The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments

International audienceTHE AUTHORS ARE EXTREMELY GRATEFUL TO GRID5000 for helping in designing and experimenting around Monte-Carlo Tree Search. In order to promote computer Go and stimulate further development and research in the field, the event activities, "Computational Intelligence Forum" and "World 99 Computer Go Championship," were held in Taiwan. This study focuses on the invited games played in the tournament, "Taiwanese Go players versus the computer program MoGo," held at National University of Tainan (NUTN). Several Taiwanese Go players, including one 9-Dan professional Go player and eight amateur Go players, were invited by NUTN to play against MoGo from August 26 to October 4, 2008. The MoGo program combines All Moves As First (AMAF)/Rapid Action Value Estimation (RAVE) values, online "UCT-like" values, offline values extracted from databases, and expert rules. Additionally, four properties of MoGo are analyzed including: (1) the weakness in corners, (2) the scaling over time, (3) the behavior in handicap games, and (4) the main strength of MoGo in contact fights. The results reveal that MoGo can reach the level of 3 Dan with, (1) good skills for fights, (2) weaknesses in corners, in particular for "semeai" situations, and (3) weaknesses in favorable situations such as handicap games. It is hoped that the advances in artificial intelligence and computational power will enable considerable progress in the field of computer Go, with the aim of achieving the same levels as computer chess or Chinese chess in the future

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

[[abstract]]An Estimation of Distribution Algorithm (EDA), which depends on explicitly sampling mechanisms based on probabilistic models with information extracted from the parental solutions to generate new solutions, has constituted one of the major research areas in the field of evolutionary computation. The fact that no genetic operators are used in EDAs is a major characteristic differentiating EDAs from other genetic algorithms (GAs). This advantage, however, could lead to premature convergence of EDAs as the probabilistic models are no longer generating diversified solutions. In our previous research [1], we have presented the evidences that EDAs suffer from the drawback of premature convergency, thus several important guidelines are provided for the design of effective EDAs. In this paper, we validated one guideline for incorporating other meta-heuristics into the EDAs. An algorithm named “EA/G-GA” is proposed by selecting a well-known EDA, EA/G, to work with GAs. The proposed algorithm was tested on the NP-Hard single machine scheduling problems with the total weighted earliness/tardiness cost in a just-in-time environment. The experimental results indicated that the EA/G-GA outperforms the compared algorithms statistically significantly across different stopping criteria and demonstrated the robustness of the proposed algorithm. Consequently, this paper is of interest and importance in the field of EDAs.[[notice]]補正完

Semi-Automatic Method to Assist Expert for Association Rules Validation

Abstract-In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process

Future Intelligent Systems and Networks 2019

In this Special Issue, we present current developments and future directions of future intelligent systems and networks. This is the second Special Issue regarding the future of the Internet. This subject remains of interest for firms applying technological possibilities to promote more innovative business models. This Special Issue widens the application of intelligent systems and networks to firms so that they can evolve to more innovative models. The five contributions highlight useful applications, business models, or innovative practices based on intelligent systems and networks. We hope our findings become an inspiration for firms operating in various industries

A Low-Complexity Model-Free Approach for Real-Time Cardiac Anomaly Detection Based on Singular Spectrum Analysis and Nonparametric Control Charts

While the importance of continuous monitoring of electrocardiographic (ECG) or photoplethysmographic (PPG) signals to detect cardiac anomalies is generally accepted in preventative medicine, there remain numerous challenges to its widespread adoption. Most notably, difficulties arise regarding crucial characteristics such as real-time capability, computational complexity, the amount of required training data, and the avoidance of too-restrictive modeling assumptions. We propose a lightweight and model-free approach for the online detection of cardiac anomalies such as ectopic beats in ECG or PPG signals on the basis of the change detection capabilities of singular spectrum analysis (SSA) and nonparametric rank-based cumulative sum (CUSUM) control charts. The procedure is able to quickly detect anomalies without requiring the identification of fiducial points such as R-peaks, and it is computationally significantly less demanding than previously proposed SSA-based approaches. Therefore, the proposed procedure is equally well suited for standalone use and as an add-on to complement existing (e.g., heart rate (HR) estimation) procedures

