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On the complexity of computing Markov perfect equilibrium in general-sum stochastic games

National science Review 2023年第1期10卷 288-301页

作者： Xiaotie Deng Ningyuan Li David Mguni Jun Wang Yaodong Yang Center on Frontiers of Computing Studies School of Computer Science Peking University Center for Multi-Agent Research Institute for AI Peking University Huawei UK Computer Science University College London

Similar to the role of Markov decision processes in reinforcement learning,Markov games(also called stochastic games) lay down the foundation for the study of multi-agent reinforcement learning and se quential agent *** introduce approximate Markov perfect equilibrium as a solution to the computational problem of finite-state sto chastic games repeated in the infinite horizon and prove its *** solution concept preserves the Markov perfect property and opens up the possibility for the success of multi-agent reinforcement learning algorithms on static two-player games to be extended to multi-agent dynamic games,expanding the reign of the PPAD-complete class.

关键词： Markov game multi-agent reinforcement learning Markov perfect equilibrium PPAD-completeness stochastic game

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ELLIPTIC EQUATION WITH CRITICAL EXPONENT AND DIPOLE POTENTIAL: EXISTENCE AND DECAY ESTIMATES

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Acta Mathematica Scientia 2025年第2期45卷 636-658页

作者： Yu SU Zhisu LIU Senli LIU School of Mathematics and Big Data Anhui University of Science and TechnologyHuainan 232001China School of Mathematics and Physics China University of GeosciencesWuhan 430074China Institute for Advanced Marine Research China University of GeosciencesGuangzhou 511462China College of Mathematics and Computing Science Hunan University of Science and TechnologyXiangtan 411201China

The paper is concerned with a class of elliptic equation with critical exponent and Dipole *** precisely,we make use of the refined Sobolev inequality with Morrey norm to obtain the existence and decay properties of n... 详细信息

关键词： Dipole potential decay estimation Hardy Sobolev critical exponent Henon Sobolev critical exponent

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An Attention-Locating Algorithm for Eliminating Background Effects in Fine-grained Visual Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5993-6006页

作者： Huang, Yueting Hechen, Zhenzhe Zhou, Mingliang Li, Zhengguo Kwong, Sam Chongqing University School of Computer Science Chongqing400044 China Agency for Science Technology and Research Institute for Infocomm Research Singapore138632 Singapore Lingnan University Department of Computing and Decision Science Hong Kong

Fine-grained visual classification (FGVC) is a challenging task characterized by interclass similarity and intraclass diversity and has broad application prospects. Recently, several methods have adopted the vision Transformer (ViT) in FGVC tasks since the data specificity of the multihead self-attention (MSA) mechanism in ViT is beneficial for extracting discriminative feature representations. However, these works focus on integrating feature dependencies at a high level, which leads to the model being easily disturbed by low-level background information. To address this issue, we propose a fine-grained attention-locating vision Transformer (FAL-ViT) and an attention selection module (ASM). First, FAL-ViT contains a two-stage framework to identify crucial regions effectively within images and enhance features by strategically reusing parameters. Second, the ASM accurately locates important target regions via the natural scores of the MSA, extracting finer low-level features to offer more comprehensive information through position mapping. Extensive experiments on public datasets demonstrate that FAL-ViT outperforms the other methods in terms of performance, confirming the effectiveness of our proposed methods. The source code is available at https://***/Yueting-Huang/FAL-ViT. © 1991-2012 IEEE.

关键词： Feature extraction Transformers Visualization Data mining computer vision Circuits and systems Training Computational modeling Classification algorithms Background noise

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2516-2533页

作者： Tietaa Maale, Gerald Arden Elorm Kuadey, Noble Arafat, Yeasin Thomas, Kwantwi Sun, Guolin Liu, Guisong Research Institute of Intelligent Computing University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China McCoy University College of Education Department of Mathematics and ICT Nadowli Ghana Research Institute of Intelligent Computing at the University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China Ho Technical University Department of Computer Science Ho Ghana University of Mines and Technology Department of Computer Science and Engineering Tarkwa Ghana Southwestern University of Finance and Economics Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Chengdu610074 China Ministry of Education Engineering Research Center of Intelligent Finance Chengdu610030 China

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines. © 2004-2012 IEEE.

关键词： Multi-task learning

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Enhancing sarcasm detection through grasshopper optimization with deep learning based sentiment analysis on social media

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1785-1791页

作者： Srinu, Nidamanuri Sivaraman, K. Sriram, M. School of Computing Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai 600073 India

Detecting sarcasm in social media presents challenges in natural language processing (NLP) due to the informal language, contextual complexities, and nuanced expression of sentiment. Integrating sentiment analysis (SA) with sarcasm detection enhances the understanding of text meaning. Deep learning (DL), utilizing neural networks to grasp lexical and contextual features, offers a method for sarcasm detection. However, current DL-based sarcasm detection methods often overlook sentiment semantics, a crucial aspect for improving detection outcomes. Therefore, this study develops a new sarcasm detection using grasshopper optimization algorithm with DL (SD-GOADL) technique. The SD-GOADL technique aims to explore the patterns that exist in social media data and detect sarcasm. To obtain this, the SD-GOADL technique undergoes data pre-processing and Glove based word embedding technique. Next, the classification of sarcasm takes place using deep belief network (DBN) system. For enhancing the detection results of the DBN approach, the SD-GOADL technique uses GOA for hyperparameter selection process. The stimulation outcome of the SD-GOADL technique is tested on a sarcasm dataset and the results highlight the significant performance of the SD-GOADL technique compared to recent models. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Deep learning Grasshopper optimization algorithm Natural language processing Sarcasm detection Sentiment analysis

