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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Remote Patient Monitoring systems Network Adaptation Scenarios

Remote Patient Monitoring Systems Network Adaptation Scenari...

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Current Development in engineering and Technology (CCET), IEEE International Conference on

作者： Halimjon Khujamatov Nurshod Akhmedov Amir Lazarev Sobiya Arsheen Dean of the faculty of Computer Engineering Tashkent University of Information Technologies Tashkent Uzbekistan Data transmission networks and systems department Tashkent University of Information Technologies Tashkent Uzbekistan Department of Computer Science and Information Technology Maulana Azad National Urdu University India

This article is devoted to the actual topic of studying scenarios for adapting a network infrastructure to a network of remote patient monitoring systems (PCMS). The paper presents an overview and analysis of scenarios for adapting the network infrastructure of monitoring systems. The stages of scenario execution have been studied and simulation models of the RPMS network infrastructure have been developed that implement various adaptation scenarios. A comparative analysis of adaptation scenarios was carried out according to several criteria, such as the speed of request delivery, delivery time, and network load. Simulation models built in the Anylogic environment, a powerful simulator of mass service system, showed the effectiveness of the mesh topology in relation to the ad-hoc topology according to several criteria. The theoretical significance of the work lies in carrying out analytical work on scenarios for adapting the network infrastructure of the RPMS, as well as in studying the network architecture. The practical significance of the work lies in the development of a simulation model that allows you to explore scenarios for adapting the network architecture.

关键词： Phase change materials Adaptation models Patient monitoring Network architecture Ad hoc networks Topology Load modeling

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A Kanban-based Approach to Manage Machine Learning Projects in Manufacturing

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Procedia CIRP 2025年 134卷 109-114页

作者： Ulf Schreier Peter Reimann Bernhard Mitschang Business Information Systems - Furtwangen University of Applied Science Robert-Gerwig-Platz 1 D-78120 Furtwangen Germany Graduate School of advanced Manufacturing Engineering (GSaME) University of Stuttgart Nobelstr. 12 D-70569 Stuttgart Germany Institute for Program Structures and Data Organization (IPD) Karlsruhe Institute of Technology (KIT) Am Fasanengarten 5 D-76131 Karlsruhe Germany Institute of Parallel and Distributed Systems (IPVS) University of Stuttgart Universitätsstr. 38 D-70569 Stuttgart Germany

A growing number of machine learning (ML) projects in manufacturing require the collaboration of various experts. In addition to data scientists, stakeholders with production engineering knowledge have to specify and prioritize individual project tasks. data engineers prepare input data, while machine learning operations (MLOps) engineers ensure that trained models are deployed and monitored within IT landscapes. Existing project management approaches, e.g., Scrum, have problems for ML projects, as they do not consider various expert roles or ML project stages. We propose a project management approach defining a Kanban workflow by readjusting stages of ML development lifecycles, e.g., CRISP DM. This makes it possible to map expert roles to stages of the Kanban workflow. An adapted Kanban board allows visualizing and reviewing the status of all project tasks. We validate our approach with specific use cases, showing that it facilitates ML project management in manufacturing.

关键词： Machine learning (ML) ML project management machine learning operations (MLOps) Kanban Scrum

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Predicting Crops Based on Soil and Environmental Characteristics Using ARIMA and LSTM Model

Predicting Crops Based on Soil and Environmental Characteris...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： U Naresh kumar K Madhana Mohan S. Pragadeeswaran Jobin M Scaria Baseera A D. Seenivasan Department of Artificial intelligence & Data science Annamacharya University Rajampet India Department of Electronics and Instrumentation Engineering Sri Sairam Engineering College Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Mt. Eriama Campus School of Information Technology IBS University Port Moresby Papua New Guinea School of Computing Science and Engineering VIT Bhopal University Sehore India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARIMA-LSTM technique, which blends deep learning and time-series forecasting. The ARIMA-LSTM model outperforms solo ARIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIMA-LSTM can boost agricultural productivity while minimizing environmental impact.

关键词： Training Accuracy Food manufacturing Biological system modeling Soil Predictive models Feature extraction Agriculture Long short term memory Meteorology

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Deep Learning Inverse Design of Broadband Dual-Frequency Metasurfaces Using Additive Manufacturing

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SSRN 2024年

作者： Li, Mengze Cai, Jiaqi Yang, Yang Deng, Li Li, Xiaopeng Iacopi, Francesca Nanjing University of Science and Technology School of Microelectronics Nanjing210094 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China University of Technology Sydney School of Electrical and Data Engineering NSW2019 Australia School of Mechanical and Manufacturing Engineering China Australian Research Council Centre of Excellence in Transformative Meta-Optical Systems UltimoNSW2007 Australia

Multi-material additive manufacturing (AM) is a promising technique for creating functional electronic devices with complex geometries and properties. In this paper, we propose a novel inverse design method based on deep neural networks for conductive and dielectric multi-material printing to achieve broadband dual frequency metasurfaces that can produce two different orbital angular momentum (OAM) states, simultaneously, at the V-band and W-band. These OAM states can be used for high-speed wireless communications. We demonstrate the effectiveness of our inverse design method by designing, printing and testing multilayer metasurface using the lights-out digital manufacturing process, which can seamlessly sinter silver nanoparticles and acrylates inks. Our results show that the proposed method can achieve high efficiency in design optimisation and precision printing. This opens new avenues for the design and fabrication of multilayer metasurfaces using multi-material AM for future wireless electronic devices. © 2024, The Authors. All rights reserved.

