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A Resource-Aware Model-Based Framework for Load Testing of WS-BPEL Compositions 1

20th International Conference on Enterprise Information Systems, ICEIS 2018

作者： Krichen, Moez MaÃ¢lej, Afef Jmal Lahami, Mariam Jmaiel, Mohamed Faculty of Computer Science and Information Technology Al-Baha University Al Bahah Saudi Arabia ReDCAD Research Laboratory National School of Engineering of Sfax University of Sfax B.P. 1173 Sfax Tunisia Research Center for Computer Science Multimedia and Digital Data Processing of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia

ISBN: (数字)9783030261696

ISBN: (纸本)9783030261689

Nowadays, Web services compositions are playing a major role in the implementation of different types of distributed architectures. Such applications usually provide services to hundreds of users simultaneously. Load Testing is considered as an important type of testing for Web services compositions, as such applications require concurrent access by many users simultaneously. In this context, load testing for these types of applications seems an important task in order to discover problems under high loads. For this goal, we propose a model-based resource aware test architecture aiming to study the behavior of WS-BPEL compositions taking into account load conditions. The main contribution of this work consists of (a) adopting the timed automata formalism to model the system under test and to generate digital-clock test suites (b) identifying the best node for hosting each tester instance, then (c) running load tests and recording performance data and finally (d) analyzing the obtained logs in order to detect problems under load. Our approach is illustrated by means of a case study from the healthcare domain. © 2019, Springer Nature Switzerland AG.

关键词： Load testing

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Exploring the Pan-Relationship Between Disease and Symptom Related to Coronary Heart Disease from Chinese Electronic Medical Records 15th

Exploring the Pan-Relationship Between Disease and Symptom R...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Yang, Ronggen Wang, Yichen Wang, Bo Gong, Lejun Faculty of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210046 China School of Geography and Bioinformatics Nanjing University of Posts and Telecommunication Nanjing China

ISBN: (纸本)9783030267650

Electronic Medical Records (EMRs) arises from the clinical treatment process, which reflects a patient’s physical condition, the disease, the treatment process, etc. It has an important significance for the recovery of the patient. As the amount of electronic medical records continues to increase, people are actively looking for ways to get the valid data contained in electronic medical records. More effective methods are urgently needed for processing EMRs. In this paper, 108 real EMRs of coronary heart disease were analyzed, through named entity recognition and entity relation extraction. Pan-relationship between entities was defined in the process. Experimental results showed the analysis of pan-relation between coronary heart disease and symptom is helpful for treatment of coronary heart disease. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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Comparing concepts of quantum and classical neural network models for image classification task

arXiv

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arXiv 2021年

作者： Potempa, Rafal Porebski, Sebastian Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

While quantum architectures are still under development, when available, they will only be able to process quantum data when machine learning algorithms can only process numerical data. Therefore, in the issues of classification or regression, it is necessary to simulate and study quantum systems that will transfer the numerical input data to a quantum form and enable quantum computers to use the available methods of machine learning. This material includes the results of experiments on training and performance of a hybrid quantum-classical neural network developed for the problem of classification of handwritten digits from the MNIST data set. The comparative results of two models: classical and quantum neural networks of a similar number of training parameters, indicate that the quantum network, although its simulation is time-consuming, overcomes the classical network (it has better convergence and achieves higher training and testing accuracy). © 2021, CC BY.

关键词： Image recognition

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A Conceptual Framework for Addressing Class Imbalance in Image data: Challenges and Strategies

A Conceptual Framework for Addressing Class Imbalance in Ima...

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International IEEE Conference on Intelligent Systems, IS

作者： Saara Asif Muhammad Uzair Akmal Leonid Koval Selvine G. Mathias Simon Knollmeyer Daniel Grossmann AIMotion Technische Hochschule Ingolstadt Ingolstadt Germany Faculty of Computer Science and Data Processing Technische Hochschule Ingolstadt Ingolstadt Germany

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

The presence of class imbalance, denoting a dis-proportionate distribution of class instances in a dataset, has emerged as a significant challenge in the era of Deep Learning (DL) where models crave abundance in data. This issue is pervasive in various real-world applications, where certain classes exhibit limited data representation. This problem is frequently encountered when dealing with image data, which exhibits an imbalanced distribution, with one class significantly outnumbering the others. Failing to address class imbalance introduces bias in machine learning and deep learning models, favoring the majority classes and leading to subpar performance for the minority classes. This research specifically delves into the recurrent problem in the context of image data, that is “class imbalance”. The research comprehensively explores the existing challenges based on data pre-processing, algorithmic techniques, hybrid methodologies, and state-of-the-art solutions.

关键词： Deep learning Accuracy Machine learning algorithms Merging Generative adversarial networks data models Intelligent systems

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data fusion using large multi-agent networks: an analysis of network structure and performance

Data fusion using large multi-agent networks: an analysis of...

