Probabilistic learning approaches for indexing and retrieval with the TREC-2 collection
Face Recognition
Introduction
Identification
– When an unknown face is input, the system determines the identity through a one-to-many matching with all the known individuals in the database.
Let the a set of training face images be represented by a X x , , x N by M matrix: 1 M N: the number of pixels in images; M: image number
T C xi mxi m
Face Recognition
[name]
Outline
Introduction Difficulties for face recognition Methods
– Feature based face recognition – Appearance based face recognition – Elastic Bunch Graph Matching Face Database
bayesian personalized ranking
1
Introduction
Recommending content is an important task in many information systems. For example online shopping websites like Amazon give each customer personalized recommendations of products that the user might be interested in. Other examples are video portals like YouTube that recommend movies to customers. PerCopyright held by author/owner.
Abstract
Item recommendation is the task of predicting a personalized ranking on a set of items (e.g. websites, movies, products). In this paper, we investigate the most common scenario with implicit feedback (e.g. clicks, purchases). There are many methods for item recommendation from implicit feedback like matrix factorization (MF) or adaptive knearest-neighbor (kNN). Even though these methods are designed for the item prediction task of personalized ranking, none of them is directly optimized for ranking. In this paper we present a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem. We also provide a generic learning algorithm for optimizing models with respect to BPR-Opt. The learning method is based on stochastic gradient descent with bootstrap sampling. We show how to apply our method to two state-of-the-art recommender models: matrix factorization and adaptive kNN. Our experiments indicate that for the task of personalized ranking our optimization method outperforms the standard learning techniques for MF and kNN. The results show the importance of optimizing models for the right criterion.
A Feature-based Classification Technique for Answering Multi-choice World History Questions
2.2 Hash Map of Item Title
For quickly checking if a word or word group is a Wikipedia item, we put all Wikipedia item titles into a hash map. The title list dataset contains 32,877,103 titles of Wikipedia items in total, and we convert all characters of them to be lowercase. Word or word groups will also be converted to be characters in lowercase when they are checked, for realizing an exact matching. When we detect items contained in a sentence, we adopt a Maximum Matching Method. For example, for a sentence with N words, we first check if this whole sentence is a Wiki item, and then check all sub-sentences with N-1 continuous words, then subsentences with length of N-2, and so on. In particular, if a detected item consist of another detected item, the latter one will be removed and the longer one will be reserved.
Knowledge Retrieval (KR)
Knowledge Retrieval(KR)Yiyu Yao,1,2Yi Zeng,2Ning Zhong,2,3and Xiangji Huang41Department of Computer Science2International WIC Institute University of Regina Beijing University of Technology Regina,Saskatchewan,Canada S4S0A2Beijing,P.R.China100022yyao@cs.uregina.ca yzeng@3Department of Information Engineering4School of Information Technology Maebashi Institute of Technology York UniversityMaebashi-City,Japan371-0816Toronto,Ontario,Canada M3J1P3 zhong@maebashi-it.ac.jp jhuang@cs.yorku.caWhere is the Life we have lost in living?Where is the wisdom we have lost in knowledge?Where is the knowledge we have lost in information?—T.S.Eliot,The Rock,1934.AbstractWith the ever-increasing growth of data and informa-tion,finding the right knowledge becomes a real challenge and an urgent task.Traditional data and information re-trieval systems that support the current web are no longer adequate for knowledge seeking tasks.Knowledge retrieval systems will be the next generation of retrieval system serv-ing those purposes.Basic issues of knowledge retrieval sys-tems are examined and a conceptual framework of such sys-tems is proposed.Theories and Technologies such as the-ory of knowledge,machine learning and knowledge discov-ery,psychology,logic and inference,linguistics,etc.are briefly mentioned for the implementation of knowledge re-trieval systems.Two applications of knowledge retrieval in rough sets and biomedical domains are presented.1.IntroductionAlthough the quest for knowledge from data and infor-mation is an old problem,it is perhaps more relevant today than ever before.In the last few decades,we have seen an unprecedented growth rate of data and information.It is necessary to reconsider the question:“Where is the knowl-edge we have lost in information?”The data-information-knowledge-wisdom hierarchy is used in information sciences to describe different levels of abstraction in human centered information processing. Computer systems can be designed for the management of each of them.Data Retrieval Systems(DRS),such as database management systems,are well suitable for the storage and retrieval of structured rmation Re-trieval Systems(IRS),such as web search engines,are very effective infinding the relevant documents or web pages that contain the information required by a user.What lacks in those systems is the management at the knowledge level.A user must read and analyze the relevant documents in or-der to extract the useful knowledge.In this paper,we pro-pose that Knowledge Retrieval Systems(KRS)is the next generation retrieval systems for supporting knowledge dis-covery,organization,storage,and retrieval.Such systems will be used by advanced and expert users to tackle the chal-lenging problem of knowledge seeking.While the growth and evolution of the Web makes knowledge retrieval systems a necessity for supporting the future generations of the Web,the extensive results from machine learning,knowledge discovery,in particular,text mining,and knowledge based systems make the implemen-tation of such systems feasible.Many proposals and research efforts have been made re-garding knowledge retrieval[8,13,17,18,26,29,34].They cover various specific aspects and provide us insights into further development of knowledge retrieval.Those efforts suggest that it is the time to study knowledge retrieval on a grand scale.In this paper,we argue that knowledge retrieval systems are the natural next step in the evolution of retrieval systems. Specifically,we examine the characteristics and main fea-tures of data retrieval systems,information retrieval systemsand knowledge retrieval systems.A conceptual framework of knowledge retrieval system is outlined.The main com-ponents in this framework are the discovery of knowledge, the construction of knowledge structures,and the inference of required knowledge based on the knowledge structures. One the one hand,results from existing studies are drawn for the study of knowledge retrieval.On the other hand, knowledge retrieval is studied in its own right by focus-ing on its unique methodologies and theories.A success of knowledge retrieval will have a great impact on future retrieval systems and future generations of the Web.2.Data and Information Retrieval vs.Knowl-edge RetrievalKnowledge retrieval systems may be considered as the next generation in the evolution of retrieval systems.Their unique features and characteristics become clear by a com-parison with existing systems.2.1Generations of Retrieval SystemsIn information and management sciences,one considers the following hierarchy[23]:−Data,−Information,−Knowledge,−Wisdom.It concisely summarizes different types of resources that we can use for problem solving.The hierarchy represents in-creasing levels of complexity that require increasing levels of understanding[30].The generations of the retrieval sys-tems may be studied based on this hierarchy.For exam-ple,Yao suggests an evolution process of retrieval systems from data retrieval to knowledge retrieval,and from infor-mation retrieval to information retrieval support(a step to-wards knowledge retrieval)[29].LaBrie argues the needs for a change from data and information retrieval to knowl-edge retrieval,and considers the problem of retrieving and searching for knowledge objects in advanced knowledge management systems[22].In the data level,we use data retrieval systems tofind relevant data and acquire knowledge.The problem to be solved is well structured,and the concept definitions are clear.Data mining could help us get interesting knowledge from a large volume of data stored in the database.In the information level,we use information retrieval systems to acquire knowledge.The problem is semi-structured,and the concept definitions are not always clear. We retrieve the relevant information by keywords or their combinations and get the implicit knowledge from retrieved results.This is effective if the scale of the information col-lection is small.However keyword based matching and page ranking cannot satisfy users needs in the modern In-ternet age.On the one hand,retrieved results containing the keywords might be huge.Even through page ranking,the most relevant results can be of hundred pages,which will be very difficult for a user to explore one by one.On the other hand,results recommended by page rank might not satisfy various user needs.Some of the most relevant results to specific users might not be ranked in the front portion of the list.In practical situations,we need to perform more tasks in addition to simple search.Taking scientific literature search as an example,most tasks are not searching for specific ar-ticles.We would want tofind out which problems have been solved,which ones have no solutions,whichfields have more audiences and which topics are promising.It will not be easy to extract such knowledge from increas-ing volume of information by using current information re-trieval systems.This may stem from a discrepancy between knowledge representation methods in retrieval systems and human thoughts.The storage methods of information in the Web,litera-ture databases,digital libraries are documents,information flows,etc.They are not closely related to the ways that hu-man organize knowledge.People need tofind,learn,and reorganize retrieved results to extract and construct knowl-edge embedded in information.Those problems cannot be solved satisfactorily in the in-formation level.The task offinding knowledge from in-formation and organizing it into the structures that human can use are the focus of knowledge retrieval systems.The process of retrieving information is changed to retrieving knowledge directly,and the retrieved results is changed from relevant documents to knowledge.Some of the hard problems in information retrieval mayfind their solutions in knowledge retrieval.2.2A Comparison of Retrieval SystemsKnowledge retrieval(KR)focuses on the knowledge level.We need to examine how to extract,represent,and use the knowledge in data and information[2].Knowledge retrieval systems provide knowledge to users in a structured way.They are different from data retrieval systems and in-formation retrieval systems in inference models,retrieval methods,result organization,etc.Table1,extending van Rijsbergen’s comparison of the difference between data re-trieval and information retrieval[27],summarizes the main characteristics of data retrieval,information retrieval,and knowledge retrieval[33].The core of data retrieval and information retrieval are retrieval subsystems.Data retrieval gets results through Boolean match[1].Information retrieval uses partial match and best match.Knowledge retrieval is also based on partialData Retrieval Information Retrieval Knowledge RetrievalMatch Boolean match partial match,best match partial match,best matchInference deductive inference inductive inference deductive inference,inductive inference,associative reasoning,analogical reasoningModel deterministic model statistical and probabilistic model semantic model+inference model Query artificial language natural language knowledge structure+natural languageOrganization table,index table,index knowledge unit andknowledge structure Representation number,rule natural language concept graph,predicate logic,markup language production rule,frame,semantic network,ontologyStorage database document collections knowledge baseRetrieved Results data set sections or documents a set of knowledge unit Table1.A Comparison of Data Retrieval,Information Retrieval,and Knowledge Retrievalmatch and best match.Considering inference perspective,data retrieval uses de-ductive inference,and information retrieval uses inductive inference[27].Considering the limitations from the as-sumptions of different logics,traditional logic systems(e.g., Horn subset offirst order logic)cannot make efficient rea-soning in a reasonable time[7].Associative reasoning,ana-logical reasoning and the idea of unifying reasoning and search may be effective methods of reasoning at the web scale[4,7].From retrieval model perspective,knowledge retrieval systems focus on the semantics and knowledge organi-zation.Data retrieval and information retrieval organize the data and documents by indexing,while knowledge re-trieval organizes knowledge by connections among knowl-edge units and the knowledge structures.3.A Conceptual Framework of KR Systems 3.1Challenges to Traditional KR SystemsThe term“knowledge retrieval”is not new and has been used by many authors.Some authors considered knowledge retrieval as a process of information retrieval[34].On the other hand,there are authors who investigated this topic in its own right.Frisch discussed knowledge retrieval based on a knowledge base(KB),and considered the entire re-trieval process as a form of inference[8].Oertel and Amir presented an approach to retrieve commonsense knowledge for autonomous decision making[18].Martin and Eklund examined different metadata languages for knowledge rep-resentation(e.g.,RDF,OML)and proposed to use general and intuitive knowledge representation languages,rather than XML-based languages to represent knowledge.They proposed methods which satisfy users requirement at dif-ferent levels of details.Kame and Quintana proposed a concept graph based knowledge retrieval system[13].In their system,sentences are converted into concept graphs. Chen and Hsiang presented a logical framework of knowl-edge retrieval by considering fuzziness in inference[5].To some extent,the framework is restricted to information re-trieval and question answering systems.Models and meth-ods for text based knowledge retrieval have also been inves-tigated[17,26,34].In the web age,the traditional understanding of knowl-edge retrieval faces new challenges.New methodologies and techniques need to be explored.