A Review on Personalized Tag based Image based Search Engines

The development of social media based on Web 2.0, amounts of images and videos spring up everywhere on the Internet. This phenomenon has brought great challenges to multimedia storage, indexing and retrieval. Generally speaking, tag-based image search is more commonly used in social media than content based image retrieval and content understanding. Thanks to the low relevance and diversity performance of initial retrieval results, the ranking problem in the tag-based image retrieval has gained researchers� wide attention. We will review some of techniques proposed by different authors for image retrieval in this paper

Between browsing and search, a new model for navigating through large documents

International audienceThis paper proposes a new model for document access, which combines the search and browsing approaches. We define a good navigation as a navigation which is as quick and direct as possible and which offers good precision and recall rates in finding the text segments that are relevant for the user's information need. Our navigation model relies on recent advances in natural language processing and it is based on two traditional cognitive principles that are inherited from works on the visualization of information. This model is implemented in a navigation prototype which is designed for physicians who want to consult official recommendations to take medical decisions. Even if this model has not been really evaluated yet, we show the dynamicity and the efficiency of our approach on few detailled examples

The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction

Non peer reviewe

Semantic linking through spaces for cyber-physical-socio intelligence:a methodology

Humans consciously and subconsciously establish various links, emerge semantic images and reason in mind, learn linking effect and rules, select linked individuals to interact, and form closed loops through links while co-experiencing in multiple spaces in lifetime. Machines are limited in these abilities although various graph-based models have been used to link resources in the cyber space. The following are fundamental limitations of machine intelligence: (1) machines know few links and rules in the physical space, physiological space, psychological space, socio space and mental space, so it is not realistic to expect machines to discover laws and solve problems in these spaces; and, (2) machines can only process pre-designed algorithms and data structures in the cyber space. They are limited in ability to go beyond the cyber space, to learn linking rules, to know the effect of linking, and to explain computing results according to physical, physiological, psychological and socio laws. Linking various spaces will create a complex space — the Cyber-Physical-Physiological-Psychological-Socio-Mental Environment CP3SME. Diverse spaces will emerge, evolve, compete and cooperate with each other to extend machine intelligence and human intelligence. From multi-disciplinary perspective, this paper reviews previous ideas on various links, introduces the concept of cyber-physical society, proposes the ideal of the CP3SME including its definition, characteristics, and multi-disciplinary revolution, and explores the methodology of linking through spaces for cyber-physical-socio intelligence. The methodology includes new models, principles, mechanisms, scientific issues, and philosophical explanation. The CP3SME aims at an ideal environment for humans to live and work. Exploration will go beyond previous ideals on intelligence and computing

Modeling Scholar Profile in Expert Recommendation based on Multi-Layered Bibliographic Graph

A recommendation system requires the profile of researchers which called here as Scholar Profile for suggestions based on expertise. This dissertation contributes on modeling unbiased scholar profile for more objective expertise evidence that consider interest changes and less focused on citations. Interest changes lead to diverse topics and make the expertise levels on topics differ. Scholar profile is expected to capture expertise in terms of productivity aspect which often signified from the volume of publications and citations. We include researcher behavior in publishing articles to avoid misleading citation. Therefore, the expertise levels of researchers on topics is influenced by interest evolution, productivity, dynamicity, and behavior extracted from bibliographic data of published scholarly articles. As this dissertation output, the scholar profile model employed within a recommendation system for recommending productive researchers who provide academic guidance. The scholar profile is generated from multi layers of bibliographic data, such as layers of author, topic, and relations between those layers to represent academic social network. There is no predefined information of topics in a cold-start situation, such that procedures of topic mapping are necessary. Then, features of productivity, dynamicity and behavior of researchers within those layers are taken from some observed years to accommodate the behavior aspect. We experimented with AMiner dataset often used in the following bibliographic data related studies to empirically investigate: (a) topic mapping strategies to obtain interest of researchers, (b) feature extraction model for productivity, dynamicity, and behavior aspects based on the mapped topics, and (c) expertise rank that considers interest changes and less focused on citations from the scholar profile. Ensuring the validity results, our experiments worked on standard expert list of AMiner researchers. We selected Natural Language Processing and Information Extraction (NLP-IE) domains because of their familiarity and interrelated context to make it easier for introducing cases of interest changes. Using the mapped topics, we also made minor contributions on transformation procedures for visualizing researchers on maps of Scopus subjects and investigating the possibilities of conflict of interest

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performanc

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

Non peer reviewe