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检索条件"任意字段=23rd International Conference on Computer Systems and Technologies, CompSysTech 2022"

共 801 条记录，以下是151-160 订阅

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Cooperative Relationship Prediction between Scholars in Heterogeneous Academic Network 23

Cooperative Relationship Prediction between Scholars in Hete...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Shi, Jia Jin, Hai Xie, Xia National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

The real academic network belongs to a heterogeneous network, therefore, for the link prediction tasks, some information on the network may be lost if only using homogeneous network methods. In order to make good use of the rich semantic information in a heterogeneous network for better predicting the cooperative relationship between scholars, this paper proposes a cooperative relationship prediction (CoRP) model. The CoRP model includes the following three steps. Firstly, a structural feature extraction module is designed based on meta path by using the similarity metric function to measure the meta path similarity between scholars. Secondly, to overcome the defects that the existing methods usually ignore the attribute information on nodes, three attribute feature presentation functions are constructed to fully utilize the attribute information of scholars in the academic network. In the end, a machine learning classifier which combines the structural similarity and attribute similarity is utilized to learn each attribute feature and complete the task of cooperative relationship prediction. The experimental results reveal that compared with the benchmark method, the proposed method has better performance and interpretability with improvement from 3% to 10% of AUC value for the AMiner academic data set. © 2021 IEEE.

关键词： Measurement Smart cities Semantics Machine learning Benchmark testing Predictive models Feature extraction

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BOBBLE: A Mixed Routing-Granularity Distributed Load Balancing for Data Center Networks 23

BOBBLE: A Mixed Routing-Granularity Distributed Load Balanci...

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作者： Xu, Zhengzhi Lu, Yifei Ma, Xu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210096 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214215 China

ISBN: (纸本)9781665494571

In current Datacenter Networks (DCNs), Equal-Cost Multipath (ECMP) is a default load balancing scheme. However, using ECMP may result in rapid growth of Flow Completion Time (FCT) due to its well-known drawbacks. In order to solve the problem of ECMP, load balancing schemes with per-packet granularity (such as RPS) and per-flowlet granularity (such as LetFlow) are proposed. These two different granularities have their own features, but there's no existing load balancing considering both their advantages. In this paper, we mix per-flowlet and per-packet in scheduling decisions and introduce the potential performance bottleneck in mixed granularities load balancing. Based on this idea, we propose a hybrid scheme for load balancing, referred to as BOBBLE. The key idea of BOBBLE is to distinguish elephant and mouse flows and schedule them with different granularity. We evaluate BOBBLE in simulations with NS-3. Experiment shows that BOBBLE slightly sacrifices elephant flows FCT but it is worthy because our results demonstrate that BOBBLE is a good trade-off which outperforms RPS and LetFlow in mouse flows FCT in both symmetric and asymmetric topology. © 2021 IEEE.

关键词： Data centers Network topology Smart cities Load management Routing Mice computer networks

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Service Recommendation based on DIKW Pyramid 23

Service Recommendation based on DIKW Pyramid

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作者： Lv, Jiamin Yu, Lei Duan, Yucong Inner Mongolia University Department of Computer Science Hohhot China School of Computer and Cyberspace Security Hainan University Hainan China

ISBN: (纸本)9781665494571

In the initial establishment of wisdom, data, information, knowledge and wisdom are a progressive relationship. When we test whether the current wisdom system is fully developed, our judgment is to verify whether the knowledge, information and data generated by the wisdom system are correct. However, the growth process of the wisdom system is not smooth sailing. It will generate wrong knowledge, information and data, and these mistakes can also be used as the raw materials for the growth of the wisdom system, that is, a more perfect wisdom system can be formed through reflection. By analyzing the process of Service Recommendation based on deep learning, we found that a Service Recommendation model based on the data, information, knowledge and wisdom has excellent performance in accuracy, reliability and other aspects. We proposed a Service Recommendation model based on DIKW Pyramid. By comparing the recommendation by the model with the service that is users' real like, the model reflects on the differences between the recommendation results and the real results, making the recommendation results closer to the real, so as to improve the accuracy of Service recommendation. © 2021 IEEE.

关键词： Deep learning Analytical models Smart cities Reflection Raw materials Data models Reliability

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23rd international conference on Information Security Applications, WISA 2022

23rd International Conference on Information Security Applic...

