Generación automática de resúmenes extractivos para un solo documento: un mapeo sistemático

La Generación Automática de Resúmenes Extractivos para un Solo Documento (GAReUD) es un área de investigación que tiene como objetivo crear una versión corta de un documento con la información más relevante y adquiere mayor importancia a diario debido a la necesidad de los usuarios de obtener rápidamente información de documentos publicados en internet. En el área de generación automática de resúmenes cada elemento debe ser evaluado y luego rankeado para conformar un resumen, de acuerdo con esto, existen tres diferentes enfoques teniendo en cuenta la cantidad de objetivos que se evalúan, así: mono objetivo, multi objetivo y de muchos objetivos. El propósito de este mapeo sistemático es brindar conocimiento sobre los métodos y técnicas utilizadas en métodos extractivos de GAReUD, analizando la cantidad de objetivos y características evaluadas, que pueden ser útiles para futuras investigaciones. Este mapeo se realizó utilizando un proceso genérico para la realización de revisiones sistemáticas donde se construye una cadena de búsqueda considerando unas preguntas de investigación, luego se utiliza un filtro con unos criterios de inclusión y exclusión para la selección de los estudios primarios con los que se realizará el análisis, adicionalmente, estos estudios se ordenan de acuerdo con la relevancia de su contenido; este proceso se resume en tres pasos principales: Planificación, Ejecución y Análisis de resultados. Al final del mapeo se identificaron las siguientes observaciones: (i) existe una preferencia por la utilización de métodos basados en aprendizaje automático de máquina y también por el uso de técnicas de agrupamiento, (ii) la importancia de usar como objetivos ambos tipos de características (estadísticas y semánticas) y (iii) la necesidad de explorar el enfoque de muchos objetivos

AN EVOLUTIONARY APPROACH TO BIBLIOGRAPHIC CLASSIFICATION

This dissertation is research in the domain of information science and specifically, the organization and representation of information. The research has implications for classification of scientific books, especially as dissemination of information becomes more rapid and science becomes more diverse due to increases in multi-, inter-, trans-disciplinary research, which focus on phenomena, in contrast to traditional library classification schemes based on disciplines.The literature review indicates 1) human socio-cultural groups have many of the same properties as biological species, 2) output from human socio-cultural groups can be and has been the subject of evolutionary relationship analyses (i.e., phylogenetics), 3) library and information science theorists believe the most favorable and scientific classification for information packages is one based on common origin, but 4) library and information science classification researchers have not demonstrated a book classification based on evolutionary relationships of common origin.The research project supports the assertion that a sensible book classification method can be developed using a contemporary biological classification approach based on common origin, which has not been applied to a collection of books until now. Using a sample from a collection of earth-science digitized books, the method developed includes a text-mining step to extract important terms, which were converted into a dataset for input into the second step—the phylogenetic analysis. Three classification trees were produced and are discussed. Parsimony analysis, in contrast to distance and likelihood analyses, produced a sensible book classification tree. Also included is a comparison with a classification tree based on a well-known contemporary library classification scheme (the Library of Congress Classification).Final discussions connect this research with knowledge organization and information retrieval, information needs beyond science, and this type of research in context of a unified science of cultural evolution

MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

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 “enablers”, 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

Seeking multiple solutions:an updated survey on niching methods and their applications

Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving

Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction

This study aims to propose a suitable TE approach, which provides a better overview of the text documents. To achieve this aim: First, A new feature selection method for TDC, that is, binary multi-verse optimizer algorithm (BMVO) is proposed to eliminate irrelevantly, redundant features and obtain a new subset of more informative features. Second, three multi-verse optimizer algorithm (MVOs), namely, basic MVO, modified MVO, hybrid MVO is proposed to solve the TDC problem; these algorithms are incremental improvements of the preceding versions. Third, a novel ensemble method for an automatic TE from a collection of text document is proposed to extract the topics from the clustered document

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms