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International Conference on Advanced Information Networking and Applications (AINA)

作者： S.M. Deen Department of Computer Science Data and Knowledge Engineering (DAKE) Group University of Keele Staffordshire UK

The paper presents a general architecture for a P2P data sharing facility within a multi-agent framework, where peers as autonomous high-level nodal agents cooperate with each other to solve global tasks. A node may have several lower level local agents including local databases and partial global ontologies. In addition there are also minder agents coordinating the activities of the peers that offer the same type of service, thus providing fault-tolerance. The ability of this architecture in data and task sharing has been demonstrated by considering query processing and directory update strategies.

关键词： Peer to peer computing Fault tolerance Query processing Spread spectrum communication Transaction databases knowledge engineering computer science computer architecture Ontologies Internet

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3D Human Pose Estimation with Dilated Sampled Frames 3

3D Human Pose Estimation with Dilated Sampled Frames

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3rd International Workshop on Intelligent Systems, IWIS 2023

作者： Cao, Ge Tang, Qing Tien Dat, Tran Uddin Russo, Ashraf Jo, Kanghyun University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of Data Science Group INTERX Ulsan Korea Republic of

ISBN: (纸本)9798350305043

Three-dimensional (3D) human pose estimation (HPE) targets to produce the 3D spatial coordinates of the human pose from 2D images. 3D HPE is a basic computer vision task for many intelligent industrial applications. Commonly, the coordinates of the predicted 3D human pose joints are calculated through the 2D keypoint from the ground truth provided by the datasets or generated by a classical and robust 2D human pose estimator. With the development of transformer-based methods, the methods with a sequence of monocular images have achieved great success in 2D-to-3D lifting human pose estimation. In this paper, the sampled frames with a dilated ratio are given as the input of the 3D human pose estimator. Extensive experiments on the public benchmark Human 3.6M demonstrate the significance and effectiveness of the proposed method. © 2023 IEEE.

关键词： 3D human pose estimation intelligent industry monocular image transformer

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A machine learning approach for instance matching based on similarity metrics

A machine learning approach for instance matching based on s...

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11th International Semantic Web Conference, ISWC 2012

作者： Rong, Shu Niu, Xing Xiang, Evan Wei Wang, Haofen Yang, Qiang Yu, Yong APEX Data and Knowledge Management Lab Shanghai Jiao Tong University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9783642351754

The Linking Open data (LOD) project is an ongoing effort to construct a global data space, i.e. the Web of data. One important part of this project is to establish owl:sameAs links among structured data sources. Such links indicate equivalent instances that refer to the same real-world object. The problem of discovering owl:sameAs links between pairwise data sources is called instance matching. Most of the existing approaches addressing this problem rely on the quality of prior schema matching, which is not always good enough in the LOD scenario. In this paper, we propose a schema-independent instance-pair similarity metric based on several general descriptive features. We transform the instance matching problem to the binary classification problem and solve it by machine learning algorithms. Furthermore, we employ some transfer learning methods to utilize the existing owl:sameAs links in LOD to reduce the demand for labeled data. We carry out experiments on some datasets of OAEI2010. The results show that our method performs well on real-world LOD data and outperforms the participants of OAEI2010. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Learning systems

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Performance Analysis of Health Care Expense Anticipation using Computational Intelligence 1

Performance Analysis of Health Care Expense Anticipation usi...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Gopalakrishnan, A. Kumarganesh, S. Divya Bharathi, G. Anthoniraj, S. Natarajan, Suresh Kumar Gochhait, Saikat Knowledge Institute of Technology Department of Artificial Intelligence and Data Science Salem India Knowledge Institute of Technology Department of Electronics and Communication Engineering Salem India Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India School of Computer Science and Engineering Ramanagara India Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Maharashtra Pune India

ISBN: (纸本)9798350348637

The escalating costs of healthcare globally necessitate the development of accurate prediction models to address the financial strain on individuals, families, businesses, and governments. This research employs linear regression models to forecast healthcare expenditures, analyzing the intricate relationship between various factors and costs. A comprehensive literature survey explores topics such as the impact of environmental pollution on healthcare spending, prediction of surgical expenses, budget impact analysis for cancer treatment, and the integration of big data in healthcare infrastructure investments. The methodology involves gathering a diverse dataset encompassing patient demographics, medical history, treatment methods, and associated expenditures. Exploratory data analysis employs Pearson's correlation coefficient to identify trends and correlations between variables. Two linear regression models are proposed and compared, with the second model excluding non-significant variables for enhanced accuracy and interpretability. Results indicate that the second model, with refined feature selection, outperforms the first in terms of mean squared error and R-squared values. A comparative analysis of different studies underscores the diverse findings in healthcare economics, ranging from the impact of CO2 emissions on health to predicting surgery costs and budgetary savings in cancer treatment. The research contributes to the growing body of knowledge in healthcare economics, providing decision-makers with a valuable tool for precise expenditure estimation. The emphasis on linear regression models aids stakeholders in understanding factors influencing healthcare costs, guiding resource allocation, policy creation, and financial planning for more sustainable and effective healthcare systems. © 2024 IEEE.

关键词： Budget control

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Assessing the quality of geospatial linked data - Experiences from ordnance survey Ireland (OSi) 14

Assessing the quality of geospatial linked data - Experience...