(1)Traditional knowledge representation methods are in-vestigated in a small scale environment.How to represent knowledge in a large scale needs to be explored.Visualized structured knowledge may be one of the possible strategy.(2)Traditional knowledge retrieval concentrates on knowledge storage,which is unfortunately not from a hu-man oriented perspective:how to provide and use knowl-edge in more convenient ways.(3)Inductive reasoning and deductive reasoning are ap-plicable in data retrieval and small-scale information re-trieval.Finding knowledge in the web context needs more effective reasoning methodologies.(4)Knowledge based systems usually provide knowl-edge in the form of text.A user needs to extract different views.In knowledge retrieval,knowledge should be exam-ined from different perspectives.(5)Traditional knowledge based systems assume that the stored contents are all trustworthy,but in the web environ-ment,this assumption is not always valid,as many knowl-edge are not reliable.Knowledge validation in the complex environment is a challenge.(6)To some extend,knowledge stored in many systems are static,but in the web environment,knowledge is dynam-InputOutputķUser need is satisfiedĸViews of knowledge structure need to be changed ĹQuery needs to reformulatedFigure1.A Typical KR Systemically changing all the time.How to update knowledge in this environment is also a challenge.3.2A Typical KR SystemKnowledge represented in a structured way is consistent with human thoughts and is easily understandable[20,26]. Sometimes,users do not know exactly what they want or are lack of contextual awareness[28].If knowledge can be provided visually in a structured way,it will be very useful for users to explore and refine the query[22].Figure1shows a conceptual framework of a typical knowledge retrieval system.The main process can be described as follows:(1)Knowledge Discovery:Discovering knowledge from sources by data mining,machine learning,knowledge acquisition and other methods.(2)Query Formulation:Formulating queries from user needs by user inputs.The inputs can be in natural languages and artificial languages.(3)Knowledge Selection:Selecting the range of possible related knowledge based on user query and knowledge discovered from data/information sources.(4)Knowledge Structure Construction:Reasoning according to different views of knowledge,domain knowl-edge,user background,etc.in order to form knowledge structures.Domain knowledge can be provided by expert er background and preference can be provided by user logs.(5)Exploration and Search:Exploring the knowledge structure to get general awareness and refine the search. Through understanding the relevant knowledge structures, users can search into details on what they are interested in to get the required knowledge.(6)Knowledge Structure Reorganization:Reorganizing knowledge structures if users need to explore other views of selected knowledge.(7)Query Reformulation:Reformulating the query if the constructed structures cannot satisfy user needs.One of the key features of knowledge retrieval systems is that knowledge are visualized in a structured way so that users could get contextual awareness of related knowl-edge and make further retrieval.Main operations for explo-ration[12,28]are browsing,zooming-in,and zooming-out. Browsing helps users to navigate.Zoom-out provides gen-eral understanding,while zoom-in presents detailed knowl-edge and its structure.We need to point out that the full process contains two levels of feedbacks.One at a local level dealing with knowl-edge structure reorganization,and the other at a global level dealing with query ers’browsing and exploration history will be stored in user logs as back-ground information for improving the personalized knowl-edge structures.Knowledge retrieval is a typical example of human-centered computing[11],and its evaluation is more related to personal judges,which makes a balance between computer automation and user intervention.3.3Knowledge StructuresThe definition,representation,generation,exploration and retrieval of knowledge structures are the main issues in knowledge retrieval.Concept is the basic unit of human thoughts.We can build knowledge based on concepts and the relations amongthem[32].Knowledge structure is built based on hierarchi-cal structures of concepts.In the context of granular com-puting,a knowledge unit can be considered as a granule. Drawing results from granular structure,knowledge struc-tures can be examined at least at three levels[31]:−internal structures of knowledge units,−collective structures of a family of knowledge units,−hierarchical structures of a web of knowledge units.A unit of knowledge may be decomposed into a family of smaller units.Their decomposition and relationship repre-sent the internal structures[15].The collective structures of a family of knowledge units describe the relations of knowl-edge units in the same level.Different levels of knowledge units form a partial ordering.The hierarchical structures describe the integrated whole of a web of knowledge units from a very high level of abstraction to the veryfinest de-tails.Tree structure and concept graph are most commonly used methods for visualizing knowledge structures[14,16, 26].Semantic network is used for knowledge representa-tion[20,24].Formal concept analysis can be used to gen-erate concept graph[9].Knowledge unit and knowledge association are the two elements of knowledge[34].Knowledge unit is consid-ered to be the basic unit of knowledge,and the complex knowledge structures are based on knowledge associations. Knowledge is organized in hierarchies.Semantic tree is one of the mainly used methods for knowledge representation[20]and the visualized tree struc-tures are convenient for understanding[20,22].A semantic tree represents a hierarchical structure[20].Knowledge can be represented as many semantic trees according to differ-ent views and understandings[6,14,20].The knowledge structures discovered and constructed by a knowledge re-trieval system should be in multilevels and multiviews.The discovery of hierarchical structures is tofind simi-lar or different features of knowledge and make partitions or coverings for it.Hierarchical clustering is a practical method for knowledge structure construction[25].The sources of knowledge may have effects on thefinal knowledge structures.Selecting reliable knowledge from the web environment is one of the key issue for knowledge structure generation.For example,in literature exploration, top journals and conference proceedings may be more im-portant and valuable than some web pages.We should not generate knowledge structures just from contents stored infiles or documents.Various views should be explored.Considering literature on the web,at least con-tent view,structure view and usage view should be explored to generate the multiviews of knowledge structures[32].When users know exactly what they want,a knowledge retrieval system should provide what they need in a more direct way.The retrieval results should be hierarchical.Coarser results provide general knowledge,whilefiner re-sults provide detailed ers can interact with the system to decide which level of results they want.4.Theories and Technologies Supporting KRIt is generally believed that new ideas may be repackag-ing or reinterpretation of old ones[10].As a new research field,knowledge retrieval can draw results from the follow-ing related theories and technologies:-Theory of Knowledge:knowledge acquisition,knowl-edge organization,knowledge representation,knowl-edge validation,knowledge management.-Machine Learning and Knowledge Discovery:prepro-cessing,classification,clustering,prediction,postpro-cessing,statistical learning theory.-Psychology:cognitive psychology,cognitive informat-ics,concept formation and learning,decision making, human-computer interaction.-Logic and Inference:propositional logic,predicate logic,attribute logic,universal logic,inductive infer-ence,deductive inference,associative reasoning,ana-logical reasoning,approximate reasoning.-Information Technology:information theory,informa-tion science,information retrieval,database systems, knowledge-based systems,rule-based systems,expert systems,decision support systems,intelligent agent technology.-Linguistics:computational linguistics,natural lan-guage understanding,natural language processing. Topics listed under each entry serve as examples and do not form a complete list.5.Applications of KRWhen scientists explore the literature,they search for the scientific facts and research results.The goal of a lit-erature exploration support system is to provide those in a knowledgable way[32].Knowledge structures provide a high level understanding of scientific literature and hints re-garding what has been done and what needs to be done.As a demonstration,we examined knowledge structures obtained from an analysis of papers of two rough sets re-lated conferences[32].Different views provide various unique understanding of the literature.By drawing results from web mining,one examines literature and provides rel-evant knowledge in multiple views and at multiple levels,based on the contents,structures,and usages of the liter-ature.The main knowledge structure is constructed based on proceedings indexes,document structures and domain knowledge.The knowledge structures not only provide a relation diagram of specified discipline,but also help re-searchers tofind the contribution of each study and possible future research topics in rough sets[32].Considering biomedical literature explorations,the rela-tionship and connections of various genes and proteins re-lated publications can be found based on the structure im-plicit in the genes and proteins.Current biomedical research is characterized by immense volume of data,accompanied by a tremendous increase in the number of gene and pro-tein related publications.However,many interesting links that connect facts,assertions or hypotheses may be missed because these publications are generated by many authors working independently and the functions of many genes and proteins are separately described in the literature.In order to build knowledge structures for biomedical knowledge retrieval,a large database of abstracts,a subset of PubMed database1[19],will be used as our information search space.For example,each gene is mapped to a docu-ment roughly discussing the gene’s biological function.The literature database is then searched for documents similar to the gene’s document.The resulting set of documents typ-ically discusses the gene’s function.Since each set corre-sponds to a gene,the similar document sets can be mapped back to their originating genes in order to establish func-tional relationships among these genes.The main knowl-edge structure is constructed based on:first,functional re-lationships among genes and proteins,on a genome-wide scale;second,the literature specifically relevant to the func-tion of these genes and proteins;third,a short-term list char-acterizing the document set,which suggests why the genes and proteins are considered relevant to each other,and what their biological functions are.It is clear that knowledge retrieval systems can greatly enhance our understanding of genetic processes for biomed-ical research.The associative organization is more closer to human intuition than conventional keyword match[21]. The visualized structures can provide medical doctors and researchers new ideas on medicine use and production.6.ConclusionThe main contribution for this paper is to suggest knowl-edge retrieval as a new researchfield.A comparative study of different levels of retrieval systems is given based on the data-information-knowledge-wisdom hierarchy.The results suggest that different retrieval systems focus on different levels of retrieval problems.A framework of knowledge 1It contains over17,000,000scientific abstracts retrieval system has been proposed.A list of theories and technologies related to KR has been discussed.The appli-cations of KR may show great impact on the future devel-opment of retrieval systems,digital libraries,and the Web.AcknowledgmentsThis study was supported in part by research grants from the Natural Sciences and Engineering Research Council (NSERC)of Canada.The paper was prepared when Yi Zeng was visiting the University of Regina withfinancial support from an NSERC discovery grant awarded to Yiyu Yao.The authors would like to thank anonymous reviewers for their constructive suggestions.References[1]Baeza-Yates,R.and Ribeiro-Neto,B.Modern Infor-mation Retrieval,Addison Wesley,1999.[2]Bellinger,G.,Castro,D.and Mills,A.Data,Informa-tion,Knowledge,and Wisdom,/dikw/dikw.htm(Accessed on August, 16th,2007).[3]Benjamins,V.R.and Fensel,munity is knowl-edge!in(KA)2,Proceedings of the1998Knowledge Acquisition Workshop,1998.[4]Berners-Lee,T.,Hall,W.,Hendler,J.A.,O’Hara,K.,Shadbolt,N.and Weitzner,D.J.A Framework for Web science,Foundations and Trends in Web Science, 2006,1(1):1-130.[5]Chen, B.C.and Hsiang,J.A logic framework ofknowledge retrieval with fuzziness,Proceedings of the2004IEEE/WIC/ACM International Conference on Web Intelligence,2004:524-528.[6]Collins,A.M.and Quillian,M.R.Retrieval time fromsemantic memory,Journal of Verbal Learning and Verbal Behavior,1969,8:240-248.[7]Fensel,D.and van Harmelen,F.Unifying reasoningand search to web scale,IEEE Internet Computing, 2007,11(2):96,94-95.[8]Frisch,A.M.Knowledge Retrieval as Specialized In-ference,Ph.D thesis,University of Rochester,1986.[9]Ganter,B.and Wille,R.Formal Concept Analysis:Mathematical Foundations,Springer-Verlag,1999. [10]Hawkins,J.and Blakeslee,S.On Intelligence,HenryHolt and Company,2004.[11]Jaimes,A.,Gatica-Perez,D.,Sebe,N.and Huang,T.S.Human-centered computing:toward a human revolu-tion,IEEE Computer,2007,40(5):30-34.[12]Jain,R.Unified access to universal knowledge:nextgeneration search experience,white paper,2004. [13]Kame,M.and Quintana,Y.A graph based knowledgeretrieval system,Proceedings of the1990IEEE Inter-national Conference on Systems,Man and Cybernet-ics,1990:269-275.[14]Keil,F.C.Concepts,Kinds,and Cognitive Develop-ment,MIT Press,1989.[15]Langseth,H.,Aamodt,A.and Winnem,O.M.Learn-ing retrieval knowledge from data,Proceedings of the1999International Joint Conference on Artificial Intelligence Workshop ML-5:Automating the Con-struction of Case-Based Reasoners,1999:77-82. [16]Martin,P.and Alpay,L.Conceptual structures andstructured documents,Proceedings of the4th Inter-national Conference on Conceptual Structures,LNAI 1115,Springer,1996:145-159.[17]Martin,P.and Eklund,P.W.Knowledge retrieval andthe World Wide Web,IEEE Intelligent Systems,2000, 15(3):18-25.[18]Oertel,P.and Amir, E.A framework for com-monsence knowledge retrieval,Proceedings of the7th International Symposium on Logic Formalizations of Commonsense Reasoning,2005.[19]PubMed database:[20]Quillian,M.R.Semantic Memory,Semantic Infor-mation Processing,Minsky,M.L(Eds.),MIT Press, 1968:216-270.[21]Robert,H.and Alfonso,V.Implementing the iHOPconcept for navigation of biomedical literature,Bioin-formatics,2005,21:252-258.[22]LaBrie,R.C.The Impact of Alternative Search Mech-anisms on the Effectiveness of Knowledge Retrieval, Ph.D thesis,Arizona State University,2004.[23]Sharma,N.The Origin of the“Data Informa-tion Knowledge Wisdom”Hierarchy,/˜n sharma/dikw origin.htm(Ac-cessed on August,16th,2007).[24]Sowa,J.F.Knowledge Representation:Logical,Philosophical,and Computational Foundations, Brooks/Cole,2000.[25]Tenenbaum,J.Knowledge Representation:Spaces,Trees,Features,Course Lecture Notes on Computa-tional Cognitive Science,MIT Open Courseware,Fall 2004.[26]Travers,M.A visual representation for knowledgestructures,Proceedings of the2nd annual ACM con-ference on Hypertext and Hypermedia,1989:147-158.[27]van Rijsbergen,rmation Retrieval,Butter-worths,1979.[28]White,R.W.,Kules,B.,Drucker,S.M.and Schraefel,M.C.Supporting exploratory search,Communications of the ACM,2006,49(4):37-39.[29]Yao,rmation retrieval support systems,Pro-ceedings of the2002IEEE International Conference on Fuzzy Systems,2002,1092-1097.[30]Yao,Y.Y.Web Intelligence:new frontiers of explo-ration,Proceedings of the2005International Confer-ence on Active Media Technology,2005:3-8. 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latex编译技巧
