Spatio-temporal block model for video indexation assistance

International audienceIn the video indexing framework, we have developed a user assistance system in order to define concept models (i.e semantic index) according to features automatically extracted from the video. Because the manual indexing is a long and tedious task, we propose to focus the user attention on pre-selected prototypes that a priori correspond to the searched concepts. The proposed system is decomposed in three steps. In the first one, some basic spatio-temporal blocks are extracted from the video, a particular block being associated to a particular property of one feature. In the second step, a Question/Answer system allows the user to define links between basic blocks in order to define concept block models. And finally, some concept blocks are extracted and proposed as prototypes of the concepts. In this paper, we present the two first steps, particularly the block structure, illustrated by an example of video indexing that corresponds to the concept running in athletic videos

Innovative Platform for Designing Hybrid Collaborative & Context-Aware Data Mining Scenarios

The process of knowledge discovery involves nowadays a major number of techniques. Context-Aware Data Mining (CADM) and Collaborative Data Mining (CDM) are some of the recent ones. the current research proposes a new hybrid and efficient tool to design prediction models called Scenarios Platform-Collaborative & Context-Aware Data Mining (SP-CCADM). Both CADM and CDM approaches are included in the new platform in a flexible manner; SP-CCADM allows the setting and testing of multiple configurable scenarios related to data mining at once. The introduced platform was successfully tested and validated on real life scenarios, providing better results than each standalone technique-CADM and CDM. Nevertheless, SP-CCADM was validated with various machine learning algorithms-k-Nearest Neighbour (k-NN), Deep Learning (DL), Gradient Boosted Trees (GBT) and Decision Trees (DT). SP-CCADM makes a step forward when confronting complex data, properly approaching data contexts and collaboration between data. Numerical experiments and statistics illustrate in detail the potential of the proposed platform.Comment: 15 figure

Exploring the Landscape of Natural Language Processing Research

As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing amount of research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent to this day. Contributing to closing this gap, we have systematically classified and analyzed research papers included in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields-of-study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.Comment: Accepted to the 14th International Conference on Recent Advances in Natural Language Processing (RANLP 2023

Inverse-free extreme learning machine with optimal information updating

2014-2015 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Asymmetry in Biological Homochirality

Chirality, or handedness, is a fundamental physical characteristic, which spans the length scales ranging from elementary particles to the chiral asymmetry of spiral galaxies. The way in which chirality in chemistry, or molecular handedness, may have emerged in a primitive terrestrial environment, and how it can be triggered, amplified, and transferred, are deeply challenging problems rooted in both fundamental scientific interests and the technological potentials for science and society. Chirality constitutes a unifying feature of the living world and is a prime driving force for molecular selection and genetic evolution in biology. In this book, we offer a selection of five distinct approaches to this problem by leading experts in the field. The selected topics range from protein chirality and its relevance to protein ageing, protein aggregation and neurodegeneration, entropy production associated with chiral symmetry breaking in closed systems, chiral oscillations in polymerization models involving higher-order oligomers, the mirror symmetry breaking in liquids and its implications for the development of homochirality in abiogenesis, the role of chirality in the chemical sciences, and some philosophical implications of chirality

Similarity and diversity: two sides of the same coin in the evaluation of data streams

The Information Systems represent the primary instrument of growth for the companies that operate in the so-called e-commerce environment. The data streams generated by the users that interact with their websites are the primary source to define the user behavioral models. Some main examples of services integrated in these websites are the Recommender Systems, where these models are exploited in order to generate recommendations of items of potential interest to users, the User Segmentation Systems, where the models are used in order to group the users on the basis of their preferences, and the Fraud Detection Systems, where these models are exploited to determine the legitimacy of a financial transaction. Even though in literature diversity and similarity are considered as two sides of the same coin, almost all the approaches take into account them in a mutually exclusive manner, rather than jointly. The aim of this thesis is to demonstrate how the consideration of both sides of this coin is instead essential to overcome some well-known problems that affict the state-of-the-art approaches used to implement these services, improving their performance. Its contributions are the following: with regard to the recommender systems, the detection of the diversity in a user profile is used to discard incoherent items, improving the accuracy, while the exploitation of the similarity of the predicted items is used to re-rank the recommendations, improving their effectiveness; with regard to the user segmentation systems, the detection of the diversity overcomes the problem of the non-reliability of data source, while the exploitation of the similarity reduces the problems of understandability and triviality of the obtained segments; lastly, concerning the fraud detection systems, the joint use of both diversity and similarity in the evaluation of a new transaction overcomes the problems of the data scarcity, and those of the non-stationary and unbalanced class distribution

Similarity and diversity: two sides of the same coin in the evaluation of data streams

The Information Systems represent the primary instrument of growth for the companies that operate in the so-called e-commerce environment. The data streams generated by the users that interact with their websites are the primary source to define the user behavioral models. Some main examples of services integrated in these websites are the Recommender Systems, where these models are exploited in order to generate recommendations of items of potential interest to users, the User Segmentation Systems, where the models are used in order to group the users on the basis of their preferences, and the Fraud Detection Systems, where these models are exploited to determine the legitimacy of a financial transaction. Even though in literature diversity and similarity are considered as two sides of the same coin, almost all the approaches take into account them in a mutually exclusive manner, rather than jointly. The aim of this thesis is to demonstrate how the consideration of both sides of this coin is instead essential to overcome some well-known problems that affict the state-of-the-art approaches used to implement these services, improving their performance. Its contributions are the following: with regard to the recommender systems, the detection of the diversity in a user profile is used to discard incoherent items, improving the accuracy, while the exploitation of the similarity of the predicted items is used to re-rank the recommendations, improving their effectiveness; with regard to the user segmentation systems, the detection of the diversity overcomes the problem of the non-reliability of data source, while the exploitation of the similarity reduces the problems of understandability and triviality of the obtained segments; lastly, concerning the fraud detection systems, the joint use of both diversity and similarity in the evaluation of a new transaction overcomes the problems of the data scarcity, and those of the non-stationary and unbalanced class distribution

Libro de Actas del Primer Congreso Internacional de Análisis de Rendimiento Deportivo y Coaching

Este libro es una compliación de las Comunicaciones y Ponencias presentadas en el I Congreso Internacional de Análisis de Rendimiento Deportivo y Coaching, celebrado en la Facultad de Ciencias de la Actividad Física y el Deporte de la Universidad de Valencia entre el 25 y 27 de Marzo de 2015. Organizado por la Universidad de Valencia y la Asociación Valenciana de Análisis de Rendimiento Deportivo y Coaching.This book is a compilation of Communications and lectures presented at the Firsth International Conference on Performance Analysis and Coaching in Sport, performed in the Faculty of Physical Activity and Sport Sciences of the University of Valencia, 25-27th of March. Organized by the University of Valencia and The Valencian Association of Perfomance Analysis and Coaching in Sport