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semantic computing AND PRIVACY: A CASE STUDY USING INFERRED GEO-LOCATION

INTERNATIONAL JOURNAL OF semantic computing 2011年第1期5卷 79-93页

作者： Friedland, Gerald Choi, Jaeyoung Int Comp Sci Inst 1947 Ctr StSuite 600 Berkeley CA 94704 USA

This paper presents an experiment that allows the inference over data published in social networks, resulting in a potentially severe privacy leak, more specifically the inference of geo-location resulting in the potential of cybercasing attacks. We present an algorithm that allows the inference of the geo-location of YouTube and Flickr videos based on the tag descriptions. Using the locations, we find people where we can infer both the home address as well as the fact that they are currently on vacation, which makes them potential targets for burglary. By doing so we repeat an experiment from the literature that was originally meant to show the potential dangers of geo-tagging but replacing the geo-tags with semantic computing methods. We conclude that the only way to tackle potential threats like this is for researchers to develop an enhanced notion of privacy for semantic computing.

关键词： semantic computing social computing privacy

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semantic computing for Education

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INTERNATIONAL JOURNAL OF semantic computing 2015年第3期9卷 395-413页

作者： Jin, Jennifer Kim, Mira Rivett, Pete Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA Adaptive Inc Irvine CA 92618 USA

There are challenges educators face in delivering the vast amount of teaching material to students. Using ontologies could solve some of these issues. We survey different types of ontologies available that could aid educators teach students. We discuss how ontologiesmay help improve the education system for K-12, higher education, curriculum creating, e-learning, etc. We analyze the efficacy of the ontologies available as well as the challenges educators face and how to make improvements.

关键词： Ontology e-learning K-12 higher education curriculum semantic computing

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semantic computing IN SOCIAL MEDIA

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INTERNATIONAL JOURNAL OF semantic computing 2013年第3期7卷 325-347页

作者： Ostrowski, David Alfred Ford Motor Res & Adv Engn Res & Innovat Ctr Dearborn MI 48124 USA

The ever-increasing amount of information flowing through Social Media presents numerous opportunities for the generation of Business Intelligence. Challenges exist in the leveraging of these data sources due to their heterogeneity and unstructured content. This paper presents the application of semantic computing to Social Media for industrial application, focusing on topic identification and behavior prediction. The methodologies described can benefit many areas of an organization including support of marketing, customer service, engineering and public relations. Results demonstrate that business operations can be substantially enhanced through application of semantic computing to Social Media.

关键词： semantic computing Social Media Social Networks

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semantic computing AND COMPUTER SCIENCE

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INTERNATIONAL JOURNAL OF semantic computing 2011年第1期5卷 95-120页

作者： Hao, Ke Gong, Zhiyuan Huo, Chengjia Sheu, Phillip C. -Y. Univ Calif Irvine Dept EECS Irvine CA 92697 USA

semantic computing is an emerging research field that has drawn much attention from both academia and industry. It addresses the derivation and matching of semantics of computational "content" where "content" may be anything including text, multimedia, hardware, network, etc. which can be mapped to many areas in Computer Science that involve analyzing and processing the intentions of humans with computational content. This paper discusses some potential applications of semantic computing in Computer Science.

关键词： semantic computing computer science software engineering programming language distributed system communication system web services ontology operating system real-time system security privacy database biomedical system natural language processing

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semantic computing for Railway Infrastructure Verification

Semantic Computing for Railway Infrastructure Verification

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7th IEEE International Conference on semantic computing (ICSC)

作者： Lodemann, Michael Luttenberger, Norbert Schulz, Elferik Univ Kiel Dept Comp Sci Kiel Germany Funkwerk AG Traff & Control Commun Kiel Germany

ISBN: (纸本)9780769551197

We present an approach for modeling, verification and debugging of railway infrastructures. The aim of this work is to improve the planning process of new railway lines. We define a set of OWL ontologies for describing the static interrelations of railway tracks and safety elements. Planning instructions-so far available in natural language only-are formally modeled by SWRL rules. The Open World Assumption underlying OWL makes it difficult to perform ontology debugging. Therefore, the concept of semantic Constraints-based on a dynamic rule composition process-is developed.