Fuzzy multicriteria analysis and its applications for decision making under uncertainty

Multicriteria decision making refers to selecting or ranking alternatives from available alternatives with respect to multiple, usually conflicting criteria involving either a single decision maker or multiple decision makers. It often takes place in an environment where the information available is uncertain, subjective and imprecise. To adequately solve this decision problem, the application of fuzzy sets theory for adequately modelling the uncertainty and imprecision in multicriteria decision making has proven to be effective. Much research has been done on the development of various fuzzy multicriteria analysis approaches for effectively solving the multicriteria decision making problem, and numerous applications have been reported in the literature. In general, existing approaches can be categorized into (a) multicriteria decision making with a single decision maker and (b) multicriteria group decision making. Existing approaches, however, are not totally satisfactory due to various shortcomings that they suffer from including (a) the inability to adequately model the uncertainty and imprecision of human decision making, (b) the failure to effectively handle the requirements of decision maker(s), (c) the tedious mathematical computation required, and (d) cognitively very demanding on the decision maker(s). This research has developed four novel approaches for effectively solving the multicriteria decision making problem under uncertainty. To effectively reduce the cognitive demand on the decision maker, a pairwise comparison based approach is developed in Chapter 4 for solving the multicriteria problem under uncertainty. To adequately meet the interest of various stakeholders in the multicriteria decision making process, a decision support system (DSS) based approach is introduced in Chapter 5. In Chapter 6, a consensus oriented approach is presented in multicriteria group decision making on which a DSS is proposed for facilitating consensus building in solving the multicriteria group decision making problem. In Chapter 7, a risk-oriented approach is developed for adequately modelling the inherent risk in multicriteria group decision making with the use of the concept of ideal solutions so that the complex and unreliable process of comparing fuzzy utilities usually required in fuzzy multicriteria analysis is avoided. Empirical studies of four real fuzzy multicriteria decision making problems are presented for illustrating the applicability of the approaches developed in solving the multicriteria decision making problem. A hospital location selection problem is discussed in Chapter 8. An international distribution centre location problem is illustrated in Chapter 9. A supplier selection problem is presented in Chapter 10. A hotel location problem is discussed in Chapter 11. These studies have shown the distinct advantages of the approaches developed respectively in this research from different perspectives in solving the multicriteria decision making problem

