The Experts below are selected from a list of 790728 Experts worldwide ranked by ideXlab platform
Yasmine H. Abdul-amir - One of the best experts on this subject based on the ideXlab platform.
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© 2010 Science Publications A Trust System Based on Multi Level Virus Detection
2010Co-Authors: Yasmine H. Abdul-amirAbstract:Abstract: Problem statement: As these detection methods were developed and implemented, the virus developers adapted to the new detectors in ways intended to defeat them. Approach: This study introduced new multilevel virus detection (MDS). Results: This system model depended on an advance behavior blocking technology. It detected virus-code by a behavior approach monitors and determined a virus activity at several protection system levels. Conclusion: This system simultaneously provided smart memory resident monitor, integrity checker and activity virus file (.BAT) checker. Key words: Computer security, system security, computer viru
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© 2010 Science Publications A Trust System Based on Multi Level Virus Detection
2010Co-Authors: Yasmine H. Abdul-amirAbstract:Abstract: Problem statement: As these detection methods were developed and implemented, the virus developers adapted to the new detectors in ways intended to defeat them. Approach: This study introduced a new Multilevel virus detection (MDS). Results: This system model depends on an advance behavior blocking technology. It detects virus-code by a behavior approach monitors and determined a virus activity at several protection system levels. Conclusion: This system simultaneously provides smart memory resident monitor, integrity checker and activity virus file (.BAT) checker. Key words: Computer security, system security, computer viru
Songchun Zhu - One of the best experts on this subject based on the ideXlab platform.
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inter gps interpretable geometry Problem solving with formal language and symbolic reasoning
Meeting of the Association for Computational Linguistics, 2021Co-Authors: Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, Songchun ZhuAbstract:Geometry Problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires Abstract Problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale benchmark, Geometry3K, consisting of 3,002 geometry Problems with dense annotation in formal language. We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the Problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively. Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step. Also, a theorem predictor is designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path. Extensive experiments on the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves significant improvements over existing methods. The project with code and data is available at https://lupantech.github.io/inter-gps.
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inter gps interpretable geometry Problem solving with formal language and symbolic reasoning
arXiv: Computation and Language, 2021Co-Authors: Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, Songchun ZhuAbstract:Geometry Problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires Abstract Problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale benchmark, Geometry3K, consisting of 3,002 geometry Problems with dense annotation in formal language. We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the Problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively. Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step. Also, a theorem predictor is designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path. Extensive experiments on the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves significant improvements over existing methods. The project with code and data is available at this https URL.
Jonathan Rosenhead - One of the best experts on this subject based on the ideXlab platform.
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Project review and learning in the construction industry: Embedding a Problem structuring method within a partnership context
European Journal of Operational Research, 2004Co-Authors: L. Alberto Franco, Mike Cushman, Jonathan RosenheadAbstract:Abstract Problem Structuring Methods have most commonly been employed in one-off interventions to address non-routine Problem situations. This paper argues that Problem structuring methods are by their nature also appropriate for routine use within multi-organizational partnerships as a means of supporting inter-organizational learning. An experience of the use of a Problem structuring methods approach in this mode is reported, within a UK construction industry partnership context. The approach which was developed has been called the Cross Organizational Learning Approach (COLA). COLA uses Strategic Choice-based workshops to identify and review critical incidents and project successes, in order to generate a limited set of key actions to feed back both to project partners and to future joint projects. The paper describes and discusses the process of developing and using this approach, with particular concentration on the apparent success in embedding it as a continuing business practice.
Raymond R Tan - One of the best experts on this subject based on the ideXlab platform.
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Problem based learning of process systems engineering and process integration concepts with metacognitive strategies the case of p graphs for polygeneration systems
Applied Thermal Engineering, 2017Co-Authors: Michael Angelo B Promentilla, Rochelle Irene G Lucas, Kathleen B Aviso, Raymond R TanAbstract:Abstract Problem-based Learning (PBL) is regarded by many education experts as superior to the traditional lecture, particularly for learning higher-order skills and concepts. PBL can be further reinforced through the utilization of metacognitive skills on the part of the students. Such approaches are particularly useful in Process Systems Engineering (PSE) and Process Integration (PI), where proficiency in the use of methodologies for Problem-solving is paramount. In this paper, we describe how such educational strategies can be used to improve learning outcomes through the use of the process graph (P-graph) framework for the synthesis of polygeneration systems, in addition to conventional approaches using lectures and drills. Practical pedagogic implications are then discussed for potential use in teaching related topics in PSE/PI.
Daniel J Cote - One of the best experts on this subject based on the ideXlab platform.
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students approaches to learning in Problem based learning taking into account professional behavior in the tutorial groups self study time and different assessment aspects
Studies in Educational Evaluation, 2013Co-Authors: Sofie M M Loyens, David Gijbels, Liesje Coertjens, Daniel J CoteAbstract:Abstract Problem-based learning (PBL) represents a major development in higher educational practice and is believed to promote deep learning in students. However, empirical findings on the promotion of deep learning in PBL remain unclear. The aim of the present study is to investigate the relationships between students’ approaches to learning (SAL) and academic achievement in a PBL environment, taking into account the role of self-study time and students’ professional behavior in the PBL tutorial groups. In addition, different knowledge categories that determine achievement (i.e., understanding of concepts, understanding of the principles that link concepts, and the linking of concepts and principles to conditions and procedures for application) were taken into account. A hypothesized structural equation model including these variables was tested. Results showed that the PBL students in this study reported more use of a surface compared to a deep approach to learning. The hypothesized model demonstrated an excellent fit of the model with the data. The relationship between SAL and academic achievement was mediated by self-study time and professional behavior. These findings imply that self-study time and professional behavior are crucial variables to take into account when studying SAL.