关键词： semantic computing modeling verification railway infrastructure semantic constraint ontology debugging

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semantic computing, Cloud computing, and semantic Search Engine

Semantic Computing, Cloud Computing, and Semantic Search Eng...

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3rd International Conference on semantic computing (ICSC 2009)

作者： Sheu, Phillip C-Y Wang, Shu Wang, Qi Hao, Ke Paul, Ray Univ Calif Irvine Dept EECS Irvine CA 92717 USA Univ Calif Irvine Dept Def Irvine CA USA Univ Calif Irvine Inst Semant Comp Irvine CA USA

ISBN: (纸本)9781424449620

semantic computing extends semantic Web both in breadth and depth. It bridges, and integrates, technologies such as software engineering, user interface, natural language processing, artificial intelligence, programming language, grid computing and pervasive computing, among others, into a complete and unified theme. Cloud computing, the dream of computing as a utility, shifts user programs and data from personal computers to the clouds, providing all kinds of resources over the Internet as services to customers and letting customers pay for them in a way much like they pay for traditional utilities such as electricity. This paper analyzes both semantic computing and Cloud computing, and introduces the semantic Search Engine, an infrastructure and implementation of semantic computing, that demonstrates how semantic computing can benefit Cloud computing.

关键词： semantic computing cloud computing semantic search engine

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Generalized rough and fuzzy rough automata for semantic computing

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第12期13卷 4013-4032页

作者： Yadav, Swati Tiwari, S. P. Kumari, Mausam Yadav, Vijay K. Indian Inst Technol Dept Math & Comp Indian Sch Mines Dhanbad 826004 Bihar India Cent Univ Rajasthan Dept Math Ajmer 305817 Rajasthan India

The classical automata, fuzzy finite automata, and rough finite state automata are some formal models of computing used to perform the task of computation and are considered to be the input device. These computational models are valid only for fixed input alphabets for which they are defined and, therefore, are less user-friendly and have limited applications. The semantic computing techniques provide a way to redefine them to improve their scope and applicability. In this paper, the concept of semantically equivalent concepts and semantically related concepts in information about real-world applications datasets are used to introduce and study two new formal models of computations with semantic computing (SC), namely, a rough finite-state automaton for SC and a fuzzy finite rough automaton for SC as extensions of rough finite-state automaton and fuzzy finite-state automaton, respectively, in two different ways. The traditional rough finite-state automata can not deal with situations when external alphabet or semantically equivalent concepts are given as inputs. The proposed rough finite-state automaton for SC can handle such situations and accept such inputs and is shown to have successful real-world applications. Similarly, a fuzzy finite rough automaton corresponding to a fuzzy automaton is also failed to process input alphabet different from their input alphabet, the proposed fuzzy finite rough automaton for SC corresponding to a given fuzzy finite automaton is capable of processing semantically related input, and external input alphabet information from the dataset obtained by real-world applications and provide better user experience and applicability as compared to classical fuzzy finite rough automaton.

关键词： semantic computing semantic relations Fuzzy finite automata Rough finite state automata Fuzzy finite rough automata

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A formal model of semantic computing

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SOFT computing 2019年第14期23卷 5411-5429页

作者： Jiang, Yuncheng South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China

