1,720,961 research outputs found
Collegamento di menzioni visive e testuali di entità con conoscenze di base.
No full text“A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed reveal different and complementary information that, if combined will result in more information than the sum of that contained in single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with the agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this thesis, after providing a precise definition of the VTKEL task, we present two datasets called VTKEL1k* and VTKEL30k. These datasets consisting of images and corresponding captions, in which the image and textual mentions are both annotated with the corresponding entities typed according to the YAGO ontology. The datasets can be used for training and evaluating algorithms of the VTKEL task. Successively, we developed an unsupervised baseline algorithm called VT-LinKEr (Visual-Textual-Knowledge-Entity Linker) for the solution of the VTKEL task. We evaluated the performances of VT-LinKEr on both datasets. We also developed a supervised algorithm called ViTKan (Visual-Textual-Knowledge-Alignment Network). During training, the ViTKan takes in the input (1) an image and applying an object detector to predict visual-objects & their typing, (ii) takes text (captions) and applying a knowledge graph extracting tool PIKES to recognized textual entity mentions and linked these entities to the knowledgebase YAGO for background knowledge extraction. We trained the ViTKan model by using the visual, textual, and ontological features data of the VTKEL1k* dataset. During prediction, the ViTKan solves the problem of alignment (mapping) between visual entities in the image with textual entities in the captions with a great accuracy. The evaluation results of ViTKan on VTKEL1k* and VTKEL30k datasets show improved results with respect to the state-of-the-art methods on grounding (localization) of textual entities on images task.“A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed reveal different and complementary information that, if combined will result in more information than the sum of that contained in single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with the agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this thesis, after providing a precise definition of the VTKEL task, we present two datasets called VTKEL1k* and VTKEL30k. These datasets consisting of images and corresponding captions, in which the image and textual mentions are both annotated with the corresponding entities typed according to the YAGO ontology. The datasets can be used for training and evaluating algorithms of the VTKEL task. Successively, we developed an unsupervised baseline algorithm called VT-LinKEr (Visual-Textual-Knowledge-Entity Linker) for the solution of the VTKEL task. We evaluated the performances of VT-LinKEr on both datasets. We also developed a supervised algorithm called ViTKan (Visual-Textual-Knowledge-Alignment Network). During training, the ViTKan takes in the input (1) an image and applying an object detector to predict visual-objects & their typing, (ii) takes text (captions) and applying a knowledge graph extracting tool PIKES to recognized textual entity mentions and linked these entities to the knowledgebase YAGO for background knowledge extraction. We trained the ViTKan model by using the visual, textual, and ontological features data of the VTKEL1k* dataset. During prediction, the ViTKan solves the problem of alignment (mapping) between visual entities in the image with textual entities in the captions with a great accuracy. The evaluation results of ViTKan on VTKEL1k* and VTKEL30k datasets show improved results with respect to the state-of-the-art methods on grounding (localization) of textual entities on images task
Aligning and linking entity mentions in image, text, and knowledge base
No full textA picture is worth a thousand words, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed reveal different and complementary information that, if combined will result in more information than the sum of that contained in a single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with the agent’s background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this article, after providing a precise definition of the VTKEL task, we present two datasets called VTKEL1k* and VTKEL30k. These datasets consisting of images and corresponding captions, in which the image and textual mentions are both annotated with the corresponding entities typed according to the YAGO ontology. The datasets can be used for training and evaluating algorithms of the VTKEL task. Successively, we introduce a baseline algorithm called VT-LinKEr (Visual-Textual-Knowledge Entity Linker) for the solution of the VTKEL task. We evaluate the performances of VT-LinKEr on both datasets. We then contribute a supervised algorithm called ViTKan (Visual-Textual- Knowledge Alignment Network). We trained the ViTKan algorithm using features data of the VTKEL1k* dataset. The experimental results on VTKEL1k* and VTKEL30k datasets show that ViTKan substantially outperforms the baseline algorithm
VT-LINKER: Visual-Textual-Knowledge Entity Linker
No full text“A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the sum of that contained in the single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, we precisely define the VTKEL task and present two datasets composed of 1k and 30k pictures, annotated with visual and textual entities and linked to the YAGO ontology. Successively, we develop the first unsupervised algorithm for the solution of VTKEL task. The evaluation of the algorithm shows promising results on both 1k and 30k VTKEL datasets
On Visual-Textual-Knowledge Entity Linking
No full textA picture is worth a thousand words, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the sum of that contained in the single media. The combination of visual and textual information can be obtained by linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. This complex task is called Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, we present a purely unsupervised algorithm for the solution of the VTKEL tasks. The evaluation on the VTKEL dataset —a dataset composed of about 30K pictures, annotated with visual and textual entities, and linked to the YAGO ontology— shows promising results
Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
Full text linkRenewable Energy Sources (RESs) such as hydro, wind, and solar are merging as preferred alternatives to fossil fuels. Among these RESs, solar energy is the most ideal solution; it is gaining extensive interest around the globe. However, due to solar energy’s intermittent nature and sensitivity to environmental parameters (e.g., irradiance, dust, temperature, aging and humidity), real-time solar plant monitoring is imperative. This paper’s contribution is to compare and analyze current IoT trends and propose future research directions. As a result, this will be instrumental in the development of low-cost, real-time, scalable, reliable, and power-optimized solar plant monitoring systems. In this work, a comparative analysis has been performed on proposed solutions using the existing literature. This comparative analysis has been conducted considering five aspects: computer boards, sensors, communication, servers, and architectural paradigms. IoT architectural paradigms employed have been summarized and discussed with respect to communication, application layers, and storage capabilities. To facilitate enhanced IoT-based solar monitoring, an edge computing paradigm has been proposed. Suggestions are presented for the fabrication of edge devices and nodes using optimum compute boards, sensors, and communication modules. Different cloud platforms have been explored, and it was concluded that the public cloud platform Amazon Web Services is the ideal solution. Artificial intelligence-based techniques, methods, and outcomes are presented, which can help in the monitoring, analysis, and management of solar PV systems. As an outcome, this paper can be used to help researchers and academics develop low-cost, real-time, effective, scalable, and reliable solar monitoring systems
Jointly linking visual and textual entity mentions with background knowledge
No full text“A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the sum of that contained in the single media. The combination of visual and textual information can be obtained through linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, after providing a precise definition of the VTKEL task, we present a dataset composed of about 30K commented pictures, annotated with visual and textual entities, and linked to the YAGO ontology. Successively, we develop a purely unsupervised algorithm for the solution of the VTKEL tasks. The evaluation on the VTKEL dataset shows promising results
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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