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作者： Greene, Derek Freyne, Jill Smyth, Barry Cunningham, Pádraig Clique Research Cluster School of Computer Science and Informatics University College Dublin Ireland CSIRO's ICT Centre Based Hobart Australia Computer Science School of Computer Science and Informatics University College Dublin Ireland ChangingWorlds Ltd. Ireland Clarity: The Centre for Sensor Web Technologies Ireland Department of Knowledge and Data Engineering School of Computer Science and Informatics University College Dublin Ireland

The European Conference on Case-Based Reasoning (CBR) in 2008 marked IS years of international and European CBR conferences where almost seven hundred research papers were published. In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both cocitation links and text similarity. It i.ç interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a subpart of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the subtopics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced. Copyright © 2010, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Case based reasoning

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Assessing the influence probability between objects: A random walker approach

Assessing the influence probability between objects: A rando...

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IEEE Symposium on Computational Intelligence and data Mining (CIDM)

作者： Pei Li Zhixu Li Jun He Xiaoyong Du Hongyan Liu Key Laboratories of Data Engineering and Knowledge Engineering MOE China & School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

Influence between objects needs to be assessed in many applications. Lots of measures have been proposed, but a domain-independent method is still expected. In this paper, we give a probabilistic definition of influence based on the random walker model on graphs. Two approaches, linear systems method and Basic InfRank algorithm, are shown and return equal results, but Basic InfRank is more efficient by iterative computation. Two variants on bipartite graphs and star graphs are discussed. Experiments show InfRank algorithms have good accuracy, fast convergent rate and high performance.

关键词： data engineering knowledge engineering Iterative algorithms Attenuation measurement Helium knowledge management engineering management Linear systems Iterative methods Bipartite graph

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IKGN: Intention-aware knowledge Graph Network for POI Recommendation

IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

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IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is available at https://***/Jungle123456/IKGN.

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Exploring Social Media data for MOOC Recommendation

Exploring Social Media Data for MOOC Recommendation

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International Conference on Systems of Collaboration (SysCo)

作者： Sara Assami Najima Daoudi Rachida Ajhoun Department of Computer Science Smart Systems Laboratory Mohammed Vth University Rabat Morocco Department of Data and Knowledge Engineering Information Sciences School (ESI) Rabat Morocco

ISBN: (数字)9781728142166

ISBN: (纸本)9781728142173

MOOCs (Massive Open Online Courses) are a key tool for open education stakeholders and high-level universities to face the problem of limited resources and make education accessible worldwide. Our study relies on the hypothesis of the influence of learning adaptation on the learners' lack of motivation in MOOCs. In this sense, a recommendation approach could stimulate the learner's interest in specific MOOCs. In previous research, we have identified the recommendation criteria to recommend MOOCs adapted to the learner's needs and motivations. Then, we modeled the ontology of the learning actors' profiles for the matching of the characteristics of learners with adapted MOOCs. Afterward, we designed the functional architecture and use cases of a semantic recommendation system to stimulate learners' interest in MOOCs. However, the recommendation process is still hindered by the learner profile on these platforms that doesn't represent the learner's interests and motivations. Yet, social media profiles extraction can be an external data source for learners' profile enrichment. So, the purpose of this research is to use social media mining for the acquisition of personal and professional data about learners. Therefore, we apply the first phases of data mining on a social media dataset to explore the potentially meaningful data for MOOC recommendation.

关键词： Social networking (online) data mining Educational courses Recommender systems Computer aided instruction

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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and data Visualization 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Alfredo Vellido Karina Gibert Cecilio Angulo José David Martín Guerrero

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GenF: A longevity predicting framework to aid public health sectors

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Informatics in Medicine Unlocked 2021年 26卷

作者： Khalid, Sadia Rasheed, Uzair Qamar, Usman Knowledge & Data Science Centre Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Sciences and Technology Islamabad Pakistan

Life expectancy at older ages has seen an increment in the past 100 years and factors influencing longevity are since being studied. Public health sectors (PHS) across the globe are trying hard to understand longevity trends to improve living conditions and provide necessary medical facilities;but there is no single tool that can help the overall cause of recording and/or measuring and/or predicting human longevity. The objectives of this study are to help PHS understand longevity-influencing factors and to offer a framework to record and predict longevity trends in any population around the globe. This study has gathered fifteen longevity-influencing factors from literature between 2000 and 2020;with a special focus on APOE genotype;and based on them, has developed a framework GenF, including a rule base dataset of 73,728 rules, to predict longevity. Furthermore, the study has also used GenF to perform feature selection on the rule base and generate another rule base for customized longevity predictions. Models, using four different machine learning classifiers, are then trained on both rule bases to predict longevity. © 2021 The Authors

关键词： Healthy aging Human longevity Public health Rule based classifier Supervised learning

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Simulation, comparison and analysis of Wireless Sensor Networks protocols: LEACH, LEACH-C, LEACH-1R, and HEED

Simulation, comparison and analysis of Wireless Sensor Netwo...

