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Co-training with Progressive Distribution Alignment and Uncertainty-Interactive Relabeling for Semi-Supervised Domain Adaptive Semantic Segmentation

Co-training with Progressive Distribution Alignment and Unce...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ruiguo Yu Yida Wang Xuewei Li Xuzhou Fu Zijian Zhang Yuan Tian Jie Gao College of Intelligence and Computing Tianjin University China Tianjin Key Laboratory of Cognitive Computing and Application China Tianjin Key Laboratory of Advanced Networking China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-training is a strong baseline for semi-supervised domain adaptive semantic segmentation. However, it inevitably introduces biased links between features and concepts in the prediction of certain "hard pixels", which may mislead the generalization of models. We consider these hard pixels to come from two aspects: First, the labels are severely imbalanced and distributed across domains and classes, which may lead to features biased towards source domains and majority classes. Second, the naive threshold filtering pseudo-label methods limit the supervision of hard pixels. To address the above problems, we propose a novel co-training framework with progressive distribution alignment and uncertainty-interactive relabeling strategies. More concretely, a progressive distribution alignment strategy is proposed to match distribution across domains while providing additional supervision for tail class pixels. Additionally, an uncertainty-interactive relabeling strategy is proposed to retain more supervisory information for hard pixels and reduce the overall uncertainty of the pseudo-labels. Experiments on two widely-used benchmarks demonstrate the effectiveness of the proposed PDAUR, achieving state-of-the-art results.

关键词： Adaptation models Uncertainty Filtering Semantic segmentation Semantics Tail Signal processing Predictive models Benchmark testing Speech processing

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Task Migration in Volunteer computing Systems

Task Migration in Volunteer Computing Systems

引用

Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Ehab Saleh Chandrasekar Shastry Department of Computer Science and Engineering Jain University Bangalore India

Volunteer computing is the use of underutilised computing power donated by volunteers who want to participate in ongoing high-throughput scientific projects. Due to the general simplicity of this concept, this type of computing attracts thousands of volunteers from all over the world. Many of them, however, leave the network, prompting the main server to initiate Task Migration, which involves transferring the remaining job to another volunteer’s device on the network. In this paper, we conduct two experiments in the task migration procedure in peer-to-peer volunteer network. In the first experiment, the server starts the migration procedure when it detects the first available volunteer, whereas in the second one, the server starts the migration after all of the server’s sub-peers have completed their jobs. To ensure the network’s heterogeneity, we select the dataset GWA-T-13 Materna, which contains performance metrics described as trace files of over 1500 VMs from the distributed Materna Data Centers in Dortmund, Germany. In both experiments, we compare the total execution time of the entire task and the maximum execution time that a peer can have to complete its assigned work. The simulation results show that in the second experiment, the main task was completed in less time than in the first experiment.

关键词： Measurement distributed processing Data centers Simulation Computer applications Peer-to-peer computing Servers

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RIFFLE: A distributed Network Operating System with Re-configurability for Multiple Domains

RIFFLE: A Distributed Network Operating System with Re-confi...

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IEEE international Symposium on Parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Boyang Zhou Chunming Wu Shuangxi Chen Zhen Li Hong Fan Zhejiang Lab Intelligent Networks Institute Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Jiaxing Key Laboratory of Industrial Internet Jiaxing China Research Center for Intelligent Technology Standardization Zhejiang Lab Hangzhou China Department of Electrical Engineering Shanghai University of Electric Power Shanghai China

The distributed network control in software-defined networking is greatly challenged by the design demands of evolving a running distributed control plane (DCP), and by the complexity of handling the control state dynamics. In this paper, we propose RIFFLE to address the problem using a distributed network operating system approach. RIFFLE enables the evolvability of control states of DCP deployed on a global scale. RIFFLE handles spatial and temporal dynamics that occur when updating and migrating the distributed control states, which overcomes the critical barrier to the evolvability. It fills the gap by enabling temporal reconfigurability in DCP. Moreover, RIFFLE reduces the complexities of building and maintaining network control services in DCP by enabling componentization and abstracting the underlying network dynamics. We evaluated the prototype RIFFLE through experiments running in PlanetLab. The results show that the prototype scales well for 135 nodes. We also validate that RIFFLE ensures the continuity and low content request delay for the supported the information-centric network supported during cache update periods owing to the enabled evolvability.

