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27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Hao, Hongsheng Wang, Liang Xia, Zenggang Yu, Zhiwen Gu, Jianhua Fu, Ning School of Computer Science Northwestern Polytechnical University Xi'an China College of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9781665408783

In urban informatics, traffic congestion prediction is of great importance for travel route planning and traffic management, and has received extensive attention from academia and industry. However, most previous works fail to implement a citywide traffic congestion prediction on fine-grained road segment, and without comprehensively considering strong spatial-temporal correlations. To overcome these concerns, in this paper, we propose a spatial-temporal context embedding and metric learning approach (STE-ML) to predict the traffic congestion level. In particular, our STE-ML consists of a traffic spatial-temporal context embedding component, and a metric learning component. From local and global perspectives, the context embedding component can simultaneously integrate local spatial-temporal correlation features and global traffic statistics information, and compress into an unified and abstract embedding representation. Meanwhile, metric learning component benefits from learning a more suitable distance function tuned to specific task. The combination of these models together could enhance traffic congestion prediction performance. We conduct extensive experiments on real traffic data set to evaluate the performance of our proposed STE-ML approach, and make comparison with other existing techniques. The experimental results demonstrate that the proposed STE-ML outperforms the existing methods. © 2021 IEEE.

关键词： Traffic congestion

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Test Case Generation for Ethereum Smart Contract based on Data Dependency Analysis of State Variable

Test Case Generation for Ethereum Smart Contract based on Da...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Jinhu Du Song Huang Xingya Wang Changyou Zheng Jinlei Sun Command & Control Engineering College Army Engineering University of PLA Nanjing China College of Computer Science and Technology Nanjing Tech University Nanjing China

An Ethereum smart contract is an agreement reached by multiple parties, which is guaranteed by blockchain technology to be executed in accordance with the terms expressed in the form of code. Its security needs are particularly prominent due to a large number of digital assets under management. Testing is an effective way to find flaws that threaten the security of smart contracts. However, current smart contract test case generation methods do not regard the impact of other functions in the smart contract on state variables, resulting in the inaccessibility of the control statements related to state variables and low branch coverage of the function under test. To alleviate this problem, this paper proposes SV-Gen. SV-Gen generates test cases for smart contracts through two steps: static analysis and dynamic search. In the first step, SV-Gen considers the read-write relationship between functions and state variables in the smart contract to generate a function invocation sequence for the function to be tested through a backtracking algorithm on state variables. Then the arguments of transactions to invoke each function in the sequence are generated through regex matching to form the primitive test case. In the second step, the primitive test cases constitute an initial population, and a genetic algorithm undertakes the task of evolving them to high branch coverage. The experimental results on one of the VeriSmart datasets show that SV-Gen can effectively enter the control constraints related to state variables and improve the branch coverage of smart contracts.

关键词： Smart contracts Software algorithms Sociology Static analysis Software quality Software reliability Security

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Fall Detection Based on Person Detection and Multi-target Tracking 11

Fall Detection Based on Person Detection and Multi-target Tr...

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Xu, Teng Chen, Jian Li, Zuoyong Cai, Yuanzheng School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9781665406796

Recently, official statistics reported that the Chinese population aged 60 and above has been 26.402 million, which accounts for 18.70% of total population. It is urgent to develop fall detection technologies for alleviating the risk causing by falling of elder person. In this paper, we propose a real-time, high-precision, and deep learning-based fall detection method with automatic person detection and tracking. Specifically, the proposed method first improves the YOLOv3 network to more efficiently detect person and extract feature maps of the object. Then, it inputs the extracted feature maps from the YOLOv3 into a multi-target tracking network for cascade matching and IOU matching in a Deep SORT algorithm, respectively. Next, it improves YOLOv5 network to detect posture anomalies. Finally, it refines the detected posture anomalies for obtaining the final fall detection result. Experimental results show that the proposed method simultaneously improves accuracy and efficiency of the fall detection. © 2021 IEEE.