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International Conference on Electrical engineering (ICEE)

作者： Mohammed Omari Soumia Laroui Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran Knowledge and Data Engineering Group Trinity College Dublin LDDI Laboratory Computer Science Department University of Colonel Ahmed Draya Adrar 01000 Algeria

Wireless Sensor Networks (WSNs) are made up of tiny sensor nodes which sense the data and communicate to the base station via other nodes. These sensor nodes are inexpensive portable devices with limited processing power and energy resources which make them in need of smart clustering protocols. Many clustering and routing protocols were proposed in the literature to serve large networks of such tiny devices. In this paper, we have implemented and analyzed different clustering protocols, namely LEACH, LEACH-C, LEACH-1R, and HEED using MATLAB environment. These clustering protocols are compared in different terms such as residual energy, data delivery to the base station, maximum number of rounds and the number of live nodes. Experimental results showed a better performance of the LEACH protocols when compared to the different versions of HEED. Moreover, LEACH-1R proved to be efficient in terms of network lifetime.

关键词： Wireless sensor networks Base stations Mathematical model Energy consumption Batteries Routing protocols

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Enabling Efficient Random Access to Hierarchically-Compressed data

Enabling Efficient Random Access to Hierarchically-Compresse...

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International Conference on data engineering

作者： Feng Zhang Jidong Zhai Xipeng Shen Onur Mutlu Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Department of Computer Science and Technology Tsinghua University BNRist Computer Science Department North Carolina State University Department of Computer Science ETH Zürich

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Recent studies have shown the promise of direct data processing on hierarchically-compressed text documents. By removing the need for decompressing data, the direct data processing technique brings large savings in both time and space. However, its benefits have been limited to data traversal operations; for random accesses, direct data processing is several times slower than the state-of-the-art baselines. This paper presents a set of techniques that successfully eliminate the limitation, and for the first time, establishes the feasibility of effectively handling both data traversal operations and random data accesses on hierarchically-compressed data. The work yields a new library, which achieves 3.1× speedup over the state-of-the-art on random data accesses to compressed data, while preserving the capability of supporting traversal operations efficiently and providing large (3.9×) space savings.

关键词： Indexing data processing data structures Technological innovation Dictionaries Law data mining

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The Direct Product of Graded Finite Posets

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Chinese Quarterly Journal of Mathematics 2008年第3期23卷 430-436页

作者： LIU Wei CHEN Shu-wei JIANG Bao-qing Institute of Data and Knowledge Engineering Henan University Kaifeng 475001 China Department of Basic Science Henan Industry and Trade Professional CollegeZhengzhou 450012 China School of Electrical Engineering Zhengzhou University Zhengzhou 450001 China

A new concept Graded Finite Poset is proposed in this paper. Through discussing some basic properties of it, we come to that the direct product of graded finite posets is connected if and only if every graded finite poset is connected. The graded function of a graded finite poset is unique if and only if the graded finite poset is connected.

关键词： Graded poset graded finite poset graded function direct product connected post

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Detection of Pulsar Candidates using Bagging Method

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Procedia Computer science 2020年 170卷 1096-1101页

作者： Mourad Azhari Abdallah Abarda Altaf Alaoui Badia Ettaki Jamal Zerouaoui Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco

The pulsar classification represents a major issue in the astrophysical area. The Bagging Algorithm is an ensemble method widely used to improve the performance of classification algorithms, especially in the case of pulsar search. In this way, our paper tries to prove how the Bagging Method can improve the performance of pulsar candidate detection in connection with four basic classifiers: Core Vector Machines (CVM), the K-Nearest-Neighbors (KNN), the Artificial Neural Network (ANN), and Cart Decision Tree (CDT). The Error Rate, Area Under the Curve (AUC), and Computation Time (CT) are measured to compare the performance of different classifiers. The High Time Resolution Universe (HTRU2) dataset, collected from the UCI Machine Learning Repository, is used in the experimentation phase.

关键词： Bagging CVM ANN KNN CDT AUC Error Rate Computation Time(CT)

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