关键词： Runtime Roads Decentralized control Information-centric networking Prototypes Ubiquitous computing Dynamic scheduling

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An SGX and Blockchain-Based System for Federated Learning in Classifying COVID-19 Images

An SGX and Blockchain-Based System for Federated Learning in...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Sihang Qin Xianjun Gu Xing Jun Haitao Li Mengqi Huang Weiqi Dai 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 Wuhan China State Grid Wuhan Power Supply Company China

Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inability of model-generating nodes to ensure computational fairness. The second challenge involves the potential leakage of private information through gradient differences. This paper introduces a federated learning system that leverages Software Guard Extensions (SGX) and blockchain technology to enhance the fairness and security of federated learning for analyzing chest imaging data. The modeling of imaging factors for chest radiography data can effectively regulate the data quality of semi-trusted nodes. A blockchain smart contract is employed for data quality-aware proof, and the quality-aware block undergoes a two-stage rewriting process across distinct nodes. A notary membership model achieves notarized cluster consensus for imaging informatics data. The multi-channel layer model quantifies the distribution of computational workload across participating nodes within the medical consortium. Upon successful verification of the enclave command in the aggregation cycle, traceable and packaged signatures are generated to support computational fairness. To ensure robust information security, SGX remote authentication is used between downstream and upstream nodes. The federated learning system, built upon SGX and blockchain, has been implemented using the Fabric federated chain platform. The ResNet model underwent training through cross-sectional federated learning to effectively classify novel coronavirus lung medical image data. Following this, multiple experimental control and security analyses were conducted, revealing that the time loss incurred due to crossing channels is approximately 21.31%, while the time overhead of SGX remains within 5.2%, as allowed by the number of nodes.

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Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning

Network link weight optimization based on antisymmetric deep...

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Next Generation Data-driven Networks (NGDN), international conference on

作者： Yuhan Chen Yingya Guo Department of Computer and Data Science Fuzhou University Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios.

关键词： Measurement Network topology Optimization methods Telecommunication traffic Feature extraction Deep reinforcement learning Routing Load management Routing protocols Next generation networking

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Detection of A Novel Dual Attack in Named Data networking

Detection of A Novel Dual Attack in Named Data Networking

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Liang Liu Silin Peng College of Safety Science and Engineering Civil Aviation University of China Tianjin China College of Electronic Information and Automation Civil Aviation University of China Tianjin China

distributed Denial of Service (DDoS) attacks in Named Data networking (NDN), such as Cache Pollution Attacks (CPA) and Improved Collusive Interest Flooding Attacks (I-CIFA), can significantly threaten the NDN network. However, most of the previous research has focused on the detection of a single attack by using Machine Learning algorithms and threshold-based methods, so the previous methods are not efficient enough for detecting dual attacks. This paper first proposes a dual attack by combining I-CIFA and CPA, and extracts the network traffic features, including the number of CacheMisses and the number of PIT entries. After analyzing the severe impact of the dual attack, a detection scheme BO-CatBoost is proposed based on Bayesian optimization and CatBoost. Finally, the experiment results show that the proposed detection scheme is robust in detecting the dual attack.

关键词： Pollution Machine learning algorithms distributed databases Telecommunication traffic Feature extraction Denial-of-service attack Bayes methods

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： A S Gowri P Jose Karthik S M S Berlin Shaheema K M Karuppasway R. Balamurugan Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department for Applied Research The Gandhigram Rural Institute (Deemed to be University) Gandhigram India Department of Artificial Intelligence and Data Science Stella Mary’s College of Engineering Azhikal India Department of Mathematics and Statistics Bharath Institute of Higher Education and Research Chennai India Department of Information Technology DMI College of Engineering Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%.

关键词： Support vector machines Accuracy Convolution Machine learning Feature extraction Data models Diabetes Data mining Convolutional neural networks Principal component analysis

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Pallas: Optimizing Userspace TCP Stack for Short-Lived Connections

Pallas: Optimizing Userspace TCP Stack for Short-Lived Conne...

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international conference on Parallel and distributed Systems (ICPADS)

作者： Haiqiang Lin Wenxin Li Wenyu Qu Tianjin Key Laboratory of Advanced Networking (TANK) College of Intelligence and Computing Tianjin University China

Short-lived TCP connections are important for modern Internet applications and call for efficient network stack support. Traditional Linux TCP stack is inefficient because of the inherent kernel overheads like context switching and memory copy. A promising alternative, called user-level TCP stack, is thus becoming attractive in this context. However, existing user-level stacks mainly focus on scalability issues when handling shortlived connections and fall short in optimizing request-response latency. To fill this gap, this paper presents Pallas, a built-in packet scheduler in the user-space mTCP stack, to prioritize the transmissions of short connections over long connections to reduce the request-response latency. Pallas schedules at packetlevel first to complete 1-packet short connections as quickly as possible and then smoothly switches to connection-level to defer the transmissions of long connections. We have implemented a Pallas prototype and evaluated it on a 4-core machine. The results show that Pallas-enhanced mTCP delivers significant performance. For example, it speeds up the overall request response time of mTCP by 1.49 times.

关键词： Schedules Scheduling algorithms Scalability Linux Prototypes Switches Scheduling

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Curse of Feature Selection: a Comparison Experiment of DDoS Detection Using Classification Techniques

Curse of Feature Selection: a Comparison Experiment of DDoS ...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Wenjia Wang Seyed Masoud Sadjadi Naphtali Rishe Knight Foundation School of Computing and Information Sciences Florida International University Miami USA

distributed denial-of-service (DDoS) attack is a malicious cybersecurity attack that has become a global threat. Machine learning (ML) as an advanced technology has been proven to be an effective way against DDoS attacks. Feature selection is a crucial step in ML, and researchers have put endless efforts to mitigate the “Curse of Dimensionality”. Feature selection is also causing problems to ML models, such as a decrease in prediction accuracy. Four supervised classification techniques, namely, Decision Tree (DT), k-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF), are tested using mutual information score ranking to study the necessity of feature selection in DDoS detection.

关键词： Training Radio frequency Measurement Predictive models Feature extraction Denial-of-service attack Decision trees

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Optimizing distributed Denial of Service (DDoS) Attack Detection Techniques on Software Defined Network (SDN) Using Feature Selection

Optimizing Distributed Denial of Service (DDoS) Attack Detec...

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Innovations in Computer Science & Software Engineering (ICONICS), international conference on

作者： Inamul Haq Salman A. Khan Nazeeruddin Mohammad Ali Zaman College of Computing and Info. Sciences Karachi Institute of Economics and Technology Karachi Pakistan Cyber security center Prince Mohammad bin Fahd University Al-Khobar Saudi Arabia Computer Science Department Shaheed Zulfiqar Ali Bhutto University Karachi Pakistan

ISBN: (数字)9798331504342

ISBN: (纸本)9798331504359

distributed denial of service (DDoS) attacks refer to a category of cyber attacks in which an attacker attempts to overwhelm a resource, such as a server, destination, or bandwidth through various means. This is usually done by either flooding the traffic or sending a heavy crunching job to exhaust the CPU or heavy amounts of data to pileup the storage. By the rise of virtualization and cloud technologies, networks have also evolved from hardware to a piece of software which virtualizes the operation of a network equipment. This virtualization is termed as a Software Defined Network (SDN). Due to the sheer impact of cyberattacks on the performance of networks, the study of DDoS attacks on SDN is vital in the present era. Contemporary approaches such as machine learning and deep learning have proven to be effective in attack detection of DDoS in SDNs. In this paper, a preliminary analysis is carried out to study the impact of feature selection to increase the throughput and response time of Machine learning techniques in detection of DDoS attacks on SDN. The results indicate that feature selection, when applied to machine learning algorithms, is an effective approach for DDoS attack detection in SDNs.

关键词： Technological innovation Machine learning algorithms Feature extraction Denial-of-service attack Throughput Software Time factors Servers Software defined networking Virtualization

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