The Experts below are selected from a list of 60210 Experts worldwide ranked by ideXlab platform
Elyes Lamine - One of the best experts on this subject based on the ideXlab platform.
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A Semantic Reasoning Method Towards Ontological Model for Automated Learning Analysis
Advances in intelligent systems and computing, 2015Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Elyes LamineAbstract:Semantic reasoning can help solve the problem of regulating the evolving and static measures of knowledge at theoretical and technological levels. The technique has been proven to enhance the capability of process models by making inferences, retaining and applying what they have learned as well as discovery of new processes. The work in this paper propose a semantic rule-based approach directed towards discovering learners interaction patterns within a Learning knowledge base, and then respond by making decision based on adaptive rules centred on captured user profiles. The method applies semantic rules and description logic queries to build ontology model capable of automatically computing the various Learning activities within a Learning Knowledge-Base, and to check the consistency of Learning object/data types. The approach is grounded on inductive and deductive logic descriptions that allows the use of a Reasoner to check that all definitions within the Learning model are consistent and can also recognise which concepts that fit within each defined class. Inductive reasoning is practically applied in order to discover sets of inferred learner categories, while deductive approach is used to prove and enhance the discovered rules and logic expressions. Thus, this work applies effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns/behaviour.
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A Semantic Rule-Based Approach Supported by Process Mining for Personalised Adaptive Learning
2014Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes LamineAbstract:Currently, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. There has been a big gap between the extraction of useful patterns from data sources to knowledge, as it is crucial that data is made valid, novel, potentially useful and understandable. To meet the needs of intended users, there is requirement for Learning systems to embody technologies that support learners in achieving their Learning goals and this process don't happen automatically. This paper propose a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns within Learning processes, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic Modelling and Process Mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.
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HPCC/CSS/ICESS - A Semantic Rule-Based Approach Towards Process Mining for Personalised Adaptive Learning
2014 IEEE Intl Conf on High Performance Computing and Communications 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security 2014 IEEE 11th Intl Con, 2014Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes LamineAbstract:In recent years, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for Automated Learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their Learning goals. This paper proposes a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic modelling and process mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.
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A Semantic Rule-Based Approach Towards Process Mining for Personalised Adaptive Learning
2014Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes LamineAbstract:In recent years, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for Automated Learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their Learning goals. This paper proposes a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic modelling and process mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.
Paul Szyszka - One of the best experts on this subject based on the ideXlab platform.
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Social foraging extends associative odor-food memory expression in an Automated Learning assay for Drosophila melanogaster.
The Journal of experimental biology, 2019Co-Authors: Aarti Sehdev, Yunusa Garba Mohammed, Cansu Tafrali, Paul SzyszkaAbstract:Animals socially interact during foraging and share information about the quality and location of food sources. The mechanisms of social information transfer during foraging have been mostly studied at the behavioral level, and its underlying neural mechanisms are largely unknown. Fruit flies have become a model for studying the neural bases of social information transfer, because they provide a large genetic toolbox to monitor and manipulate neuronal activity, and they show a rich repertoire of social behaviors. Fruit flies aggregate, they use social information for choosing a suitable mating partner and oviposition site, and they show better aversive Learning when in groups. However, the effects of social interactions on associative odor-food Learning have not yet been investigated. Here, we present an Automated Learning and memory assay for walking flies that allows the study of the effect of group size on social interactions and on the formation and expression of associative odor-food memories. We found that both inter-fly attraction and the duration of odor-food memory expression increase with group size. This study opens up opportunities to investigate how social interactions during foraging are relayed in the neural circuitry of Learning and memory expression.
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Social foraging extends associative odor–food memory expression in an Automated Learning assay for Drosophila melanogaster
The Journal of Experimental Biology, 2019Co-Authors: Aarti Sehdev, Yunusa Garba Mohammed, Cansu Tafrali, Paul SzyszkaAbstract:ABSTRACT Animals socially interact during foraging and share information about the quality and location of food sources. The mechanisms of social information transfer during foraging have been mostly studied at the behavioral level, and its underlying neural mechanisms are largely unknown. Fruit flies have become a model for studying the neural bases of social information transfer, because they provide a large genetic toolbox to monitor and manipulate neuronal activity, and they show a rich repertoire of social behaviors. Fruit flies aggregate, they use social information for choosing a suitable mating partner and oviposition site, and they show better aversive Learning when in groups. However, the effects of social interactions on associative odor–food Learning have not yet been investigated. Here, we present an Automated Learning and memory assay for walking flies that allows the study of the effect of group size on social interactions and on the formation and expression of associative odor–food memories. We found that both inter-fly attraction and the duration of odor–food memory expression increase with group size. This study opens up opportunities to investigate how social interactions during foraging are relayed in the neural circuitry of Learning and memory expression.