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International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： A. Knoll J. Meinkoehn Department of Computer Science Faculty of Technology University of Bielefeld Bielefeld Germany National Research Corporation for Mathematics and Data Processing Berlin Germany

Concerns the multi-agent paradigm for structuring the data fusion in large networks of information sources. The general goal is the maximisation of the quality of the result at minimum cost. To this end agents compete for scarce resources or they work in parallel. Both the general granularity of the agent society and the competence assigned to each individual agent determine the information flow. The multitude of parameters involved makes it difficult to optimally adapt the network structure to a given class of sensing tasks. We outline possible network structures and present an approach for analysing a number of important parameters characterising the network. The abstraction enables comparison of different structures. The methods for the analysis may be readily refined to evaluate a specific problem. A model of lateral coordination control in proposed as a result. It is based on the notion of negotiated cooperation between pairs of autonomous sensor agents. The cooperation phase is preceded by a bidding scheme to establish logical communication links. The cooperation is modelled on human social behaviour.< >

关键词： Performance analysis Sensor fusion Costs Fuses Sensor systems Cameras Information analysis computer science Mathematics data processing

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Improving artificial bee colony algorithm with historical archive 11th

Improving artificial bee colony algorithm with historical ar...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Zhou, Yalan Wang, Jiahai Gao, Shangce Yang, Xing Yin, Jian College of Information Guangdong University of Finance & Economics Guangzhou510320 China Department of Computer Science Sun Yat-sen University Guangzhou510006 China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Faculty of Engineering University of Toyama Toyama-shi930-8555 Japan

ISBN: (纸本)9789811036132

In this study, an artificial bee colony with historical archive (HAABC) is proposed to help ABC escape from stagnation situation. The proposed framework keeps track of the search history and stores excellent successful solutions into an archive. Once stagnation is detected in scout bees phase of ABC, a new individual is generated by utilizing the historical archive. Experimental results on 28 benchmark functions show that the proposed framework significantly improves the performance of basic ABC and five state-of-the-art ABC algorithms. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Benchmarking

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Brain-inspired artificial intelligence research: A review

引用

science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques

arXiv

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arXiv 2023年

作者： Qazanfari, Hamed Alyan Nezhadi, Mohammad M. Khoshdaregi, Zohreh Nozari Image Processing and Data Mining Lab Shahrood University of Technology Shahrood Iran Faculty of Science University of Science and Technology of Mazandaran Behshahr Iran Department of Computer Engineering University of Bojnord Iran

Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visual content rather than relying solely on metadata. This survey paper presents a comprehensive overview of CBIR, emphasizing its role in object detection and its potential to identify and retrieve visually similar images based on content features. Challenges faced by CBIR systems, including the semantic gap and scalability, are discussed, along with potential solutions. It elaborates on the semantic gap, which arises from the disparity between low-level features and high-level semantic concepts, and explores approaches to bridge this gap. One notable solution is the integration of relevance feedback (RF), empowering users to provide feedback on retrieved images and refine search results iteratively. The survey encompasses long-term and short-term learning approaches that leverage RF for enhanced CBIR accuracy and relevance. These methods focus on weight optimization and the utilization of active learning algorithms to select samples for training classifiers. Furthermore, the paper investigates machine learning techniques and the utilization of deep learning and convolutional neural networks to enhance CBIR performance. This survey paper plays a significant role in advancing the understanding of CBIR and RF techniques. It guides researchers and practitioners in comprehending existing methodologies, challenges, and potential solutions while fostering knowledge dissemination and identifying research gaps. By addressing future research directions, it sets the stage for advancements in CBIR that will enhance retrieval accuracy, usability, and effectiveness in various application domains. © 2023, CC BY.

关键词： Object detection

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AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing

arXiv

引用

arXiv 2023年

作者： Chen, Zhaoliang Wu, Zhihao Lin, Zhenghong Wang, Shiping Plant, Claudia Guo, Wenzhong The College of Computer and Data Science Fuzhou University Fuzhou350116 China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China The Faculty of Computer Science Austria The research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors. Recent works have devoted effort to addressing over-smoothing problems, including establishing residual connection structure or fusing predictions from multi-layer models. Because of the indistinguishable embeddings from deep layers, it is reasonable to generate more reliable predictions before conducting the combination of outputs from various layers. In light of this, we propose an Alternating Graph-regularized Neural Network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL). GEL is derived from the graph-regularized optimization containing Laplacian embedding term, which can alleviate the over-smoothing problem by periodic projection from the low-order feature space onto the high-order space. With more distinguishable features of distinct layers, an improved Adaboost strategy is utilized to aggregate outputs from each layer, which explores integrated embeddings of multi-hop neighbors. The proposed model is evaluated via a large number of experiments including performance comparison with some multi-layer or multi-order graph neural networks, which reveals the superior performance improvement of AGNN compared with state-of-the-art models. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Neighborhood collaborative classifiers

Neighborhood collaborative classifiers

引用

2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Xu, Suping Yang, Xibei Tsang, Eric C. C. Mantey, Eric A. School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang212003 China School of Economics and Management Nanjing University of Science and Technology Nanjing210094 China Faculty of Information Technology Macau University of Science and Technology C-Macau519020 China Intelligent Information Processing Key Laboratory of Shanxi Province Shanxi University Taiyuan030006 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China

ISBN: (纸本)9781509003891

In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC;2) NCC reduces the size of dictionary when compared with CRC. © 2016 IEEE.

关键词： Classification (of information)

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