Optimization Methods and SoftwareVol.00,No.00,January2009,1–18GUIDEOptimization Methods and Software—L A T E X2εstyle guide for authors(Style2+References Style S)Taylor&Francis a∗and I.T.Consultant ba4Park Square,Milton Park,Abingdon,OX144RN,UK;b Institut f¨u r Informatik, Albert-Ludwigs-Universit¨a t,D-79110Freiburg,Germany(v4.4released October2008)This guide is for authors who are preparing papers for the Taylor&Francis journal Optimiza-tion Methods and Software(gOMS)using the L A T E X2εdocument preparation system and the Classfile gOMS2e.cls,which is available via the journal homepage on the Taylor&Francis website(see Section8).Authors planning to submit their papers in L A T E X2εare advised to use gOMS2e.cls as early as possible in the creation of theirfiles.Keywords:submission instructions;sourcefile coding;environments;references citation;fonts;numbering(Authors:Please provide three to six keywords taken from terms used in your manuscript)AMS Subject Classification:F1.1;F4.3(...for example;authors are encouragedto provide two to six AMS Subject Classification codes)Index to information contained in this guide1.Introduction1.1.The gOMS document style1.2.Submission of L A T E X2εarticlesto the journaling the gOMS Classfilendscape pages3.Additional features3.1.Footnotes to article titlesand authors’names3.2.Abstracts3.3.Lists4.Some guidelines for usingstandard features4.1.Sections4.2.Illustrations(figures)4.3.Tables4.4.Running headlines4.5.Maths environments4.6.Typesetting mathematics4.6.1.Displayed mathematics4.6.2.Bold math italic symbols4.6.3.Bold Greek4.6.4.Upright lowercase Greek characters4.7.Acknowledgements4.8.Notes4.9.Appendices4.10.References4.10.1.References cited in thetext4.10.2.The list of references4.11.gOMS macros5.Example of a section heading with small caps,lowercase,italic,and bold Greek such asκ6.gOMS journal style6.1.Punctuation6.2.Spelling6.3.Hyphens,n-rules,m-rules andminus signs6.4.References6.5.Maths fonts7.Troubleshooting7.1.Fixes for coding problems8.Obtaining the gOMS2e Classfile8.1Via the Taylor&Francis website8.2Via e-mailPlease note that the index following the abstract in this guide is provided for information only.An index is not required in submitted papers.∗Corresponding author.Email:latex.helpdesk@ISSN:1055-6788print/ISSN1029-4937onlinec 2009Taylor&FrancisDOI:10.1080/1055678xxxxxxxxxxxxx2Taylor&Francis and I.T.Consultant1.IntroductionAuthors are encouraged to submit manuscripts to Optimization Methods and Soft-ware(gOMS)electronically.Electronic submissions should be sent as e-mail at-tachments using a standard word processing program,such as Microsoft Word or PDF created from L A T E X2εsourcefiles(see Section1.2).gOMS does not accept Microsoft Word2007documents.Please use Word’s‘Save As’option to save your document as an older(.doc)file type.If e-mail submission is not possible,please send an electronic version on disc.The layout design for gOMS has been implemented as a L A T E X2εClassfile.The gOMS Classfile is based on mands that differ from the standard L A T E X2εinterface,or which are provided in addition to the standard interface,are explained in this guide.This guide is not a substitute for the L A T E X2εmanual itself.This guide can be used as a template for composing an article for submission by cutting,pasting,inserting and deleting text as appropriate,using the LaTeX environments provided(e.g.\begin{equation},\begin{corollary}).1.1.The gOMS document styleThe use of L A T E X2εdocument styles allows a simple change of style(or style option) to transform the appearance of your document.The gOMS2e Classfile preserves the standard L A T E X2εinterface such that any document that can be produced using the standard L A T E X2εarticle style can also be produced with the gOMS style. However,the measure(or width of text)is narrower than the default for article, therefore line breaks will change and long equations may need re-formatting. When your article appears in the print edition of the gOMS journal,it is typeset in Monotype Times.As most authors do not own this font,it is likely that the page make-up will change with the change of font.For this reason,we ask you to ignore details such as slightly long lines,page stretching,orfigures falling out of synchronization with their citations in the text,because these details will be dealt with at a later stage.1.2.Submission of L A T E X2εarticles to the journalSubmissions to be considered for publication in Optimization Methods and Soft-ware should be sent to the appropriate Editor at one of the following addresses: O.Burdakov(Co-Editor),Division of Optimization,Department of Mathemat-ics,Link¨o ping University,58183Link¨o ping,Sweden(e-mail:burdakov@mai.liu.se);A.Griewank(Co-Editor),Humboldt University Berlin,Mat Nat Faculty II, Department of Mathematics,Unter den Linden6,10099Berlin,Germany(e-mail:griewank@mathematik.hu-berlin.de);T.Tsuchiya(Regional Editor,Asia), The Institute of Statistical Mathematics,4-6-7Minami-Azabu,Minato-ku,Tokyo 106-8569,Japan(e-mail:tsuchiya@sun312.ism.ac.jp);S.Ulbrich(Regional Edi-tor,Europe),Technische Universit¨a t Darmstadt,Fachbereich Mathematik,AG10, Schloßgartenstr.7,64289Darmstadt,Germany(e-mail:ulbrich@mathematik.tu-darmstadt.de);F.A.Potra(Regional Editor,Americas),Department of Mathe-matics and Statistics,University of Maryland,Baltimore,MD21250,USA(e-mail: potra@);or M.Anitescu(Software Editor),Mathematics and Com-puter Science,Argonne National Laboratory,9700South Cass Avenue,Argonne, IL60439-4844,USA(e-mail:anitescu@).Optimization Methods and Software3 Authors are encouraged to submit manuscripts electronically.If e-mail submis-sion is not possible,please send an electronic version on disc.General Instructions for Authors may be found at (/journals/authors/gomsauth.asp).Only‘open-source’L A T E X2εshould be used,not proprietary systems such as TCI LaTeX or Scientific WorkPlace.Similarly,Classfiles such as REVTex4that produce a document in the style of a different publisher and journal should not be used for preference.Appropriate gaps should be left forfigures,for which original electronicfiles should be supplied.Authors should ensure that theirfigures are suitable(in terms of lettering size,etc.)for the reductions they intend.Authors who wish to incorporate Encapsulated PostScript artwork directly in their articles can do so by using Tomas Rokicki’s EPSF macros(which are supplied with the DVIPS PostScript driver).See Section2.1,which also demonstrates how to treat landscape pages.Please remember to supply any additionalfigure macros you use with your article in the preamble before begin{document}.Authors should not attempt to use implementation-specific\special’s directly.Ensure that any author-defined macros are gathered together in the sourcefile, just before the\begin{document}command.Please note that,if serious problems are encountered with the coding of a paper (missing author-defined macros,for example),it may prove necessary to divert the paper to conventional typesetting,i.e.it will be re-keyed.ing the gOMS ClassfileIf thefile gOMS2e.cls is not already in the appropriate system directory for L A T E X2εfiles,either arrange for it to be put there,or copy it to your working folder.The gOMS document style is implemented as a complete document style, not a document style option.In order to use the gOMS style,replace‘article’by ‘gOMS2e’in the\documentclass command at the beginning of your document: \documentclass{article}is replaced by\documentclass{gOMS2e}In general,the following standard document style options should not be used with the gOMS style:(1)10pt,11pt,12pt—unavailable;(2)oneside(no associated stylefile)—oneside is the default;(3)leqno and titlepage—should not be used;(4)singlecolumn—is not necessary as it is the default style.ndscape pagesIf a table or illustration is too wide tofit the standard measure,it must be turned,with its caption,through90◦ndscape illustra-tions and/or tables can be produced directly using the gOMS2e stylefile us-ing\usepackage{rotating}after\documentclass{gOMS2e}.The following com-mands can be used to produce such pages.\setcounter{figure}{2}\begin{sidewaysfigure}4Taylor&Francis and I.T.Consultant\centerline{\epsfbox{fig1.eps}}\caption{This is an example of figure caption.}\label{landfig}\end{sidewaysfigure}\setcounter{table}{0}\begin{sidewaystable}\tbl{The Largest Optical Telescopes.}\begin{tabular}{@{}llllcll}...\end{tabular}\label{tab1}\end{sidewaystable}Before anyfloat environment,use the\setcounter command as above tofix the numbering of the caption.Subsequent captions will then be automatically renum-bered accordingly.3.Additional featuresIn addition to all the standard L A T E X2εdesign elements,gOMS style includes a separate command for specifying short versions of the authors’names and the journal title for running headlines on the left-hand(verso)and right-hand(recto) pages,respectively(see Section4.4).In general,once you have used this additional gOMS2e.cls feature in your document,do not process it with a standard L A T E X2εstylefile.3.1.Footnotes to article titles and authors’namesOn the title page,the\thanks command may be used to produce a footnote to either the title or authors’names.Footnote symbols should be used in the order:†(coded as\dagger),‡(\ddagger),§(\S),¶(\P), (\|),††(\dagger\dagger),‡‡(\ddagger\ddagger),§§(\S\S),¶¶(\P\P), (\|\|).Note that footnotes to the text will automatically be assigned the superscript symbols1,2,3,...by the Classfile,beginning afresh on each page.1The title,author(s)and affiliation(s)should be followed by the\maketitle com-mand.3.2.AbstractsAt the beginning of your article,the title should be generated in the usual way using the\maketitle command.Immediately following the title you should include an abstract.The abstract should be enclosed within an abstract environment.For example,the titles for this guide were produced by the following source code:\title{Optimization Methods and Software:\LaTeXe\style%1These symbols will be changed to the style of the journal by the typesetter during preparation of your proofs.Optimization Methods and Software5guide for authors}\author{Taylor\&Francis Limited,4Park Square,Milton Park,Abingdon,OX144RN,UK}\received{v4.4released October2008} \maketitle\begin{abstract}This guide is for authors who are preparing papers for the Taylor\&% Francis journal{\em Optimization Methods and Software}%({\it gOMS}\,)using the\LaTeXe\document preparation system and%the Class file{\tt gOMS2e.cls},which is available via the journal% homepage on the Taylor\&Francis website(see Section~\ref{FTP}).% Authors planning to submit their papers in\LaTeXe\are advised to%use{\tt gOMS2e.cls}as early as possible in the creation of their%files.\end{abstract}(Please note that the percentage signs at the ends of lines that quote source code in this document are not part of the coding but have been inserted to achieve line wrapping at the appropriate points.)3.3.ListsThe gOMS style provides numbered and unnumbered lists using the enumerate environment and bulleted lists using the itemize environment.The enumerated list numbers each list item with arabic numerals:(1)first item(2)second item(3)third itemAlternative numbering styles can be achieved by inserting a redefinition of the number labelling command after the\begin{enumerate}.For example,the list(i)first item(ii)second item(iii)third itemwas produced by:\begin{enumerate}\item[(i)]first item\item[(ii)]second item\item[(iii)]third item\end{enumerate}Unnumbered lists are also provided using the enumerate environment.For example, First unnumbered indented item without label.Second unnumbered item.Third unnumbered item.was produced by:\begin{enumerate}\item[]First unnumbered indented item...6Taylor&Francis and I.T.Consultant\item[]Second unnumbered item.\item[]Third unnumbered item.\end{enumerate}Bulleted lists are provided using the itemize environment.For example,•First bulleted item•Second bulleted item•Third bulleted itemwas produced by:\begin{itemize}\item First bulleted item\item Second bulleted item\item Third bulleted item\end{itemize}4.Some guidelines for using standard featuresThe following notes may help you achieve the best effects with the gOMS2e Class file.4.1.SectionsL A T E X2εprovidesfive levels of section headings and they are all defined in the gOMS2e Classfile:(1)\section(2)\subsection(3)\subsubsection(4)\paragraph(5)\subparagraphNumbering is automatically generated for section,subsection,subsubsection and paragraph headings.If you need additional text styles in the headings,see the examples in Section5.4.2.Illustrations(figures)The gOMS style will cope with most positioning of your illustrations and you should not normally use the optional positional qualifiers of the figure environ-ment,which would override these decisions.See‘Instructions for Authors’in the journal’s homepage on the Taylor&Francis website for how to submit artwork (/journals/authors/gomsauth.asp).Figure captions should be below thefigure itself,therefore the\caption command should appear after thefigure.For example,Figure1with caption and sub-captions is produced using the following commands:\begin{figure}\begin{center}\subfigure[]{\resizebox*{5cm}{!