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Social foraging extends associative odor-food memory expression in an Automated Learning assay for Drosophila melanogaster
bioRxiv, 2019Co-Authors: Aarti Sehdev, Yunusa Garba Mohammed, Cansu Tafrali, Paul SzyszkaAbstract:Animals socially interact during foraging and share information about the quality and location of food sources. The mechanisms of social information transfer during foraging have been mostly studied at the behavioral level, and its underlying neural mechanisms are largely unknown. The fruit fly Drosophila melanogaster has become a model for studying the neural bases of social information transfer, as fruit flies show a rich repertoire of social behaviors and provide a well-developed genetic toolbox to monitor and manipulate neuronal activity. Social information transfer has already been characterized for fruit flies9 egg laying, mate choice, foraging and aversive associative Learning, however the role of social information transfer on associative odor-food Learning during foraging are unknown. Here we present an Automated Learning and memory assay for walking flies that allows studying the effect of group size on social interactions and on the formation and expression of associative odor-food memories. We found that both inter-fly attraction and the duration of odor-food memory expression increase with group size. We discuss possible behavioral and neural mechanisms of this social effect on odor-food memory expression. This study opens up opportunities to investigate how social interactions are relayed in the neural circuitry of Learning and memory expression.
Kingsley Okoye - One of the best experts on this subject based on the ideXlab platform.
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A Semantic Reasoning Method Towards Ontological Model for Automated Learning Analysis
Advances in intelligent systems and computing, 2015Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Elyes LamineAbstract:Semantic reasoning can help solve the problem of regulating the evolving and static measures of knowledge at theoretical and technological levels. The technique has been proven to enhance the capability of process models by making inferences, retaining and applying what they have learned as well as discovery of new processes. The work in this paper propose a semantic rule-based approach directed towards discovering learners interaction patterns within a Learning knowledge base, and then respond by making decision based on adaptive rules centred on captured user profiles. The method applies semantic rules and description logic queries to build ontology model capable of automatically computing the various Learning activities within a Learning Knowledge-Base, and to check the consistency of Learning object/data types. The approach is grounded on inductive and deductive logic descriptions that allows the use of a Reasoner to check that all definitions within the Learning model are consistent and can also recognise which concepts that fit within each defined class. Inductive reasoning is practically applied in order to discover sets of inferred learner categories, while deductive approach is used to prove and enhance the discovered rules and logic expressions. Thus, this work applies effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns/behaviour.
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A Semantic Rule-Based Approach Supported by Process Mining for Personalised Adaptive Learning
2014Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes LamineAbstract:Currently, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. There has been a big gap between the extraction of useful patterns from data sources to knowledge, as it is crucial that data is made valid, novel, potentially useful and understandable. To meet the needs of intended users, there is requirement for Learning systems to embody technologies that support learners in achieving their Learning goals and this process don't happen automatically. This paper propose a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns within Learning processes, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic Modelling and Process Mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.
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HPCC/CSS/ICESS - A Semantic Rule-Based Approach Towards Process Mining for Personalised Adaptive Learning
2014 IEEE Intl Conf on High Performance Computing and Communications 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security 2014 IEEE 11th Intl Con, 2014Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes LamineAbstract:In recent years, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for Automated Learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their Learning goals. This paper proposes a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic modelling and process mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.
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A Semantic Rule-Based Approach Towards Process Mining for Personalised Adaptive Learning
2014Co-Authors: Kingsley Okoye, Abdel-rahman H. Tawil, Usman Naeem, Rabih Bashroush, Elyes LamineAbstract:In recent years, Automated Learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for Automated Learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their Learning goals. This paper proposes a novel approach for Automated Learning that is capable of detecting changing trends in Learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the Learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic modelling and process mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to Automated discovery of Learning patterns or behaviour.
Sholom M. Weiss - One of the best experts on this subject based on the ideXlab platform.
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towards language independent Automated Learning of text categorization models
International ACM SIGIR Conference on Research and Development in Information Retrieval, 1994Co-Authors: Chidanand Apte, Fred Damerau, Sholom M. WeissAbstract:We describe the results of extensive machine Learning experiments on large collections of Reuters’ English and German newswires. The goal of these experiments was to automatically discover classification patterns that can be used for assignment of topics to the individual newswires. Our results with the English newswire collection show a very large gain in performance as compared to published benchmarks, while our initial results with the German newswires appear very promising. We present our methodology, which seems to be insensitive to the language of the document collections, and discuss issues related to the differences in results that we have obtained for the two collections.
