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A human vision system-based approach to improve computing efficiency in information systems

International Journal of Information systems and Change Management 2019年第1期11卷 44-55页

作者： Li, Jianxun Chen, Jim Q. Li, Weiqian Department of Management Science and Engineering School of Economics and Management Xi'an University of Technology Xi'an China Department of Information Systems Herberger Business School St. Cloud State University St. CloudMN United States Department of Software Engineering School of Computer Science Xi'an Polytechnic University Xi'an China

While a lot of research has been done in system design, reducing computational complexity is still a core problem in information systems. In order to break through the bottleneck of computing efficiency, this paper draws on the powerful processing capability of the human vision system (HVS) to propose a pixel domain computing framework. It converts the original problem into pixel operations, and pixels are taken as the basic unit to change the construction process of data analysis. The framework significantly decreases computational complexity, simplifies programming logic, and enhances the automaticity of information system. Our results demonstrate that the efficiency of the containment test is improved by 30 times, the logic complexity of the intersection area measurement is reduced by a factor of five, and the automaticity of root finding is significantly heightened. © 2019 Inderscience Enterprises Ltd.

关键词： Information systems

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Machine Knowledge and Human Cognition

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Big Data Mining and Analytics 2020年第4期3卷 292-299页

作者： Fashen Li Lian Li Jianping Yin Liang Huang Qingguo Zhou Ning An Yong Zhang Li Liu Jialin Zhang Kun Kuang Lei Yang Zhixi Wu Lianchun Yu the School of Physical Science and Technology Lanzhou UniversityLanzhou 730000China the School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China the School of Cyber Science and Engineering Dongguan University of TechnologyDongguan 523808China the Institute of Computational Physics and Complex Systems Lanzhou UniversityLanzhou 730000China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China the Department of Physics Xiamen UniversityXiamen 361005China the School of Big Data&Software Engineering Chongqing UniversityChongqing 400044China the Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China the College of Computer Science and Technology Zhejiang UniversityHangzhou 310000China the Institute ofModern Physics Chinese Academy of SciencesLanzhou 730000China

Intelligent machines are knowledge systems with unique knowledge structure and *** this paper,we discuss issues including the characteristics and forms of machine knowledge,the relationship between knowledge and human cognition,and the approach to acquire machine *** issues are of great significance to the development of artificial intelligence.

关键词： intelligent machine machine knowledge human cognition knowledge interpretation principle of functional similarity Probable Approximative Correction(PAC)model

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Comparative analysis of time series model and machine testing systems for crime forecasting

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Neural Computing and Applications 2020年第17期33卷 10621-10636页

作者： Jha, Sudan Yang, Eunmok Almagrabi, Alaa Omran Bashir, Ali Kashif Joshi, Gyanendra Prasad School of Computer Science and Engineering Lovely Professional University Phagwara India School of Software Soongsil University Seoul South Korea Department of Information Systems King Abdul Aziz University Jeddah Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan University Manchester UK School of Electrical Engineering and Computer Science NUST Islamabad Pakistan Department of Computer Science and Engineering Sejong University Seoul South Korea

Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

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Proposing Model for Security of IoT Devices in Smart Logistics: A Review

Proposing Model for Security of IoT Devices in Smart Logisti...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Arbab Waseem Abbas Safdar Nawaz Khan Marwat Salman Ahmed Abdul Hafeez Khalil Ullah Ihsan Ullah Khan Department of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan Department of Computer Science University of Engineering & Technology (Jalozai Campus) Pakistan Department of Software Engg University of Malakand Dir Lower Pakistan Frequency Allocation Board (FAB) Islamabad Pakistan

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

The information technology's new revolution is Internet of Things (IoT), where physical world is connected to the internet. IoT applications are diversified such as smart health, smart industry, smart homes and most importantly smart logistics for perishable items conservation. Although IoT devices have revolutionized world but due to their constrained nature they are at the cost of a greater risk of personal data loss, security breaches and misuse. In this research, a review is presented for proposing two state of the art modules i.e. communication model of smart logistics and security of IoT devices in Smart Logistics. As the smart logistics have perishable items i.e. fruits, vegetables, meat, medicine, chemicals, and cosmetics etc. which are sensitive to temperature, humidity and pressure etc. If the sensors monitoring these items have been handled abnormally due to security attacks, it will give huge loss. Therefore, security of smart logistics is of paramount importance in this research. In order to expedite research in this emergent area of IoT, in this research paper a brief review on progress of IoT in smart logistics, and its security has been presented.

关键词： Logistics Security Containers Protocols Monitoring Intelligent sensors

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software Rejuvenation Under Persistent Attacks in Constrained Environments

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IFAC-PapersOnLine 2020年第2期53卷 4088-4094页

作者： Raffaele Romagnoli Paul Griffioen Bruce H. Krogh Bruno Sinopoli Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA 15213 Software Engineering Institute Carnegie Mellon UniversityPittsburgh PA USA 15213 Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis MO USA 63130

software rejuvenation has been proposed to guarantee safety of cyber-physical systems (CPSs) against cyber-attacks. Recent work has demonstrated how this method can be applied to more general control problems such as tracking control. Despite this progress, there are still limitations in applying software rejuvenation to real situations where the presence of persistent attacks and physical environment constraints exist. In this paper we address these issues and propose a secure recovery algorithm that can be deployed not only for recovery against persistent attacks but also in situations where physical environment constraints do not allow the system to tolerate any attack. The effectiveness of the approach is illustrated with a simulation of a quadrotor landing on the ground during recovery from a persistent attack.

