https://www.inf.ed.ac.uk/teaching/courses/seoc/2005_2006/resources/statecharts.pdf

Statecharts: A visual formalism for complex systems

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

Systems composed of interacting autonomous agents offer a promising software engineering approach for developing applications in complex domains. However, this multiagent system paradigm introduces a number of new abstractions and design/development issues when compared with more traditional approaches to software development. Accordingly, new analysis and design methodologies, as well as new tools, are needed to effectively engineer such systems. Against this background, the contribution of this article is twofold. First, we synthesize and clarify the key abstractions of agent-based computing as they pertain to agent-oriented software engineering. In particular, we argue that a multiagent system can naturally be viewed and architected as a computational organization, and we identify the appropriate organizational abstractions that are central to the analysis and design of such systems. Second, we detail and extend the Gaia methodology for the analysis and design of multiagent systems. Gaia exploits the aforementioned organizational abstractions to provide clear guidelines for the analysis and design of complex and open software systems. Two representative case studies are introduced to exemplify Gaia's concepts and to show its use and effectiveness in different types of multiagent system.

/pdf/developing-multiagent-systems-the-gaia-methodology-3qm0mwov2h.pdf

Developing multiagent systems: The Gaia methodology

Enticing online consumers: an extended technology acceptance perspective

Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.

/pdf/a-comprehensive-survey-of-clustering-algorithms-2webn4t8if.pdf

A Comprehensive Survey of Clustering Algorithms

Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.

A Cluster-Matching-Based Method for Video Face Recognition

Lunch that day wa5 at a very n1ce re5taurant near the c0nference 51te (0f c0ur5e, French cu151ne w0u1d mer1t a rep0rt 0f 1t5 0wn). At the ta61e, 50me0ne remarked that he d1dn • t u5ua11y have a fu11 mea1 at 1unch, t0 wh1ch 5h0wed h0w the 06ject 0r1ented Hypermed1a De519n Meth0d (00HDM) can he1p 1n 5eparat1n9 the d1ver5e de519n c0ncern5 (c0nceptua1, nav19at10na1 and 1nterface), f0cu51n9 0n what mu5t 6e 5pec1f1ed at the 1nterface 1eve1.

International Workshop on Hypermedia Design

Documents, sites and in general applications in the World-Wide Web are undergoing constant evolution. Yet we would argue that not everything changes but that a large proportion of design decisions are of lasting quality. We propose that such design decision be captured explicitly through complimentary use of design patterns in design methods, and illustrate this with the use of patterns in the Object-Oriented Hypermedia Design Method (OOHDM). Further, we propose the use of an object-oriented web implementation technology, the WebComposition Markup Language (WCML), to facilitate straightforward mapping of object-oriented design patterns to an implementation, and so to facilitate the tracking of higher-level design decisions in a web implementation.

Patterns and Components: Capturing the Lasting Amidst the Changing

A multigranularity locking model for RDF

Modern day interfaces must deal with a large number of heterogeneity factors, such as varying user profiles and runtime hardware and software platforms. These conditions require interfaces that can adapt to the changes in the triad. The Model-Based User Interface approach has been proposed as a way to deal with these requirements. In this paper we present a data-driven, rule-based interface definition model capable of taking into account the semantics of the data it is manipulating, especially in the case of Linked Data. An implementation architecture based on the Synth environment supporting this model is presented.

/pdf/semantic-data-driven-interfaces-for-web-applications-28hnncoex7.pdf

Daniel Schwabe

Papers

A Cluster-Matching-Based Method for Video Face Recognition

International Workshop on Hypermedia Design

Patterns and Components: Capturing the Lasting Amidst the Changing

A multigranularity locking model for RDF

Semantic data driven interfaces for web applications