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Application of association rules mining in maritime casualties analysis

ICIC Express Letters, Part B: Applications 2015年第1期6卷 81-87页

作者： Shi, Guoyou Chen, Peng Liu, Shuang College of Navigation Dalian Maritime University No. 1 Linghai Road Dalian China Department of Computer Science and Technology Dalian Neusoft University of Information No. 8 Software Park Road Dalian China School of Computer Science and Engineering Dalian Nationalities University No. 18 Liaohe West Road Jinzhou New District Dalian China

Association rules mining technology is used to mine hidden information from maritime casualties in order to help maritime sectors to enforce supervision of ship’s navigation to reduce maritime casualties. Five thousand domestic and overseas reports of maritime casualties are collected. Sixteen features such as time, ship type, ship length, sailing speed, navigational status, meteorology environment, sea condition, accident reason are extracted from each accident, then association rules mining algorithm is used to mine associate rules from these accidents. On the c ondition of 5% support and 50% confidence, 369 association rules are mined. These rules represent some universal rules and can provide reference for maritime sectors and shipping enterprises.

关键词： Data mining

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Towards a framework for adaptive resource provisioning in large-scale distributed agent-based simulation

Towards a framework for adaptive resource provisioning in la...

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Lecture Notes in computer {1.

作者： Hanai, Masatoshi Suzumura, Toyotaro Ventresque, Anthony Shudo, Kazuyuki Tokyo Institute of Technology Dept. of Mathematical and Computing Sciences 2-12-1 Ookayama MeguroTokyo152-8552 Japan School of Computer Science and Informatics University College Dublin Ireland Lero The Irish Software Engineering Research Centre Damastown Industrial Estate Mulhuddart Dublin 15 Ireland Smarter Cities Technology Centre IBM Research Damastown Industrial Estate Mulhuddart Dublin 15 Ireland

ISBN: (纸本)9783319143248

Large scale distributed agent-based simulations run on several computing units (e.g., virtual machines in the Cloud, nodes in a supercomputer). Classically, these systems try to (re-)load-balance the nodes as overloaded nodes slow down the *** another challenge in large scale distributed simulations is that the overall load evolves. In this paper we leverage on commodity computing to adapt resource provisioning (number of computing units) to the load during the execution of the simulation. We also propose an asynchronous migration mechanism that migrate workload between computing nodes efficiently when nodes wait for synchronisation barriers to happen. We validate our implementation on a scenario simulating one day of vehicular traffic in Tokyo, running on 2 to 8 machines depending on the demand. Our evaluation shows a 26% reduction in data migration time compared to a naive migration approach between computing units. © Springer International Publishing Switzerland 201..

关键词： Supercomputers

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Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems

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IEEE Communications Surveys and Tutorials 2024年

作者： Wang, Xiaofei Zhao, Yunfeng Qiu, Chao Hu, Qinghua Leung, Victor C. M. Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen518000 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

Amidst the robust impetus from artificial intelligence (AI) and big data, edge intelligence (EI) has emerged as a nascent computing paradigm, synthesizing AI with edge computing (EC) to become an exemplary solution for unleashing the full potential of AI services. Nonetheless, challenges in communication costs, resource allocation, privacy, and security continue to constrain its proficiency in supporting services with diverse requirements. In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI. SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents within the EI system. SL not only enhances the system's adaptability but also optimizes communication, and networking processes, essential for distributed intelligence across diverse devices and platforms. Therefore, a combination of SL and EI may greatly facilitate the development of collaborative intelligence in the future network. This paper presents the findings of a literature review on the integration of EI and SL, summarizing the latest achievements in existing research on EI and SL. Subsequently, we delve comprehensively into the limitations of EI and how it could benefit from SL. Special emphasis is placed on the communication challenges and networking strategies and other aspects within these systems, underlining the role of optimized network solutions in improving system efficiency. Based on these discussions, we elaborate in detail on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses. Finally, we identify some possible future applications of combining SL and EI, discuss open problems and suggest some future research, with the hope of arousing the research community's interest in this emerging and exciting interdisciplinary field. © 1

关键词： Resource allocation

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Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Niyato, Dusit Kang, Jiawen Jamalipour, Abbas Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Automation Guangdong University of Technology Guangzhou510006 China The School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

