing multiple inputs in parallel. This parallel architecture allows for faster and efficientcomputations, making neural networks suitable for handling large-scale datasets and real-time applications.3. Distributed Representation:Neural networks utilize distributed representation to process and store information. In traditional computing systems, data is stored in a centralized manner. However, neural networks distribute information across interconnected neurons, enabling efficient storage, retrieval, and association of knowledge. This distributed representation enhances their ability to learn complex patterns and generalize from limited training examples.4. Adaptability:Neural networks exhibit a high degree of adaptability, enabling them to adjust their internal parameters and optimize their performance based on changing input. Through a process called backpropagation, neural networks continuously learn from the errors they make during training. This iterative learning process allows them to adapt to new data and improve their accuracy over time. The adaptability of neural networks makes them robust to noise, varying input patterns, and changing environments.5. Feature Extraction:Neural networks are adept at automatically extracting relevant features from raw data. In traditional machine learning approaches, feature engineering is often a time-consuming and manual process. However, neural networks can learn to identify important features directly from the input data. This property eliminates the need for human intervention and enables neuralnetworks to handle complex, high-dimensional data without prior knowledge or domain expertise.6. Capacity for Representation:Neural networks possess an impressive capacity for representation, making them capable of modeling intricate relationships in data. Deep neural networks, in particular, with multiple layers, can learn hierarchies of features, capturing both low-level and high-level representations. This property allows neural networks to excel in tasks such as image recognition, where they can learn to detect complex shapes, textures, and objects.Conclusion:The intriguing properties of neural networks, such as non-linearity, parallel processing, distributed representation, adaptability, feature extraction, and capacity for representation, contribute to their exceptional performance in various domains. These properties enable neural networks to tackle complex problems, make accurate predictions, and learn from diverse datasets. As researchers continue to explore and enhance the capabilities of neural networks, we can expect these models to revolutionize fields such as healthcare, finance, and autonomous systems.。

鹦鹉的生活特征英语作文

鹦鹉的生活特征英语作文Parrots: Adaptations and Behaviors for Life in the Trees.Parrots, members of the order Psittaciformes, are a diverse group of birds renowned for their intelligence, vibrant plumage, and remarkable vocal abilities. They inhabit a wide range of habitats, from dense tropical forests to arid scrublands, and have evolved a suite of adaptations and behaviors that enable them to thrive in their treetop homes.Physical Adaptations.Parrots possess a number of physical adaptations that facilitate their arboreal lifestyle. Their strong feet, equipped with powerful toes and sharp claws, allow them to grasp branches and climb with ease. Their prehensile zygodactyl feet, with two toes facing forward and two facing backward, provide them with an exceptional level ofdexterity for manipulating food and objects.The parrot's beak is another remarkable adaptation. Its powerful hooked shape enables them to crack open nuts and seeds, while its serrated edges serve to slice through fruits and leaves. The tongue, which is usually thick and fleshy, often plays a vital role in the parrot's feeding habits. Some species, such as the nectar-eating lories, have tongues specially adapted for extracting nectar from flowers.Parrots also have a keen sense of vision, with eyesthat are laterally placed on their heads, providing them with a broad field of view. Their color vision is particularly acute, enabling them to distinguish between different fruits and leaves.Social Behavior.Parrots are highly social animals that live in flocks, which can vary in size from a few individuals to hundreds. Flocks provide parrots with a number of benefits, includingprotection from predators, shared resources, and social interactions.Within flocks, parrots establish complex social hierarchies, with dominant individuals typically securing access to the best food and nesting sites. Communication plays a vital role in maintaining these hierarchies and fostering cooperation within the flock. Parrots have a wide range of vocalizations, including calls, whistles, and screams, that they use to communicate with each other. Some species, such as the African grey parrot, are renowned for their ability to mimic human speech.Foraging and Feeding.Parrots are primarily herbivores, with their diets consisting of a wide variety of plant material, including fruits, nuts, seeds, and leaves. Some species are also known to consume insects, small vertebrates, and carrion.Parrots have evolved specialized foraging behaviorsthat allow them to access food in their treetop habitats.Some species, such as the macaws, have powerful beaks that enable them to crack open nuts and seeds. Others, such as the lories, have specialized tongues for extracting nectar from flowers.Nesting and Breeding.Parrots typically nest in tree cavities, abandoned woodpecker holes, or other enclosed spaces. They build large, untidy nests using a variety of materials, including twigs, leaves, and feathers. Parrots lay clutches of two to six eggs, which are incubated by both parents. The chicks hatch after about three weeks and are altricial, meaningthat they are completely dependent on their parents forfood and care.Ecological Importance.Parrots play vital ecological roles in their ecosystems. As herbivores, they help to disperse seeds and pollinate plants. Their vocalizations and foraging activities contribute to the overall biodiversity and health offorests.However, many parrot species are facing threats totheir survival due to habitat loss, illegal trade, and other human activities. Conservation efforts are crucial to protect these remarkable birds and ensure their continued existence in the wild.。

