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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Ilampari, M. Amina Begum, M. Mahesh Babu, N. Gopi, S. Mythreyee, M. Deivasigamani, Shanmugavel SIMATS Saveetha School of Law India Oxford College of Engineering Department of ECE Chennai India Anurag Engineering College Suryapet Department of Information Technology Telangana Kodad India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India Dr. MGR Educational and Research Institute Department of EEE Chennai India Jeppiaar Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. These are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal utilization of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. The proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimizations in network performance, energy efficiency and lifetime over known protocols. This method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will utilize reliable and sustainable WSNs. © 2024 IEEE.

关键词： Sensor nodes

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FedCosA: Optimised Federated Learning Model Using Cosine Annealing

FedCosA: Optimised Federated Learning Model Using Cosine Ann...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： John, Binu Menon, Varun G. Adhikari, Mainak Scms School of Engineering and Technology Department of Computer Science & Engineering Kerala Ernakulam683576 India Apj Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India School of Data Science Indian Institute of Science Education and Research Kerala Thiruvananthapuram695551 India

ISBN: (纸本)9798331507541

The Federated Learning paradigm allows multiple devices or nodes to contribute to model training without sharing raw data, ensuring that sensitive information remains on the client side while still benefiting from collective learning. However, FL suffers several problems, including slow convergence, overfitting, and communication cost, particularly in Non-Independent and Identically Distributed (Non-IID) data settings. To solve these concerns, we offer an improved Federated Averaging (FedAvg) method that includes the Adam optimizer, cosine annealing learning rate scheduler, and weight decay (L2 regularisation). The Adam optimizer generates adaptive learning rates, but the cosine annealing schedule refines the learning rate over time for greater stability. Weight decay reduces overfitting by penalizing big model parameters. Furthermore, picking a subset of customers in each cycle lowers communication costs. Experimental findings on the MNIST dataset show that our strategy outperforms existing FL algorithms like FedAvg, FedSGD, FedNova, and FedSAM in test accuracy and loss minimization while significantly lowering communication load and training time. These enhancements make the suggested approach more effective and scalable for FL across several settings. © 2025 IEEE.

关键词： Federated learning

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Real-time Student Classroom Feedback with the RFMNet Framework in a Smart Classroom

Real-time Student Classroom Feedback with the RFMNet Framewo...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xianpiao Tang Pengyun Hu Dewei Yu Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Key Laboratory of Information and Computing Science Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

Facial expression recognition is the ability to interpret human feelings from facial features. Teachers can use it to visualize the state of emotion and estimate students' learning effects to take corresponding intervention strategies to improve teaching. The current technology has shortcomings such as network performance degradation and feature information loss, which cannot meet the needs of sentiment analysis of student feedback. Therefore, RFMNet, a novel network model based on deep learning theory, is proposed in this paper. Featuring higher extraction ability, it is conducive to accurately analysing students' expressions. First of all, this model introduces a relation-aware global attention (RGA) module to better integrate the more discriminative expression features in the image and achieve more refined sentiment analysis. Second, the Mish activation function and the focal loss function are employed. The Mish activation function is introduced to avoid disappearing feature information due to neuron deactivation when the ReLU gradient is 0. The model achieves an average recognition accuracy of 89.62%, as confirmed through a sequence of trials conducted on the public dataset FERPlus. The model helps achieve intelligent processing of teaching information and obtaining real-time feedback by detecting, extracting, and analysing the students' facial expressions.

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Exceptional point enhanced nanoparticle detection in deformed Reuleaux‑triangle microcavity

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Frontiers of Optoelectronics 2024年第3期17卷 83-91页

作者： Jinhao Fei Xiaobei Zhang Qi Zhang Yong Yang Zijie Wang Chuanlu Deng Yi Huang Tingyun Wang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China

In this paper,we propose a deformed Reuleaux-triangle resonator(RTR)to form exceptional point(EP)which results in the detection sensitivity enhancement of *** introducing single nanoparticle to the deformed RTR at EP,frequency splitting obtains an enhancement of more than 6 times compared with non-deformed *** addition,EP induced a result that the far feld pattern of chiral mode responses signifcantly to external perturbation,corresponding to the change in internal ***,single nanoparticle with far distance of more than 4000 nm can be detected by measuring the variation of far feld directional *** to traditional frequency splitting,the far feld pattern produced in deformed RTR provides a cost-efective and convenient path to detect single nanoparticle at a long distance,without using tunable laser and external *** structure indicates great potential in high sensitivity sensor and label-free detector.

关键词： Exceptional point Deformed microcavity Nanoparticle detection Far-feld pattern

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VULSEYE: Detect Smart Contract Vulnerabilities via Stateful Directed Graybox Fuzzing

arXiv

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arXiv 2024年

作者： Liang, Ruichao Chen, Jing Wu, Cong He, Kun Wu, Yueming Cao, Ruochen Du, Ruiying Liu, Yang Zhao, Ziming Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China Cyber Security Lab College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science and Engineering University at Buffalo New York United States

Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in decentralized systems. Fuzzing, a prominent security testing technique, is extensively explored to detect vulnerabilities. But current smart contract fuzzers fall short of expectations in testing efficiency for two primary reasons. Firstly, smart contracts are stateful programs, and existing approaches, primarily coverage-guided, lack effective feedback from the contract state. Consequently, they struggle to effectively explore the contract state space. Secondly, coverage-guided fuzzers, aiming for comprehensive program coverage, may lead to a wastage of testing resources on benign code areas. This wastage worsens in smart contract testing, as the mix of code and state spaces further complicates comprehensive testing. To address these challenges, we propose VULSEYE, a stateful directed graybox fuzzer for smart contracts guided by vulnerabilities. Different from prior works, VULSEYE achieves stateful directed fuzzing by prioritizing testing resources to code areas and contract states that are more prone to vulnerabilities. We introduce Code Targets and State Targets into fuzzing loops as the testing targets of VULSEYE. We use static analysis and pattern matching to pinpoint Code Targets, and propose a scalable backward analysis algorithm to specify State Targets. We design a novel fitness metric that leverages feedback from both the contract code space and state space, directing fuzzing toward these targets. With the guidance of code and state targets, VULSEYE alleviates the wastage of testing resources on benign code areas and achieves effective stateful fuzzing. In comparison with state-of-the-art fuzzers, VULSEYE demonstrated superior effectiveness and efficiency. Notably, it uncovered 4,845 vulnerabilities in 42,738 rea