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APPROXIMATELY ARTINIAN(NOETHERIAN)C*-ALGEBRAS

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Acta Mathematica Scientia 2024年第6期44卷 2485-2497页

作者： Mohammad ROUZBEHANI Massoud AMINI Mohammad B.ASADI School of Mathematics Statistics and Computer ScienceCollege of ScienceUniversity of TehranTehranIran Department of Mathematics Faculty of Mathematical SciencesTarbiat Modares UniversityTehran 14115-134Iran School of Mathematics Institute for Research in Fundamental Sciences(IPM)Tehran 19395-5746Iran

In this article,we introduce and study the class of approximately Artinian(Noetherian)C^(*)-algebras,called AR-algebras(AN-algebras),which is a simultaneous generalization of Artinian(Noetherian)C*-algebras and *** study properties such as the ideal property and topological dimension zero for *** particular,we show that a faithful AR or AN algebra is strongly purely infinite iff it is purely infinite iff it is weakly purely *** extends the Kirchberg's O_(∞)-absorption theorem,and implies that a weakly purely infinite C^(*)-algebra is Noetherian iff every its ideal has a full projection.

关键词： C^(*)-algebra graph C^(*)-algebra Artinian Noetherian inductive limit (weakly)purely infinite ideal property

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Activation Control of Vision Models for Sustainable AI Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3470-3481页

作者： Burton-Barr, Jonathan Fernando, Basura Rajan, Deepu Institute of High-Performance Computing The Centre for Frontier AI Research Agency for Science Technology and Research 138632 Singapore Nanyang Technological University Nanyang School of Computer Science and Engineering 639798 Singapore

As artificial intelligence (AI) systems become more complex and widespread, they require significant computational power, increasing energy consumption. Addressing this challenge is essential for ensuring the long-term sustainability of AI technology. AI-on-AI control refers to a system with a set of AI functions controlled by an upper-level AI model. Previous work in AI-on-AI control focuses on boosting accuracy or expanding system capability by increasing overall system cost. Alternatively, we focus on applying AI-on-AI control to decrease system cost and increase the sustainability and viability of a system with multiple AI functions. Our supervised image classification evaluative controller (SICEC) is a cost-reduction oriented AI-on-AI controller that learns when vision models within an AI system should be activated based on input features. The function controller (FC) preprocesses an input and activates relevant functions, functions being distinct units of AI functionality within the system. Some functions have a set of same functional models (SFMs). These models take the same input and produce the same output but have architectural differences. We introduce a same functional controller to select a SFM using the FC's decision confidence. Results are promising, with a decrease of up to 48.9% in inference time, 67.8% in floating point operations (FLOPs), and 66.4% in energy usage. With SICEC showing significant reductions in inference time and energy cost, our work contributes to limited resource computing and sustainable AI technology. © 2020 IEEE.

关键词： Energy utilization

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Which is more faithful,seeing or saying? Multimodal sarcasm detection exploiting contrasting sentiment knowledge

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CAAI Transactions on Intelligence Technology 2025年第2期10卷 375-386页

作者： Yutao Chen Shumin Shi Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications BeijingChina

Using sarcasm on social media platforms to express negative opinions towards a person or object has become increasingly ***,detecting sarcasm in various forms of communication can be difficult due to conflicting *** this paper,we introduce a contrasting sentiment-based model for multimodal sarcasm detection(CS4MSD),which identifies inconsistent emotions by leveraging the CLIP knowledge module to produce sentiment features in both text and ***,five external sentiments are introduced to prompt the model learning sentimental preferences among ***,we highlight the importance of verbal descriptions embedded in illustrations and incorporate additional knowledge-sharing modules to fuse such imagelike *** results demonstrate that our model achieves state-of-the-art performance on the public multimodal sarcasm dataset.

关键词： CLIP image-text classification knowledge fusion multi-modal sarcasm detection

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PalmSense: Real-Time Anemia Detection Using Deep Learning on Palm Images 2

PalmSense: Real-Time Anemia Detection Using Deep Learning on...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Prahladan, G. Rithik Sriraam, A.L. Sivakumar, Soubraylu Shanmugam, Selvanayaki Kolandapalayam Ajay, S. Pradeep, S. Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Mathematics and Computer Science Ashland University OH United States

ISBN: (纸本)9798331541217

Anaemia does pose a challenge to the health sector of the world, especially in resource poor settings where most diagnostic techniques are invasive, expensive, and inaccessible. This study aims to develop a very accurate, non-invasive anaemia detection solution using deep learning models on palm images. Such tests, like blood tests, cannot be implemented in many low-resource settings. It is for this reason that the current research gap, particularly as regards the diagnostic capability of palm images using state-of-the-art deep learning methods, falls within the opportunity that may be contemplated here. This work used the ConvNext Tiny model, trained over a dataset of images taken from the palms of people in Ghana using transfer learning and heavy preprocessing of data, other CNN models including ResNet and VGG16. The results showed that the accuracy for ConvNext Tiny was 96.8% and rendered the highest data accuracy and also provided high speed compared with all other models. This approach promotes early detection of anemia, and not only is this a cost-effective method, but it is also accessible enough to be used in such sites of improving healthcare delivery where resources may be scarce. © 2024 IEEE.

关键词： Diseases

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