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Distributed Nash Equilibrium Seeking for General Networked Games With Bounded Disturbances

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 376-387页

作者： Maojiao Ye Danhu Li Qing-Long Han Lei Ding IEEE the School of Automation Nanjing University of Science and TechnologyNanjing 210094China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China

This paper is concerned with anti-disturbance Nash equilibrium seeking for games with partial ***,reduced-order disturbance observer-based algorithms are proposed to achieve Nash equilibrium seeking for games with firstorder and second-order players,*** the developed algorithms,the observed disturbance values are included in control signals to eliminate the influence of disturbances,based on which a gradient-like optimization method is implemented for each ***,a signum function based distributed algorithm is proposed to attenuate disturbances for games with secondorder integrator-type *** be more specific,a signum function is involved in the proposed seeking strategy to dominate disturbances,based on which the feedback of the velocity-like states and the gradients of the functions associated with players achieves stabilization of system dynamics and optimization of players'objective *** Lyapunov stability analysis,it is proven that the players'actions can approach a small region around the Nash equilibrium by utilizing disturbance observerbased strategies with appropriate control ***,exponential(asymptotic)convergence can be achieved when the signum function based control strategy(with an adaptive control gain)is *** performance of the proposed algorithms is tested by utilizing an integrated simulation platform of virtual robot experimentation platform(V-REP)and MATLAB.

关键词： Distributed networks disturbance rejection Nash equilibrium networked games

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Review of DDoS Attack Detection in Big Data with Cloud using Machine Learning 2

Review of DDoS Attack Detection in Big Data with Cloud using...

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Radhika, P. Kamalakkannan, S. Kavitha, P. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798350340235

Cloud computing (CC) is a massive breakthrough in Information technology (IT) that provides end users to access flexible andvirtualizedsources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC Data Centres (DCs). The Distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined. © 2023 IEEE.

关键词： Big Data Cloud computing Deep learning Distributed Denial of Service attacks Machine Learning

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Enhancing Large-Scale Pre-Trained Models with Mixer for Far-Field ASR 5

Enhancing Large-Scale Pre-Trained Models with Mixer for Far-...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Niu, Tong Chen, Yaqi Qu, Dan Hu, Hengbo Information Engineering University Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (纸本)9798331528911

Although the significant improvements of Automatic Speech Recognition systems is achieved by the end-to-end modeling technology, especially the use of large-scale pretrained models. Far-Field Speech Recognition still faces the challenges due to signal degradation, such as low signal-to-noise ratio, reverberation, and background noise. To overcome these issues, we introduce an innovative data augmentation technique called Mixer to enhance the performance of large-scale pretrained models in FSR, by interpolated and mixed the features to represent the speech samples and the corresponding losses. Experimental results show that compared to the Whisper acrossed all version, including SpecAugment, the proposed method has reduced 3.6% Word Error Rate. © 2024 IEEE.

关键词： Signal to noise ratio

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Predictive Modeling for Asset Bubble Detection in Financial Markets

Predictive Modeling for Asset Bubble Detection in Financial ...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering, ICSSEEC 2024

作者： Murugan, Suganiya Sivakumar, Pradeep Kumar Goyal, Arpit Goenka, Pranay Srm Institute of Science and Technology Department of Computing Technologies Chennai India Vels Institute of Science Technology and Advanced Studies Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350378177

In response to the volatile nature of financial markets, our project, titled 'A Comprehensive Approach to Stock Market Crash Prediction using Time Series Forecasting, Machine Learning, and Deep Learning Techniques,' endeavors to create a robust prediction model for anticipating stock market crashes. Leveraging a diverse dataset gathered from various open-source links, with a primary source being YahooFinance, we employ an extensive feature set including time stamps, economic indicators such as unemployment, consumer price index (CPI), price-to-earning ratio (P/E), and market yields. Our innovative approach involves labeling major crashes on the S&P 500 chart and correlating them with diverse economic data. By filtering and marking significant crash phases, we create a binary classifier. Forecasting S&P 500 values for 24 months using ARIMA and SARIMAX models allows for better evaluation and economic analysis. The project integrates various machine learning algorithms, with random forests and decision trees emerging as top performers. Deep learning techniques using Keras, such as Convolutional Neural Networks (CNNs) and Dense Neural Networks (DNNs), achieve notable accuracy. Additionally, real-time stock-related tweets obtained through the Twitter API and market sentiments from the Alpha Vantage API enhance the predictive capabilities. The model employs ARIMA and SARIMAX for forecasting, alongside a CNN architecture for crash classification. The UI/UX showcases data visualizations, sentiment analysis, and predictive models. The technology stack includes REACT, NODE JS, TENSORFLOW, A WS, and more. This work presents a holistic strategy to equip investors with valuable insights and aid in making informed decisions during potential market downturns. © 2024 IEEE.

关键词： Convolutional neural networks

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SST-HAR: Simultaneous Spatial-temporal Information Encoding Network for Radar-based Human Activity Recognition 9

SST-HAR: Simultaneous Spatial-temporal Information Encoding ...

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9th IEEE Smart World Congress, SWC 2023

作者： Ye, Xudong Fan, Feiyi Gu, Yang Shen, Jianfei Zhengzhou University Henan Institute of Advanced Technology Zhengzhou China Chinese Academy of Sciences Institute of Computing Technology Beijing China Shandong Academy of Intelligent Computing Technology Shandong China

ISBN: (纸本)9798350319804

Radar-based human activity recognition (HAR) is a time-series classification challenge, which is widely applied in intelligent healthcare, ubiquitous computing, autonomous driving, etc. However, the received radar signal is much noisier and less intuitive than images, which makes it harder to capture the human posture information and achieve a high recognition accuracy. In this paper, we use a radar array to collect activity data from different subjects and propose a novel simultaneous spatial-temporal information encoding network (SST-HAR) to extract the posture features from the raw radar signals. The spatial and temporal information are extracted in two branches respectively at the same time, and a lightweight external attention (EA) module allows for the extraction of more relevant features from the fused spatial-temporal information for radar-based HAR. We evaluate the SST-HAR method through extensive experiments and achieve a mean accuracy of 97.91% on our dataset and 94.12% on a public dataset, which shows the effectiveness of our method in radar-based HAR tasks. © 2023 IEEE.

关键词： deep learning external Attention human activity recognition radar spatial-temporal features

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Dalea:A Persistent Multi-Level Extendible Hashing with Improved Tail Performance

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Journal of Computer Science & technology 2023年第5期38卷 1051-1073页

作者：熊子威蒋德钧熊劲 Ren Ren Center for Advanced Computer Systems Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 101408China Huawei Technology Co. Ltd.Shanghai 201206China

Persistent memory(PM)promises byte-addressability,large capacity,and *** memory systems,such as key-value stores and in-memory databases,benefit from such features of *** to the great popularity of hash-ing index in main memory systems,a number of research efforts are made to provide high average performance persistent ***,suboptimal tail performance in terms of tail throughput and tail latency is still observed for existing persistent *** this paper,we analyze major sources of suboptimal tail performance from key design issues of persis-tent *** identify the global hash structure and concurrency control as remaining explorable design spaces for im-proving tail *** propose Directory-sharing Multi-level Extendible Hashing(Dalea)for *** designs an-cestor link-based extendible hashing as well as fine-grained transient lock to address the two main sources(rehashing and locking)affecting tail *** evaluation results show that,compared with state-of-the-art persistent hashing Dash,Dalea achieves increased tail throughput by 4.1x and reduced tail latency by ***,in order to provide de-sign guidelines for improving tail performance,we adopt Dalea as a testbed to identify different impacts of four factors on tail performance,including fine-grained rehashing,transient locking,memory pre-allocation,and fingerprinting.

关键词： persistent memory persistent hashing indexing structure

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Numerical Simulation Study of Oil-Water Emulsion Separation in an Ultrasonic Field:Effect of Coupling between Acoustic and Flow Field Parameters

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China Petroleum Processing & Petrochemical technology 2024年第1期26卷 116-125页

作者： Wang Heping Lin Yinchao Li Yanggui Zhang Xiaohang Wu Yi Key Laboratory of Engineering Mathematics and Advanced Computing of Nanchang Institute of Technology School of SciencesNanchang Institute of TechnologyNanchang 330099People’s Republic of China Dongguan Key Laboratory of Kunpeng Computing School of Computer ScienceGuangdong University of Science&TechnologyDongguan 523083China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016China