引用

23rd international conference on Information Security Applications, WISA 2022

ISBN: (纸本)9783031256585

The proceedings contain 25 papers. The special focus in this conference is on Information Security Applications. The topics include: On Membership Inference Attacks to Generative Language Models Across Language Domains;a Joint Framework to Privacy-Preserving Edge Intelligence in Vehicular Networks;Recovering Yaw Rate from Signal Injection Attack to Protect RV’s Direction;a Survey on Sensor False Data Injection Attacks and Countermeasures in Cyber-Physical and Embedded systems;dazzle-attack: Anti-Forensic Server-side Attack via Fail-Free Dynamic State Machine;vkTracer: Vulnerable Kernel Code Tracing to Generate Profile of Kernel Vulnerability;ARMing-Sword: Scabbard on ARM;optimized Implementation of Quantum Binary Field Multiplication with Toffoli Depth One;Time-Optimal Design of Finite Field Arithmetic for SIKE on Cortex-M4;Provably Secure Password-Authenticated Key Exchange Based on SIDH;analysis of Radioactive Decay Based Entropy Generator in the IoT Environments;a Novel Metric for Password Security Risk Against Dictionary Attacks;towards Evaluating the Security of Human Computable Passwords Using Neural Networks;markov Decision Process for Automatic Cyber Defense;Influence Through Cyber Capacity Building: Network Analysis of Assistance, Cooperation, and Agreements Among ASEAN Plus Three Countries;Chameleon DNN Watermarking: Dynamically Public Model Ownership Verification;group Signatures with Designated Traceability over Openers’ Attributes in Bilinear Groups;Grover on SPARKLE;quality-of-Service Degradation in Distributed Instrumentation systems Through Poisoning of 5G Beamforming Algorithms;an Effective Approach for Stepping-Stone Intrusion Detection Using Packet Crossover;software-Defined Network Based Secure Internet-Enabled Video Surveillance System;TLS Goes Low Cost: When TLS Meets Edge;5G-AKA, Revisited.

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Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits 23

Simultaneously Achieving Group Exposure Fairness and Within-...

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23rd international conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Pokhriyal, Subham Jain, Shweta Ghalme, Ganesh Dhamal, Swapnil Gujar, Sujit Indian Institute of Technology Ropar Rupnagar India Indian Institute of Technology Hyderabad Hyderabad India International Institute of Information Technology Hyderabad Hyderabad India

Existing approaches to fairness in stochastic multi-armed bandits (MAB) primarily focus on exposure guarantee to individual arms. When arms are naturally grouped by certain attribute(s), we propose Bi-Level Fairness, which considers two levels of fairness. At the first level, Bi-Level Fairness guarantees a certain minimum exposure to each group. To address the unbalanced allocation of pulls to individual arms within a group, we consider meritocratic fairness at the second level, which ensures that each arm is pulled according to its merit within the group. Our work shows that we can adapt a UCB-based algorithm to achieve a Bi-Level Fairness by providing (i) anytime Group Exposure Fairness guarantees and (ii) ensuring individual-level Meritocratic Fairness within each group. We first show that one can decompose regret bounds into two components: (a) regret due to anytime group exposure fairness and (b) regret due to meritocratic fairness within each group. Our proposed algorithm BF-UCB balances these two regrets optimally to achieve the upper bound of O(√T) on regret;T being the stopping time. With the help of simulated experiments, we further show that BF-UCB achieves sub-linear regret;provides better group and individual exposure guarantees compared to existing algorithms;and does not result in a significant drop in reward with respect to UCB algorithm, which does not impose any fairness constraint. © 2024 international Foundation for Autonomous Agents and Multiagent systems.

关键词： Stochastic systems

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Microgrid Forecasting Using Multiple Deep learning Models 23

Microgrid Forecasting Using Multiple Deep learning Models

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作者： Sakubu, Daxelle Liu, Qi School of Computer and Software Nanjing University of Information Science & Technology Nanjing China

ISBN: (纸本)9781665494571

To achieve electrical grid decentralization, microgrids and distributed energy resources are widely used. However, these systems are dependent on inherently unstable renewable energy sources such as wind and solar energy and thus, forecasting electrical generation and demand is crucial for decision-making and energy control. The effectiveness of the three variants of Recurrent Neural Networks (RNNs): Long Short-Term Memory (LSTM), standard-Recurrent Neural Network (s-RNN), and Gated Recurrent Unit (GRU) models for short-term forecasting of solar and wind energy is investigated in this study. Using the root mean square error (RMSE) to evaluate accuracy, it was identified that GRU performed best with RMSE of 0.035 kW. LSTM followed in terms of accuracy with an RMSE of 0.038 kW. The s-RNN had the poorest results with RMSEs being measured at 0.042 kW in photovoltaic (PV) forecasting. In the case of wind forecasting the GRU led the best result with 0.134 kW of RMSE, 0.138 kW for LSTM and 0.142 kW for s-RNN. In forecasting of load demand, the GRU and LSTM got the same result with RMSE of 0.029 kW and 0.032 kW for s-RNN. Nevertheless, the LSTM and GRU had longer computational times of 10 seconds compared to the s-RNN's of 4 seconds. © 2021 IEEE.

关键词： Wind energy generation Renewable energy sources Recurrent neural networks Wind energy Demand forecasting Microgrids Solar energy

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DDoS Defense Scheme Based on Machine Learning in Software-Defined Networking 23

DDoS Defense Scheme Based on Machine Learning in Software-De...