In this paper, we propose a distributed collaborative beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the low Earth orbit (LEO) satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that variously balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we perform a multi-objective optimization analysis and formulate a long-term optimization problem. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal multi-objective Markov decision process. Then, we propose an evolutionary multi-objective deep reinforcement learning algorithm to obtain the desirable policies, in which the low-value actions are masked to speed up the training process. As such, the applicability of a onetime trained model can cover more changing terminal-satellite uplink scenarios. Simulation results show that the proposed algorithm outmatches various baselines, and draw some useful insights. Specifically, it is found that DCB enables terminals that cannot reach the uplink achievable threshold to achieve efficient direct uplink transmission, which thus reveals that DCB is an effective solution for enabling direct ground-space communications. Moreover, it reveals that the proposed algorithm achieves multiple policies favoring different objectives and achieving near-optimal uplink achievable rates with low switching frequency. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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An improved Bayesian network structure learning algorithm and its application in an intelligent B2C portal

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Web Intelligence and Agent Systems 2007年第2期5卷 127-138页

作者： Ji, Junzhong Liu, Chunnian Yan, Jing Zhong, Ning College of Computer Science and Technology Beijing University of Technology Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology Beijing 100022 China Department of Information Engineering Maebashi Institute of Technology 460-1 Kamisadori-Cho Maebashi-City 371-0816 Japan

Web Intelligence (WI) is a new and active research field in current AI and IT. Intelligent B2C Portals are an important research topic in WI. In this paper, we first investigate and analyze the architecture of a B2C portal for personalized recommendation from the viewpoint of conceptual levels of WI. Aiming at knowledge-level data mining in a B2C portal, we present a new improved learning algorithm of Bayesian Networks, which consists of two major contributions, namely, reducing Conditional Independence (CI) test costs by few lower order CI tests and accelerating search process by means of sort order for candidate parent nodes. Experimental results on benchmark ALARM data sets show that the improved algorithm has high accuracy, and is more efficient in the time performance than other algorithms. Finally, we apply this algorithm to learning Customer Shopping Model (CSM) in an intelligent recommendation system. By a number of experiments on real world data, we find that the recommendation method based on the learned CSM outperforms some traditional ones in rates of coverage and precision. © 2007 - IOS Press and the authors. All rights reserved.

关键词： Bayesian networks

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Cross-Layer Scheduling and Beamforming in Smart-Grid Powered Cellular Networks With Heterogeneous Energy Coordination

arXiv

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arXiv 2020年

作者： Dong, Yanjie Hossain, Md Jahangir Cheng, Julian Leung, Victor C.M. Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada School of Engineering University of British Columbia KelownaBCV1V 1V7 Canada College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

—User scheduling, beamforming and energy coordination are investigated in smart-grid powered cellular networks (SGPCNs), where the base stations are powered by a smart grid and natural renewable energy sources. Heterogeneous energy coordination is considered in SGPCNs, namely energy merchandizing with the smart grid and energy exchanging among the base stations. A long-term grid-energy expenditure minimization problem with proportional-rate constraints is formulated for SGPCNs. Since user scheduling is coupled with the beamforming vectors, the formulated problem is challenging to handle via standard convex optimization methods. In practice, the beamforming vectors need to be updated over each slot according to the channel variations. User scheduling needs to be updated over several slots (frame) since the frequent scheduling of user equipment can cause reliability issues. Therefore, the Lyapunov optimization method is used to decouple the problem. A practical two-scale algorithm is proposed to schedule users at each frame, and obtain the beamforming vectors and amount of exchanged natural renewable energy at each slot. We prove that the proposed two-scale algorithm can asymptotically achieve the optimal solutions via tuning a control parameter. Numerical results verify the performance of the proposed two-scale algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Wireless networks

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A Novel Deep Learning-Based Method for Real-Time Face Spoof Detection

SSRN

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SSRN 2023年

作者： Malik, Muhammad Amir Mazhar, Tehseen Haq, Inayatul Shahzad, Tariq Ghadi, Yazeed Yaseen Mallek, Fatma Hamam, Habib Department of Computer Science and Software Engineering International Islamic Unversity Islamabad44000 Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan School of Information Engineering Zhengzhou University Zhengzhou450001 China Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering Al Ain University Abu Dhabi15322 United Arab Emirates Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada National College Of Business Administration & Economics Canada

This research aims to improve the detection accuracy of face spoof recognition systems by employing a hybrid model of machine learning and computer vision-based approaches. Differences, including Decision Tree, Nave Bayes, K-nearest Neighbor, Support Vector Machine, Convolutional Neural Network (CCN), and Recurrent Neural Networks are used for face spoof detection. For face spoof detection, the proposed model is a hybrid variant of the CNN-based classifier used in the proposed face spoof detection model. This study improves real-time face fake detection using machine learning and computer vision. The proposed system is based on a CNN-based classification approach with optimized hyper parameters that detect real-time face spoofing attacks using print, video, and repeat attacks, improving detection accuracy. IDIAP, USSA & and MSFD datasets are used in the simulation;the proposed model has achieved a maximum accuracy of 87.5%. a high sensitivity score of 92.45%,. © 2023, The Authors. All rights reserved.

