2,740 research outputs found
Gazo bunseki to kanren joho o riyoshita gazo imi rikai ni kansuru kenkyu
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CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and âenablersâ, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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Proceedings of QG2010: The Third Workshop on Question Generation
These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge".
QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
Applications of opinion mining to data journalism
Dissertação de mest., Processamento de Linguagem Natural e IndĂșstrias da LĂngua, Faculdade de CiĂȘncias Humanas e Sociais, Univ. do Algarve, 2013Nowadays social media play a central role in every day life. A huge volume
of user-generated data spins around online social networks, such as Twitter, having
an extraordinary impact on the media industry and on the usersâ everyday life. More
and more users and people use social networks from their computers and
smartphones to share their emotions and opinions about the facts happening in the
world. Natural language processing and, in particular, sentiment analysis are key
technologies to make sense out of the data about news that circulates in the online
social networks. The application of opinion mining to news-oriented user-generated
contents, such as news-linking tweets, can provide novel views on the news
audience behaviour and help to interpret the evolution of sentiments. Applying this
capability in the social news-sphere permits (i) to measure the impact of news onto
readers and (ii) to gather elements that contain stories.
From a broad perspective, the main aim of this research is to face this
challenge, that is, to explore how opinion mining (or sentiment analysis) can be
adopted into the field of digital media and data-driven journalism
Personalized question-based cybersecurity recommendation systems
En ces temps de pandĂ©mie Covid19, une Ă©norme quantitĂ© de lâactivitĂ© humaine est modifiĂ©e pour se faire Ă distance, notamment par des moyens Ă©lectroniques. Cela rend plusieurs personnes et services vulnĂ©rables aux cyberattaques, dâoĂč le besoin dâune Ă©ducation gĂ©nĂ©ralisĂ©e ou du moins accessible sur la cybersĂ©curitĂ©. De nombreux efforts sont entrepris par les chercheurs, le gouvernement et les entreprises pour protĂ©ger et assurer la sĂ©curitĂ© des individus contre les pirates et les cybercriminels. En raison du rĂŽle important jouĂ© par les systĂšmes de recommandation dans la vie quotidienne de l'utilisateur, il est intĂ©ressant de voir comment nous pouvons combiner les systĂšmes de cybersĂ©curitĂ© et de recommandation en tant que solutions alternatives pour aider les utilisateurs Ă comprendre les cyberattaques auxquelles ils peuvent ĂȘtre confrontĂ©s. Les systĂšmes de recommandation sont couramment utilisĂ©s par le commerce Ă©lectronique, les rĂ©seaux sociaux et les plateformes de voyage, et ils sont basĂ©s sur des techniques de systĂšmes de recommandation traditionnels.
Au vu des faits mentionnés ci-dessus, et le besoin de protéger les internautes, il devient important de fournir un systÚme personnalisé, qui permet de partager les problÚmes, d'interagir avec un systÚme et de trouver des recommandations.
Pour cela, ce travail propose « Cyberhelper », un systÚme de recommandation de cybersécurité personnalisé basé sur des questions pour la sensibilisation à la cybersécurité.
De plus, la plateforme proposĂ©e est Ă©quipĂ©e d'un algorithme hybride associĂ© Ă trois diffĂ©rents algorithmes basĂ©s sur la connaissance, les utilisateurs et le contenu qui garantit une recommandation personnalisĂ©e optimale en fonction du modĂšle utilisateur et du contexte. Les rĂ©sultats expĂ©rimentaux montrent que la prĂ©cision obtenue en appliquant l'algorithme proposĂ© est bien supĂ©rieure Ă la prĂ©cision obtenue en utilisant d'autres mĂ©canismes de systĂšme de recommandation traditionnels. Les rĂ©sultats suggĂšrent Ă©galement qu'en adoptant l'approche proposĂ©e, chaque utilisateur peut avoir une expĂ©rience utilisateur unique, ce qui peut l'aider Ă comprendre l'environnement de cybersĂ©curitĂ©.With the proliferation of the virtual universe and the multitude of services provided by the World Wide Web, a major concern arises: Security and privacy have never been more in jeopardy. Nowadays, with the Covid 19 pandemic, the world faces a new reality that pushed the majority of the workforce to telecommute. This thereby creates new vulnerabilities for cyber attackers to exploit. Itâs important now more than ever, to educate and offer guidance towards good cybersecurity hygiene. In this context, a major effort has been dedicated by researchers, governments, and businesses alike to protect people online against hackers and cybercriminals.
With a focus on strengthening the weakest link in the cybersecurity chain which is the human being, educational and awareness-raising tools have been put to use. However, most researchers focus on the âone size fits allâ solutions which do not focus on the intricacies of individuals. This work aims to overcome that by contributing a personalized question-based recommender system. Named âCyberhelperâ, this work benefits from an existing mature body of research on recommender system algorithms along with recent research on non-user-specific question-based recommenders.
The reported proof of concept holds potential for future work in adapting Cyberhelper as an everyday assistant for different types of users and different contexts
Review of Semantic Importance and Role of using Ontologies in Web Information Retrieval Techniques
The Web contains an enormous amount of information, which is managed to accumulate, researched, and regularly used by many users. The nature of the Web is multilingual and growing very fast with its diverse nature of data including unstructured or semi-structured data such as Websites, texts, journals, and files. Obtaining critical relevant data from such vast data with its diverse nature has been a monotonous and challenging task. Simple key phrase data gathering systems rely heavily on statistics, resulting in a word incompatibility problem related to a specific word's inescapable semantic and situation variants. As a result, there is an urgent need to arrange such colossal data systematically to find out the relevant information that can be quickly analyzed and fulfill the users' needs in the relevant context. Over the years ontologies are widely used in the semantic Web to contain unorganized information systematic and structured manner. Still, they have also significantly enhanced the efficiency of various information recovery approaches. Ontological information gathering systems recover files focused on the semantic relation of the search request and the searchable information. This paper examines contemporary ontology-based information extraction techniques for texts, interactive media, and multilingual data types. Moreover, the study tried to compare and classify the most significant developments utilized in the search and retrieval techniques and their major disadvantages and benefits
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