}{\includegraphics{senu_gr1.eps}}}%\subfigure[]{\resizebox*{5cm}{!}{\includegraphics{senu_gr2.eps}}}%Optimization Methods and Software7(a)(b)Figure1.Example of a two-partfigure with individual sub-captions showingthat all lines offigure captions range left.Table1.Radio-band beaming model parameters for FSRQsand BL Lacs.Class aγ1γ2b γ G fθcBL Lacs5367−4.01.0×10−210◦FSRQs54011−2.30.5×10−214◦a This is not as accurate,owing to numerical error.b An example table footnote to show the text turning overwhen a long footnote is inserted.\caption{\label{fig2}Example of a two-part figure with individual% sub-captions showing that all lines of figure captions range left.}% \label{sample-figure}\end{center}\end{figure}The control sequences\epsfig{},\subfigure{}and\includegraphics{}re-quire epsfig.sty,subfigure.sty and graphicx.sty.These are called by the Classfile gOMS2e.cls and are included with the LaTeX package for this journal for conve-nience.To ensure thatfigures are correctly numbered automatically,the\label{}com-mand should be inserted just after\caption{}4.3.TablesThe gOMS style will cope with most positioning of your tables and you should not normally use the optional positional qualifiers of the table environment,which would override these decisions.The table caption appears above the body of the table in gOMS style,therefore the\tbl command should appear before the body of the table.The tabular environment can be used to produce tables with single thick and thin horizontal rules,which are allowed,if desired.Thick rules should be used at the head and foot only and thin rules elsewhere.Commands to redefine quantities such as\arraystretch should be omitted. For example,table1is produced using the following commands.Note that\rm will produce a roman character in math mode.There are also\bf and\it,which produce bold face and text italic in math mode.\begin{table}\tbl{Radio-band beaming model parameters8Taylor&Francis and I.T.Consultantfor{FSRQs and BL Lacs.}}{\begin{tabular}{@{}lcccccc}\topruleClass$^{\rm a}$&$\gamma_1$&$\gamma_2$$^{\rm b}$&$\langle\gamma\rangle$&$G$&$f$&$\theta_{c}$\\\colruleBL Lacs&5&36&7&$-4.0$&$1.0\times10^{-2}$&10$^\circ$\\FSRQs&5&40&11&$-2.3$&$0.5\times10^{-2}$&14$^\circ$\\\botrule\end{tabular}}\tabnote{$^{\rm a}$This is not as accurate,owing tonumerical error.}\tabnote{$^{\rm b}$An example table footnote to show thetext turning over when a long footnote is inserted.}%\label{symbols}\end{table}To ensure that tables are correctly numbered automatically,the\label{}com-mand should be inserted just before\end{table}.4.4.Running headlinesIn gOMS style,the authors’names and the title of the journal are used throughout the article as running headlines at the top of alternate pages. An abbreviated list of authors’names in italic format appears on even-numbered pages(versos)—e.g.‘J.Smith and P.Jones’,or‘J.Smith et al.’for three or more authors—and the journal title in italic format is used on odd-numbered pages(rectos).To achieve this,the\markboth command is used.The running headlines for this guide were produced using the following code:\markboth{Taylor\&Francis and I.T.Consultant}{Optimization Methods and Software}.The\pagestyle and\thispagestyle commands should not be used.4.5.Maths environmentsThe gOMS style provides for the following maths environments.Lemma4.1More recent algorithms for solving the semidefinite programming relax-ation are particularly efficient,because they explore the structure of the MAX-CUT.Theorem4.2More recent algorithms for solving the semidefinite programming relaxation are particularly efficient,because they explore the structure of the MAX-CUT.Corollary4.3More recent algorithms for solving the semidefinite programming relaxation are particularly efficient,because they explore the structure of the MAX-CUT.Proposition4.4More recent algorithms for solving the semidefinite programming relaxation are particularly efficient,because they explore the structure of the MAX-CUT.Optimization Methods and Software9 Proof More recent algorithms for solving the semidefinite programming relaxation are particularly efficient,because they explore the structure of the MAX-CUT.Remark1More recent algorithms for solving the semidefinite programming relax-ation are particularly efficient,because they explore the structure of the MAX-CUT problem.Algorithm1More recent algorithms for solving the semidefinite programming re-laxation are particularly efficient,because they explore the structure of the MAX-CUT problem.These were produced by:\begin{lemma}More recent algorithms for solving the semidefiniteprogramming relaxation are particularly efficient,because they explore the structure of the MAX-CUT.\end{lemma}\begin{theorem}......\end{theorem}\begin{corollary}......\end{corollary}\begin{proposition}......\end{proposition}\begin{proof}......\end{proof}\begin{remark}......\end{remark}\begin{algorithm}......\end{algorithm}10Taylor&Francis and I.T.Consultant4.6.Typesetting mathematics4.6.1.Displayed mathematicsThe gOMS style will set displayed mathematics centred on the measure without equation numbers,provided that you use the L A T E X2εstandard control sequences open(\[)and close(\])square brackets as delimiters.The equationpλi=trace(S)i∈Ri=1was typeset in the gOMS style using the commands\[\sum_{i=1}^p\lambda_i={\rm trace}({\textrm{\bf S}})\qquadi\in{\mathbb R}\].For those of your equations that you wish to be automatically numbered sequen-tially throughout the text,use the equation environment,e.g.pλi=trace(S)i∈R(1)i=1was typeset using the commands\begin{equation}\sum_{i=1}^p\lambda_i={\rm trace}({\textrm{\bf S}})quadi\in{\mathbb R}\end{equation}Part numbers for sets of equations may be generated using the subequations environment,e.g.ερw tt(s,t)=N[w s(s,t),w st(s,t)]s,(2a)w tt(1,t)+N[w s(1,t),w st(1,t)]=0,(2b) which was generated using the control sequences\begin{subequations}\label{subeqnexample}\begin{equation}\varepsilon\rho w_{tt}(s,t)=N[w_{s}(s,t),w_{st}(s,t)]_{s},\label{subeqnpart}\end{equation}\begin{equation}w_{tt}(1,t)+N[w_{s}(1,t),w_{st}(1,t)]=0,\end{equation}\end{subequations}This is made possible by the package subeqn,which is called by the Classfile.If you put the\label{}just after the\begin{subequations}line,references will be to the collection of equations,‘(2)’in the example above.Or,like the example code above,you can reference each equation individually—e.g.‘(2a)’.4.6.2.Bold math italic symbolsTo get bold math italic you can use\bm,which works for all sizes,e.g.\sffamily\begin{equation}{\rm d}({\bm s_{t_{\bm u}})=\langle{\bm\alpha({\sf{\textbf L}})}% [RM({\bm X}_y+{\bm s}_t)-RM({\bm x}_y)]^2\rangle.\end{equation}\normalfontproduces)= α(L)[RM(X y+s t)−RM(x y)]2 .(3)d(s tuNote that subscript,superscript,subscript to subscript,etc.sizes will take care of themselves and are italic,not bold,unless coded individually.\bm produces the same effect as\boldmath.\sffamily...\normalfont allows upright sans serif fonts to be created in math mode by using the control sequence‘\sf’.4.6.3.Bold GreekBold lowercase as well as uppercase Greek characters can be obtained by {\bm\gamma},which givesγ,and{\bm\Gamma},which givesΓ.4.6.4.Upright lowercase Greek characters and the upright partial derivative sign Upright lowercase Greek characters can be obtained with the Classfile by insert-ing the letter‘u’in the control code for the character,e.g.\umu and\upi produce µ(used,for example,in the symbol for the unit microns—µm)andπ(the ratio of the circumference to the diameter of a circle).Similarly,the control code for the upright partial derivative∂is\upartial.4.7.AcknowledgementsAn unnumbered section,e.g.\section*{Acknowledgement(s)},should be used for thanks,grant details,etc.and placed before any Notes or References sections.4.8.NotesAn unnumbered section,e.g.\section*{Note(s)},may be inserted after any Ac-knowledgements and before any References section.4.9.AppendicesAppendices should be set after the references,beginning with the command \appendices followed by the command\section for each appendix title,e.g.\appendices\section{This is the title of the first appendix}\section{This is the title of the second appendix}producesAppendix A.This is the title of thefirst appendixAppendix B.This is the title of the second appendixSubsections,equations,theorems,figures,tables,etc.within appendices will then be automatically numbered as appropriate.4.10.References4.10.1.References cited in the textReferences cited in the text should be quoted by their number as they are listed in the alphabetical References list towards the end of the document, not the order of citation,so thefirst reference cited in the text might be [23].For example,these may take the forms[32],[5,6,14],[21–55](not[21]–[55]).To produce the References list,the bibliographic data about each refer-ence item should be listed in thebibliography environment in alphabetical or-der.Each bibliographical entry has a key,which is assigned by the author and used to refer to that entry in the text.In this document,the key glov00in the citation form\cite{glov00}produces‘[5]’,and the keys ed84and aiex02 in the citation form\cite{ed84,aiex02}produce‘[1,3]’.The citation for a range of bibliographic entries(e.g.‘[2,4,6–10]’)will automatically be produced by\cite{doniz00,fzf88,GHGsoft,lam86,mtw73,neu83,fwp88}.Optional notes may be included at the end of a citation by the use of square brackets, e.g. \cite[see][and references therein]{fzf88}produces‘[see4,and references therein]’.4.10.2.The list of referencesThe following listing shows some references prepared in the style of the journal; note that references having the same author(s)are listed chronologically,beginning with the earliest.References[1]R.M.Aiex,Conjectured statistics for the q,t-Catalan numbers,preprint(2002),to appear in Advancesin Math.Available at /∼rmaiex.[2]G.Donizetti,C.M.von Weber,et,C.P.E.Bach,R.Strauss,and L.van Beethoven,Computingtools for modelling orchestral performance,Tech.Rep.DAMTP2000/NA10,Department of Applied Mathematics and Theoretical Physics,University of Cambridge,Cambridge,UK,2000.[3] D.M.F.Edwards and I.R.McDonald,Positive bases in numerical optimization,Comput.Optim.Appl.21(1984),pp.169–175.[4] F.French,English title of a chapter in the translation of a book in a foreign language,in Title ofa Book in Another Language(Quoted in that Language)[English translation],P.Smith(Transl.),Dover,New York(1988),original work published1923.[5] F.Glover,Multi-start and strategic oscillation methods—principles to exploit adaptive memory,inComputing Tools for Modeling,Optimization and Simulation:Interfaces in Computer Science and Operations Research,guna and J.L.Gonz´a les-Velarde,eds.,2nd ed.,Vol.6,Kluwer Academic, Boston,MA,2000,pp.1–24.[6]T.G.Golda,P.D.Hough,and G.Gay,APPSPACK(Asynchronous parallel pattern search package);software available at /appspack.[7]mport,Hilbert modular forms and the Galois representations associated to Hilbert–Blumenthalabelian varieties,Ph.D.diss.,School of Engineering and Applied Sciences,Harvard University,Cam-bridge,MA,1986.[8] C.W.Misner(ed.),Nonlinear Operators and Nonlinear Equations of Evolution in Banach Spaces,Proceedings of Symposia in Pure Mathematics Vol.18,Part2,American Mathematical Society, Providence,RI,1973,pp.231–256.[9]M.Neumann,Parallel GRASP with path-relinking for job shop scheduling,Mol.Phys.50(1983),pp.841–843.[10] F.W.Patel,Title of a Book,Monographs on Technical Aspects Vol.II,Dover,New York,1988.[11]H.Quorn,The resurgent Japanese economy and a Japan–United States free trade agreement,in4thInternational Conference on the Restructuring of the Economic and Political System in Japan andEurope,Milan,Italy,21–25May1996,World Scientific,Singapore,1997,pp.147–156.This list was produced by:\begin{thebibliography}{12}\bibitem[1]{aiex02}%1R.M.Aiex,{\em Conjectured statistics for the{$q,t$}-Catalan%numbers},preprint(2002),to appear in Advances in Math.Available%at /$\sim$rmaiex.\bibitem[2]{doniz00}%2G.Donizetti, C.M.{{v}on~Weber},et,C.P.E.Bach,R.Strauss,%and L.{{v}an~Beethoven},{\em Computing tools for modelling orchestral% performance},Tech.Rep.DAMTP2000/NA10,Department of Applied%Mathematics and Theoretical Physics,University of Cambridge,Cambridge,%UK,2000.\bibitem[3]{ed84}%3D.M.F.Edwards and I.R.McDonald,{\em Positive bases in numerical% optimization},Comput.Optim.Appl.21(1984),pp.169--175.\bibitem[4]{fzf88}%4F.French,{\em{English title of a chapter in the translation of a%book in a foreign language}},in{\itshape Title of a Book in Another%Language(Quoted in that Language)}[{\itshape English translation}],%P.Smith(Transl.),Dover,New York(1988),original work published1923.\bibitem[5]{glov00}%5F.Glover,{\it{Multi-start and strategic oscillation methods---principles%to exploit adaptive memory}},in{\it Computing Tools for Modeling,%Optimization and Simulation:Interfaces in Computer Science and Operations% Research},guna and J.L.Gonz\’{a}les-Velarde,eds.,2nd ed.,Vol.6,Kluwer% Academic,Boston,MA,2000,pp.1--24.\bibitem[6]{GHGsoft}%6T.G.Golda,P.D.Hough,and G.Gay,{\em{APPSPACK(Asynchronous parallel pattern% search package);}}software available at /appspack.\bibitem[7]{lam86}%7mport,{\em Hilbert modular forms and the Galois representations%associated to Hilbert--Blumenthal abelian varieties},Ph.D.diss.,School of% Engineering and Applied Sciences,Harvard University,Cambridge,MA,1986.\bibitem[8]{mtw73}%8C.W.Misner(ed.),{\em{Nonlinear Operators and Nonlinear Equations of Evolution% in Banach Spaces}},Proceedings of Symposia in Pure Mathematics Vol.18,Part%2,American Mathematical Society,Providence,RI,1973,pp.231--256.\bibitem[9]{neu83}%9M.Neumann,{\em Parallel GRASP with path-relinking for job shop%scheduling},Mol.Phys.50(1983),pp.841--843.。
统计语言模型(fandywang 20121106)
Economy is good
Einstein met with UN officials in Princeton
17
Shannon Game: Word Prediction
• Claude E.Shannon. “Prediction and Entropy of Printed English”, Bell System Technical Journal 30:30-64. 1951 • How well can the next letter(or word) of a text be predicted when the preceding letters(or words) are known