关键词： Decision trees

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Integrate chinese semantic knowledge into word sense disambiguation

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International Journal of Hybrid Information Technology 2015年第4期8卷 105-116页

作者： Chun-Xiang, Zhang Lu-Rong, Sun Xue-Yao, Gao Zhi-Mao, Lu Yong, Yue School of Software Harbin University of Science and Technology Harbin150080 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China Department of Computer Science and Technology University of Bedfordshire Park Square LutonLU1 3JU United Kingdom

Word sense disambiguation is important for many applications in natural language processing fields including machine translation, information retrieval and automatic summarization. In this paper, left word unit and right word unit are extracted for improving the quality of word sense disambiguation (WSD) starting from the target polysemous word. Their semantic knowledge is mined from Tongyici Cilin which is a Chinese semantic lexicon. A new method of word sense disambiguation is proposed with semantic information of left word unit and right word unit. The classifier of word sense disambiguation is built based on bayesian model. SemEval-2007: Task#5 is used as training corpus and test corpus. Experimental results show that the disambiguation classifier’s precision is improved and demonstrate the effectiveness of the method. © 201. SERSC

关键词： Bayesian networks

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Perspective-based reading: A replicated experiment focused on individual reviewer effectiveness

Perspective-based reading: A replicated experiment focused o...

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作者： Maldonado, José C. Carver, Jeffrey Shull, Forrest Fabbri, Sandra Dória, Emerson Martimiano, Luciana Mendonça, Manoel Basili, Victor 400-Centro Av. Trabalhador São-Carlense 13560 São Carlos SP Brazil Department of Computer Science and Engineering 300 Butler Hall Mississippi State University Mississippi State MS 39762 United States Fraunhofer Center for Experimental Software Engineering Maryland 4321 Hartwick Road College Park MD 20740 United States Departamento de Computação Universidade Federal de São Carlos Rodovia Washington Luiz km235 Caixa Postal 676 São Carlos SP 13565-905 Brazil Universidade do Oeste Paulista Faculdade de Informática de Presidente Prudente Campus I 700 Bloco H - 1o andar CEP:19050-680 São Carlos SP Brazil 400-Centro Av. Trabalhador São-Carlense 13560 São Carlos SP Brazil Departamento de Ciências Exatas-NUPERC Universidade Salvador Rua Ponciano de Salvador-BA 41950 Oliveira 126 275 Brazil Fraunhofer Center for Experimental Software Engineering Department of Computer Science University of Maryland College Park MD 20742 United States ACM IEEE Computer Society Computer Science and Engineering Department Mississippi State University Department of Computer Science Federal University of São Carlos São Paulo Brazil Faculdade de Informática de Presidente Prudente Universidade do Oeste Paulista Brazilian Computer Society Brazil Department of Computer Science University of Maryland NASA/GSFC

This paper describes a replication conducted to compare the effectiveness of inspectors using Perspective Based Reading (PBR) to the effectiveness of inspectors using a checklist. The goal of this replication was to better understand the complementary aspects of the PBR perspectives. To this end, a brief discussion of the original study is provided as well as a more detailed description of the replication. A detailed statistical analysis is then provided along with analysis of the PBR perspectives. For the individual PBR perspectives, we saw an interesting dichotomy: In the original study there was little overlap among the sets of defects found by each of the three perspectives, while in the replication two of the three perspectives found similar sets of defects on one of the two documents used in the study. Interestingly this document was the only case where the users of PBR were not more effective than the users of a checklist. This result leads to a new hypothesis that the complementary aspect of the PBR perspectives is the characteristic that provides the benefit over other defect detection techniques. © Springer science + Business Media, Inc. 2006.

关键词： software {1.

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Performance of OTFS-NOMA Scheme for Coordinated Direct and Relay Transmission Networks in High-Mobility Scenarios

arXiv

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arXiv 2023年

作者： Xu, Yao Du, Zhen Yuan, Weijie Jia, Shaobo Leung, Victor C.M. School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In this paper, an orthogonal time frequency space (OTFS) based non-orthogonal multiple access (NOMA) scheme is investigated for the coordinated direct and relay transmission system, where a source directly communicates with a near user with high mobile speed, and it needs the relaying assistance to serve the far user also having high mobility. Due to the coexistence of signal superposition coding and multi-domain transformation, the performance of OTFS-based NOMA is usually challenging to be measured from a theoretical perspective. To accurately evaluate the system performance of the proposed scheme, we derive the closed-form expressions for the outage probability and the outage sum rate by using the Inversion formula and characteristic function. Numerical results verify the performance superiority and the effectiveness of the proposed scheme. Copyright © 2023, The Authors. All rights reserved.

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