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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Li, Aichen Liu, Yang Wu, Qingqing Shi, Qingjiang Zhao, Jun Dalian University of Technology School of Information and Communication Engineering China University of Macau Department of Electrical and Computer Engineering China Tongji Univ. Shanghai Shenzhen Research Institute of Big Data School of Software Eng. Shenzhen China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9781665459754

This paper considers exploiting reconfigurable intelligent surface (RIS) in a multi-user multi-access mobile computing (MEC) system. To precisely model the traffic behaviors of the tasks, we consider queuing procedures at both mobile users and the cloud server. This consideration leads to concatenation of queues and makes our problem much more difficult than existing literature since analytic expression of the objective is impossible. Our goal is to jointly optimize the task scheduling, RIS configuration and computation resource allocation to improve the MEC network's overall computation rate in the long run while maintaining the stability of the queues. To tackle this highly challenging stochastic problem, we develop a Lyapunov guided deep reinforcement learning (DRL) solution, which borrows the Lyapunov optimization theory to stabilize queues and at the same time can effectively optimize the intractable objective in an online manner. Numerical results will be presented to verify the effectiveness of our solution and the performance boost due to the deployment of RIS. © 2022 IEEE.

关键词： Edge computing

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Intent Classification of Nepali Factoid Questions

Intent Classification of Nepali Factoid Questions

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Sushil Timilsina Ashim Khadka Jianchu Lin Faculty of Science and Technology Gandaki College of Engineering and Science Pokhara Nepal Faculty of Science and Technology Nepal College of Information Technology Lalitpur Nepal Faculty of Computer and Software Engineer Huaiyin Institute of Technology Jiangsu China

Question Classification plays a vital role in identifying the accurate answer to any question, and is considered as a core component of Question Answering Systems. In Nepali Natural Language Processing, the area of Question Answering has not even been initiated due to a lack of dataset. The dataset of Nepali factual questions has been created by collecting questions from general knowledge books and labelling them with developed taxonomy. The problem of Nepali factoid question classification has been studied with a Support Vector Machine, which works as a baseline classification algorithm and obtained a 0.74 weighted F1-score in the imbalance dataset. The experimental results of the proposed model show the effectiveness of the approach in classifying factoid questions accurately compared with the state-of-the-art approaches.

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Enhancing Feature Selection in IoT Intrusion Detection Using the Ensemble Stacking

Enhancing Feature Selection in IoT Intrusion Detection Using...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Zhijian Zheng Weilin Gai Peng Zhang Ming Zhou School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China TCA Institute of Software CAS CNCERT/CC School of Computer Science and Technology UCAS Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The Internet of Things (IoT) is increasingly vulnerable to security risks due to new network attacks. Deep learning-based intrusion detection systems (DL-IDS) have emerged as a key solution, but they face challenges like imbalanced datasets and lengthy training times in complex environments. While feature selection algorithms are commonly employed to mitigate these issues, mainstream methods can yield inconsistent results, failing to reflect data characteristics accurately and potentially introducing noise. To address this problem, we propose an ensemble stacking approach to combine multiple feature selection algorithms, thereby minimizing errors from individual approaches. Each feature selection method acts as a base learner to assess feature importance, while logistic regression is a meta-learner to integrate the outputs into a final result. Additionally, we developed a CNN-based intrusion detection model enhanced with BiLSTM and attention mechanisms to improve detection performance. Our approach was tested on the UNSW-NB15 and CIC-IDS2017 datasets, with results indicating a significant improvement in detection performance compared to using a single feature selection method.

关键词： Training Attention mechanisms Stacking Noise Intrusion detection Bidirectional long short term memory Feature extraction Robustness Internet of Things Security

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A Novel Algorithm for Vertex Sum Distinguishing Total Coloring Based on Multiobjective Optimization

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Journal of engineering Science and technology Review 2021年第6期14卷 107-115页

作者： Huanping, Zhao Dangqin, Xue Huojie, Shi School of Computer and Software Nanyang Institute of Technology Nanyang473004 China School of Intelligent Manufactuing Nanyang Institute of Technology Nanyang473004 China Department of Biological Systems Engineering Washington State University PullmanMI98101 United States

Graph coloring is an important research field in graph theory. In vertex sum distinguishing total coloring, the constraint condition, that is, the color sum of each vertex is different from that of its associated edges is added on the basis of the normal total coloring. A novel algorithm was designed to increase the vertex sum distinguishing total coloring effciency. Subobjective functions were designed according to the vertex constraint, edge constraint, vertex-edge constraint, and vertex-edge sum constraint to improve the vertex sum distinguishing total coloring efficiency of random graphs. In each subobjective function, iterative swap was performed step by step using the coloring matrix and color set complement, in which the conflict was handled following the idea of impressed variation, thereby realizing the vertex sum distinguishing total coloring of random graphs. In the end, the lower bound of chromatic number in the vertex sum distinguishing total coloring was proven through the theoretical analysis and experimental comparison. Results demonstrate that when the order of a graph is smaller than 1000 and the edge density is smaller than 0.1, the conflict handling function can be converged at a high rate. When the order of the graph is greater than 1000, the convergence rate of the algorithm declines rapidly, and the accurate coloring result can be obtained only by increasing the coloring quantity, indicating that the vertex sum distinguishing total chromatic number of a connected graph with the order of not smaller than 3 ranged from μΣt(G) to μΣt(G)+3. This finding is consistent with the algorithm analysis result. On this basis, this algorithm can effectively complete the vertex sum distinguishing total coloring of random graphs, and its time complexity does not exceed O(n3). The proposed algorithm provides evidence for exploring complex networks, transportation, and network security © 2021 school of Science, IHU. All rights reserved

关键词： Multiobjective optimization

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Definition and Detection of Centralization Defects in Smart Contracts

arXiv

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arXiv 2024年

作者： Lin, Zewei Chen, Jiachi Wu, Jiajing Zhang, Weizhe Zheng, Zibin School of Software Engineering Sun Yat-sen University Zhuhai China Peng Cheng Laboratory Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

In recent years, security incidents stemming from centralization defects in smart contracts have led to substantial financial losses. A centralization defect refers to any error, flaw, or fault in a smart contract’s design or development stage that introduces a single point of failure. Such defects allow a specific account or user to disrupt the normal operations of smart contracts, potentially causing malfunctions or even complete project shutdowns. Despite the significance of this issue, most current smart contract analyses overlook centralization defects, focusing primarily on other types of defects. To address this gap, our paper introduces six types of centralization defects in smart contracts by manually analyzing 597 Stack Exchange posts and 117 audit reports. For each defect, we provide a detailed description and code examples to illustrate its characteristics and potential impacts. Additionally, we introduce a tool named CDRipper (Centralization Defects Ripper) designed to identify the defined centralization defects. Specifically, CDRipper constructs a permission dependency graph (PDG) and extracts the permission dependencies of functions from the source code of smart contracts. It then detects the sensitive operations in functions and identifies centralization defects based on predefined patterns. We conduct a large-scale experiment using CDRipper on 244,424 real-world smart contracts and evaluate the results based on a manually labeled dataset. Our findings reveal that 82,446 contracts contain at least one of the six centralization defects, with our tool achieving an overall precision of 93.7%. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart contract

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ICLTR: A Input-split Inverters and C-elements based Low-Cost Latch with Triple-Node-Upset Recovery

ICLTR: A Input-split Inverters and C-elements based Low-Cost...

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International Test Conference in Asia (ITC-Asia)

作者： Jiajia Zhang Zhenmin Li Gaoyang Shan Jie Song Xing Guo Xiaoqing Wen School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China Department of Software and Computer Engineering Ajou University Suwon Korea Department of Computer Science and Networks Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798331540333

ISBN: (纸本)9798331540340

As the semiconductor technology continues to advance, integrated circuits (ICs) are becoming increasingly sensitive to soft errors, e.g., double-node upsets (DNUs) and triplenode upsets (TNUs), induced by harsh radiation. In this paper, a low-cost latch design, namely ICLTR, using input-split inverters (ISIs) and C-elements to provide complete TNU recovery, is proposed. ICLTR consists of seven ISIs, seven 2-input C-elements and a clock-gated inverter, and all these elements are interlocked. Simulation results show the complete TNU recovery for ICLTR. The simulation results also show that ICLTR can save 59.5% of the transmission delay, 36.1% of the power consumption and 81.6% of the delay-area-power product (DAPP) on average when compared with the same type of TNU recovery latch designs.

关键词： Integrated circuits Latches Sensitivity Power demand Radiation hardening (electronics) Simulation Reliability engineering

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Neurocomputational Modelling of EEG Connectivity: Links Between Depression, Inflammation, and Gut-Microbiome 17th

Neurocomputational Modelling of EEG Connectivity: Links Betw...

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17th International Conference on Brain Informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios

A Method Based on Genetic Programming to Automatically Const...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yan Ma Changsheng Zhang Yan Gao Ying Guo School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated.

关键词： Industries Genetic programming Prediction algorithms Knowledge discovery Boosting Decision trees Stock markets Random forests Fuzzy systems

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Accelerating temporal action proposal generation via high performance computing

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Frontiers of computer Science 2022年第4期16卷 59-68页

作者： Tian Wang Shiye Lei Youyou Jiang Choi Chang Hichem Snoussi Guangcun Shan Yao Fu Institute of Artificial Intelligence Beihang UniversityBeijing100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191China School of Software Tsinghua UniversityBeijing100084China Department of Computer Engineering Gachon UniversitySeongnam13120South Korea Institute Charles Delaunay-LM2S FRE CNRS 2019 University of Technology of TroyesTroyes10010France School of Instrumentation Science and Opto-electronics Engineering Beihang UniversityBeijing100191China Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchun130033China

Temporal action proposal generation aims to output the starting and ending times of each potential action for long videos and often suffers from high computation *** address the issue,we propose a new temporal convolution network called Multipath Temporal ConvNet(MTCN).In our work,one novel high performance ring parallel architecture based is further introduced into temporal action proposal generation in order to respond to the requirements of large memory occupation and a large number of ***,the total data transmission is reduced by adding a connection between multiple-computing load in the newly developed *** to the traditional Parameter Server architecture,our parallel architecture has higher efficiency on temporal action detection tasks with multiple *** conduct experiments on ActivityNet-1.3 and THUMOS14,where our method outperforms-other state-of-art temporal action detection methods with high recall and high temporal *** addition,a time metric is further proposed here to evaluate the speed performancein the distributed training process.

关键词： temporal convolution temporal action proposal generation deep learning

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Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Shumeng Qi, Lei Yu, Qian Huo, Jing Shi, Yinghuan Gao, Yang Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University National Institute of Healthcare Data Science China School of Computer Science and Engineering China Shandong Women's University School of Data and Computer Science China

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce the annotation cost and maintain satisfactory performance, in this work, we leverage the capabilities of SAM for establishing semi-supervised medical image segmentation models. Rethinking the requirements of effectiveness, efficiency, and compatibility, we propose a three-stage framework, i.e., Stitching, Fine-tuning, and Re-training (SFR). The current fine-tuning approaches mostly involve 2D slice-wise fine-tuning that disregards the contextual information between adjacent slices. Our stitching strategy mitigates the mismatch between natural and 3D medical images. The stitched images are then used for fine-tuning SAM, providing robust initialization of pseudo-labels. Afterwards, we train a 3D semi-supervised segmentation model while maintaining the same parameter size as the conventional segmenter such as V-Net. Our SFR framework is plug-and-play, and easily compatible with various popular semi-supervised methods. We also develop an extended framework SFR+ with selective fine-tuning and re-training through confidence estimation. Extensive experiments validate that our SFR and SFR+ achieve significant improvements in both moderate annotation and scarce annotation across five datasets. In particular, SFR framework improves the Dice score of Mean Teacher from 29.68% to 74.40% with only one labeled data of LA dataset. The code is available at https://***/ShumengLI/SFR. © 1982-2012 IEEE.

关键词： Image segmentation

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