semantic知识点总结

semantic知识点总结Definition and Importance of SemanticsSemantics is the study of meaning in language and the interpretation of words, phrases, and sentences. It examines how words and symbols convey meaning, how meanings are structured and organized, and how meanings are used in communication. Semantics is a fundamental aspect of language and communication, as it enables people to understand and convey meaning effectively.The importance of semantics lies in its role in language comprehension, communication, and reasoning. It allows individuals to understand the meaning of the words and sentences they encounter, to interpret and infer meaning from context, and to express themselves effectively. Semantics also plays a crucial role in the development of language, as it helps children and language learners to acquire and understand the meanings of words and symbols.Role of Semantics in Language UnderstandingSemantics plays a crucial role in language understanding, as it enables individuals to comprehend the meaning of words, phrases, and sentences. It involves several key processes, including lexical semantics (the meanings of individual words), compositional semantics (the derivation of meaning from word combinations), and pragmatic semantics (the use of language in context).Lexical semantics focuses on the meanings of individual words and how they are organized and structured in the mental lexicon. It examines the different types of word meanings, including denotation (the literal meaning of a word) and connotation (the associated or suggested meanings of a word). Lexical semantics also explores the relationships between words, such as synonyms (words with similar meanings) and antonyms (words with opposite meanings), and the polysemy (multiple meanings) and homonymy (same form, different meanings) of words.Compositional semantics is concerned with how the meaning of a phrase or sentence is derived from the meanings of its constituent words and the syntactic structure of the sentence. It involves processes such as semantic composition, which combines word meanings to form sentence meanings, and semantic ambiguity resolution, which resolves multiple possible interpretations of a sentence. Compositional semantics also considers the influence of context and pragmatic information on meaning derivation, such as the use of inference and presupposition in language understanding.Pragmatic semantics focuses on the use of language in context and the interpretation of meaning in communication. It considers how speakers and listeners use context, background knowledge, and communicative intentions to convey and infer meaning. Pragmatic semantics also examines various communicative phenomena, such as implicature (indirect or implied meaning), speech acts (the performative function of language), anddiscourse coherence (the organization and connection of utterances in a conversation or text).Aspects of Semantic Knowledge in Linguistics and Cognitive ScienceSemantic knowledge is a central topic in linguistics and cognitive science, as it provides insights into the nature, structure, and processing of meaning in language and cognition. It encompasses various aspects of language and cognition, including lexical semantics, conceptual semantics, and computational semantics.Lexical semantics is the branch of semantics that focuses on the meanings of individual words and how they are organized and structured in the mental lexicon. It examines the different types of word meanings, semantic relations between words, and the representation and processing of word meanings. Lexical semantics also considers the influence of semantic properties, such as imageability (the ease with which a word evokes mental images) and concreteness (the degree to which a word refers to tangible objects or experiences), on word processing and memory.Conceptual semantics is concerned with the representation and organization of concepts and meanings in the mind. It explores how people categorize and classify the world, how they form and distinguish concepts, and how they encode and retrieve meaning from memory. Conceptual semantics also investigates the relationships between language and thought, such as the influence of linguistic categories and structures on conceptual organization and the influence of conceptual knowledge on language comprehension and production.Computational semantics is the area of semantics that addresses the computational modeling and processing of meaning in language and cognition. It focuses on developing formal and computational models of meaning representation, meaning inference, and meaning generation. Computational semantics also considers the use of natural language processing (NLP) techniques, such as semantic parsing, semantic role labeling, and semantic similarity measurement, to extract and analyze semantic information from texts and to build intelligent systems that understand and generate natural language.In addition, there are other important aspects of semantic knowledge in linguistics and cognitive science, such as cross-linguistic semantics (the study of semantic universals and variation across languages), diachronic semantics (the study of semantic change over time), and psycholinguistic semantics (the study of the cognitive processes and mechanisms underlying language understanding and production). These aspects contribute to our understanding of how meaning is structured and processed in language and cognition and how semantic knowledge is represented and used in different linguistic and cognitive contexts.In conclusion, semantic knowledge is a crucial aspect of human cognition and communication. It plays a central role in language understanding, as it enables individuals to comprehend and convey meaning effectively. Semantic knowledge encompasses variousaspects of language and cognition, such as lexical semantics, conceptual semantics, and computational semantics, and provides insights into the nature, organization, and processing of meaning in language and cognition. By exploring and understanding semantic knowledge, we can gain a deeper understanding of how language and thought are intertwined and how we make sense of the world through meaning.。