关键词： software rejuvenation cyber-physical systems resilience security

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CHANCES AND CHALLENGES OF CHATGPT AND SIMILAR MODELS FOR EDUCATION IN M&S

TechRxiv

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TechRxiv 2023年

作者： Scherer, William T. Tolk, Andreas Loper, Margaret L. Barry, Philip Rabadi, Ghaith Yilmaz, Levent The MITRE Corporation Modeling and Analysis Innovation Center 1001 Research Park Blvd #200 CharlottesvilleVA22911 United States Georgia Tech Research Institute Georgia Institute of Technology AtlantaGA30308 United States University of Virgina Department of Systems and Information Engineering CharlottesvilleVA22904 United States L3Harris Corporation Mission Analysis Group 2235 Monroe Street HerndonVA20171 United States University of Central Florida School of Modeling Simulation and Training 3100 Technology Pkwy OrlandoFL32826 United States Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering AuburnAL36849 United States

The position paper summarizes the inputs of a set of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within the Modelling and Simulation education. They also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. © 2023, CC BY.

关键词： Modeling languages

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Deception and cloud integration: A multi-layered approach for DDoS detection, mitigation, and attack surface minimization in SD-IoT networks

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computers and Electrical engineering 2025年 126卷

作者： Ameer El-Sayed Ahmed A. Toony Amr Tolba Fayez Alqahtani Yasser Alginahi Wael Said Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig 44511 Egypt Department of Business Information Systems High Institute for Commercial Sciences (Aboukir High Institutes) Ministry of Higher Education and Scientific Research Alexandria Egypt Computer Science Department Community College King Saud University Riyadh 11437 Saudi Arabia Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia Department of Computer Science Adrian College Michigan USA Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig 44511 Egypt

Detecting Distributed Denial of Service (DDoS) attacks in software-Defined Internet of Things (SD-IoT) networks is challenging due to vulnerabilities in single-controller architectures, the limitations of the OpenFlow protocol, evolving DDoS strategies, and resource constraints. This research proposes a multi-layered security framework that integrates deception-based security, cloud-integrated machine learning (ML), a new hierarchically distributed multi-controller (HDMC) architecture, P4-enabled real-time traffic monitoring, and adaptive mitigation. The framework includes dynamic time-based windowing for enhanced detection, a decoy network to divert attackers, and a cloud-based multi-task ML model (MT-EDD) for attack classification. It also features a synchronized multi-control design for secure communication and coordinated actions among multiple controllers and a dynamic monitoring algorithm for real-time traffic analysis. P4 switches extract features from network traffic and send them to a cloud-based server for preprocessing and analysis by a pre-trained ensemble learning model (MT-EDD), which predicts attack states and communicates results to the central controller for mitigation. The controller then enforces appropriate mitigation actions on P4 switches. This approach offloads computationally intensive tasks to the cloud, improving scalability and detection accuracy. Evaluations show the framework achieves an average accuracy of 98.42%, precision of 96.17%, recall of 94.72%, F1-score of 95.39%, and specificity of 98.22%. The proposed P4-enabled solution consumes 30% less bandwidth and 25% less CPU, reduces detection times by 54.3%, and improves detection accuracy by 5.2% compared to the OpenFlow-enabled method. The HDMC architecture, evaluated against a single-controller setup, demonstrated 40% higher throughput and 32% lower latency, confirming its superior performance across multiple metrics.

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Location-based collaborative filtering for web service recommendation

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Recent Patents on computer Science 2019年第1期12卷 34-40页

作者： Venkatachalaappaswamy, Mareeswari Ramaraj, Vijayan Ravichandran, Saranya Department of Computer Applications and Creative Media School of Information Technology and Engineering Vellore Institute of Technology-VIT VelloreTamil Nadu India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology-VIT VelloreTamil Nadu India Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology-VIT VelloreTamil Nadu India

Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms. © 2019 Bentham Science Publishers.

关键词： Collaborative filtering

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DP-Net: Dynamic programming guided deep neural network compression

arXiv

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arXiv 2020年

作者： Yang, Dingcheng Yu, Wenjian Zhou, Ao Mu, Haoyuan Yao, Gary Wang, Xiaoyi BNRist Dept. Computer Science & Tech. Tsinghua University Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Department of Electrical Engineering and Computer Science Case Western Reserve University

—In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs). It includes a novel dynamic programming (DP) based algorithm to obtain the optimal solution of weight quantization and an optimization process to train a clustering-friendly DNN. Experiments showed that the DP-Net allows larger compression than the state-of-the-art counterparts while preserving accuracy. The largest 77X compression ratio on Wide ResNet is achieved by combining DP-Net with other compression techniques. Furthermore, the DP-Net is extended for compressing a robust DNN model with negligible accuracy loss. At last, a custom accelerator is designed on FPGA to speed up the inference computation with DP-Net. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamic programming

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Corrigendum to “An analysis of urban sprawl growth and prediction using remote sensing and machine learning techniques” [J. S. Am. Earth Sci. Volume 142 , 15 August 2024, 104988]

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Journal of South American Earth Sciences 2025年 152卷

作者： Alanoud Al Mazroa Mashael Maashi Fadoua Kouki Kamal M. Othman Nahla Salih Mohamed Ahmed Elfaki Sabarunisha Begum S Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Po box 103786 Riyadh 11543 Saudi Arabia Department of Financial and Banking Sciences Applied College at Muhail Aseer King Khalid University Saudi Arabia Department of Electrical Engineering College of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Department of Biotechnology P.S.R. Engineering College Sivakasi 626140 India

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