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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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IEEE International Conference on Web Services (ICWS)

作者： Renjie Zhou Zhenming Jin Jian Wan Jilin Zhang Xingyu Guo Qiang Liu Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education ITMO Joint Institute Hangzhou Dianzi University Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Dept. of Computer and Information Technology Zhejiang Police College Hangzhou China

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user’s shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods.

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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Analysis and optimization control of demand response via networked evolutionary game with memory

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Chaos, Solitons and Fractals 2025年 199卷

作者： Zhou, Mengyu Liu, Xingwen Liu, Yaojun Hu, Qi Shu, Feng Key Laboratory of Electronic Information of State Ethnic Affairs Commission College of Electrical Engineering Southwest Minzu University Sichuan Chengdu 610225 China Center for Intelligent Complex Systems College of Electrical Engineering Southwest Minzu University Sichuan Chengdu 610225 China School of Software Technology Dalian University of Technology Dalian 116620 China

Demand response (DR) is a crucial mechanism for dynamically regulating electricity consumption in smart grids. Conventional DR studies often neglect the role of historical information on current decision-making and cost constraints providing users with an upper affordable threshold. This paper proposes a networked evolutionary game (NEG) model for DR that incorporates multi-step memory and cost constraints to more realistically capture user behavior. By employing the semi-tensor product method, the dynamics of the DR system is represented algebraically, enabling tractable analysis of equilibria and controller design. An optimization control strategy is developed to steer the system toward a desired state, reducing both total energy consumption and users’ cost, while ensuring that individual costs remain within a predetermined threshold. The proposed approach provides an effective theoretical framework for DR implementation in smart grids. © 2025 Elsevier Ltd

关键词： Demand response Memory Networked evolutionary games Optimization control Semi-tensor product of matrices

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Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation

SSRN

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SSRN 2023年

作者： Li, Ping Zhang, Yu Yuan, Li Xiao, Huaxin Lin, Binbin Xu, Xianghua School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Electrical and Computer Engineering Peking University Beijing China College of Systems Engineering National University of Defense Technology Changsha China College of Computer Science Zhejiang University Hangzhou China

Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial-temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high © 2023, The Authors. All rights reserved.

关键词： Pixels

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Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

Gradient Projection Differential Neural Solution for Quadrat...

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IEEE International Conference on systems, Man and Cybernetics

作者： Ying Liufu Mei Liu Long Jin Feiyue Wang School of Information Science and Engineering Lanzhou University Lanzhou China College of Computer Qinghai Normal University Xining China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems.

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Accelerating zk-SNARK with Group and Zone Optimization on GPU

Accelerating zk-SNARK with Group and Zone Optimization on GP...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Runnan Shen Liang Wang Haotian Luo Rui Yang Jinqian Yang Jinquan Wang Qiancheng Sun Limin Xiao Jin Dong State Key Laboratory of Complex Key Software Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Shen Yuan Honors College Beihang University Beijing China Beijing Academy of Blockchain and Edge Computing (BABEC) Beijing China

Zero-knowledge proof (ZKP) is a popular cryptographic strategy for building a trusted environment, which can be applied to blockchain, electronic voting, and other scenarios. However, ZKP involves a number of computationally intensive operations that limit its widespread adoption in time-sensitive practical applications. The multi-scalar multiplication (MSM) dominates the computations and takes over 70% of the total computation time. This paper proposes a GPU-based acceleration method for ZKP by designing several optimization techniques for MSM. First, this paper constructs a formal mathematical formula of the Pippenger algorithm, which provides a theoretical optimization framework for MSM. Second, by parallelizing the prefix sum, the time complexity of the bucket reduction part of MSM is reduced from $\mathcal{O}\left( {3 \times {2^C}} \right)$ to $\mathcal{O}\left( {2 \times {2^C}} \right)$. Finally, this paper also analyzes the influence of group size on the final calculation time under different data scales and gives a suitable range of group sizes. Compared to the state-of-the-art method, our method can achieve 1.01× to 1.12× for throughput.

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DeepDir: a deep learning approach for API directive detection

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Science China(Information Sciences) 2021年第9期64卷 238-240页

作者： Jingxuan ZHANG He JIANG Shuai LU Ge LI Xin CHEN College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education School of Software Dalian University of Technology School of Electronic Engineering and Computer Science Peking University School of Computer Science and Technology Hangzhou Dianzi University

Dear editor,software developers tend to reuse existing libraries to facilitate their development process and implement certain functionalities by invoking application programming interfaces(APIs) [1]. However, it remains a challenging task for developers to correctly use APIs [2], so they often consult API learning resources [3, 4]. As one of the most important API learning resources,

关键词： Information systems and Communication Service

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Pursuit-evasion differential games of players with different speeds in spaces of different dimensions

arXiv

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arXiv 2022年

作者： Li, Shuai Wang, Chen Xie, Guangming The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China The National Engineering Research Center of Software Engineering Peking University Beijing100871 China

We study pursuit-evasion differential games between a faster pursuer moving in 3D space and an evader moving in a plane. We first extend the well-known Apollonius circle to 3D space, by which we construct the isochron for the considered two players. Then both cases with and without a static target are considered and the corresponding optimal strategies are derived using the concept of isochron. In order to guarantee the optimality of the proposed strategies, the value functions are given and are further proved to be the solution of Hamilton-Jacobi-Isaacs equation. Simulations with comparison between the proposed strategies and other classical strategies are carried out and the results show the optimality of the proposed strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

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