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作者： Zhong, Hong Yu, Chao Cui, Jie Sun, Xiuwen Gu, Chengjie Liu, Lu School of Computer Science and Technology Anhui University Hefei China Security Research Institute New H3C Group Hefei China

ISBN: (纸本)9781665494571

Since software-defined networking (SDN) has the ad-vantages of central control programmability and global view, it is suitable for industrial network. However, large-scale security risks such as distributed denial of service (DDoS) attacks endanger the security and reliability of services in the SDN-based system. Therefore, a comprehensive and efficient solution to this issue is crucial. This paper proposes a scheme for mitigating DDoS attack traffic deployed on an SDN application plane. The framework periodically obtains the traffic statistics of the switch ports and flow tables. After obtaining the traffic characteristics, a machine learning model is used for training. The trained model can identify benign and malicious traffic. After the attack is found, the rules are sent to the switch to control the flow and resist DDoS attacks. We use the CICDDoS2019 dataset to perform experiments. The experimental results show that compared to other schemes, the proposed scheme achieves more accurate classification. © 2021 IEEE.

关键词： Training Support vector machines Switches Machine learning Denial-of-service attack Feature extraction Control systems

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Knowledge-Assisted DRL for Energy Harvesting Based Multi-Access Wireless Communications 23

Knowledge-Assisted DRL for Energy Harvesting Based Multi-Acc...

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作者： Luo, Sheng Zhang, Haodi Li, Qifan Wu, Kaishun College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665494571

This paper investigates the use of deep reinforcement learning (DRL) to maximize the long-term throughput of multi-access wireless communication systems. Specifically, we consider the scenario in which an access point (AP) without dedicated power supply harvests energy from the ambient environment and uses the harvested energy to deliver data packets to multiple users. We formulate the access control problem as a Markov Decision Process (MDP) with unknown system dynamics and apply the deep reinforcement learning (DRL) approach to solve this problem, i.e., the optimal access strategy is approximated by using a double deep Q-network (DDQN). As the considered system usually has large state and action spaces, some domain knowledge is embedded into the training process to improve the performance of DDQN. It is shown that with the help of domain knowledge, access control strategies which can achieve a higher system throughput can be found by the knowledge-embedded DDQN. Moreover, the performance of the access control policy obtained by using the knowledge-embedded DDQN is compared with that of several conventional transmission schemes. Experiment results show that the transmission policy obtained by using our proposed model can achieve better performance. © 2021 IEEE.

关键词： Access control Wireless communication Training System dynamics Smart cities Power supplies Process control

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EEG and Forehead EOG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks 23

EEG and Forehead EOG-Based Driver Fatigue Classification Usi...

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作者： Zhang, Jie Xiao, Nanfeng Guangzhou Xinhua University Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9781665494571

In this study, we fuse electroencephalography (EEG) signals and forehead electrooculogram (EOG) signals to classify the driver's fatigue and alert states. Based on time-varying power spectral density (PSD) and Hilbert-Huang spectrum entropy extracted from EEG, 36 eye movement features from EOG, we develop novel sparse-deep belief networks (Sparse-DBN) for detecting driver drowsiness. PSD, four modalities, and support vector machine (SVM) classifier are used for comparison. The experimental results show that EEG and forehead EOG feature fusion can improve performance compared with two single modalities. Furthermore, the results show that the classification performance achieves an improved classification performance using two feature fusions and Sparse-DBN classifier with the sensitivity, specificity, accuracy,and area under the curve of 97.99% /96.72%, 94.73% /95.51%, 97.93% /97.91%, and 0.96273/ 0.96376, respectively. Additionally, EEG and forehead EOG features can be collected simultaneously using only four shared forehead sites and achieve comparative performance compared with traditional sites, which is the preferred approach for practical applications. © 2021 IEEE.

关键词： Support vector machines Electrooculography Forehead Sensitivity Fuses Fatigue Feature extraction

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A Real-time Demand Response Strategy of Home Energy Management by Using Distributed Deep Reinforcement Learning 23

A Real-time Demand Response Strategy of Home Energy Manageme...

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作者： Liu, Weirong Wang, Yujie Jiang, Fu Cheng, Yijun Rong, Jieqi Wang, Chenglong Peng, Jun School of Computer Science and Engineering Central South University Changsha China School of Automation Central South University Changsha China

ISBN: (纸本)9781665494571

Home energy management system (HEMS) autonomously schedules the energy usage of home electricity consuming devices to reduce the electricity cost based on real-time electricity price. However, it is very challenging to design an optimal energy management strategy under the uncertainty of the resident's behavior, outdoor temperature and renewable generation. To solve this problem, a real-time demand response (DR) strategy based on deep reinforcement learning (DRL) is proposed. Firstly, the real-time DR management problem is modeled as a Markov game, and then a home energy management algorithm is proposed based on Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). It is worth mentioning that the proposed algorithm builds the thermal dynamics model and takes energy storage system (ESS) into account, which improves the utilization of renewable power generation effectively. The simulation result based on real-world data demonstrates the effectiveness of the proposed algorithm. © 2021 IEEE.

关键词： Renewable energy sources Schedules Costs Uncertainty Heuristic algorithms Simulation Reinforcement learning

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