关键词： 3D printing

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On the Impact of Interlayer Misalignment for Dual-Layer data Detection in Three Dimensional Magnetic Recording

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SSRN 2024年

作者： Luo, Ke Liao, Yanzhe Zhang, Kezheng Jian, Yugen Wang, Shaobing Chen, Jincai Lu, Ping Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China School of Public Health Hubei University of Medicine Shiyan442000 China

Three-dimensional magnetic recording (3DMR) is a crucial technology for significantly increasing the storage capacity of hard disk drives (HDDs). However, the presence of inter-symbol interference (ISI), inter-track interference (ITI), and inter-layer interference (ILI) poses significant challenges to the accurate detection of data stored on multiple layers. This study addresses the impact of interlayer misalignment on the bit error rate (BER) performance in 3DMR systems. We evaluate the performance based on a neural network estimator for reconstructing the top-layer read response signal using feedback from a Viterbi detector, enabling the separation of bottom-layer signals by subtraction from the mixed readback signal. We introduce a dual-layer partial response maximum likelihood (PRML) detector for simultaneous bit retrieval from both layers. We investigate methods of a per-layer binary classifier and a dual-layer four-class classifier based on neural networks. Our study demonstrates the BER performance of these detection schemes influenced by the interlayer misalignment, especially when the offset is 0, 10%, 50%, and 90% of the bit dimensions between the two layers. The results show that the neural network-based reconstruction and separation method achieves better bottom-layer BER performance under a slight interlayer misalignment. The BER performance benefits more from a mild downtrack offset compared with the crosstrack offset. Both the separation detection based on a neural network and the dual-layer PRML achieve the lowest top-layer BER and the worst bottom-layer BER when the interlayer misalignment is 50% of a bit size. © 2024, The Authors. All rights reserved.

关键词： Bit error rate

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Research on Hardware-in-the-Loop Test System for the Main Control Unit of Unmanned Vehicles

Research on Hardware-in-the-Loop Test System for the Main Co...

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Chinese Intelligent systems Conference, CISC 2020

作者： Ni, Haoyuan Yu, Guizhen Zhou, Bin Liu, Guoqiang School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beihang University Beijing100191 China School of Electronic Information Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789811584572

At present, there are more researches on the automatic driving technology of unmanned vehicles. It needs a main control unit, which enables the vehicle to automatically drive. The main control unit supports the access of various sensors and controllers. It can process data of each device and control the movement state of vehicles. Therefore, the main control unit needs to be tested repeatedly to ensure its functionality and stability. Based on Prescan simulation software and Matlab/Simulink software, this paper studies a hardware-in-the-loop (HIL) test system for testing the main control unit of unmanned vehicles according to the communication protocol and communication method of the real sensor and controller hardware devices. At the same time, this paper studies a simulation method of the main control unit, which can effectively verify the function of the test system and conduct the feasibility test of the communication protocol and communication method used. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Simulink

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Hybrid malware detection approach with feedback-directed machine learning

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science China(information sciences) 2020年第3期63卷 240-242页

作者： Zhetao LI Wenlin LI Fuyuan LIN Yi SUN Min YANG Yuan ZHANG Zhibo WANG Hunan Provincial Key Lab of Internet of Things and Information Security Xiangtan University The College of Information Engineering Xiangtan University Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science Fudan University Shanghai Institute of Intelligent Electronics & Systems Shanghai Institute for Advanced Communication and Data Science School of Cyber Science and Engineering Wuhan University

Dear editor,In recent years, the number of mobile malware has increased at an alarming rate. According to a report from G data [1], there are approximately 9000 new Android malware instances each *** malicious applica... 详细信息

关键词： Hybrid malware detection approach with feedback-directed machine learning

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Proposal for a Knowledge Map in Programming Education Curriculum for IT Talent Development in Japan

Proposal for a Knowledge Map in Programming Education Curric...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Hiroto Yamakawa Haruki Ueno Kana Ohashi Akihiro Matsuda Osamu Hasegawa Hiroshi Komatsugawa Department of Information Systems Engineering Chitose Institute of Science and Technology Chitose Hokkaido Japan Faculty of Science and Technology Chitose Institute of Science and Technology Chitose Hokkaido Japan Graduate School of Science and Technology Chitose Institute of Science and Technology Chitose Hokkaido Japan Chitose Hokuyou High School Chitose Hokkaido Japan Department of Data Science Musashino University Ariake Koto-Ku Tokyo

This study proposes a knowledge map for standardizing programming education across Japan and focuses on bridging the curriculum gaps between elementary, middle, and higher education. Utilizing a computer adaptive testing system for empirical validation, a pilot implementation in a high school class revealed knowledge gaps in advancing levels of comprehension and application, especially when transitioning to complex elements such as a pseudocode.

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Usage of Continuous Medical Monitoring in Human-Machine systems to Improve Their Reliability and Emergency Safety

Usage of Continuous Medical Monitoring in Human-Machine Syst...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Yaroslav Turovskiy Alexander Surovtsev Laboratory of Control Based on Incomplete Data V.A. Trapeznikov Institute of Control Science of RAS Moscow Russia Department of Digital Technologies Voronezh State University Voronezh Russia Department of Information and Control Systems Voronezh State University of Engineering Technologies Voronezh Russia

A method of conducting continuous medical monitoring of human operators of human-machine systems is proposed. This method assumes building a decision support system that selects the control mode of the technological process. This paper has simulation modeling results of the technological process conditions changing from a potentially faulty state to a proper state.

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