APPLICATIONS

APPLICATIONS,
INCREMENTAb INTERPRETATION: T H E O R Y , A N D RF, L A T I O N S H I P T O D Y N A M I C David Milward & Robin Cooper
SEMANTICS*
Centre for Cognitive Science, University of Edinburgh 2, Buccleuch Place, Edinburgh, EH8 91,W, Scot,land, davidm@
APPLICATIONS
Following the work of, for example, Marslen-Wilson (1973), .lust and Carpenter (1980) and Altma.nn al]d Steedrnan (1988), it has heroine widely accepted that semantic i11terpretation in hnman sentence processing can occur beibre sentence boundaries and even before clausal boundaries. It is less widely accepted that there is a need for incremental inteபைடு நூலகம்pretation in computational applications. In the [970s and early 1980s several compntational implementations motivated the use of' incremental in-. terpretation as a way of dealing with structural and lexical ambiguity (a survey is given in Haddock 1989). A sentence snch as the following has 4862 different syntactic parses due solely to attachment ambiguity (Stabler 1991). 1) I put the bouquet of flowers that you gave me for Mothers' Day in the vase that you gave me for my birthday on the chest of drawers that you gave me lbr Armistice Day. Although some of the parses can be ruled out using structural preferences during parsing (such as [,ate C'losure or Minimal Attachment (Frazier 1979)), ex traction of the correct set of plausible readings requires use of real world knowledge. Incremental interpretation allows on-line semantic tiltering, i.e. parses of initial fragments which have an implausible or anolnalous interpretation are rqiected, thereby preven*'.lPhis research was supported by the UK Science and Gnglneerlng l~.esearch Council, H,esearch G r a n t 1tR30718.