Melhoramento genético da cana-de-açucar para obtenção de cana energia

Orientador : Prof. Dr. Edeclaiton DarosCo-orientadores : Prof. Dr. Márcio Henrique Pereira Barbosa e Engº Heroldo WeberTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Agronomia. Defesa: Curitiba, 08/12/2014Inclui referênciasÁrea de concentração: Produção vegetalResumo: A busca por fontes alternativas de energia limpa tem sido uma constante nos últimos anos. Dentre essas fontes, a utilização de materiais lignocelulósicos para a geração de etanol de segunda geração e combustão para co-geração de energia elétrica apareceu como uma das soluções mais promissoras. A cana-de-açúcar é uma das culturas agrícolas mais eficientes na conversão de energia solar em energia química e os melhoristas, desde o princípio, vêm explorando a ampla variabilidade genética presente no complexo Saccharum. Os tradicionais programas de melhoramento genético têm voltado suas atenções majoritariamente ao desenvolvimento de cultivares para a maior produção de açúcar e etanol. No entanto, recentemente, diversos estudos vêm sendo desenvolvidos, visando avaliar o seu desempenho e otimizar o seu potencial energético a partir de cultivares com maior teor de fibra e elevada produção de biomassa. Com a nova demanda energética, a Rede Interuniversitária para o Desenvolvimento do Setor Sucroenergético (RIDESA) iniciou um programa de seleção de cultivares cana energia e vem realizando hibridações envolvendo acessos de Saccharum spontaneum, Saccharum robustum e as variedades cultivadas atualmente (Saccharum spp.). Visando contribuir com a nova linha de pesquisa da RIDESA, o objetivo desse trabalho foi avaliar a diversidade genética entre 50 genitores potenciais para obtenção de cana energia, selecionar os melhores cruzamentos (famílias) e os clones promissores presentes na população segregante, além de definir as estratégias a serem adotadas na avaliação e seleção das famílias. A caracterização dos 50 genótipos ocorreu no Banco Ativo de Germoplasma de Cana-de-açúcar, localizado na Serra do Ouro, município de Murici, AL e pertencente a Universidade Federal de Alagoas. A análise de diversidade e o coeficiente de parentesco entre os genótipos permitiu a identificação de dois grupos heteróticos (G1 e G2) com genitores potenciais para obtenção de cultivares cana energia. O grupo G1 foi formado por 13 genótipos que apresentam elevado teor de fibra, baixo teor de sacarose e descendem de Saccharum spontaneum e Saccharum robustum. No grupo G2 a maioria dos 37 genótipos são cultivares modernas (Saccharum spp.) desenvolvidas pela RIDESA. Após a realização de hibridações envolvendo esses genótipos, foram obtidas 50 famílias de irmãos completos. Essas famílias foram avaliadas na Estação de Pesquisa da Universidade Federal do Paraná, localizada no município de Paranavaí, PR. Após a coleta de dados dos caracteres de produção: tonelada de cana por hectare (TCH), tonelada de fibra por hectare (TFH), tonelada de lignina por hectare (TLH) e tonelada de sacarose por hectare (TSH), além dos componentes da produção: número médio de colmos (NC), peso médio de colmos (PMC), diâmetro de colmo (DC), altura de colmo (AC), teor de fibra (FIB), pol percento cana ou teor de sacarose aparente (PC) e teor de lignina (LIG), foi possível identificar via análise de trilha, que a seleção das famílias de cana energia deve ser realizada com base TCH, que pode ser estimado via NC e PMC, pois esses dois componentes de produção são os principais responsáveis pela determinação da TCH, TFH e TLH. Após a avaliação e ordenamento das famílias para TCH, TFH e TSH, foram selecionados as 22 famílias com as maiores médias genotípicas para esses caracteres. Nessas famílias foram selecionados 199 clones, muitos deles com elevado teor de fibra (>16%) associado a um bom teor de sacarose (>12%), além de clones com teor de fibra próximo de 20% e baixo teor de sacarose, assim como clones que apresentam os mesmos teores de fibra (12%) e sacarose (13%) das cultivares atuais. Também foi possível identificar os genitores de maior destaque do grupo G1, sendo eles: KRAKATAU, IM76-228, IM76-229 e US85-1008, assim como os melhores genitores do grupo G2: RB867515 e RB93509. A seleção desses genitores é recomendada com base na avaliação do desempenho de suas progênies. No entanto, recomenda-se a introdução de novos acessos de S. spontaneum e S. robustum, de forma a ampliar a variabilidade genética presente no grupo G1 e também fazer uso de outras cultivares e clones modernos desenvolvidos pela RIDESA e que não foram avaliados nesse trabalho. Para o sucesso na obtenção de cultivares cana energia é necessário realizar o melhoramento das populações heteróticas (G1 e G2) a partir de hibridações entre os melhores genitores dentro de cada grupo, assim como realizar cruzamentos interpopulacionais visando explorar os desvios de dominância. Portanto, o uso da