This book constitutes the refereed proceedings of the International Second International Multi-Conference on Artificial Intelligence Technology, M-CAIT 2013, held in Shah Alam, in August 2013. The 25 revised full papers presented were carefully reviewed and selected from 110 submissions. M-CAIT 2013 hosted four special tracks in a single event: Intelligence Computation on Pattern Analysis and Robotics (ICPAIR 2013), Data Mining and Optimization (DMO 2013), Semantic Technology and Information Retrieval (STAIR 2013) and Industrial Computing & Applied Informatics (IComp 2013). The papers address issues of state-of-the-art research, development, implementation and applications within the four focus areas in CAIT: pattern recognition, data mining and optimization, knowledge technology and industrial computing.

The three volume set provides a systematic overview of theories and technique on social network analysis. Volume 1 of the set mainly focuses on the structure characteristics, the modeling, and the evolution mechanism of social network analysis. Techniques and approaches for virtual community detection are discussed in detail as well. It is an essential reference for scientist and professionals in computer science.

Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets. We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods. We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation.

Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.

A large international conference on Advances in Machine Learning and Data Analysis was held in UC Berkeley, California, USA, October 22-24, 2008, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2008). This volume contains sixteen revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Advances in Machine Learning and Data Analysis offers the state of the art of tremendous advances in machine learning and data analysis and also serves as an excellent reference text for researchers and graduate students, working on machine learning and data analysis.

This book constitutes the thoroughly refereed proceedings of the 8th International Workshop on Computational Processing of the Portuguese Language, PROPOR 2012, held in Coimbra, Portugal in April 2012. The 24 revised full papers and 23 revised short papers presented were carefully reviewed and selected from 86 submissions. These papers cover the areas related to phonology, morphology and POS-Tagging, acquisition, language resources, linguistic description, syntax and parsing, semantics, opinion analysis, natural language processing applications, speech production and phonetics, speech resources, speech processing and applications.

Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB(r).

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between Grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the fifth issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains nine selected full-length papers, focusing on the topics of query processing, information extraction, management of dataspaces and contents, and mobile applications.

This book constitutes the thoroughly refereed conference proceedings of the First International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2012, held in conjunction with ECML/PKDD 2012, in Bristol, UK, in September 2012. The 15 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on mining rich (relational) datasets, mining complex patterns from miscellaneous data, mining complex patterns from trajectory and sequence data, and mining complex patterns from graphs and networks.

This book constitutes the refereed proceedings of the 9th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2012, held in Houston, TX, USA during in July 2012. The 16 revised full papers presented were carefully reviewed and selected from 23 submissions. The papers are organized in topical sections on relativistic heavy ions and DNA damage; image segmentation; proteomics; RNA and DNA sequence analysis; RNA, DNA, and SNP microarrays; semi-supervised/unsupervised cluster analysis.

Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world "friend of a friend" recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required. This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user's interest similarity. To identify the interest similarity, a user's personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data. Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this book valuable.

Foreword------------------------------------- SGML is misunderstood and underestimated. I have always wanted to write this book. I am pleased that two people with whom I have had the pleasure to work were finally able to do so. Since I have always been a bit of an evangelist, I feel pride when my "students" become recognized "teachers." In the early years of SGML we struggled to define a language that would bring the information to its rightful place. We succeeded. Then we had to explain these idea to technical adoptors. Again, I think we have succeeded. We have learned much about SGML in the process of implementing it. These experiences must now also be shared, along with comprehensible information on the lan guage itself. The word must move out of the lab and the computer center and reach the business people, the users, the movers and shakers. The next generation will do things with SGML that we can't even imagine yet- it is that versatile."

The International conference series on Computer Science, Engineering & Applications (ICCSEA) aims to bring together researchers and practitioners from academia and industry to focus on understanding computer science, engineering and applications and to establish new collaborations in these areas. The Second International Conference on Computer Science, Engineering & Applications (ICCSEA-2012), held in Delhi, India, during May 25-27, 2012 attracted many local and international delegates, presenting a balanced mixture of intellect and research both from the East and from the West. Upon a strenuous peer-review process the best submissions were selected leading to an exciting, rich and a high quality technical conference program, which featured high-impact presentations in the latest developments of various areas of computer science, engineering and applications research.

This two-volume set, consisting of LNCS 7181 and LNCS 7182, constitutes the thoroughly refereed proceedings of the 13th International Conference on Computer Linguistics and Intelligent Processing, held in New Delhi, India, in March 2012. The total of 92 full papers were carefully reviewed and selected for inclusion in the proceedings. The contents have been ordered according to the following topical sections: NLP system architecture; lexical resources; morphology and syntax; word sense disambiguation and named entity recognition; semantics and discourse; sentiment analysis, opinion mining, and emotions; natural language generation; machine translation and multilingualism; text categorization and clustering; information extraction and text mining; information retrieval and question answering; document summarization; and applications.

This book constitutes the thoroughly refereed post-workshop proceedings of 5 workshops, held at the 10th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2011, in Taipei, Taiwan, May 2-6, 2011. The 37 revised full papers presented together with 1 invited paper were carefully reviewed and selected from numerous submissions. The papers are organized in sections on the workshops Agent-Based Modeling for Policy Engineering (AMPLE), Agent-Oriented Software Engineering (AOSE), Autonomous Robots and Multirobot Systems (ARMS), Data Oriented Constructive Mining and Multi-Agent Simulation, Massively Multi-Agent Systems: Models, Methods and Tools (DOCM(3)AS), and Infrastructures and Tools for Multiagent Systems (ITMAS).

Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the "noise" that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

The two-volume set LNCS 7649 + 7650 constitutes the refereed proceedings of the 11th International Semantic Web Conference, ISWC 2012, held in Boston, MA, USA, in November 2012. The International Semantic Web Conference is the premier forum for Semantic Web research, where cutting edge scientific results and technological innovations are presented, where problems and solutions are discussed, and where the future of this vision is being developed. It brings together specialists in fields such as artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, human-computer interaction, natural language processing, and the social sciences. Volume 1 contains a total of 41 papers which were presented in the research track. They were carefully reviewed and selected from 186 submissions. Volume 2 contains 17 papers from the in-use track which were accepted from 77 submissions. In addition, it presents 8 contributions to the evaluations and experiments track and 7 long papers and 8 short papers of the doctoral consortium.

This book constitutes the refereed proceedings of the 5th International Conference on Data Management in Grid and Peer-to-Peer Systems, Globe 2012, held in Vienna, Austria, in September 2012 in conjunction with DEXA 2012. The 9 revised full papers presented were carefully reviewed and selected from 15 submissions. The papers are organized in topical sections on data management in the cloud, cloud MapReduce and performance evaluation, and data stream systems and distributed data mining.

About two years ago, while attending yet another international standards meeting, a few of the meeting participants were discussing the utility and applicability of the standards we were designing. After all, if standards are not used, and used effectively, why are we spending all this time and money designing them? The ultimate test of the utility of computer standards is the number of implementations that are developed and the number of end-users that successfully use these within their own application. The number of implementations is related to the quality of a standard because vendors cannot produce correct implementations without clear, precise and unambiguous semantics within the standard. The number of users of implementations of the standards is an even greater measure of success of the standard because users will only purchase these implementations if they are useful for their applications. "How could we determine whether or not graphics standards are useful?" we asked ourselves. " Let's ask both implementors and users about the experiences they've had with our standards. Let them tell us about the successes and the problems as well. " Thus, an idea was born - the idea of a series of workshops, each one devoted to the usability of a different computer graphics standard. The only thing left to do in planning this workshop was to choose the appropriate standard to serve as the focus of the first workshop. There were only a few viable candidates.

The two-volume set LNAI 7802 and LNAI 7803 constitutes the refereed proceedings of the 5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013, held in Kuala Lumpur, Malaysia in March 2013. The 108 revised papers presented were carefully reviewed and selected from numerous submissions. The papers included are grouped into topical sections on: innovations in intelligent computation and applications; intelligent database systems; intelligent information systems; tools and applications; intelligent recommender systems; multiple modal approach to machine learning; engineering knowledge and semantic systems; computational biology and bioinformatics; computational intelligence; modeling and optimization techniques in information systems, database systems and industrial systems; intelligent supply chains; applied data mining for semantic Web; semantic Web and ontology; integration of information systems; and conceptual modeling in advanced database systems.

The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed proceedings of the 7th International Conference on Advanced Data Mining and Applications, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised full papers and 29 short papers presented together with 3 keynote speeches were carefully reviewed and selected from 191 submissions. The papers cover a wide range of topics presenting original research findings in data mining, spanning applications, algorithms, software and systems, and applied disciplines.

The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed proceedings of the 7th International Conference on Advanced Data Mining and Applications, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised full papers and 29 short papers presented together with 3 keynote speeches were carefully reviewed and selected from 191 submissions. The papers cover a wide range of topics presenting original research findings in data mining, spanning applications, algorithms, software and systems, and applied disciplines.

The five volume set LNCS 7663, LNCS 7664, LNCS 7665, LNCS 7666 and LNCS 7667 constitutes the proceedings of the 19th International Conference on Neural Information Processing, ICONIP 2012, held in Doha, Qatar, in November 2012. The 423 regular session papers presented were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The 5 volumes represent 5 topical sections containing articles on theoretical analysis, neural modeling, algorithms, applications, as well as simulation and synthesis.

Anonymization of Electronic Medical Records to Support Clinical Analysis closely examines the privacy threats that may arise from medical data sharing, and surveys the state-of-the-art methods developed to safeguard data against these threats.

To motivate the need for computational methods, the book first explores the main challenges facing the privacy-protection of medical data using the existing policies, practices and regulations. Then, it takes an in-depth look at the popular computational privacy-preserving methods that have been developed for demographic, clinical and genomic data sharing, and closely analyzes the privacy principles behind these methods, as well as the optimization and algorithmic strategies that they employ. Finally, through a series of in-depth case studies that highlight data from the US Census as well as the Vanderbilt University Medical Center, the book outlines a new, innovative class of privacy-preserving methods designed to ensure the integrity of transferred medical data for subsequent analysis, such as discovering or validating associations between clinical and genomic information.

Anonymization of Electronic Medical Records to Support Clinical Analysis is intended for professionals as a reference guide for safeguarding the privacy and data integrity of sensitive medical records. Academics and other research scientists will also find the book invaluable.

This book constitutes the proceedings of the 5th International Conference on Knowledge Science, Engineering and Management, KSEM 2011, held in Irvine, CA, USA, in December 2011. The 34 revised full papers presented together with 7 short papers were carefully reviewed and selected from numerous submissions.