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Posters and Demos Track of the 14th International Conference on Semantic Systems, SEMPDS 2018

作者： Debattista, Jeremy Clinton, Eamon Brennan, Rob Knowledge and Data Engineering Group ADAPT Centre School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Ordnance Survey Ireland Phoenix Park Dublin 8 Ireland

Ordnance Survey Ireland (OSi) is Ireland's national mapping agency that is responsible for the digitisation of the island's infrastructure in terms of mapping. Generating data from various sensors (e.g. spatial sensors), OSi build its knowledge in the Prime2 framework, a subset of which is transformed into geo-Linked data. In this paper we discuss how the quality of the generated sematic data fares against datasets in the LOD cloud. We set up Luzzu, a scalable Linked data quality assessment framework, in the OSi pipeline to continuously assess produced data in order to tackle any quality problems prior to publishing. © 2018 CEUR-WS. All Rights Reserved.

关键词： Linked data

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Speech emotion recognition with acoustic and lexical features 40

Speech emotion recognition with acoustic and lexical feature...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Jin, Qin Li, Chengxin Chen, Shizhe Wu, Huimin Computer Science Department School of Information Renmin University of China Beijing China Key Lab of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing China

ISBN: (纸本)9781467369978

In this paper we explore one of the key aspects in building an emotion recognition system: generating suitable feature representations. We generate feature representations from both acoustic and lexical levels. At the acoustic level, we first extract low-level features such as intensity, F0, jitter, shimmer and spectral contours etc. We then generate different acoustic feature representations based on these low-level features, including statistics over these features, a new representation derived from a set of low-level acoustic codewords, and a new representation from Gaussian Supervectors. At the lexical level, we propose a new feature representation named emotion vector (eVector). We also use the traditional Bag-of-Words (BoW) feature. We apply these feature representations for emotion recognition and compare their performance on the USC-IEMOCAP database. We also combine these different feature representations via early fusion and late fusion. Our experimental results show that late fusion of both acoustic and lexical features achieves four-class emotion recognition accuracy of 69.2%. © 2015 IEEE.

关键词： Acoustic features Emotion lexicon Emotion recognition Lexical features Support vector machine

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Towards Policy Decomposition for Autonomic Systems Governance by Applying Biologically Inspired Techniques.

Towards Policy Decomposition for Autonomic Systems Governanc...

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IEEE Network Operations and Management Symposium Workshops (NOMS Workshops)

作者： John Keeney Vincent Wade Knowledge & Data Engineering Group School of Computer Science and Statistics Dublin Ireland

ISBN: (纸本)9781424420674

Policy-based management systems use declarative rules to govern their operation whilst satisfying the goals of the system. One of the fundamental issues in policy engineering remains the inability to automatically refine high-level goals to low level achievable goals, many of which will be interdependent and conflicting. This paper introduces the automated decomposition and refinement of management policies through the optimisation of balancing constraints by the innovative integration metaheuristic algorithms.

关键词： Humans data engineering computer science Statistics Educational institutions knowledge management engineering management Innovation management Constraint optimization Multidimensional systems

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Efficient querying of correlated uncertain data with cached results

Efficient querying of correlated uncertain data with cached ...

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Chen, Jinchuan Zhang, Min Xie, Xike Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China School of Information Renmin University of China China Department of Computer Science Aalborg University Denmark

ISBN: (纸本)9783642374869

Although there have been many efforts for management of uncertain data, evaluating probabilistic inference queries, a known NP-hard problem, is still a big challenge, especially for querying data with highly correlations. The state-of-art exact algorithms for accelerating the evaluation of inference queries are based on special indices. Besides, with the observation of the existence of many frequent queries, some researchers try to improve efficiency by reusing previously queried results. Indexing depends on the static properties like data distributions, whereas caching is in favor of the dynamic features like query workload. In this paper we propose a new approach for speeding up the evaluation of inference queries by caching frequent results in a junction tree-based hierarchical index. To the best of our knowledge, this is the first effort on utilizing both the static (data) and dynamic (query workload) properties to efficiently evaluate probabilistic inference queries. Moreover, according to our experience, different caching strategies may significantly affect the query performance. Basically a good caching strategy needs to have high cache hit ratio with limited space *** on these considerations, we propose a novel caching approach, called FVEC, and present corresponding algorithms for efficiently querying correlated uncertain data. We further conduct a series of extensive experiments on large uncertain datasets in order to illustrate the effectiveness and efficiency of our proposed approaches. As illustrated by the results, compared with previous solutions, our method could greatly improve the query performance. © Springer-Verlag 2013.

关键词： Efficiency

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Performance Evaluation of Application Layer IoT Protocols: MQTT and CoAP 4

Performance Evaluation of Application Layer IoT Protocols: M...

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Debnath, Manika Modak, Subrata Sarkar, Dhrubasish Supreme Knowledge Foundation Group of Institutions Department of Computer Science & Engineering West Bengal Mankundu India Supreme Institute of Management and Technology Computer Science & Information Technology West Bengal Mankundu India

ISBN: (纸本)9798350389128

The Internet of Things (IoT) is one of the most intriguing technological revolutions of the last few years due to its rapid expansion. The advancement of IoT applications is dependent on standard and real-time communication protocols. The IoT protocols are designed to enable reliable messaging. However, the selection of a standard and efficient messaging protocol is a difficult and intimidating task for any organization. One of the main challenging issues facing the loT is wireless sen-sor networks (WSNs). The devices that make up these networks have low levels of memory, processing power, and energy. Certain restricted protocols must be in place to function in WSNs due to their constrained nature. In the context of a business application for monitoring, this paper shows the experimental performance of two protocols: Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP). © 2024 IEEE.

关键词： HTTP

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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