seleção recorrente recíproca (SRR) e/ou da seleção recorrente recíproca individual com famílias endogâmicas (SRRI-S1) na condução do programa de melhoramento genético da cana energia irá alavancar os ganhos genéticos por ciclo de recombinação. Palavras-chave: Saccharum, Bioenergia, Melhoramento genético, Cana energiaAbstract: The search for alternative sources of clean energy has been constant in recent years in Brazil and worldwide. Among these sources, the use of lignocellulosic materials for the generation of second-generation ethanol and co-combustion for electricity generation appear as one of the most promising solutions. The sugarcane is one of the most efficient crops in converting solar energy into chemical energy and breeders, from the beginning, have been exploring the wide genetic variability present in the Saccharum complex. Traditional breeding programs have focused their attention mainly to the development of cultivars for increased production of sugar and ethanol. However, recently, several studies have been conducted with cane sugar, to evaluate its performance and optimize its energy potential from cultivars with higher fiber content and high biomass production. With the new energy demand, the Interuniversity Network for the Development of Sugarcane Industry (RIDESA) initiated a program of energy sugarcane cultivar selection and has been performing hybridization with bouts of Saccharum spontaneum, Saccharum robustum and currently cultivated varieties (Saccharum spp.). Aiming to contribute to the new line of research RIDESA, the aim of this study was to evaluate the genetic diversity among 50 potential parents for obtaining energy cane, selecting the best families and promising clones present in segregating population, and defines the strategies to be adopted in the evaluation and selection of families. Evaluation of 50 genotypes present in the Active Germplasm Bank of Sugarcane, located in the Serra do Ouro, municipality of Murici, AL and owned by Federal University of Alagoas, allowed the identification of two heterotic groups (G1 and G2) with potential parents for obtaining energy cane cultivars. The G1 group consisted of 13 genotypes that have high fiber, low sucrose content and descended from Saccharum spontaneum and Saccharum robustum. G2 most 37 genotypes are modern cultivars (Saccharum spp.) Developed by RIDESA. After performing hybridization with these genotypes, 50 full-sib families were obtained. These families were evaluated at the Federal University of Paraná Research Station, located in the municipality of Paranavaí, PR. After collecting data for yield: ton of cane per hectare (TCH), tons of fiber per acre (TFH), lignin ton per hectare (TLH) and tons of sucrose per hectare (TSH), in addition to the components of the production: average number of culms (NC), stem diameter (DC), stem height (AC), fiber content (FIB), sucrose content (PC) and lignin content (LIG), was identified via analysis track, the selection of families cane energy should be made based on the character ton of cane per hectare (TCH), which can be estimated via average number of stems (NC) and mean weight of stem (PMC), because these two components of production are primarily responsible for determining the productivity of sugarcane (TCH), fiber (TFH) and lignin (TLH). After the evaluation and ranking of families for the following characters of production: TCH, TFH and TSH and 22 families were selected the highest genotypic averages for these characters. 199 clones in these families, many with high fiber content (> 16%) associated with a good sucrose content (> 12%), clones with fiber content around 20% and low sucrose content were selected as clones Where the same fiber content (12%) and sucrose (13%) of current cultivars. It was also possible to identify the most outstanding parents of G1, namely: KRAKATAU, IM76-228, IM76-229 US85-1008 and, like the best parents in G2: RB867515 and RB93509. The selection of these parents is recommended based on the evaluation of the performance of their progeny. However, the introduction of new accesses and S. spontaneum and S. robustum, in order to amplify the genetic variability present in G1 and also make use of other modern and cultivars developed by RIDESA clones that were not evaluated, it is recommended that work. For success in obtaining energy cane cultivars should seek to achieve the improvement of heterotic populations (G1 and G2) from crosses between the best parents within each group, as well as performing interpopulation crosses for exploring the dominance deviations. Therefore, the use of reciprocal recurrent selection (RRS) and / or the individual reciprocal recurrent selection with inbred families (SRRI-S1) in conducting the energy sugarcane breeding program can leverage the genetic gain per cycle of recombination. Keywords: Saccharum, Bioenergy, Crop breeding, Energy can