关键词： Fall detection

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Build a Cross-modality Bridge for Image-to-Point Cloud Registration

Build a Cross-modality Bridge for Image-to-Point Cloud Regis...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Lin Bie Shouan Pan Kai Cheng Li Han School of Software Tsinghua University Beijing China Command Control College Army Engineering University Nanjing China School of Computer Science and Artificial Intelligence Liaoning Normal University Dalian China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Although the fusion of images and LiDAR point clouds is crucial to many applications in computer vision, the relative poses of cameras and LiDAR scanners often need to be discovered. The general 2D-3D registration pipeline establishes correspondences and performs pose estimation based on the generated matches. However, 2D-3D correspondences are inherently challenging to establish due to the large gap between images and LiDAR point clouds. To this end, we build a bridge to alleviate the 2D-3D gap and propose a practical framework to align LiDAR point clouds to the virtual points generated by images. In this way, the modality gap is converted to the domain gap of point clouds. Furthermore, we propose a registration method with cross-domain feature extraction, frustum classification, and domain-agnostic correspondence pruning to narrow the domain gap to establish 3D-3D correspondences between real and virtual point clouds. Experimental results demonstrate that our image-to-point cloud registration method achieves state-of-theart performance on the KITTI Odometry and Oxford RobotCar datasets.

关键词： Point cloud compression Bridges Cloud computing Laser radar Three-dimensional displays Pipelines Pose estimation Feature extraction Cameras Odometry

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Performance Improvement Strategy for Differentiated Services in Multi-rate IEEE 802.11 Wireless Networks 12

Performance Improvement Strategy for Differentiated Services...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Xu, Haihong Lu, Shejie Hu, Siping Gao, Jinfeng Liu, Fang Wu, Haitao Hubei University of Science and Technology Information Center Institute of Engineering and Technology Xianning437100 China Huanghuai University International Joint Laboratory of Behavior Optimization Control for Smart Robots College of Computer and Artificial Intelligence Zhumadian463000 China

ISBN: (纸本)9798350310153

With the wide application of wireless LAN, the real-time and differentiated services of IEEE 802.11 MAC are becoming more and more important. IEEE 802.11 uses CSMA/CA mechanism in MAC layer to coordinate access, but it does not meet the different requirements of different users for QoS. Considering the different requirements of different priority users for QoS, a solution is proposed for this problem. The core idea of this strategy is: in a multi rate network, according to the priority of nodes in the network, through adaptive optimization of relevant parameters, the probability of high priority nodes accessing the channel can be improved, and its throughput can be improved accordingly;The channel access probability of nodes with lower priority is reduced accordingly, and the throughput is reduced accordingly. Therefore, this policy can expand the throughput of each priority and realize the service differentiation function. © 2022 IEEE.

关键词： IEEE Standards

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Fog-Cloud-IoT Enabled WSN Architecture 3

Fog-Cloud-IoT Enabled WSN Architecture

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Anitha Christy Angelin, P. Samuel Devakumar, P.V. Al-Farhani, Liwa H. Kumar, Gandam Vijay Deep, Shaurya Mohammad, Shoaib PSNA College of Engineering and Technology Tamilnadu Dindigul India Karunya Institute of Technology and Sciences Tamilnadu Coimbatore India RUDN University System Analysis Control and Information Processing Academy of Engineering Moscow Russia Career Point University Computer Science and Engineering Rajasthan India IIMT College of Management Greater Noida India IMS Unison University Department of Law Uttarakhand Dehradun India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Remote Because of the exciting innovation it introduces, the WSN likely to be a subject of research in recent time. Because of the impromptu distant connections, adaptability, and simplicity of execution, this has all the makings of becoming the most maintainable innovation for ecological detecting, whether it's about limited or huge scope watching. In any case, the main drawbacks stem from the limited capacity, processing, and accessibility of organization centers for information. Virtual machines resources have been adopted to overcome such constraints, devices to expanded capacity, computation, and easy-to-understand availability. This was a natural progression from traditional WSN designs among pattern in recent theories evolved along advent of Internet-of-Things advancements. Against the growing popularity of WSN associated cloud checking frameworks, still the issues are detected because of the distributed comp ting's disadvantages, like dormancy with the capacity charges. In this paper, the advancements achieved to a fog-cloud-IoT associated with WSN architecture of putting a bit of computing at the network part are elaborated, a process, which follows the smart Fog associated with Cloud-computing concept of dissecting also following up on IoT data. A similar investigation was carried out to illustrate the improvements made possible by processing at the organization's edge. © 2022 IEEE.