P(W) or P(wn|w1,w2…wn-1) is called a language model
20
How to compute P(W)
• How to compute this joint probability:
– P(its, water, is, so, transparent, that, the)
✓ ✗
The
13th
Shanghai International Film Festival…
Party May 27 add
Information extraction (IE)
You’re invited to our dinner party, Friday May 27 at 8:30
9
什么是自然语言处理
– Word Segment
ReliabilityEngineeringandSystemSafety91(2006)992–1007
Reliability Engineering and System Safety 91(2006)992–1007Multi-objective optimization using genetic algorithms:A tutorialAbdullah Konak a,Ã,David W.Coit b ,Alice E.Smith caInformation Sciences and Technology,Penn State Berks,USA bDepartment of Industrial and Systems Engineering,Rutgers University cDepartment of Industrial and Systems Engineering,Auburn UniversityAvailable online 9January 2006AbstractMulti-objective formulations are realistic models for many complex engineering optimization problems.In many real-life problems,objectives under consideration conflict with each other,and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives.A reasonable solution to a multi-objective problem is to investigate a set of solutions,each of which satisfies the objectives at an acceptable level without being dominated by any other solution.In this paper,an overview and tutorial is presented describing genetic algorithms (GA)developed specifically for problems with multiple objectives.They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity.r 2005Elsevier Ltd.All rights reserved.1.IntroductionThe objective of this paper is present an overview and tutorial of multiple-objective optimization methods using genetic algorithms (GA).For multiple-objective problems,the objectives are generally conflicting,preventing simulta-neous optimization of each objective.Many,or even most,real engineering problems actually do have multiple-objectives,i.e.,minimize cost,maximize performance,maximize reliability,etc.These are difficult but realistic problems.GA are a popular meta-heuristic that is particularly well-suited for this class of problems.Tradi-tional GA are customized to accommodate multi-objective problems by using specialized fitness functions and introducing methods to promote solution diversity.There are two general approaches to multiple-objective optimization.One is to combine the individual objective functions into a single composite function or move all but one objective to the constraint set.In the former case,determination of a single objective is possible with methods such as utility theory,weighted sum method,etc.,but theproblem lies in the proper selection of the weights or utility functions to characterize the decision-maker’s preferences.In practice,it can be very difficult to precisely and accurately select these weights,even for someone familiar with the problem pounding this drawback is that scaling amongst objectives is needed and small perturbations in the weights can sometimes lead to quite different solutions.In the latter case,the problem is that to move objectives to the constraint set,a constraining value must be established for each of these former objectives.This can be rather arbitrary.In both cases,an optimization method would return a single solution rather than a set of solutions that can be examined for trade-offs.For this reason,decision-makers often prefer a set of good solutions considering the multiple objectives.The second general approach is to determine an entire Pareto optimal solution set or a representative subset.A Pareto optimal set is a set of solutions that are nondominated with respect to each other.While moving from one Pareto solution to another,there is always a certain amount of sacrifice in one objective(s)to achieve a certain amount of gain in the other(s).Pareto optimal solution sets are often preferred to single solutions because they can be practical when considering real-life problems/locate/ress0951-8320/$-see front matter r 2005Elsevier Ltd.All rights reserved.doi:10.1016/j.ress.2005.11.018ÃCorresponding author.E-mail address:konak@ (A.Konak).since thefinal solution of the decision-maker is always a trade-off.Pareto optimal sets can be of varied sizes,but the size of the Pareto set usually increases with the increase in the number of objectives.2.Multi-objective optimization formulationConsider a decision-maker who wishes to optimize K objectives such that the objectives are non-commensurable and the decision-maker has no clear preference of the objectives relative to each other.Without loss of generality, all objectives are of the minimization type—a minimization type objective can be converted to a maximization type by multiplying negative one.A minimization multi-objective decision problem with K objectives is defined as follows: Given an n-dimensional decision variable vector x¼{x1,y,x n}in the solution space X,find a vector x* that minimizes a given set of K objective functions z(x*)¼{z1(x*),y,z K(x*)}.The solution space X is gen-erally restricted by a series of constraints,such as g j(x*)¼b j for j¼1,y,m,and bounds on the decision variables.In many real-life problems,objectives under considera-tion conflict with each other.Hence,optimizing x with respect to a single objective often results in unacceptable results with respect to the other objectives.Therefore,a perfect multi-objective solution that simultaneously opti-mizes each objective function is almost impossible.A reasonable solution to a multi-objective problem is to investigate a set of solutions,each of which satisfies the objectives at an acceptable level without being dominated by any other solution.If all objective functions are for minimization,a feasible solution x is said to dominate another feasible solution y (x1y),if and only if,z i(x)p z i(y)for i¼1,y,K and z j(x)o z j(y)for least one objective function j.A solution is said to be Pareto optimal if it is not dominated by any other solution in the solution space.A Pareto optimal solution cannot be improved with respect to any objective without worsening at least one other objective.The set of all feasible non-dominated solutions in X is referred to as the Pareto optimal set,and for a given Pareto optimal set,the corresponding objective function values in the objective space are called the Pareto front.For many problems,the number of Pareto optimal solutions is enormous(perhaps infinite).The ultimate goal of a multi-objective optimization algorithm is to identify solutions in the Pareto optimal set.However,identifying the entire Pareto optimal set, for many multi-objective problems,is practically impos-sible due to its size.In addition,for many problems, especially for combinatorial optimization problems,proof of solution optimality is computationally infeasible.There-fore,a practical approach to multi-objective optimization is to investigate a set of solutions(the best-known Pareto set)that represent the Pareto optimal set as well as possible.With these concerns in mind,a multi-objective optimization approach should achieve the following three conflicting goals[1]:1.The best-known Pareto front should be as close aspossible to the true Pareto front.Ideally,the best-known Pareto set should be a subset of the Pareto optimal set.2.Solutions in the best-known Pareto set should beuniformly distributed and diverse over of the Pareto front in order to provide the decision-maker a true picture of trade-offs.3.The best-known Pareto front should capture the wholespectrum of the Pareto front.This requires investigating solutions at the extreme ends of the objective function space.For a given computational time limit,thefirst goal is best served by focusing(intensifying)the search on a particular region of the Pareto front.On the contrary,the second goal demands the search effort to be uniformly distributed over the Pareto front.The third goal aims at extending the Pareto front at both ends,exploring new extreme solutions.This paper presents common approaches used in multi-objective GA to attain these three conflicting goals while solving a multi-objective optimization problem.3.Genetic algorithmsThe concept of GA was developed by Holland and his colleagues in the1960s and1970s[2].GA are inspired by the evolutionist theory explaining the origin of species.In nature,weak and unfit species within their environment are faced with extinction by natural selection.The strong ones have greater opportunity to pass their genes to future generations via reproduction.In the long run,species carrying the correct combination in their genes become dominant in their population.Sometimes,during the slow process of evolution,random changes may occur in genes. If these changes provide additional advantages in the challenge for survival,new species evolve from the old ones.Unsuccessful changes are eliminated by natural selection.In GA terminology,a solution vector x A X is called an individual or a chromosome.Chromosomes are made of discrete units called genes.Each gene controls one or more features of the chromosome.In the original implementa-tion of GA by Holland,genes are assumed to be binary digits.In later implementations,more varied gene types have been introduced.Normally,a chromosome corre-sponds to a unique solution x in the solution space.This requires a mapping mechanism between the solution space and the chromosomes.This mapping is called an encoding. In fact,GA work on the encoding of a problem,not on the problem itself.GA operate with a collection of chromosomes,called a population.The population is normally randomly initia-lized.As the search evolves,the population includesfitterA.Konak et al./Reliability Engineering and System Safety91(2006)992–1007993andfitter solutions,and eventually it converges,meaning that it is dominated by a single solution.Holland also presented a proof of convergence(the schema theorem)to the global optimum where chromosomes are binary vectors.GA use two operators to generate new solutions from existing ones:crossover and mutation.The crossover operator is the most important operator of GA.In crossover,generally two chromosomes,called parents,are combined together to form new chromosomes,called offspring.The parents are selected among existing chromo-somes in the population with preference towardsfitness so that offspring is expected to inherit good genes which make the parentsfitter.By iteratively applying the crossover operator,genes of good chromosomes are expected to appear more frequently in the population,eventually leading to convergence to an overall good solution.The mutation operator introduces random changes into characteristics of chromosomes.Mutation is generally applied at the gene level.In typical GA implementations, the mutation rate(probability of changing the properties of a gene)is very small and depends on the length of the chromosome.Therefore,the new chromosome produced by mutation will not be very different from the original one.Mutation plays a critical role in GA.As discussed earlier,crossover leads the population to converge by making the chromosomes in the population alike.Muta-tion reintroduces genetic diversity back into the population and assists the search escape from local optima. Reproduction involves selection of chromosomes for the next generation.In the most general case,thefitness of an individual determines the probability of its survival for the next generation.There are different selection procedures in GA depending on how thefitness values are used. Proportional selection,ranking,and tournament selection are the most popular selection procedures.The procedure of a generic GA[3]is given as follows:Step1:Set t¼1.Randomly generate N solutions to form thefirst population,P1.Evaluate thefitness of solutions in P1.Step2:Crossover:Generate an offspring population Q t as follows:2.1.Choose two solutions x and y from P t based onthefitness values.ing a crossover operator,generate offspringand add them to Q t.Step3:Mutation:Mutate each solution x A Q t with a predefined mutation rate.Step4:Fitness assignment:Evaluate and assign afitness value to each solution x A Q t based on its objective function value and infeasibility.Step5:Selection:Select N solutions from Q t based on theirfitness and copy them to P t+1.Step6:If the stopping criterion is satisfied,terminate the search and return to the current population,else,set t¼t+1go to Step2.4.Multi-objective GABeing a population-based approach,GA are well suited to solve multi-objective optimization problems.A generic single-objective GA can be modified tofind a set of multiple non-dominated solutions in a single run.The ability of GA to simultaneously search different regions of a solution space makes it possible tofind a diverse set of solutions for difficult problems with non-convex,discon-tinuous,and multi-modal solutions spaces.The crossover operator of GA may exploit structures of good solutions with respect to different objectives to create new non-dominated solutions in unexplored parts of the Pareto front.In addition,most multi-objective GA do not require the user to prioritize,scale,or weigh objectives.Therefore, GA have been the most popular heuristic approach to multi-objective design and optimization problems.Jones et al.[4]reported that90%of the approaches to multi-objective optimization aimed to approximate the true Pareto front for the underlying problem.A majority of these used a meta-heuristic technique,and70%of all meta-heuristics approaches were based on evolutionary ap-proaches.Thefirst multi-objective GA,called vector evaluated GA (or VEGA),was proposed by Schaffer[5].Afterwards, several multi-objective evolutionary algorithms were devel-oped including Multi-objective Genetic Algorithm (MOGA)[6],Niched Pareto Genetic Algorithm(NPGA) [7],Weight-based Genetic Algorithm(WBGA)[8],Ran-dom Weighted Genetic Algorithm(RWGA)[9],Nondomi-nated Sorting Genetic Algorithm(NSGA)[10],Strength Pareto Evolutionary Algorithm(SPEA)[11],improved SPEA(SPEA2)[12],Pareto-Archived Evolution Strategy (PAES)[13],Pareto Envelope-based Selection Algorithm (PESA)[14],Region-based Selection in Evolutionary Multiobjective Optimization(PESA-II)[15],Fast Non-dominated Sorting Genetic Algorithm(NSGA-II)[16], Multi-objective Evolutionary Algorithm(MEA)[17], Micro-GA[18],Rank-Density Based Genetic Algorithm (RDGA)[19],and Dynamic Multi-objective Evolutionary Algorithm(DMOEA)[20].Note that although there are many variations of multi-objective GA in the literature, these cited GA are well-known and credible algorithms that have been used in many applications and their performances were tested in several comparative studies. Several survey papers[1,11,21–27]have been published on evolutionary multi-objective optimization.Coello lists more than2000references in his website[28].Generally, multi-objective GA differ based on theirfitness assign-ment procedure,elitisim,or diversification approaches.In Table1,highlights of the well-known multi-objective with their advantages and disadvantages are given.Most survey papers on multi-objective evolutionary approaches intro-duce and compare different algorithms.This paper takes a different course and focuses on important issues while designing a multi-objective GA and describes common techniques used in multi-objective GA to attain the threeA.Konak et al./Reliability Engineering and System Safety91(2006)992–1007 994goals in multi-objective optimization.This approach is also taken in the survey paper by Zitzler et al.