In the existing works of semantic computing (SC), the word computing in the phrase semantic computing means computational implementations of semantics reasoning (e.g., ontology reasoning, rule reasoning, semantic query, and semantic search) but is irrelevant to the formal theory of computation (e.g., computational models such as finite automaton, pushdown automaton, and Turing machine). In this paper, we propose a different understanding of semantic computing from a computation theory perspective. Concretely, we present a formal model of SC in terms of automata and discuss SC for the two most important and simplest types of automata, namely finite automata and pushdown automata. For each automaton, we first consider a simple case (equivalent concepts) and then we further investigate a general situation (semantically related concepts). That is, some new automata for SC are provided: finite (or pushdown) automaton for SC under equivalent concepts, finite (or pushdown) automaton for SC w.r.t. external words, nondeterministic finite automaton for SC under equivalent concepts (or w.r.t. external words), fuzzy finite (or pushdown) automaton for SC under semantically related concepts, and fuzzy finite (or pushdown) automaton for SC w.r.t. external words. Furthermore, we give some properties of these new automata for SC and prove that these new automata are extensions (or enlargements) of traditional (fuzzy) automata.

关键词： semantic computing Finite automata Pushdown automata Fuzzy finite automata Fuzzy pushdown automata

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Mobile Medicine: semantic computing management for health care applications on desktop and mobile devices

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MULTIMEDIA TOOLS AND APPLICATIONS 2012年第1期58卷 41-79页

作者： Bellini, Pierfrancesco Bruno, Ivan Cenni, Daniele Fuzier, Alice Nesi, Paolo Paolucci, Michela Univ Florence Dept Syst & Informat Distributed Syst & Internet Technol Lab Florence Italy

In many health care situations, powerful mobile tools may help to make decisions and provide support for continuous education and training. They can be useful in emergency conditions and for the supervised application of protocols and procedures. To this end, content models and formats with semantic and intelligence have more flexibility to provide medical personnel (both in off-line and on-line conditions) with more powerful tools than those currently on the market. In this paper, we are presenting Mobile Medicine solution, which exploits a collection of semantic computing technologies together with intelligent content model and tools to provide innovative services for medical personnel. Most of the activities of semantic computing are performed on the back office on a cloud computing architecture for: clustering, recommendations, intelligent content production and adaptation. The mobile devices have been endowed with a content organizer to collect local data, provide local suggestions, while supporting taxonomical searches and local queries on PDA and iPhone. The proposed solution is under usage at the main hospital in Florence. The smart content has been produced by medical personnel, with the adoption of the new ADF-Design authoring tool, which produces content in MPEG-21 format. The mobile content distribution service is integrated with a collaborative networking portal, for discussion on procedures and content, thus suggestions are provided on both PC and Mobiles (PDA and iPhone).

关键词： Mobile Medicine Content distribution Cross media content Automated production mpeg-21 pda iphone semantic computing DRM

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Genre-Adaptive semantic computing and Audio-Based Modelling for Music Mood Annotation

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IEEE TRANSACTIONS ON AFFECTIVE computing 2016年第2期7卷 122-135页

作者： Saari, Pasi Fazekas, Gyorgy Eerola, Tuomas Barthet, Mathieu Lartillot, Olivier Sandler, Mark Univ Jyvaskyla Dept Mus Jyvaskyla 40014 Finland Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Univ Durham Dept Mus Durham DH1 3RL England Aalborg Univ Dept Architecture Design & Media Technol DK-9000 Aalborg Denmark

This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling are proposed. A technique called the ACTwg employs genre-adaptive semantic computing of mood-related social tags, whereas ACTwg-SLPwg combines semantic computing and audio-based modelling, both in a genre-adaptive manner. The proposed techniques are experimentally evaluated at predicting listener ratings related to a set of 600 popular music tracks spanning multiple genres. The results show that ACTwg outperforms a semantic computing technique that does not exploit genre information, and ACTwg-SLPwg outperforms conventional techniques and other genre-adaptive alternatives. In particular, improvements in the prediction rates are obtained for the valence dimension which is typically the most challenging core affect dimension for audio-based annotation. The specificity of genre categories is not crucial for the performance of ACTwg-SLPwg. The study also presents analytical insights into inferring a concise tag-based genre representation for genre-adaptive music mood analysis.

关键词： Music information retrieval mood prediction social tags semantic computing music genre genre-adaptive

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