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Proceedings of the Annual Hawaii International Conference on System Sciences 2012年 5339-5339页

作者： Alves-Foss, Jim Linger, Rick Woody, Carol Center for Secure and Dependable Systems University of Idaho United States Cyber Security and Information Intelligence Research Group Oak Ridge National Laboratory United States Software Engineering Institute Carnegie Mellon University United States

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Reference service description framework

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Journal of Harbin Institute of Technology (New Series) 2008年第SUPPL.期15卷 88-93页

作者： Zhang, Li Zhang, Naiyue Chen, Zhong Institute of Software School of Electronics Engineering and Computer Science Peking University Beijing 100000 China Key Laboratory of High Confidence Software Technologies Peking University Beijing 100000 China ACOM Financial Information Research Center Peking University Beijing 100000 China School of Software and Microelectronics Peking University Beijing 100000 China

This paper consists of two parts. In part one;we give a new reference definition for service, based on some fundamental concepts in economics such as homo economics and utility. Part two is primarily concerned with a service description framework, which includes the service provider, service receiver, service content, service medium and service environment, together with some examples.

关键词： Industrial management

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Dimension reconstruction for visual exploration of subspace clusters in high-dimensional data

Dimension reconstruction for visual exploration of subspace ...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Fangfang Zhou Juncai Li Wei Huang Ying Zhao Xiaoru Yuan Xing Liang Yang Shi School of Information Science and Engineering Central South University School of Software Central South University Key Laboratory of Machine Perception (Ministry of Education) Peking University Informatics & Decision Systems Engineering Arizona State University

ISBN: (纸本)9781509014521

Subspace-based analysis has increasingly become the preferred method for clustering high-dimensional data. A visually interactive exploration of subspaces and clusters is a cyclic process. Every meaningful discovery will motivate users to re-search subspaces that can provide improved clustering results and reveal the relationships among clusters that can hardly coexist in the original subspaces. However, the combination of dimensions from the original subspaces is not always effective in finding the expected subspaces. In this study, we present an approach that enables users to reconstruct new dimensions from the data projections of subspaces to preserve interesting cluster information. The reconstructed dimensions are included into an analytical workflow with the original dimensions to help users construct target-oriented subspaces which clearly display informative cluster structures. We also provide a visualization tool that assists users in the exploration of subspace clusters by utilizing dimension reconstruction. Several case studies on synthetic and real-world data sets have been performed to prove the effectiveness of our approach. Lastly, further evaluation of the approach has been conducted via expert reviews.

关键词： Data visualization Visualization Clustering algorithms Minerals Data mining Algorithm design and analysis Pipelines

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ROS package search for robot software development: a knowledge graph-based approach

arXiv

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arXiv 2023年

作者： Wang, Shuo Mao, Xinjun Yang, Shuo Wu, Menghan Zhang, Zhang College of Computer National University of Defense Technology Changsha410000 China Key Laboratory of Software Engineering for Complex Systems Changsha410000 China College of Systems Engineering National University of Defense Technology Changsha410000 China College of Computer Science and Technology Zhejiang University Hangzhou310058 China

In recent years, ROS (Robot Operating System) packages have become increasingly popular as a type of software artifact that can be effectively reused in robotic software development. Indeed, finding suitable ROS packages that closely match the software’s functional requirements from the vast number of available packages is a nontrivial task using current search methods. The traditional search methods for ROS packages often involve inputting keywords related to robotic tasks into general-purpose search engines (e.g., Google) or code hosting platforms (e.g., Github) to obtain approximate results of all potentially suitable ROS packages. However, the accuracy of these search methods remains relatively low because the task-related keywords may not precisely match the functionalities offered by the ROS packages. To improve the search accuracy of ROS packages, this paper presents a novel semantic-based search approach that relies on the semantic-level ROS Package Knowledge Graph (RPKG) to automatically retrieve the most suitable ROS packages. Firstly, to construct the RPKG, we employ multi-dimensional feature extraction techniques to extract semantic concepts, including code file name, category, hardware device, characteristics, and function, from the dataset of ROS package text descriptions. The semantic features extracted from this process result in a substantial number of entities (32,294) and relationships (54,698). Subsequently, we create a robot domain-specific small corpus and further fine-tune a pre-trained language model, BERT-ROS, to generate embeddings that effectively represent the semantics of the extracted features. These embeddings play a crucial role in facilitating semantic-level understanding and comparisons during the ROS package search process within the RPKG. Secondly, we introduce a novel semantic matching-based search algorithm that incorporates the weighted similarities of multiple features from user search queries, which searches out more accurate

关键词： Robots

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning

arXiv

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arXiv 2023年

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, The Authors. All rights reserved.