[1].However,the discussion in this paper is aimed at introducing the components of multi-objective GA to researchers and practitioners without a background on the multi-objective GA.It is also import to note that although several of the state-of-the-art algorithms exist as cited above,many researchers that applied multi-objective GA to their problems have preferred to design their own customized algorithms by adapting strategies from various multi-objective GA.This observation is another motivation for introducing the components of multi-objective GA rather than focusing on several algorithms.However,the pseudo-code for some of the well-known multi-objective GA are also provided in order to demonstrate how these proce-dures are incorporated within a multi-objective GA.Table1A list of well-known multi-objective GAAlgorithm Fitness assignment Diversity mechanism Elitism ExternalpopulationAdvantages DisadvantagesVEGA[5]Each subpopulation isevaluated with respectto a differentobjective No No No First MOGAStraightforwardimplementationTend converge to theextreme of each objectiveMOGA[6]Pareto ranking Fitness sharing byniching No No Simple extension of singleobjective GAUsually slowconvergenceProblems related to nichesize parameterWBGA[8]Weighted average ofnormalized objectives Niching No No Simple extension of singleobjective GADifficulties in nonconvexobjective function space Predefined weightsNPGA[7]Nofitnessassignment,tournament selection Niche count as tie-breaker in tournamentselectionNo No Very simple selectionprocess with tournamentselectionProblems related to nichesize parameterExtra parameter fortournament selectionRWGA[9]Weighted average ofnormalized objectives Randomly assignedweightsYes Yes Efficient and easyimplementDifficulties in nonconvexobjective function spacePESA[14]Nofitness assignment Cell-based density Pure elitist Yes Easy to implement Performance depends oncell sizesComputationally efficientPrior information neededabout objective spacePAES[29]Pareto dominance isused to replace aparent if offspringdominates Cell-based density astie breaker betweenoffspring and parentYes Yes Random mutation hill-climbing strategyNot a population basedapproachEasy to implement Performance depends oncell sizesComputationally efficientNSGA[10]Ranking based onnon-dominationsorting Fitness sharing bynichingNo No Fast convergence Problems related to nichesize parameterNSGA-II[30]Ranking based onnon-dominationsorting Crowding distance Yes No Single parameter(N)Crowding distance worksin objective space onlyWell testedEfficientSPEA[11]Raking based on theexternal archive ofnon-dominatedsolutions Clustering to truncateexternal populationYes Yes Well tested Complex clusteringalgorithmNo parameter forclusteringSPEA-2[12]Strength ofdominators Density based on thek-th nearest neighborYes Yes Improved SPEA Computationallyexpensivefitness anddensity calculationMake sure extreme pointsare preservedRDGA[19]The problem reducedto bi-objectiveproblem with solutionrank and density asobjectives Forbidden region cell-based densityYes Yes Dynamic cell update More difficult toimplement than othersRobust with respect to thenumber of objectivesDMOEA[20]Cell-based ranking Adaptive cell-baseddensity Yes(implicitly)No Includes efficienttechniques to update celldensitiesMore difficult toimplement than othersAdaptive approaches toset GA parametersA.Konak et al./Reliability Engineering and System Safety91(2006)992–10079955.Design issues and components of multi-objective GA 5.1.Fitness functions5.1.1.Weighted sum approachesThe classical approach to solve a multi-objective optimization problem is to assign a weight w i to each normalized objective function z 0i ðx Þso that the problem is converted to a single objective problem with a scalar objective function as follows:min z ¼w 1z 01ðx Þþw 2z 02ðx ÞþÁÁÁþw k z 0k ðx Þ,(1)where z 0i ðx Þis the normalized objective function z i (x )and P w i ¼1.This approach is called the priori approach since the user is expected to provide the weights.Solving a problem with the objective function (1)for a given weight vector w ¼{w 1,w 2,y ,w k }yields a single solution,and if multiple solutions are desired,the problem must be solved multiple times with different weight combinations.The main difficulty with this approach is selecting a weight vector for each run.To automate this process;Hajela and Lin [8]proposed the WBGA for multi-objective optimization (WBGA-MO)in the WBGA-MO,each solution x i in the population uses a different weight vector w i ¼{w 1,w 2,y ,w k }in the calculation of the summed objective function (1).The weight vector w i is embedded within the chromosome of solution x i .Therefore,multiple solutions can be simulta-neously searched in a single run.In addition,weight vectors can be adjusted to promote diversity of the population.Other researchers [9,31]have proposed a MOGA based on a weighted sum of multiple objective functions where a normalized weight vector w i is randomly generated for each solution x i during the selection phase at each generation.This approach aims to stipulate multiple search directions in a single run without using additional parameters.The general procedure of the RWGA using random weights is given as follows [31]:Procedure RWGA:E ¼external archive to store non-dominated solutions found during the search so far;n E ¼number of elitist solutions immigrating from E to P in each generation.Step 1:Generate a random population.Step 2:Assign a fitness value to each solution x A P t by performing the following steps:Step 2.1:Generate a random number u k in [0,1]for each objective k ,k ¼1,y ,K.Step 2.2:Calculate the random weight of each objective k as w k ¼ð1=u k ÞP K i ¼1u i .Step 2.3:Calculate the fitness of the solution as f ðx Þ¼P K k ¼1w k z k ðx Þ.Step 3:Calculate the selection probability of each solutionx A P t as follows:p ðx Þ¼ðf ðx ÞÀf min ÞÀ1P y 2P t ðf ðy ÞÀf minÞwhere f min ¼min f f ðx Þj x 2P t g .Step 4:Select parents using the selection probabilities calculated in Step 3.Apply crossover on the selected parent pairs to create N offspring.Mutate offspring with a predefined mutation rate.Copy all offspring to P t +1.Update E if necessary.Step 5:Randomly remove n E solutions from P t +1and add the same number of solutions from E to P t +1.Step 6:If the stopping condition is not satisfied,set t ¼t þ1and go to Step 2.Otherwise,return to E .The main advantage of the weighted sum approach is a straightforward implementation.Since a single objective is used in fitness assignment,a single objective GA can be used with minimum modifications.In addition,this approach is computationally efficient.The main disadvan-tage of this approach is that not all Pareto-optimal solutions can be investigated when the true Pareto front is non-convex.Therefore,multi-objective GA based on the weighed sum approach have difficulty in finding solutions uniformly distributed over a non-convex trade-off surface [1].5.1.2.Altering objective functionsAs mentioned earlier,VEGA [5]is the first GA used to approximate the Pareto-optimal set by a set of non-dominated solutions.In VEGA,population P t is randomly divided into K equal sized sub-populations;P 1,P 2,y ,P K .Then,each solution in subpopulation P i is assigned a fitness value based on objective function z i .Solutions are selected from these subpopulations using proportional selection for crossover and mutation.Crossover and mutation are performed on the new population in the same way as for a single objective GA.Procedure VEGA:N S ¼subpopulation size (N S ¼N =K )Step 1:Start with a random initial population P 0.Set t ¼0.Step 2:If the stopping criterion is satisfied,return P t .Step 3:Randomly sort population P t .Step 4:For each objective k ,k ¼1,y K ,perform the following steps:Step 4.1:For i ¼1þðk 21ÞN S ;...;kN S ,assign fit-ness value f ðx i Þ¼z k ðx i Þto the i th solution in the sorted population.Step 4.2:Based on the fitness values assigned in Step 4.1,select N S solutions between the (1+(k À1)N S )th and (kN S )th solutions of the sorted population to create subpopulation P k .Step 5:Combine all subpopulations P 1,y ,P k and apply crossover and mutation on the combined population to create P t +1of size N .Set t ¼t þ1,go to Step 2.A similar approach to VEGA is to use only a single objective function which is randomly determined each time in the selection phase [32].The main advantage of the alternating objectives approach is easy to implement andA.Konak et al./Reliability Engineering and System Safety 91(2006)992–1007996computationally as efficient as a single-objective GA.In fact,this approach is a straightforward extension of a single objective GA to solve multi-objective problems.The major drawback of objective switching is that the popula-tion tends to converge to solutions which are superior in one objective,but poor at others.5.1.3.Pareto-ranking approachesPareto-ranking approaches explicitly utilize the concept of Pareto dominance in evaluatingfitness or assigning selection probability to solutions.The population is ranked according to a dominance rule,and then each solution is assigned afitness value based on its rank in the population, not its actual objective function value.Note that herein all objectives are assumed to be minimized.Therefore,a lower rank corresponds to a better solution in the following discussions.Thefirst Pareto ranking technique was proposed by Goldberg[3]as follows:Step1:Set i¼1and TP¼P.Step2:Identify non-dominated solutions in TP and assigned them set to F i.Step3:Set TP¼TPF i.If TP¼+go to Step4,else set i¼iþ1and go to Step2.Step4:For every solution x A P at generation t,assign rank r1ðx;tÞ¼i if x A F i.In the procedure above,F1,F2,y are called non-dominated fronts,and F1is the Pareto front of population P.NSGA[10]also classifies the population into non-dominated fronts using an algorithm similar to that given above.Then a dummyfitness value is assigned to each front using afitness sharing function such that the worst fitness value assigned to F i is better than the bestfitness value assigned to F i+1.NSGA-II[16],a more efficient algorithm,named the fast non-dominated-sort algorithm, was developed to form non-dominated fronts.Fonseca and Fleming[6]used a slightly different rank assignment approach than the ranking based on non-dominated-fronts as follows:r2ðx;tÞ¼1þnqðx;tÞ;(2) where nq(x,t)is the number of solutions dominating solution x at generation t.This ranking method penalizes solutions located in the regions of the objective function space which are dominated(covered)by densely populated sections of the Pareto front.For example,in Fig.1b solution i is dominated by solutions c,d and e.Therefore,it is assigned a rank of4although it is in the same front with solutions f,g and h which are dominated by only a single solution.SPEA[11]uses a ranking procedure to assign better fitness values to non-dominated solutions at underrepre-sented regions of the objective space.In SPEA,an external list E of afixed size stores non-dominated solutions that have been investigated thus far during the search.For each solution y A E,a strength value is defined assðy;tÞ¼npðy;tÞN Pþ1,where npðy;tÞis the number solutions that y dominates in P.The rank r(y,t)of a solution y A E is assigned as r3ðy;tÞ¼sðy;tÞand the rank of a solution x A P is calculated asr3ðx;tÞ¼1þXy2E;y1xsðy;tÞ.Fig.1c illustrates an example of the SPEA ranking method.In the former two methods,all non-dominated solutions are assigned a rank of1.This method,however, favors solution a(in thefigure)over the other non-dominated solutions since it covers the least number of solutions in the objective function space.Therefore,a wide, uniformly distributed set of non-dominated solutions is encouraged.Accumulated ranking density strategy[19]also aims to penalize redundancy in the population due to overrepre-sentation.This ranking method is given asr4ðx;tÞ¼1þXy2P;y1xrðy;tÞ.To calculate the rank of a solution x,the rank of the solutions dominating this solution must be calculatedfirst. Fig.1d shows an example of this ranking method(based on r2).Using ranking method r4,solutions i,l and n are ranked higher than their counterparts at the same non-dominated front since the portion of the trade-off surface covering them is crowded by three nearby solutions c,d and e. Although some of the ranking approaches described in this section can be used directly to assignfitness values to individual solutions,they are usually combined with variousfitness sharing techniques to achieve the second goal in multi-objective optimization,finding a diverse and uniform Pareto front.5.2.Diversity:fitness assignment,fitness sharing,and nichingMaintaining a diverse population is an important consideration in multi-objective GA to obtain solutions uniformly distributed over the Pareto front.Without taking preventive measures,the population tends to form relatively few clusters in multi-objective GA.This phenom-enon is called genetic drift,and several approaches have been devised to prevent genetic drift as follows.5.2.1.Fitness sharingFitness sharing encourages the search in unexplored sections of a Pareto front by artificially reducingfitness of solutions in densely populated areas.To achieve this goal, densely populated areas are identified and a penaltyA.Konak et al./Reliability Engineering and System Safety91(2006)992–1007997。
OptiStruct_Optimization
• Shape: is an automated way to modify the structure shape based on a predefined
shape variables to find the optimal shape.
• Size: is an automated way to modify the structure parameters (Thickness, 1D
Copyright © 2008 Altair Engineering, Inc. All rights reserved.