A soft computing decision support framework for e-learning

Tesi per compendi de publicacions.Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break both physical and cultural distances, to disseminate knowledge and decrease the costs of the teaching-learning process allows it to reach anywhere and anyone. The educational community is divided as to its role in the future. It is believed that by 2019 half of the world's higher education courses will be delivered through e-Learning. While supporters say that this will be the educational mode of the future, its detractors point out that it is a fashion, that there are huge rates of abandonment and that their massification and potential low quality, will cause its fall, assigning it a major role of accompanying traditional education. There are, however, two interrelated features where there seems to be consensus. On the one hand, the enormous amount of information and evidence that Learning Management Systems (LMS) generate during the e-Learning process and which is the basis of the part of the process that can be automated. In contrast, there is the fundamental role of e-tutors and etrainers who are guarantors of educational quality. These are continually overwhelmed by the need to provide timely and effective feedback to students, manage endless particular situations and casuistics that require decision making and process stored information. In this sense, the tools that e-Learning platforms currently provide to obtain reports and a certain level of follow-up are not sufficient or too adequate. It is in this point of convergence Information-Trainer, where the current developments of the LMS are centered and it is here where the proposed thesis tries to innovate. This research proposes and develops a platform focused on decision support in e-Learning environments. Using soft computing and data mining techniques, it extracts knowledge from the data produced and stored by e-Learning systems, allowing the classification, analysis and generalization of the extracted knowledge. It includes tools to identify models of students' learning behavior and, from them, predict their future performance and enable trainers to provide adequate feedback. Likewise, students can self-assess, avoid those ineffective behavior patterns, and obtain real clues about how to improve their performance in the course, through appropriate routes and strategies based on the behavioral model of successful students. The methodological basis of the mentioned functionalities is the Fuzzy Inductive Reasoning (FIR), which is particularly useful in the modeling of dynamic systems. During the development of the research, the FIR methodology has been improved and empowered by the inclusion of several algorithms. First, an algorithm called CR-FIR, which allows determining the Causal Relevance that have the variables involved in the modeling of learning and assessment of students. In the present thesis, CR-FIR has been tested on a comprehensive set of classical test data, as well as real data sets, belonging to different areas of knowledge. Secondly, the detection of atypical behaviors in virtual campuses was approached using the Generative Topographic Mapping (GTM) methodology, which is a probabilistic alternative to the well-known Self-Organizing Maps. GTM was used simultaneously for clustering, visualization and detection of atypical data. The core of the platform has been the development of an algorithm for extracting linguistic rules in a language understandable to educational experts, which helps them to obtain patterns of student learning behavior. In order to achieve this functionality, the LR-FIR algorithm (Extraction of Linguistic Rules in FIR) was designed and developed as an extension of FIR that allows both to characterize general behavior and to identify interesting patterns. In the case of the application of the platform to several real e-Learning courses, the results obtained demonstrate its feasibility and originality. The teachers' perception about the usability of the tool is very good, and they consider that it could be a valuable resource to mitigate the time requirements of the trainer that the e-Learning courses demand. The identification of student behavior models and prediction processes have been validated as to their usefulness by expert trainers. LR-FIR has been applied and evaluated in a wide set of real problems, not all of them in the educational field, obtaining good results. The structure of the platform makes it possible to assume that its use is potentially valuable in those domains where knowledge management plays a preponderant role, or where decision-making processes are a key element, e.g. ebusiness, e-marketing, customer management, to mention just a few. The Soft Computing tools used and developed in this research: FIR, CR-FIR, LR-FIR and GTM, have been applied successfully in other real domains, such as music, medicine, weather behaviors, etc.Soportado por el desarrollo tecnológico y su impacto en las diferentes actividades cotidianas, el e-Learning (o aprendizaje electrónico) y el b-Learning (Blended Learning o aprendizaje mixto), han experimentado un crecimiento vertiginoso principalmente en la educación superior y la capacitación. Su habilidad inherente para romper distancias tanto físicas como culturales, para diseminar conocimiento y disminuir los costes del proceso