描写松鼠桂鱼的英语作文

描写松鼠桂鱼的英语作文The squirrel scurries across the branch, its bushy tail swishing behind it. With nimble movements, it leaps from one tree to the next, its tiny paws gripping the bark with ease. Its large, expressive eyes dart around, constantly on the lookout for any potential threats or sources of food. The squirrel's fur is a rich, reddish-brown color, providing camouflage among the leaves and branches. As it pauses to nibble on an acorn, its small, sharp teeth make quick work of the hard shell, extracting the precious nut inside.Squirrels are fascinating creatures, known for their industrious nature and impressive agility. These small rodents are found in a variety of habitats, from dense forests to urban parks, and they play a vital role in the ecosystem. Their primary function is to gather and store food for the winter months, when resources are scarce. This behavior is often observed as they scurry about, gathering nuts, seeds, and other edible items and stashing them away in various hiding spots.One of the most remarkable aspects of squirrels is their ability to navigate their environment with incredible precision. They can leapfrom tree to tree, seemingly defying gravity, and their sharp claws allow them to climb with ease. Squirrels also have excellent problem-solving skills, often finding creative ways to access food sources that may be difficult to reach. For example, they have been known to use their tails as counterbalances when jumping or to use their nimble paws to manipulate objects in order to obtain a desired item.In addition to their physical prowess, squirrels are also highly social creatures. They often live in family groups and engage in various forms of communication, such as high-pitched chirps and tail-flicking. These social interactions serve a variety of purposes, including establishing dominance hierarchies, coordinating group activities, and warning others of potential dangers.Another fascinating aspect of squirrels is their adaptability. These animals have evolved to thrive in a wide range of environments, from the dense forests of the Pacific Northwest to the urban landscapes of major cities. They have even been known to adapt to the presence of humans, often becoming quite comfortable in residential areas and even learning to raid bird feeders or raid gardens for food.Despite their ubiquity and apparent abundance, squirrels face a number of threats in the modern world. Habitat loss due to deforestation and urban development is a significant concern, as it reduces the available resources and safe havens for these creatures.Additionally, squirrels are often targeted by predators, such as hawks, snakes, and even domestic cats and dogs. As a result, many conservation efforts have been put in place to protect and preserve squirrel populations.In contrast to the nimble and agile squirrel, the gourami fish is a more serene and tranquil creature. These freshwater fish are known for their distinctive, labyrinth-like breathing apparatus, which allows them to breathe atmospheric air and survive in low-oxygen environments. Gouramis are found in various regions of Asia, including Thailand, Indonesia, and India, and they are prized by aquarium enthusiasts for their vibrant colors and unique behaviors.One of the most striking features of the gourami fish is its appearance. These fish come in a variety of shapes and sizes, with some species, such as the dwarf gourami, being quite small, while others, like the giant gourami, can grow to impressive lengths. Regardless of their size, gouramis are often adorned with a stunning array of colors, ranging from deep blues and greens to vibrant reds and oranges. Their bodies are typically elongated and laterally compressed, with a distinctive dorsal fin that often extends along the length of their backs.Gouramis are not only visually captivating but also possess a fascinating behavioral repertoire. These fish are known for theirintricate courtship and breeding rituals, which often involve elaborate displays and vocalizations. Male gouramis, in particular, are known to engage in elaborate displays, such as flaring their fins, changing colors, and even building bubble nests to attract females. The females, in turn, will carefully inspect the males and their nests before deciding to lay their eggs.Once the eggs have been laid, the male gourami takes on the responsibility of guarding and caring for them. He will diligently tend to the nest, ensuring that the eggs are well-oxygenated and protected from predators. This parental care is a testament to the intelligence and social complexity of these fish, as they demonstrate a level of nurturing behavior that is often associated with more advanced vertebrates.In addition to their fascinating behaviors, gouramis are also known for their adaptability and resilience. These fish are able to thrive in a wide range of aquatic environments, from slow-moving streams and ponds to more heavily-planted aquarium setups. Their labyrinth organ allows them to survive in low-oxygen conditions, making them a popular choice for aquarium enthusiasts who may have difficulty maintaining high levels of dissolved oxygen in their tanks.Despite their popularity in the aquarium trade, gouramis also play an important role in their natural habitats. These fish serve as importantcomponents of freshwater ecosystems, acting as both predators and prey. They help to maintain the balance of aquatic communities by consuming smaller organisms and providing a food source for larger predators. Additionally, their presence in these environments is often used as an indicator of water quality, as they are sensitive to changes in their surrounding environment.In conclusion, both the squirrel and the gourami fish are captivating creatures that offer a glimpse into the remarkable diversity and complexity of the natural world. The squirrel's agility and industriousness, combined with the gourami's vibrant colors and intricate behaviors, demonstrate the incredible adaptations and survival strategies that have evolved in these species. As we continue to explore and appreciate the wonders of the natural world, it is essential that we also work to protect and preserve the habitats and ecosystems that support these incredible creatures.。

FRKDNet：基于知识蒸馏的特征提炼语义分割网络

第 38 卷第 11 期2023 年 11 月Vol.38 No.11Nov. 2023液晶与显示Chinese Journal of Liquid Crystals and DisplaysFRKDNet：基于知识蒸馏的特征提炼语义分割网络蒋诗怡1，徐杨1，2*，李丹杨1，范润泽1（1.贵州大学大数据与信息工程学院，贵州贵阳 550025；2.贵阳铝镁设计研究院有限公司，贵州贵阳 550009）摘要：传统的语义分割知识蒸馏方法仍然存在知识蒸馏不完全、特征信息传递不显著等问题，且教师网络传递的知识情况复杂，容易丢失特征的位置信息。

针对以上问题，本文提出了一种基于知识蒸馏的特征提炼语义分割模型FRKDNet。

首先根据前景特征与背景噪声的特点，设计了一种特征提炼方法来将蒸馏知识中的前景内容进行分离，过滤掉教师网络的伪知识后将更准确的特征内容传递给学生网络，从而提高特征的表现能力。

同时，在特征空间的隐式编码中提取类间距离与类内距离从而得到相应的特征坐标掩码，学生网络通过模拟特征位置信息来最小化与教师网络特征位置的差距，并分别和学生网络进行蒸馏损失计算，从而提高学生网络的分割精度，辅助学生网络更快地收敛。

最后在公开数据集Pascal VOC和Cityscapes上实现了优秀的分割性能，MIoU分别达到74.19%和76.53%，比原始学生网络分别提高了2.04%和4.48%。

本文方法相比于主流方法具有更好的分割性能和鲁棒性，为语义分割知识蒸馏提供了一种新方法。

关键词：语义分割；神经网络；知识蒸馏；特征提炼；深度学习中图分类号：TP391.41 文献标识码：A doi：10.37188/CJLCD.2023-0010FRKDNet： feature refine semantic segmentation network basedon knowledge distillationJIANG Shi-yi1，XU Yang1，2*，LI Dan-yang1，FAN Run-ze1（1.College of Big Data and Information Engineering， Guizhou University， Guiyang 550025， China；2.Guiyang Aluminum-magnesium Design and Research Institute Co.Ltd.， Guiyang 550009， China）Abstract： The traditional semantic segmentation knowledge distillation schemes still have problems such as incomplete distillation and insignificant feature information transmission which affect the performance of network， and the complex situation of knowledge transferred by teachers’ network which makes it easy to lose the location information of feature. To solve these problems， this paper presents feature refine semantic segmentation network based on knowledge distillation. Firstly， a feature extraction method is designed to separate the foreground content and background noise in the distilled knowledge， and the pseudo knowledge of the teacher network is filtered out to pass more accurate feature content to the student network， so as to improve the performance of the feature. At the same time， the inter-class distance and intra-class distance 文章编号：1007-2780（2023）11-1590-10收稿日期：2023-01-10；修订日期：2023-02-22.基金项目：贵州省科技计划（黔科合支撑［2021］一般176）Supported by Guizhou Science and Technology Planning （Guizhou Science and Technology CooperationSupport ［2021］ General 176）*通信联系人，E-mail：xuy@第 11 期蒋诗怡，等：FRKDNet：基于知识蒸馏的特征提炼语义分割网络are extracted in the implicit encoding of the feature space to obtain the corresponding feature coordinate mask. Then， the student network minimizes the output of the feature location with the teacher network by simulating the feature location information，and calculates the distillation loss with the student network respectively，so as to improve the segmentation accuracy of the student network and assist the student network to converge faster. Finally， excellent segmentation performance is achieved on the public datasets Pascal VOC and Cityscapes， and the MIoU reaches 74.19% and 76.53% respectively， which is 2.04% and 4.48% higher than that of the original student network. Compared with the mainstream methods， the method in this paper has better segmentation performance and robustness，and provides a new method for semantic segmentation knowledge distillation.Key words： semantic segmentation； neural network； knowledge distillation； feature refine； deep learning1 引言语义分割的目的是为输入图像的每个像素进行分类，也是计算机视觉中一项基础且富有挑战性的任务，目前已广泛应用到多个领域，如自动驾驶［1-2］、行人分割［3］、遥感监测［4-5］等。