Day 1 Agenda
• • • Introduction Structural Optimization Concepts OptiStruct Features: FEA Solver and Optimizer
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• • • •
Exercise 5.4: Creating Shapes
Exercise 5.5: Pre-processing the Shape Optimization of a Channel Exercise 5.6: Shape Optimization of a Rail Joint Exercise 5.7: Shape optimization of a 3-D bracket model using Free-Shape method
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Shape Optimization Concepts (Morphing based and Free Shape)
• •
• • •
Exercise 5.1: Basics of Domains and Handles Exercise 5.2: Morph Volume Exercise 5.3: Mapping a mesh to a new geometry
Suwon University, Gyeonggi-do, Korea SPONSORED BY
FINAL PROGRAMTHE 2007 ACM SIGAPPSYMPOSIUM ON APPLIED COMPUTING/conferences/sac/sac2007Seoul, Korea March 11 - 15, 2007Organizing CommitteeRoger L. Wainwright Hisham M. Haddad Sung Y. ShinSascha Ossowski Ronaldo MenezesLorie M. Liebrock Mathew J. Palakal Jaeyoung Choi Tei-Wei Kuo Jiman HongSeong Tae Jhang Yookun Cho Yong Wan KooH OSTED BYSeoul National University, Seoul, Korea Suwon University, Gyeonggi-do, KoreaSPONSORED BYSAC 2007 I NTRODUCTIONSAC 2007 is a premier international conference on applied com-puting and technology. Attendees have the opportunity to hear from expert practitioners and researchers about the latest trends in research and development in their fields. SAC 2007 features 2 keynote speakers on Monday and Wednesday, from 8:30 to 10:00. The symposium consists of Tutorial and Technical programs. The Tutorial Program offers 3 half-day tutorials on Sunday March 11, 2007, starting at 9:00am. The Technical Program offers 38 tracks on a wide number of different research topics, which run from Monday March 12 through Thursday March 15, 2007. Regular sessions start at 8:30am and end at 5:00pm in 4 parallel sessions. Honorable ChairsYookun Cho, Honorable Symposium ChairSeoul National University, KoreaYong Wan Koo, Honorable Program ChairUniversity of Suwon, KoreaOrganizing CommitteeRoger L. Wainwright, Symposium ChairUniversity of Tulsa, USAHisham M. Haddad, Symposium Chair, Treasurer, Registrar Kennesaw State University, USASung Y. Shin, Symposium ChairSouth Dakota State University, USASascha Ossowski, Program ChairUniversity Rey Juan Carlos, Madrid, SpainRonaldo Menezes, Program ChairFlorida Institute of Technology, Melbourne, FloridaJaeyoung Choi, Tutorials ChairSoongsil University, KoreaTei-Wei Kuo, Tutorials ChairNational Taiwan University, ChinaMathew J. Palakal, Poster ChairIndiana University Purdue University, USALorie M. Liebrock, Publication ChairNew Mexico Institute of Mining and Technology, USAJiman Hong,Local Organization ChairKwangwoon University, KoreaSeong Tae Jhang,Local Organization ChairUniversity of Suwon, KoreaSAC 2007 Track OrganizersArtificial Intelligence, Computational Logic, and Image Analysis (AI)C.C. Hung, School of Computing and Soft. Eng., USAAgostinho Rosa, LaSEEB –ISR – IST, PortugalAdvances in Spatial and Image-based Information Systems (ASIIS)Kokou Yetongnon, Bourgogne University, FranceChristophe Claramunt, Naval Academy Research Institute, France Richard Chbeir, Bourgogne University, FranceKi-Joune Li, Prusan National University, KoreaAgents, Interactions, Mobility and Systems (AIMS)Marcin Paprzycki, SWPS and IBS PAN, PolandCostin Badica, University of Craiova, RomaniaMaria Ganzha, EUH-E and IBS PAN, PolandAlex Yung-Chuan Lee, Southern Illinois University, USAShahram Rahimi, Southern Illinois University, USAAutonomic Computing (AC)Umesh Bellur, Indian Institute of Technology, IndiaSheikh Iqbal Ahamed, Marquette University, USABioinformatics (BIO)Mathew J. 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Preston, University of East London, UKComputer Forensics (CF)Brajendra Panda, University of Arkansas, USAKamesh Namuduri, Wichita State University, USAComputer Networks (CN)Mario Freire, University of Beira Interior, PortugalTeresa Vazao, INESC ID/IST, PortugalEdmundo Monteiro, University of Coimbra, PortugalManuela Pereira, University of Beira Interior, PortugalComputer Security (SEC)Giampaolo Bella, Universita' di Catania, ItalyPeter Ryan, University of Newcastle upon Tyne, UKComputer-aided Law and Advanced Technologies (CLAT) Giovanni Sartor, University of Bologna, ItalyAlessandra Villecco Bettelli, University of Bologna, ItalyLavinia Egidi, University of Piemonte Orientale, ItalyConstraint Solving and Programming (CSP)Stefano Bistarelli, Università degli studi "G. D'Annunzio" di Chieti-Pescara, ItalyEric Monfroy, University of Nantes, FranceBarry O'Sullivan, University College Cork, IrelandCoordination Models, Languages and Applications (CM) Alessandro Ricci, Universita di Bologna, ItalyBernhard Angerer, Michael Ignaz Schumacher, EPFL IC IIF LIA, SwitzerlandData Mining (DM)Hasan M. Jamil, Wayne State University, USAData Streams (DS)Jesus S. Aguilar-Ruiz, Pablo de Olavide University, SpainFrancisco J. Ferrer-Troyano, University of Seville, SpainJoao Gama, University of Porto, PortugalRalf Klinkenberg, University of Dortmund, GermanyDatabase Theory, Technology, and Applications (DTTA) Ramzi A. Haraty, Lebanese American University, LebanonApostolos N. Papadopoulos, Aristotle University, GreeceJunping Sun, Nova Southeastern University, USADependable and Adaptive Distributed Systems (DADS)Karl M. Göschka, Vienna University of Technology, AustriaSvein O. Hallsteinsen, SINTEF ICT, NorwayRui Oliveira, Universidade do Minho, PortugalAlexander Romanovsky, University of Newcastle upon Tyne, UK Document Engineering (DE)Rafael Dueire Lins, Universidade Federal de Pernambuco, Brazil Electronic Commerce Technologies (ECT)Sviatoslav Braynov, University of Illinois at Springfield, USADaryl Nord, Oklahoma State University, USAFernando Rubio, Universidad Complutense de Madrid, Spain Embedded Systems: Applications, Solutions and Techniques (EMBS)Alessio Bechini, University of Pisa, ItalyCosimo Antonio Prete, University of Pisa, ItalyJihong Kim, Seoul National University, KoreaEvolutionary Computation (EC)Bryant A. Julstrom, St. Cloud State University, USA Geoinformatics and Technology (GT)Dong-Cheon Lee, Sejong University, KoreaGwangil Jeon, Korea Polytechnic University, KoreaGeometric Computing and Reasoning (GCR)Xiao-Shan Gao, Chinese Academy of Sciences, ChinaDominique Michelucci, Universite de Bourgogne, FrancePascal Schreck, Universite Louis Pasteur, FranceHandheld Computing (HHC)Qusay H. Mahmoud, University of Guelph, CanadaZakaria Maamar, Zayed University, UAEInformation Access and Retrieval (IAR)Fabio Crestani, University of Strathclyde, UKGabriella Pasi, University of Milano Bicocca, ItalyMobile Computing and Applications (MCA)Hong Va Leong, Hong Kong Polytechnic University, Hong KongAlvin Chan, Hong Kong Polytechnic University, Hong KongModel Transformation (MT)Jean Bézivin, University of Nantes, FranceAlfonso Pierantonio, Università degli Studi dell’Aquila, ItalyAntonio Vallecillo, Universidad de Malaga, SpainJeff Gray, University of Alabama at Birmingham, USAMultimedia and Visualization (MMV)Chaman L. Sabharwal, University of Missouri-Rolla, USAMingjun Zhang, Agilent Technologies, USAObject-Oriented Programming Languages and Systems (OOP) Davide Ancona, DISI - Università di Genova, ItalyMirko Viroli, Università di Bologna, ItalyOperating Systems and Adaptive Applications (OSAA)Jiman Hong, Kwangwoon University, KoreaTei-Wei Kuo, National Taiwan University, TaiwanOrganizational Engineering (OE)José Tribolet, Technical University of Lisbon, PortugalRobert Winter, University of St. Gallen, SwitzerlandArtur Caetano, Technical University of Lisbon, Portugal Programming for Separation of Concerns (PSC)Corrado Santoro, Catania University, ItalyEmiliano Tramontana, Catania University, ItalyIan Welch, Victoria University, New ZealandYvonne Coady, Victoria Univeristy, CanadaProgramming Languages (PL)Chang-Hyun Jo, California State University at Fullerton, USAMarjan Mernik, University of Maribor, SloveniaBarrett Bryant, University of Alabama at Birmingham, USAReliable Computations and their Applications (RCA)Martine Ceberio, University of Texas at El Paso, USAVladik Kreinovich, University of Texas at El Paso, USAMichael Rueher, Universite de Nice ESSI, FranceSemantic Web and Application (SWA)Hyoil Han, Drexel University, USASemantic-Based Resource Discovery, Retrieval and Composition (SDRC)Eugenio Di Sciascio, SinsInfLab Politecnico di Bari, ItalyFrancesco M. Donini, University of Tuscia, ItalyTommaso Di Noia, SinsInfLab Politecnico di Bari, ItalyMassimo Paolucci, DoCoMo Euro-Labs, GermanySoftware Engineering (SE)W. Eric Wong, University of Texas at Dallas, USAChang-Oan Sung, Indiana University Southeast, USASoftware Verification (SV)Zijiang Yang, Western Michigan University, USALunjin Lu, Oakland University, USAFausto Spoto, Universita di Verona, ItalySystem On Chip Design and Software Supports (SODSS) Seong Tae Jhang, Suwon University, KoreaSung Woo Chung, Korea University, KoreaTrust, Recommendations, Evidence and other Collaborative Know-how (TRECK)Jean-Marc Seigneur, University of Geneva, SwitzerlandJeong Hyun Yi, Samsung Advanced Institute of Technology, South Korea Ubiquitous Computing: Digital Spaces, Services and Content (UC)Achilles Kameas, Hellenic Open University, GreeceGeorge Roussos, University of London, UKWeb Technologies (WT)Fahim Akhter , Zayed University, UAEDjamal Benslimane, University of Lyon, FranceZakaria Maamar, Zayed University, UAEQusay H. Mahmoud, University of Guelph, CanadaLocal SupportLocal support for SAC 2007 is provided by the Seoul National University in Seoul, Suwon University in Gyeonggi-do, Ministry of Education and Human Resources Development, Samsung, mds technology, KETI, MIC, CVB, and ETRI. The SAC organizing committee acknowledges and thanks the local supporters for their generous contributions to SAC 2007. Their support has been essential to the success of Symposium, and is greatly appreciated. ACM SIGAPPThe ACM Special Interest Group on Applied Computing is ACM's primary applications-oriented SIG. Its mission is to further the interests of the computing professionals engaged in the development of new computing applications and applications areas and the transfer of computing technology to new problem domains. SIGAPP offers practitioners and researchers the opportunity to share mutual interests in innovative application fields, technology transfer, experimental computing, strategic research, and the management of computing. SIGAPP also promotes widespread cooperation among business, government, and academic computing activities. Its annual Symposium on Applied Computing (SAC) provides an international forum for presentation of the results of strategic research and experimentation for this inter-disciplinary environment. SIGAPP membership fees are: $30.00 for ACM Non-members, $15.00 for ACM Members, and $8.00 for Student Members. For information contact Barrett Bryant at bryant@. Also, checkout the SIGAPP website at /sigapp/M ESSAGE FROM THE S YMPOSIUM C HAIRSRoger WaiwrightUniversity of Tulsa, USAHisham M. HaddadKennesaw State University, USASung Y. ShinSouth Dakota State University, USAOn behalf of the Organization Committee, it is our pleasure to welcome you to the 22nd Annual ACM Symposium on Applied Computing (SAC 2007). This year, the conference is hosted by Seoul National University and Suwon University in Gyeonggi-do, Korea. Many thanks for your participation in this international event dedicated to computer scientists, engineers, and practitioners seeking innovative ideas in various areas of computer applications. The sponsoring SIG of this Symposium, the ACM Special Interest Group on Applied Computing, is dedicated to further the interests of computing professionals engaged in the design and development of new computing applications, interdisciplinary applications areas, and applied research. The conference provides a forum for discussion and exchange of new ideas addressing computational algorithms and complex applications. This goal is reflected in its wide spectrum of application areas and tutorials designed to provide variety of discussion topics during this event. The conference is composed of various specialized technical tracks and tutorials. As in past successful meetings, talented and dedicated Track Chairs and Co-Chairs have organized SAC 2007 tracks. Each track maintains a program committee and group of highly qualified reviewers. We thank the Track Chairs, Co-Chairs, and participating reviewers for their commitment to making SAC 2007 another high quality conference. We also thank our invited keynote speakers for sharing their knowledge with SAC attendees. Most of all, special thanks to the authors and presenters for sharing their experience with the rest of us and to all attendees for joining us in Seoul, Korea.The local organizing committee has always been a key to the success of the conference. This year, we thank our local team from Seoul National University and Suwon University. In particular, we thank Dr. Jiman Hong, from Kwangwoon University, and Dr. Seong Tae Jhang, from Suwon University, for chairing the local organization effort. We also thank Dr. Jaeyoung Choi, from Soongsil University, and Dr. Tei-Wei Kuo, from National Taiwan University, for organizing the Tutorials Program. Other committee members we also would like to thank are Lorie Liebrock for her tremendous effort putting together the conference proceedings, Mathew Palakal for coordinating another successful Posters Program, and Sascha Ossowski and Ronaldo Menezes for bringing together the Technical Program. Finally, we extend outthanks and gratitude to our honorable Symposium and Program Chairs Drs. Yookun Cho of Seoul National University and Dr. Yong Wan Koo of Suwon University. Many thanks for hosting the conference and coordinating governmental and local support. Again, we