关键词： Software testing

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Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract) 39

Copula Guided Parallel Gibbs Sampling for Nonparametric and ...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Lin, Lihui Rao, Yanghui Xie, Haoran Lau, Raymond Y. K. Yin, Jian Wang, Fu Lee Li, Qing Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Lingnan University Department of Computing and Decision Sciences New Territories Hong Kong City University of Hong Kong Department of Information Systems Kowloon Hong Kong Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Hong Kong Metropolitan University School of Science and Technology Kowloon Hong Kong The Hong Kong Polytechnic University Department of Computing Kowloon Hong Kong

ISBN: (纸本)9798350322279

In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop. © 2023 IEEE.

关键词： Information management

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Object Detection Algorithm Based on Second-Order Pooling Network and Gaussian Mixture Attention 5

Object Detection Algorithm Based on Second-Order Pooling Net...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Li, Ningbo Yang, Xiaobao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To improve the feature representation ability of the YOLOX algorithm and obtain better detection performance, an object detection algorithm based on second-order pooling network and gaussian mixture attention is proposed. Firstly, the second-order pooling network is added after the PAFPN, and the higher-order statistical information is obtained by calculating the covariance matrix between different channels, to enhance the non-linear modeling capability. Secondly, the mixture attention based on the gaussian function is added after the SPP to model global contexts in the spatial and channel dimensions respectively, which improves the network performance with almost no extra parameters. The experimental results show that the detection accuracy of the proposed algorithm on the PASCAL VOC dataset reaches 82.6 %, which is 1.6 % higher than the YOLOX algorithm. © 2022 ACM.

关键词： Object detection

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LLGS: Illuminating Gaussian Splatting via absorptance Modulation

LLGS: Illuminating Gaussian Splatting via absorptance Modula...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gan, Jianwen Li, Wenxin Zheng, Bo Wang, Chengliang Wu, Yingbo National Elite Institute of Engineering Chongqing University Chongqing China AI Lab Chongqing Changan Automobile Co. Ltd. Chongqing China College of Computer Science Chongqing University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350368741

Low-light images are typically characterized by low pixel intensity and color distortion, presenting a significant challenge for accurate 3D reconstruction with 3D Gaussian Splatting (3DGS). Traditional 2D enhancement methods fail to maintain consistent illumination, affecting reconstruction quality. We propose Low-Light Gaussian (LLGS), which can directly leverage low-light images for 3D reconstruction and synthesizing normal-light novel views. LLGS incorporates absorptance to simulate light behavior in low-light conditions, assuming objects maintain normal illumination while reflected light intensity attenuates due to absorptance during rendering. This approach enables the capture of authentic color information in dimly lit scenes. LLGS outperforms current enhancement algorithms and Neural Radiance Fields (NeRF) in image quality and processing efficiency, making it highly effective for low-light 3D scene reconstruction and novel view synthesis. © 2025 IEEE.

关键词： 3D gaussians Low-light image enhancement Novel view synthesis Radiance fields

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Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Deng, Shidi Schiffer, Maximilian Bichler, Martin School of Management Technical University of Munich Germany School of Management Munich Data Science Institute Technical University of Munich Germany School of Computation Information and Technology Technical University of Munich Germany

Nowadays, a significant share of the business-to-consumer sector is based on online platforms like Amazon and Alibaba and uses Artificial Intelligence (AI) for pricing strategies. This has sparked debate on whether pricing algorithms may tacitly collude to set supra-competitive prices without being explicitly designed to do so. Our study addresses these concerns by examining the risk of collusion when Reinforcement Learning (RL) algorithms are used to decide on pricing strategies in competitive markets. Prior research in this field focused on Tabular Q-learning (TQL) and led to opposing views on whether learning-based algorithms can result in supra-competitive prices. Building on this, our work contributes to this ongoing discussion by providing a more nuanced numerical study that goes beyond TQL, additionally capturing off- and on-policy Deep Reinforcement Learning (DRL) algorithms, two distinct families of DRL algorithms that recently gained attention for algorithmic pricing. We study multiple Bertrand oligopoly variants and show that algorithmic collusion depends on the algorithm used. In our experiments, we observed that TQL tends to exhibit higher collusion and price dispersion. Moreover, it suffers from instability and disparity, as agents with higher learning rates consistently achieve higher profits, and it lacks robustness in state representation, with pricing dynamics varying significantly based on information access. In contrast, DRL algorithms, such as Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), generally converge to lower prices closer to the Nash equilibrium. Additionally, we show that when pre-trained TQL agents interact with DRL agents, the latter quickly outperforms the former, highlighting the advantages of DRL in pricing competition. Lastly, we find that competition between heterogeneous DRL algorithms, such as PPO and DQN, tends to reduce the likelihood of supra-competitive pricing. Copyright © 2025, The Authors. All rights rese

关键词： Deep reinforcement learning

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds

Layered Structure Aware Dependent Microservice Placement Tow...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Deze Zeng Hongmin Geng Lin Gu Zhexiong Li School of Computer Science China University of Geosciences Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies.

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