METHOD FOR THE EXTRACTION OF A SUBSTANCE FROM A S

专利名称：METHOD FOR THE EXTRACTION OF ASUBSTANCE FROM A STARTING MATERIALAND EXTRACTION APPARATUS FORCARRYING OUT THE METHOD发明人：LANGELAAN, Hubertus, Cornelis,BARTELS, Paul, Vincent,HULLEMAN, Stephan, Henrick,Dick申请号：NL1998000662申请日：19981119公开号：WO99/026707P1公开日：19990603专利内容由知识产权出版社提供摘要：The invention relates to a counter-current extraction process with which the starting material to be extracted is conveyed in an extruder through zones of high and low pressure. The zones of high and low pressure can, for example, be formed by different screw elements of opposing pitch. Extractant is fed in in the high pressure zones which are located upstream of the screw elements of opposing pitch. The extractant then flows in counter-current to a discharge opening which is located in or close to the low pressure zone, downstream of a screw element of opposing pitch. With the extraction method according to the present invention a stable counter-current extraction process can be obtained over a very short extraction length and a high extraction yield can be achieved within a short time by intensive mixing of the starting material with the extractant. Furthermore, high pressures can be used in the extraction apparatus according to the present invention since the discharge openings are located close to thelow pressure zones. As a result the extraction yield is further increased and extraction fluids in supercritical state can be used.申请人：LANGELAAN, Hubertus, Cornelis,BARTELS, Paul, Vincent,HULLEMAN, Stephan, Henrick, Dick地址：NL,NL,NL,NL国籍：NL,NL,NL,NL代理机构：DE BRUIJN, Leendert, C.更多信息请下载全文后查看。

基于语义相关的视频关键帧提取算法

随着多媒体信息的发展，视频成为人们获取信息的重要途径，面对海量的视频，如何从视频中提取关键部分，提高人们看视频的效率已经成为人们所关注的问题。

视频摘要技术正是解决这一问题的关键，在视频摘要技术中的核心部分就是关键帧的提取。

关键帧的提取可以分为以下六类：（1）基于抽样的关键帧提取基于抽样的方法是通过随机抽取或在规定的时间间隔内随机抽取视频帧。

这种方法实现起来最为简单，但存在一定的弊端，在大多数情况下，用随机抽取的方式得到的关键帧都不能准确地代表视频的主要信息，有时还会抽到相似的关键帧，存在极大的冗余和信息缺失现象，导致视频提取效果不佳[1]。

（2）基于颜色特征的关键帧提取基于颜色特征的方法是将视频的首帧作为关键帧，将后面的帧依次和前面的帧进行颜色特征比较，如果发生了较大的变化，则认为该帧为关键帧，以此得到后续的一系列关键帧。

该方法针对相邻帧进行比较，不相邻帧之间无法进行比较，对于视频整体关键帧的提取造成一定的冗余。

（3）基于运动分析的关键帧提取比较普遍的运动分析算法是将视频片段中的运动信息根据光流分析计算出来，并提取关键帧。

如果视频中某个动作出现停顿，即提取为关键帧，针对不同结构的镜头，可视情况决定提取关键帧的数量。

但它的缺点也十分突出，由于需要计算运动量选择局部极小点，这基于语义相关的视频关键帧提取算法王俊玲，卢新明山东科技大学计算机科学与工程学院，山东青岛266500摘要：视频关键帧提取是视频摘要的重要组成部分，关键帧提取的质量直接影响人们对视频的认识。

传统的关键帧提取算法大多都是基于视觉相关的提取算法，即单纯提取底层信息计算其相似度，忽略语义相关性，容易引起误差，同时也造成了一定的冗余。

对此提出了一种基于语义的视频关键帧提取算法。

该算法首先使用层次聚类算法对视频关键帧进行初步提取；然后结合语义相关算法对初步提取的关键帧进行直方图对比，去掉冗余帧，确定视频的关键帧；最后与其他算法比较，所提算法提取的关键帧冗余度相对较小。

Extracting syntactic relations using heuristics

chunks. Part of speech tags are used in this identi cation. A noun phrase is de ned as a maximal sequence consisting of an optional determiner, any number of adjectives (possible none) and terminated by one or more nouns. The head noun is also marked, this is de ned as the rightmost noun in the sequence.
Chaptc Relations using Heuristics
Mark Stevenson
Abstract. In language processing it is not always necessary to fully parse a text in order to extract the syntactic information needed. We present a shallow parser which extracts a small number of grammatical links between words from unannotated text. The approach used operates by part of speech tagging the text and then applying a set of heuristics. We evaluated our parser by comparing its output against manually identi ed relations for the same sentences. These results were compared with those reported for a parser constructed by Carroll and Briscoe Carroll & Briscoe, 1996]. We found that although our results were lower than those reported our system still had a number of advantages: it is computationally far cheaper than full parsing and will process any English sentence.