welcome you to SAC 2007 in the lively city of Seoul. We hope you enjoy your stay in Seoul and leave this event enriched with new ideas and friends. Next year, we invite you to participate in SAC 2008 to be held in the costal city of Fortaleza, Brazil. The symposium will be hosted by the University of Fortaleza (UNIFOR) and the Federal University of Ceará (UFC). We hope to see there!M ESSAGE FROM THE P ROGRAM C HAIRSSascha OssowskiUniversity Rey Juan Carlos, SpainRonaldo MenezesFlorida Institute of Technology, USAWelcome to the 22nd Symposium on Applied Computing (SAC 2007). Over the past 21 years, SAC has been an international forum for researchers and practitioners to present their findings and research results in the areas of computer applications and technology. The SAC 2007 Technical Program offers a wide range of tracks covering major areas of computer applications. Highly qualified referees with strong expertise and special interest in their respective research areas carefully reviewed the submitted papers. As part of the Technical Program, this year the Tutorial Program offers several half-day tutorials that were carefully selected from numerous proposals. Many thanks to Jaeyoung Choi from the Soongsil University and Tei-Wei Kuo from the National Taiwan University for chairing the Tutorial Program. Also, this is the fourth year for SAC to incorporate poster papers into the Technical Program. Many thanks to Mathew Palakal from Indiana University Purdue University for chairing the poster sessions. SAC 2007 would not be possible without contributions from members of the scientific community. As anyone can imagine, many people have dedicated tremendous time and effort over the period of 10 months to bring you an excellent program. The success of SAC 2007 relies on the effort and hard work of many volunteers. On behalf of the SAC 2007 Organizing Committee, we would like to take this opportunity to thank all of those who made this year's technical program a reality, including speakers, referees, track chairs, session chairs, presenters, and attendees. We also thank the local arrangement committee lead by Jiman Hong from the Kwangwoon University and Seong Tae Jhang from Suwon University. We also want to thank Hisham Haddad from Kennesaw State University for his excellent job again as the SAC Treasurer, Webmaster, and Registrar.SAC's open call for Track Proposals resulted in the submission of 47 track proposals. These proposals were carefully evaluated by the conference Executive Committee. Some proposals were rejected on the grounds of either not being appropriate for the areas that SAC covers traditionally or being of rather narrow and specialized nature. Some others tracks were merged to form a single track. Eventually, 38 tracks were established, which then went on to produce their own call for papers. In response to these calls, 786 papers were submitted, from which 256 papers were strongly recommended by the referees for acceptance and inclusion in the Conference Proceedings. This gives SAC 2007 an acceptance rate of 32.5% across all tracks. SAC is today one of the most popular and competitive conferences in the international field of applied computing.We hope you will enjoy the meeting and have the opportunity to exchange your ideas and make new friends. We also hope you will enjoy your stay in Seoul, Korea and take pleasure from the many entertainments and activities that the city and Korea has to offer. We look forward to your active participation in SAC 2008 when for the first time SAC will be hosted in South America, more specifically in Fortaleza, Brazil. We encourage you and your colleagues to submit your research findings to next year's technical program. Thank you for being part of SAC 2007, and we hope to see you in sunny Fortaleza, Brazil for SAC 2008.O THER A CTIVITIESReview Meeting: Sunday March 11, 2007, from 18:00 to 19:00 in Room 311A. Open for SAC Organizing Committee and Track Chairs and Co-Chairs.SAC 2008 Organization Meeting: Monday March 12, 2007, from 18:00 to 19:00 in Room 311A. Open for SAC Organizing Committee.SAC Reception: Monday March 12, 2007 at 19:00 to 22:00. Room 402. Open for all registered attendees.Posters Session: Tuesday March 13, 2007, from 13:30 to 17:00 in the Room 311C. Open to everyone.SIGAPP Annual Business Meeting: Tuesday March 13, 2007, from 17:15 to 18:15 in Room 311A. Open to everyone.SAC Banquet: Wednesday March 14, 2007. Rooms 331-334. Open for Banquet Ticket holders. See your tickets for full details. Track-Chairs Luncheon: Thursday April 27, 2006, from 12:00 to 13:30. Hosu (Lake) Food-mall. Open for SAC Organizing Committee, Track Chairs and Co-Chairs.SAC 2008SAC 2008 will be held in Fortaleza, Ceará, Brazil, March 16 – 20, 2008. It is co-hosted by the University of Fortaleza (UNIFOR) and the Federal University of Ceará (UFC). Please check the registration desk for handouts. You can also visit the website at /conferences/sac/sac2008/.M ONDAY K EYNOTE A DDRESSA New DBMS Architecture for DB-IRIntegrationDr. Kyu-Young WhangDirector of Advanced Information Technology Research Center, Korea Advanced Institute ofScience and Technology, Daejeon, Korea M ONDAY M ARCH 12, 2007, 9:00 – 10:00ROOM 310 A, B AND CABSTRACTNowadays, there is an increasing need to integrate the DBMS (for structured data) with Information Retrieval (IR) features (for unstructured data). DB-IR integration becomes one of major challenges in the database area. Extensible architectures provided by commercial ORDBMS vendors can be used for DB-IR integration. Here, extensions are implemented using a high-level (typically, SQL-level) interface. We call this architecture loose-coupling. The advantage of loose-coupling is that it is easy to implement. But, it is not preferable for implementing new data types and operations in large databases when high performance is required. In this talk, we present a new DBMS architectureapplicable to DB-IR integration, which we call tight-coupling. In tight-coupling, new data types and operations are integrated into the core of the DBMS engine in the extensible type layer. Thus, they are incorporated as the "first-class citizens" within the DBMS architecture and are supported in a consistent manner with high performance. This tight-coupling architecture is being used to incorporate IR features and spatial database features into the Odysseus ORDBMS that has been under development at KAIST/AITrc for over 16 years. In this talk, we introduce Odysseus and explain its tightly-coupled IR features (U.S. patented in 2002). Then, we demonstrate excellence of tight-coupling by showing benchmark results. We have built a web search engine that is capable of managing 20~100 million web pages in a non-parallel configuration using Odysseus. This engine has been successfully tested in many commercial environments. In a parallel configuration, it is capable of managing billons of web pages. This work won the Best Demonstration Award from the IEEE ICDE conference held in Tokyo, Japan in April 2005.W EDNESDAY K EYNOTE A DDRESS The Evolution of Digital Evidence asa Forensic ScienceDr. Marc RogersChair of the Cyber Forensics Program,Department of Computer and InformationTechnology, Purdue University, USAW EDNESDAY M ARCH 14, 2007, 9:00 –10:00ROOMS 310 A, B AND CABSTRACTThe field of Digital Evidence while garnering significant attention by academia, the public, and the media, has really just begun its journey as a forensic science. Digital Forensic Science (DFS) in general is an immature discipline in comparison to the other more traditional forensic sciences such as latent fingerprint analysis. Digital Evidence, which falls under the larger umbrella of DFS, truly encompasses the notion of being an applied multi-disciplinary science. The areas of Computer Science, Technology, Engineering, Mathematics, Law, Sociology, Psychology, Criminal Justice etc. all have played and will continue to play a very large role in maturing and defining this scientific field. The presentation will look at the history of Digital Forensic Science and Digital Evidence, the current state of the field, and what might be in store for the future.S EOUL R EPRESENTATIVE A DDRESSKoran IT policy - IT839Dr. Jung-hee SongAssistant MayorChief of Information OfficerInformation System Planning DivisionSeoul Metropolitan Government, KoreaW EDNESDAY M ARCH 14, 2007, 18:30 – 19:00ROOMS 331-334(DURING BANQUET)ABSTRACTKorean IT policy initiated by Ministry of Information and Communication called IT839 Strategy will be introduced. By defining government role in the u-Korea vision pursuit, it removes uncertainties for IT industry and increases its active participation. As capital of Korea, Seoul presented a grand plan to be u-Seoul. An overview of u-Seoul masterplan will be delivered with introduction of 5 specific projects.SAC 2007 S CHEDULES UNDAY M ARCH 11, 200709:00 – 17:00 L OBBYR EGISTRATION09:00 – 10:30 R OOMS 310 A AND BAM T UTORIALS IT1: Introduction to Security-enhanced Linux(SELinux)Dr. Haklin Kimm, Professor, omputer Science Department, ast Stroudsburg University of Pennsylvania, USAT2: Similarity Search - The Metric Space Approach Pavel Zezula, Masaryk University, Brno, Czech RepublicGiuseppe Amato, ISTI-CNR, Pisa, ItalyVlastislav Dohnal, Masaryk University, Brno, Czech Republic10:30 – 11:00 L OBBYC OFFEE B REAK11:00 – 12:30 R OOMS 310 A AND BAM T UTORIALS IIT1: Introduction to Security-enhanced Linux(SELinux)Dr. Haklin Kimm, Professor, omputer Science Department, ast Stroudsburg University of Pennsylvania, USAT2: Similarity Search - The Metric Space Approach Pavel Zezula, Masaryk University, Brno, Czech RepublicGiuseppe Amato, ISTI-CNR, Pisa, ItalyVlastislav Dohnal, Masaryk University, Brno, Czech Republic 12:00 – 13:30 H OSU (L AKE) F OOD-MALL,1ST F LOORL UNCH B REAK13:30 – 15:00 R OOM 310 APM T UTORIAL IT3: Introduction to OWL Ontology Developmentand OWL ReasoningYoung-Tack Park, Professor, School of Computing, SoongsilUniversity,Seoul, Korea15:00 – 15:30 L OBBYC OFFEE B REAK15:30 – 17:00 R OOM 310 APM T UTORIAL IIT3: Introduction to OWL Ontology Developmentand OWL ReasoningYoung-Tack Park, Professor, School of Computing, SoongsilUniversity,Seoul, Korea18:00 – 19:00 R OOM 311A SAC 2007 R EVIEW M EETINGM ONDAY M ARCH 12, 200708:00 – 17:00 L OBBYR EGISTRATION08:30 – 09:00 R OOM 310O PENING R EMARKS09:00 – 10:00 R OOM 310K EYNOTE A DDRESSA New DBMS Architecture for DB-IRIntegrationDr. Whang, Kyu-YoungDirector of Advanced Information TechnologyResearch CenterKorea Advanced Institute of Science andTechnologyDaejeon, Korea10:00 – 10:30 L OBBYC OFFEE B REAK10:30 – 12:00 R OOM 310A(DS) Data StreamsJoao Gama, University of Porto (UP), Portugal RFID Data Management for Effective ObjectsTrackingElioMasciari, CNR, ItalyA Priority Random Sampling Algorithm for Time-based Sliding Windows over Weighted StreamingDataZhang Longbo, Northwestern Polytechnical University, China Li Zhanhuai, Northwestern Polytechnical University, ChinaZhao Yiqiang, Shandong University of Technology, ChinaMin Yu, Northwestern Polytechnical University, China Zhang Yang, Northwest A&F University, ChinaOLINDDA: A Cluster-based Approach forDetecting Novelty and Concept Drift in DataStreamsEduardo Spinosa, University of Sao Paulo (USP), BrazilAndré Carvalho, University of Sao Paulo (USP), Brazil Joao Gama, University of Porto (UP), PortugalA Self-Organizing Neural Network for DetectingNoveltiesMarcelo Albertini, Universidade de Sao Paulo, BrazilRodrigo Mello, Universidade de São Paulo, Brazil10:30 – 12:00 R OOM 310B (AI) Artificial Intelligence, ComputationalLogic and Image AnalysisChih-Cheng Hung, Southern Polytechnic State University, USA Toward a First-Order Extension of Prolog'sUnification using CHRKhalil Djelloul, University of Ulm, GermanyThi-Bich-Hanh Dao, University d'Orléans, FranceThom Fruehwirth, University of Ulm, GermanyA Framework for Prioritized Reasoning Based onthe Choice EvaluationLuciano Caroprese, University of Calabria, ItalyIrina Trubitsyna, University of Calabria, ItalyEster Zumpano, University of Calabria, ItalyA Randomized Knot Insertion Algorithm for Outline Capture of Planar Images using CubicSplineMuhammad Sarfraz, King Fahd University of Petroleum andMinerals, Saudi ArabiaAiman Rashid, King Fahd University of Petroleum and Minerals,Saudi ArabiaEstraction of Arabic Words from Complex ColorImagesRadwa Fathalla, AAST, EgyptYasser El Sonbaty, AAST College of Computing, Egypt Mohamed Ismail, Alexandria University, Egypt10:30 – 12:00 R OOM 310C (PL) Programming LanguagesMarjan Mernik, University of Maribor, Slovenia Implementing Type-Based Constructive Negation Lunjin Lu, Oakland University, USATowards Resource-Certified Software: A Formal Cost Model for Time and its Application to anImage-Processing ExampleArmelle Bonenfant, University of St Andrews, UKZehzi Chen, Heriot-Watt University, UKKevin Hammond, Univestiy of St Andrews, UKGreg Michaelson, Heriot-Watt University, UKAndy Wallace, Heriot-Watt University, UKIain Wallace, Heriot-Watt University, UK。
06_TermWeightingForScoring
Use query optimization heuristics as before
Field retrieval
In some cases, must retrieve field values
E.g., ISBN numbers of books by s*trup
Maintain “forward” index – for each doc, those field values that are “retrievable”
Linear zone combinations
First generation of scoring methods: use a linear combination of Booleans:
E.g., Score = 0.6*<sorting in Title> + 0.3*<sorting in Abstract> + 0.05*<sorting in Body> + 0.05*<sorting in Boldface> Each expression such as <sorting in Title> takes on a value in {0,1}. Then the overall score is in [0,1]. For this example the scores can only take on a finite set of values – what are they?
A parametric search interface allows the user to combine a full-text query with selections on these field values e.g.,