In this study,the separation and coalescence of oil-in-water emulsions are explored in an ultrasonic field using the lattice Boltzmann *** simulating the propagation of ultrasonic waves,this study focuses on examining the effects of acoustic wave frequency,the ratio of oil to water components,and the aspect ratio of the boundary on the emulsification and separation processes of oil-water *** following conclusions are drawn.①Frequency affects the speed of oil droplet separation,leading to an increase in droplet size over *** droplets are found near the source,while smaller droplets are distributed throughout the wave web.②As the boundary aspect ratio increases,the emulsification efficiency of the droplets weakens,and the system takes longer to stabilize.③Emulsions with a higher component of oil can better resist acoustic waves.④At the same acoustic frequency,longer wavelength ultrasonic fields promote the formation of uniformly distributed,smaller oil droplets,which is beneficial to the storage of *** numerical simulation results offer insights for optimizing conditions for oil-in-water separation and serve as a numerical reference for the study of oil-in-water emulsion separation in ultrasonic environments.

关键词： lattice Boltzmann method phase separation ultrasound field

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Research and design of distributed testing framework for 5G network 23

Research and design of distributed testing framework for 5G ...

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4th International Conference on computing, Networks and Internet of Things, CNIOT 2023

作者： He, Yugang Wang, Fengli Sun, Qian Tian, Lin Wang, Yuanyuan Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400700705

As the 5G network continues to evolve with micro-services, the protocol implementation in the network is decentralised to individual network elements, with multiple network elements collaborating to provide services to the terminals. The functional testing of the network therefore requires the simultaneous testing of multiple network *** current test system cannot test multiple network elements simultaneously and has the problem that test anomalies are difficult to *** paper proposes a distributed testing framework for 5G networks, which is based on the TTCN-3 testing framework as a prototype and designed using keyword-driven technology and remote procedure call (RPC) technology based on C/S *** framework has proven to have the following advantages: good scalability of the test nodes, the ability to test multiple network elements simultaneously, and the ability to locate test anomalies at the element signalling level. © 2023 ACM.

关键词： 5G mobile communication systems

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SEA: SPARSE LINEAR ATTENTION WITH ESTIMATED ATTENTION MASK 12

SEA: SPARSE LINEAR ATTENTION WITH ESTIMATED ATTENTION MASK

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12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Heejun Kim, Jina Willette, Jeffrey Hwang, Sung Ju School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Graduate School of AI Korea Advanced Institute of Science and Technology Daejeon Korea Republic of DeepAuto.ai Daejeon Korea Republic of

The transformer architecture has driven breakthroughs in recent years on tasks which require modeling pairwise relationships between sequential elements, as is the case in natural language understanding. However, long seqeuences pose a problem due to the quadratic complexity of the attention operation. Previous research has aimed to lower the complexity by sparsifying or linearly approximating the attention matrix. Yet, these approaches cannot straightforwardly distill knowledge from a teacher's attention matrix, and often require complete retraining from scratch. Furthermore, previous sparse and linear approaches lose interpretability if they cannot produce full attention matrices. To address these challenges, we propose SEA: Sparse linear attention with an Estimated Attention mask. SEA estimates the attention matrix with linear complexity via kernel-based linear attention, then subsequently creates a sparse attention matrix with a top-kˆ selection to perform a sparse attention operation. For language modeling tasks (Wikitext2), previous linear and sparse attention methods show roughly two-fold worse perplexity scores over the quadratic OPT-1.3B baseline, while SEA achieves better perplexity than OPT-1.3B, using roughly half the memory of OPT-1.3B. Moreover, SEA maintains an interpretable attention matrix and can utilize knowledge distillation to lower the complexity of existing pretrained transformers. We believe that our work will have a large practical impact, as it opens the possibility of running large transformers on resource-limited devices with less memory. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Matrix algebra

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Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation 1

Gender Lost In Translation: How Bridging The Gap Between Lan...

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1st Workshop on Gender-Inclusive Translation Technologies, GITT 2023

作者： Cabrera, Lena Niehues, Jan Department of Advanced Computing Sciences Maastricht University Netherlands Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany

ISBN: (纸本)9789464857177

Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation between grammatical gender languages which requires preserving the inherent gender information from the source in the target language. We study the effect of encouraging language-agnostic hidden representations on models’ ability to preserve gender and compare pivot-based and zero-shot translation regarding the influence of the bridge language (participating in all language pairs during training) on gender preservation. We find that language-agnostic representations mitigate zero-shot models’ masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement. © 2023 The Authors.

关键词： Zero-shot learning

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