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Computational Intelligence and Security
The two volume set LNAI 3801 and LNAI 3802 constitute the refereed proceedings of the annual International Conference on Computational Intelligence and Security, CIS 2005, held in Xi'an, China, in December 2005. The 338 revised papers presented - 254 regular and 84 extended papers - were carefully reviewed and selected from over 1800 submissions. The first volume is organized in topical sections on learning and fuzzy systems, evolutionary computation, intelligent agents and systems, intelligent information retrieval, support vector machines, swarm intelligence, data mining, pattern recognition, and applications. The second volume is subdivided in topical sections on cryptography and coding, cryptographic protocols, intrusion detection, security models and architecture, security management, watermarking and information hiding, web and network applications, image and signal processing, and applications.
Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges
The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the p...
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Joint Training for Neural Machine Translation
This book presents four approaches to jointly training bidirectional neural machine translation (NMT) models. First, in order to improve the accuracy of the attention mechanism, it proposes an agreement-based joint training approach to help the two complementary models agree on word alignment matrices for the same training data. Second, it presents a semi-supervised approach that uses an autoencoder to reconstruct monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based neural machine translation, which can be used to mitigate the data scarcity problem. Lastly it describes an end-to-end bidirectional NMT model to connect the source-to-target and target-to-source translation models, allowing the interaction of parameters between these two directional models.
Knowledge Graphs
A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence. The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.
Text, Speech and Dialogue
This book constitutes the refereed proceedings of the 11th International Conference on Text, Speech and Dialogue, TSD 2008, held in Brno, Czech Republic, September 8-12, 2008. The 79 revised full papers presented together with 4 invited papers were carefully reviewed and selected from 173 submissions. The topics of the conference include, but are not limited to, text corpora and tagging; transcription problems in spoken corpora; sense disambiguation; links between text and speech oriented systems; parsing issues; parsing problems in spoken texts; multi-lingual issues; multi-lingual dialogue systems; information retrieval and information extraction; text/topic summarization; machine translation; semantic networks and ontologies; semantic web; speech modeling; speech segmentation; speech recognition; search in speech for IR and IE; text-to-speech synthesis; dialogue systems; development of dialogue strategies; prosody in dialogues; emotions and personality modeling; user modeling; knowledge representation in relation to dialogue systems; assistive technologies based on speech and dialogue; applied systems and software; facial animation; and visual speech synthesis
Generating Creative Language - Theories, Practice and Evaluation
This thesis presents approaches to computationally creative natural language generation focusing on theoretical foundations, practical solutions and evaluation. I defend that a theoretical definition is crucial for computational creativity and that the practical solution must closely follow the theoretical definition. Finally, evaluation must be based on the underlying theory and what was actually modelled in the practical solution. A theoretical void in the existing theoretical work on computational creativity is identified. The existing theories do not explicitly take into account the communicative nature of natural language. Therefore, a new theoretical framework is elaborated that identi...
Rough Sets and Current Trends in Computing
This book constitutes the refereed proceedings of the 5th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2006, held in Kobe, Japan in November 2006. The 91 revised full papers presented together with five invited papers and two commemorative papers were carefully reviewed and selected from 332 submissions.
Legal Knowledge and Information Systems
Technological advances related to legal information, knowledge representation, engineering, and processing have aroused growing interest within the research community and the legal industry in recent years. These advances relate to areas such as computational and formal models of legal reasoning, legal data analytics, legal information retrieval, the application of machine learning techniques to different legal tasks, and the experimental evaluation of these systems. This book presents the proceedings of JURIX 2023, the 36th International Conference on Legal Knowledge and Information Systems, held from 18–20 December 2023 in Maastricht, the Netherlands. This annual conference has become re...
Image and Video Retrieval
Here are the refereed proceedings of the 5th International Conference on Image and Video Retrieval, CIVR 2006, held in Singapore in July 2006. Presents 18 revised full papers and 30 poster papers, together with extended abstracts of 5 papers of 1 special session and those of 10 demonstration papers. These cover interactive image and video retrieval, semantic image retrieval, visual feature analysis, learning and classification, image and video retrieval metrics, and machine tagging.