enseñanza aprendizaje le permite llegar a cualquier sitio y a cualquier persona. La comunidad educativa se encuentra dividida en cuanto a su papel en el futuro. Se cree que para el año 2019 la mitad de los cursos de educación superior del mundo se impartirá a través del e-Learning. Mientras que los partidarios aseguran que ésta será la modalidad educativa del futuro, sus detractores señalan que es una moda, que hay enormes índices de abandono y que su masificación y potencial baja calidad, provocará su caída, reservándole un importante papel de acompañamiento a la educación tradicional. Hay, sin embargo, dos características interrelacionadas donde parece haber consenso. Por un lado, la enorme generación de información y evidencias que los sistemas de gestión del aprendizaje o LMS (Learning Management System) generan durante el proceso educativo electrónico y que son la base de la parte del proceso que se puede automatizar. En contraste, está el papel fundamental de los e-tutores y e-formadores que son los garantes de la calidad educativa. Éstos se ven continuamente desbordados por la necesidad de proporcionar retroalimentación oportuna y eficaz a los alumnos, gestionar un sin fin de situaciones particulares y casuísticas que requieren toma de decisiones y procesar la información almacenada. En este sentido, las herramientas que las plataformas de e-Learning proporcionan actualmente para obtener reportes y cierto nivel de seguimiento no son suficientes ni demasiado adecuadas. Es en este punto de convergencia Información-Formador, donde están centrados los actuales desarrollos de los LMS y es aquí donde la tesis que se propone pretende innovar. La presente investigación propone y desarrolla una plataforma enfocada al apoyo en la toma de decisiones en ambientes e-Learning. Utilizando técnicas de Soft Computing y de minería de datos, extrae conocimiento de los datos producidos y almacenados por los sistemas e-Learning permitiendo clasificar, analizar y generalizar el conocimiento extraído. Incluye herramientas para identificar modelos del comportamiento de aprendizaje de los estudiantes y, a partir de ellos, predecir su desempeño futuro y permitir a los formadores proporcionar una retroalimentación adecuada. Así mismo, los estudiantes pueden autoevaluarse, evitar aquellos patrones de comportamiento poco efectivos y obtener pistas reales acerca de cómo mejorar su desempeño en el curso, mediante rutas y estrategias adecuadas a partir del modelo de comportamiento de los estudiantes exitosos. La base metodológica de las funcionalidades mencionadas es el Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés), que es particularmente útil en el modelado de sistemas dinámicos. Durante el desarrollo de la investigación, la metodología FIR ha sido mejorada y potenciada mediante la inclusión de varios algoritmos. En primer lugar un algoritmo denominado CR-FIR, que permite determinar la Relevancia Causal que tienen las variables involucradas en el modelado del aprendizaje y la evaluación de los estudiantes. En la presente tesis, CR-FIR se ha probado en un conjunto amplio de datos de prueba clásicos, así como conjuntos de datos reales, pertenecientes a diferentes áreas de conocimiento. En segundo lugar, la detección de comportamientos atípicos en campus virtuales se abordó mediante el enfoque de Mapeo Topográfico Generativo (GTM), que es una alternativa probabilística a los bien conocidos Mapas Auto-organizativos. GTM se utilizó simultáneamente para agrupamiento, visualización y detección de datos atípicos. La parte medular de la plataforma ha sido el desarrollo de un algoritmo de extracción de reglas lingüísticas en un lenguaje entendible para los expertos educativos, que les ayude a obtener los patrones del comportamiento de aprendizaje de los estudiantes. Para lograr dicha funcionalidad, se diseñó y desarrolló el algoritmo LR-FIR, (extracción de Reglas Lingüísticas en FIR, por sus siglas en inglés) como una extensión de FIR que permite tanto caracterizar el comportamiento general, como identificar patrones interesantes. En el caso de la aplicación de la plataforma a varios cursos e-Learning reales, los resultados obtenidos demuestran su factibilidad y originalidad. La percepción de los profesores acerca de la usabilidad de la herramienta es muy buena, y consideran que podría ser un valioso recurso para mitigar los requerimientos de tiempo del formador que los cursos e-Learning exigen. La identificación de los modelos de comportamiento de los estudiantes y los procesos de predicción han sido validados en cuanto a su utilidad por los formadores expertos. LR-FIR se ha aplicado y evaluado en un amplio conjunto de problemas reales, no todos ellos del ámbito educativo, obteniendo buenos resultados. La estructura de la plataforma permite suponer que su utilización es potencialmente valiosa en aquellos dominios donde la administración del conocimiento juegue un papel preponderante, o donde los procesos de toma de decisiones sean una pieza clave, por ejemplo, e-business, e-marketing, administración de clientes, por mencionar sólo algunos. Las herramientas de Soft Computing utilizadas y desarrolladas en esta investigación: FIR, CR-FIR, LR-FIR y GTM, ha sido aplicadas con éxito en otros dominios reales, como música, medicina, comportamientos climáticos, etc.Postprint (published version

PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE