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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Jing Huang Wenyu Wu Weihong Huang Yufeng Xiao Li Lisi Jinxi Sun School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China The Bilingual Experimental School Attached to HNSDFZ Changsha China

Vehicle edge computing (VEC) has become an important research field in recent years. In VEC, computation offloading moves computationally intensive tasks from resource-constrained devices to the network edge, it provides service closer to the end-users. By processing tasks with abundant idle resources at the network edge, low-latency demands for some tasks can be met. However, the mobility and uncertainty of vehicles pose significant challenges to vehicle computation offloading. This paper focuses on the decision-making process of vehicle computation offloading, specifically task partitioning and scheduling decisions. This paper summarizes some hot problems and solutions, including latency optimization, reliability optimization, energy efficiency optimization, cost optimization, and mobility support. This study will help researchers discover important features of vehicle computation offloading and find the most suitable scheme to solve the vehicle offloading problem in different scenarios.

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Intrusion Detection in Novel WSN-Leach Dos Attack Dataset using Machine Learning based Boosting Algorithms

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Procedia computer Science 2023年 230卷 90-99页

作者： Dharini N Jeevaa Katiravan Sruthi Priya D M Sakthi Sneghaa V A Associate Professor Department of Computer Science Engineering(Cyber Security) R.M.K. College of Engineering and Technology Thiruvallur 601206 Professor Department of Information Technology Velammal Engineering College Surapet Chennai 600066 Software Engineer embedUR Chennai 600013 Jr. Customer Service Executive Tata Communications Transformation Services Chennai 600037

Security poses a substantial challenge in the context of wireless sensor networks (WSNs). WSNs, which are frequently employed for real-time monitoring in various Internet of Things (IoT)-based applications, are particularly vulnerable to a range of Denial of Service (DoS) attacks. These attacks represent a significant security concern and are among the most prevalent issues confronting WSNs *** our research, we have introduced a WSN DoS dataset using the Matrix Laboratory (MATLAB) tool. This dataset was developed through the utilization of the Leach Protocol and incorporates various types of DoS attacks, including Wormhole, Blackhole, Grayhole, Flooding, and Time Division Multiple Access (TDMA) attacks. In our study, we assessed the performance of several machine learning techniques using our dataset. We employed various performance evaluation metrics that gauge both the classification accuracy and the complexity of these machine learning models. These metrics included parameters such as the number of accurately classified and misclassified instances, along with the time required for constructing the model. This comprehensive evaluation aimed to demonstrate the effectiveness of our proposed *** results of our analysis revealed that the Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Bagging algorithms exhibited strong performance on our dataset. They achieved impressive accuracy levels of up to 99.16%, 99.16%, and 99.8%, respectively, when using 14 features. Additionally, these algorithms achieved accuracy rates of 99.16%, 98.75%, and 99.8%, respectively, with 5 features, and 95.41%, 95%, and 93.75%, respectively, when considering only 3 features such as node ID and Energy-Consumed, Attack_type. Notably, LightGBM outperformed the others in terms of speed, as it employs a leaf-wise growth strategy, consumes less memory, and requires less time for model construction. Consequently, LightGBM emerged as the most efficien

关键词： Denial of Service (DoS) attacks Intrusion Detection System (IDS) Leach Protocol WSN DoS dataset Ensemble Learning Feature Selection Accurately Classified and Misclassified instance Light Gradient Boosting Machine (LightGBM)

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A Method of Multidimensional software Aging Prediction Based on Ensemble Learning: A Case of Android Os

SSRN

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SSRN 2023年

作者： Nie, Yuge Chen, Yulei Jiang, Yujia Wu, Huayao Yin, Beibei Cai, Kai-Yuan Beihang University School of Automation Science and Electrical Engineering Beijing100083 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China

Context: software aging refers to the phenomenon of performance degradation, increasing failure rate, or system crash due to resource consumption and error accumulation in software systems running for a long time. Precisely predicting software aging is crucial for enabling proactive measures before the situation substantially deteriorates. Previous machine learning (ML) methods for predicting aging predominantly rely on a single ML modeland a single labeling metric to attain their objectives. Furthermore, a common limitation shared by these methods is that training and prediction data stem from identical operational profiles. This could pose challenges for these models in delivering the desired performance when confronted with diverse user scenarios. Objective: In this paper, we aim to find a method that can both consistently and precisely predict aging trends across a wide range of application scenarios. Method: We propose a method of multidimensional software aging prediction based on ensemble learning called MSAP. Unlike the previous full-process single-threaded approaches, in the framework of MSAP, five dimensions including datasets, labeling metrics, labeling thresholds, algorithms, and model decisions are extracted and diversified to protest against diverse user scenarios and aging situations. Results: When subjected to identical workloads, MSAP demonstrates comparable performance to the majority of unidimensional models, albeit without significant prominence. Under different workloads, MSAP consistently outperforms unidimensional models whose performance drops dramatically in terms of precision, maintaining a notable lead while exhibiting minimal fluctuations. Conclusion: MSAP exhibits exceptional stability while concurrently upholding outstanding prediction precision across a spectrum of user scenarios. Therefore, it has better generalization characteristics and application prospects. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Positive Sample Augmentation with Graph Mixup for DDI Prediction

Positive Sample Augmentation with Graph Mixup for DDI Predic...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Hailong Yu Xinyu Ma Xu Chu Yasha Wang Rengyu Wu Qiang Zhou School of Computer Science Peking University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Shenzhen Center for Prehospital Care Shenzhen China

Unexpected drug-drug interactions (DDIs) may occur when drugs are taken at the same time. Harmful DDIs can reduce drug efficacy and even increase unintended toxicity, putting patients treated with different drugs at risk. Previous works on DDI prediction mainly rely on hand-engineered domain knowledge, which is laborious to obtain and cannot handle the problem of sparse positive samples in DDI prediction. Even though some works attempt to handle data sparsity from the perspective of data distribution to improve the generalization ability of the model, they are limited to negative samples distribution. To address these limitations, we propose a novel method, Graph Mixup for Positive Sample Augmentation (GMPSA), to effectively combine positive drug molecule graphs and extend the training distribution to alleviate the problem of sparse positive samples. To model the graph signals and graph structures jointly, we map source graphs to the fused gromov-wasserstein metric space, then minimizes the weighted sum of transportation distances between the distributions of the source graphs and the objective graph in this metric space. By doing so, interpolated positive samples improve the the generalization error of the model. Experiments results on a real-word dataset show that our method effectively improve the perfomermance in DDI prediction on a state-of-the-art architecture. Further studies also verify the robustness and generalizability of GMPSA under different GNN backbones and parameter settings.

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Union Variable Neighborhood Descent Algorithm for Multi-product Hybrid Disassembly Line Balancing Problem Considering Workstation Resource Configuration

Union Variable Neighborhood Descent Algorithm for Multi-prod...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhu, JinTing Han, Yunping Guo, XiWang Wang, Jiacun Qin, Shujin Qi, Liang Zhao, Jian Liaoning Petrochemical University Information and Control Fushun China Liaoning Petrochemical University Monmouth University Information and Control United States Monmouth University Computer Science and Software Engineering Wes Long Branch United States Shangqiu Normal University Economics and Management Shangqiu China Shandong University of Science and Technology Computer Intelligent Science and Technology Qingdao China University of Science and Technology Anshan Liaoning China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Nowadays, the recycling of waste products has attracted extensive attention in academia and industry. In the layout design of disassembly lines, single-row and U-shaped hybrid disassembly lines have different application scenarios. Considering workstation resource configuration, disassembly line cycle time, and disassembly task precedence relationship, we address a Multi-product Hybrid-disassembly-line-balancing Problem (MHP), and establish its mathematical model with the objective of disassembly profit maximization. In addition, the union variable neighborhood descent (U-VND) algorithm is used to solve the problem, in which two kinds of neighborhood structures composed of different actions is designed. Experimental results and comparative analysis show that the proposed algorithm can quickly obtain stable and high-quality solutions, which verifies the validity of the neighborhood structure and the correctness of the model. © 2022 IEEE.

关键词： Industries Deep learning Analytical models Petri nets Layout Mathematical models Workstations

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Face Mask Detection and Social Distance Monitoring System for COVID-19 Pandemic

TechRxiv

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TechRxiv 2022年

作者： Javed, Iram Butt, Muhammad Atif Khalid, Samina Shehryar, Tehmina Amin, Rashid Syed, Adeel Muzaffar Sadiq, Marium Department of Computer Science and Information Technology Mirpur University of Science and Technology Azad Jammu and Kashmir Pakistan Information Technology University Punjab Lahore Pakistan Department of Software Engineering Mirpur University of Science and Technology Azad Jammu and Kashmir Pakistan Department of Computer Science University of Engineering and Technology Taxila Pakistan Department of Software Engineering Bahria University Islamabad Pakistan

Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learning-based visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillance-based dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10,000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy 1 © 2022, CC BY.

关键词： Coronavirus

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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Timely Random Access: Packet-based or Connection-based?

Timely Random Access: Packet-based or Connection-based?

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IEEE Conference on Vehicular technology (VTC)

作者： Jian Feng Haoyuan Pan Tse-Tin Chan College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong SAR China

This paper studies random access protocols for timely status update systems with information freshness requirements, measured in terms of age of information (AoI). Considering an extensive network, a fundamental problem is how to schedule massive transmitters to access the wireless channel to achieve low network-wide AoI. In conventional packet-based random access protocols, transmitters contend for the channel by sending the whole data packet. When the packet duration is long, the time wasted due to packet collisions is considerable. In contrast, connection-based random access protocols first establish connections with the receiver before the data packet is transmitted. From the information freshness perspective, there should be conditions favoring either side. We present a comparative study of the average AoI of packet-based and connection-based random access protocols. Specifically, we consider frame slotted Aloha (FSA) as a representative of packet-based random access and design a request-then-access (RTA) protocol for connection-based random access. Our analyses indicate that whether packet-based or connection-based protocols should be used depends mainly on the payload size of data packets. Except for the case where the payload size of an update packet is very tiny, RTA outperforms FSA in average AoI because the established connections help avoid direct collisions of data packets.

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PATR: Periodicity-Aware Trajectory Recovery for Express System via Seq2Seq Model

PATR: Periodicity-Aware Trajectory Recovery for Express Syst...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Xinrui Zhang Xingyuan Liang Hai Wang Shuai Wang Tian He College of Software Engineering Southeast University School of Computer Science and Engineering Southeast University Department of Technology and Data Intelligence JD Logistics

ISBN: (纸本)9781665435413

The analysis of express trajectory is essential for delivery service optimization and courier management. Express trajectory consists of a large amount of community paths which is different from the trajectory of transportation tools such as taxis which run in the main road of a city. Due to the complexity of the community road networks and the energy constraint of devices, trajectories are collected under a low sampling rate and accuracy. These trajectories need to be recovered and mapped to the road networks for further analysis. Current recovery approaches utilize two-stage or end-to-end models to generate the upsampled trajectories. These methods fail to fully exploit the periodicity relation in historical data, which leads to accuracy losses for complex community road networks. In this paper, we design PATR, a periodicity-aware Seq2Seq model, to recover the couriers' trajectories by leveraging the periodical patterns of the historical data. We conduct experimental evaluations based on real-world GPS data of JD logistics consisting of 836,959 points and 25,726 road segments, and our model outperforms the state-of-the-art baselines by reducing recovery error by 2.4%-21.9%.

关键词： Roads Urban areas Data models Trajectory Complexity theory Global communication Public transportation

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Memristive Network-based Artificial Fish Swarm Algorithm

Memristive Network-based Artificial Fish Swarm Algorithm

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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Wang, Mingxiao Yu, Yongbin Deng, Quanxin Zhou, Chen School of Electrical and Computer Engineering University of Southern California Los AngelesCA United States School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665412872

This paper explores the relation between Artificial Fish Swarm Algorithm and memristive network. There are some relations between swarm intelligence and memristive network. Inspired by the correlation between colony optimization and memristive network, mapping rules between AFSA's 4 main behaviors and memristive network has highlighted in this paper. In this paper the motion of single fish is first simulated, then the motion of more fish is simulated. Finally, we make comparison of the trend of motion fish swarm and concept model. © 2021 IEEE.

关键词： Correlation Neuromorphic engineering Fish Market research Particle swarm optimization Optimization

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