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SIGIR - Towards language independent Automated Learning of text categorization models
SIGIR ’94, 1994Co-Authors: Chidanand Apte, Fred Damerau, Sholom M. WeissAbstract:We describe the results of extensive machine Learning experiments on large collections of Reuters’ English and German newswires. The goal of these experiments was to automatically discover classification patterns that can be used for assignment of topics to the individual newswires. Our results with the English newswire collection show a very large gain in performance as compared to published benchmarks, while our initial results with the German newswires appear very promising. We present our methodology, which seems to be insensitive to the language of the document collections, and discuss issues related to the differences in results that we have obtained for the two collections.
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Automated Learning of decision rules for text categorization
ACM Transactions on Information Systems, 1994Co-Authors: Chidanand Apte, Fred Damerau, Sholom M. WeissAbstract:We describe the results of extensive experiments using optimized rule-based induction methods on large document collections. The goal of these methods is to discover automatically classification patterns that can be used for general document categorization or personalized filtering of free text. Previous reports indicate that human-engineered rule-based systems, requiring many man-years of developmental efforts, have been successfully built to “read” documents and assign topics to them. We show that machine-generated decision rules appear comparable to human performance, while using the identical rule-based representation. In comparison with other machine-Learning techniques, results on a key benchmark from the Reuters collection show a large gain in performance, from a previously reported 67% recall/precision breakeven point to 80.5%. In the context of a very high-dimensional feature space, several methodological alternatives are examined, including universal versus local dictionaries, and binary versus frequency-related features.
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Automated Learning of Decision Text Categorization
1994Co-Authors: Chidanand Apte, Fred Damerau, Sholom M. WeissAbstract:We describe the results of extensive experiments using optimized rule-based induction methods on large document collections. The goal of these methods is to discover automatically classification patterns that can be used for general document categorization or personalized filtering of free text. Previous reports indicate that human-engineered rule-based systems, requiring many man-years of developmental efforts, have been successfully built to “read” documents and assign topics to them. We show that machine-generated decision rules appear comparable to human performance, while using the identical rule-based representation. In comparison with other machine-Learning techniques, results on a key benchmark from the Reuters collection show a large gain in performance, from a previously reported 67% recall/precision breakeven point to 80.5%. In the context of a very high-dimensional feature space, several methodological alternatives are examined, including universal versus local dictionaries, and binary versus frequencyrelated features.
Joêl Fagot - One of the best experts on this subject based on the ideXlab platform.
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Using Automated Learning Devices for Monkeys (ALDM) to study social networks
Behavior Research Methods, 2017Co-Authors: Nicolas Claidière, Julie Gullstrand, Aurélien Latouche, Joêl FagotAbstract:Social network analysis has become a prominent tool to study animal social life, and there is an increasing need to develop new systems to collect social information automatically, systematically, and reliably. Here we explore the use of a freely accessible Automated Learning Device for Monkeys (ALDM) to collect such social information on a group of 22 captive baboons (Papio papio). We compared the social network obtained from the co-presence of the baboons in ten ALDM testing booths to the social network obtained through standard behavioral observation techniques. The results show that the co-presence network accurately reflects the social organization of the group, and also indicate under which conditions the co-presence network is most informative. In particular, the best correlation between the two networks was obtained with a minimum of 40 days of computer records and for individuals with at least 500 records per day. We also show through random permutation tests that the observed correlations go beyond what would be observed by simple synchronous activity, to reflect a preferential choice of closely located testing booths. The use of automatized cognitive testing therefore presents a new way of obtaining a large and regular amount of social information that is necessary to develop social network analysis. It also opens the possibility of studying dynamic changes in network structure with time and in relation to the cognitive performance of individuals.
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Assessment of social cognition in non-human primates using a network of computerized Automated Learning device (ALDM) test systems.
Journal of Visualized Experiments, 2015Co-Authors: Joêl Fagot, Julie Gullstrand, Yousri Marzouki, Pascal Huguet, Nicolas ClaidièreAbstract:Fagot & Paleressompoulle1 and Fagot & Bonte2 have published an Automated Learning device (ALDM) for the study of cognitive abilities of monkeys maintained in semi-free ranging conditions. Data accumulated during the last five years have consistently demonstrated the efficiency of this protocol to investigate individual/physical cognition in monkeys, and have further shown that this procedure reduces stress level during animal testing3. This paper demonstrates that networks of ALDM can also be used to investigate different facets of social cognition and in-group expressed behaviors in monkeys, and describes three illustrative protocols developed for that purpose. The first study demonstrates how ethological assessments of social behavior and computerized assessments of cognitive performance could be integrated to investigate the effects of socially exhibited moods on the cognitive performance of individuals. The second study shows that batteries of ALDM running in parallel can provide unique information on the influence of the presence of others on task performance. Finally, the last study shows that networks of ALDM test units can also be used to study issues related to social transmission and cultural evolution. Combined together, these three studies demonstrate clearly that ALDM testing is a highly promising experimental tool for bridging the gap in the animal literature between research on individual cognition and research on social cognition.