关键词： Students

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Manjula R Jagadeesan S Venkadeshan Ramalingam Vetriselvi T Johnpeter T Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Information Technology Department University of Technology and Applied Sciences Sultanate of Oman Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user.

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Application of a neural network to automatic gray level adjustment for medical images

Application of a neural network to automatic gray level adju...

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International Joint Conference on Neural Networks (IJCNN)

作者： A. Ohhashi S. Yamada K. Haruki H. Hatano K. Nishimura Y. Fujii K. Yamaguchi H. Ogata Medical Engineering Laboratory Toshiba Corporation Otawara Tochigi Japan Systems & Software Engineering Laboratory Toshiba Corporation Kawasaki Kanagawa Japan Toshiba Information Systems Corporation Kawasaki Kanagawa Japan Toshiba Medical Engineering Company Limited Otawara Tochigi Japan Department of Radiology Toshiba Central Hospital Shinagawa Tokyo Japan

The authors have developed a system to automatically adjust the gray level of magnetic resonance (MR) images using a neural network. The gray level of an MR image is adjusted by setting the display window (gray-level) width and level (WWL). The authors define an index, EW, for the evaluation of displayed image clarity, and they prove its effectiveness. They use a neural network to learn the relationship between image histogram features and displayed image clarity. The authors calculated image clarity using the NN, performed two-stage searching, and determined the best possible WWL. They also evaluated the WWL adjusted by the system using the clarity index, EW.< >

关键词： Neural networks Biomedical imaging Humans Biological neural networks Displays Histograms Education Biomedical engineering Signal design System testing

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Feature-Modeling and Aspect-Oriented Programming: Integration and Automation

Feature-Modeling and Aspect-Oriented Programming: Integratio...

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ACIS International Conference on software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Kwanwoo Lee Goetz Botterweck Steffen Thiel Department of Information Systems Engineering Hansung University Seoul South Korea Lero – the Irish Software Engineering Research Centre University of Limerick Limerick Ireland Department of Computer Science Furtwangen University of Applied Sciences Furtwangen Germany

Feature modeling is an essential activity for modeling and managing the variability of a software product line. On the other hand, aspect-oriented programming provides effective means for modularizing feature implementation. Although current AOP tools (e.g., AJDT) provide a mechanism for switching aspect modules on and off to configure a product, this becomes infeasible in the context of large-scale product lines with thousands of variations. In this paper, we describe how feature modeling can be integrated with aspect-oriented programming to perform automated product derivation efficiently and effectively in the context of large-scale product lines.

关键词： Automatic programming Automation software engineering Switches Artificial intelligence Intelligent networks Distributed computing Parallel programming Management information systems systems engineering and theory

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Deep Label Distribution Learning for Apparent Age Estimation

Deep Label Distribution Learning for Apparent Age Estimation

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Xu Yang Bin-Bin Gao Chao Xing Zeng-Wei Huo Xiu-Shen Wei Ying Zhou Jianxin Wu Xin Geng School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Southeast University Nanjing Jiangsu CN

In the age estimation competition organized by ChaLearn, apparent ages of images are provided. Uncertainty of each apparent age is induced because each image is labeled by multiple individuals. Such uncertainty makes this age estimation task different from common chronological age estimation tasks. In this paper, we propose a method using deep CNN (Convolutional Neural Network) with distribution-based loss functions. Using distributions as the training tasks can exploit the uncertainty induced by manual labeling to learn a better model than using ages as the target. To the best of our knowledge, this is one of the first attempts to use the distribution as the target of deep learning. In our method, two kinds of deep CNN models are built with different architectures. After pre-training each deep CNN model with different datasets as one corresponding stream, the competition dataset is then used to fine-tune both deep CNN models. Moreover, we fuse the results of two streams as the final predicted ages. In the final testing dataset provided by competition, the age estimation performance of our method is 0.3057, which is significantly better than the human-level performance (0.34) provided by the competition organizers.

关键词： Face Estimation Streaming media Training Hidden Markov models Uncertainty Standards

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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