关键词： Fog computing

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Probabilistic spatial clustering based on the Self Discipline Learning (SDL) model of autonomous learning

arXiv

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arXiv 2022年

作者： Cu, Zecang Sun, Xiaoqi Sun, Yuan Zhang, Fuquan Apollo Japan Co. Ltd Japan College of Computer and Control Engineering Minjiang University China

Unsupervised clustering algorithm can effectively reduce the dimension of high-dimensional unlabeled data, thus reducing the time and space complexity of data processing. However, the traditional clustering algorithm needs to set the upper bound of the number of categories in advance, and the deep learning clustering algorithm will fall into the problem of local optimum. In order to solve these problems, a probabilistic spatial clustering algorithm based on the Self Discipline Learning(SDL) model is proposed. The algorithm is based on the Gaussian probability distribution of the probability space distance between vectors, and uses the probability scale and maximum probability value of the probability space distance as the distance measurement judgment, and then determines the category of each sample according to the distribution characteristics of the data set itself. The algorithm is tested in Laboratory for Intelligent and Safe Automobiles(LISA) traffic light data set, the accuracy rate is 99.03%, the recall rate is 91%, and the effect is achieved. © 2022, CC BY.

关键词： Learning algorithms

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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An Optimal Pricing Strategy for Ethereum Gasprice Based on Non-Preemptive Priority Queuing Game

SSRN

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SSRN 2023年

作者： Ma, Yu Wang, Xingya Gu, Xin Zhao, Zhihong College of Computer and Information Engineering College of Artificial Intelligence Nanjing Tech University Jiangsu Nanjing211816 China College of Command and Control Engineering Army Engineering University of PLA Jiangsu Nanjing210001 China College of Computing Science and Artificial Intelligence Suzhou City University Jiangsu Suzhou215104 China

In Ethereum, a game-theoretic relationship exists among transactions competing to be included in blocks. Miners prioritize transactions in the memory pool by their gasPrice, and select higher-ranked transactions for confirmation in blocks firstly. Consequently, from the perspective of transactions, this process of selection and packaging can be likened to a queuing system. To assist users in saving transaction fees and reducing waiting time, this paper utilizes a queuing game model to describe the transaction confirmation competition process and proposes an optimal pricing strategy for Ethereum transaction gasPrice that satisfies the Nash equilibrium. Specifically, the paper first models the queuing competition for transaction confirmation as a non-preemptive queuing model with priority. Then, it analyzes the impact of gasPrice on waiting time, deducing a functional relationship between waiting time and gasPrice, thereby deriving the users’ Nash equilibrium payment strategy. Experimental results demonstrate that compared to two state of the art machine learning-based pricing strategies and one based on Poisson regression, applying the pricing strategy proposed in this paper increases the Packing Rates of transactions being included in the next block by 56.21%, 50.95%, and 39.15%, respectively, and achieves cost savings of 21 times, 297 times, and 3 times for users. The results indicate that the gasPrice optimal pricing strategy proposed in this paper helps users effectively balance waiting time and cost. © 2023, The Authors. All rights reserved.

关键词： Game theory

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Improved Malaria Cells Detection Using Deep Convolutional Neural Network

Improved Malaria Cells Detection Using Deep Convolutional Ne...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Sarmad Nozad Mahmood Swash Sami Mohammed Ayad Ghany Ismaeel Hülya Gükalp Clarke Iman Nozad Mahmood Dlnya Abdulahad Aziz Sameer Alani Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk Iraq Electrical and Electronics Engineering Ondokuz Mayıs University Samsun Turkey Kirkuk General Education Directorate Kirkuk Iraq Computer Center University of Anbar Anbar Iraq

This research presents a deep convolutional neural network (CNN) as a solution for identifying malarial cells that are infected. The AI model suggested in this work comprises a three-layered CNN and a two-layered dense neural network. The model can capture both minor and significant features by utilizing CNN, thereby extracting a maximum amount of information from the input data. The model is trained over 20 epochs and evaluated using the binary cross entropy loss function and accuracy metric to assess its performance. Remarkably, the proposed model achieved an impressive accuracy of 96% and maintained a loss value below 0.2 for both the training and validation datasets. Ultimately, this research demonstrates promising potential for automating the detection of malaria through parasite cell counting.

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