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Biosignal Sequence Real-time Prediction for Game Users Based on Features Fusion of Local-Global and Time-Frequency Domain

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IEEE Transactions on Games 2025年

作者： Li, Rongyang Ding, Jianguo Ning, Huansheng Mao, Lingfeng Hebei Normal University of Science and Technology China Department of Computer Science Blekinge Institute of Technology Sweden School of Computer and Communication Engineering University of Science and Technology Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing China

Biosignal sequence real-time prediction (BSRP) is essential for predicting the future emotional experience of game users. However, BSRP for game users faces challenges, including poor real-time performance and limited feature fusion dimensions. To address these issues, we proposed a method for BSRP based on the features fusion of Local-Global and Time-Frequency domain (LGTF) for game users, which integrates real-time capabilities with multi-dimensional features fusion. Specifically, LGTF meets real-time requirements and achieves the features fusion of Local-Global through multi-channel synchronized adaptive convolution. In addition, LGTF implements the features fusion of inter- and intra-band in the frequency domain and the features fusion of time-frequency domain by incorporating the Self-Attention mechanism and Fourier Transform. Furthermore, we conducted comprehensive validation experiments on LGTF using the public dataset. The results indicate that: 1) In the comparison study, LGTF outperformed other methods, achieving the lowest average MSE and MAE values across different prediction lengths of 0.61 and 0.47, respectively. 2) Ablation studies revealed that the addition of time-frequency domain feature fusion (TF) and local-global feature fusion (LG) both have the positive effect on the prediction performance, reducing the average MSE by 0.11 and 0.09, respectively. 3) Generalization study shows that LGTF exhibits stable performance and generalization across different subjects and shows performance advantages in specific game scenarios. 4) Time performance analysis suggests LGTF has the real-time performance.5) Case study demonstrates that LGTF is practical for predicting game users' future emotions and enhancing their emotional experiences. © 2018 IEEE.

关键词： Frequency domain analysis

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Indoor Architectural Environment Quality Perception and Differentiation Analysis Using computer Vision Simulation 4th

Indoor Architectural Environment Quality Perception and Diff...

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4th International Civil engineering and Architecture Conference, CEAC 2024

作者： Liu, Zheng Huang, Na Zhang, Zhenguo School of Management Shanghai University of Engineering Science Shanghai201620 China School of Computer Science and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819754762

The indoor luminous environment of buildings is composed of daylighting and artificial lighting. Through visual effects, non-visual effects and perception, it has a wide impact on people's vision health, emotional cognition, metabolism and immune biorhythms. This study is based on on-site research, investigating three educational buildings and distributing 1154 valid questionnaires. The daylighting environmental score of educational buildings is significantly higher than the artificial lighting environmental score, and key indicators at different time periods include illuminance, correlated color temperature, and color rendering index. In order to improve the classroom luminous environment satisfaction from "neutral" to "satisfactory", the horizontal illuminance of the desktop should be greater than 500 lx, the color temperature must be greater than 5000 K, and the color rendering index must be greater than 85. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Daylighting

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A disentanglement mamba network with a temporally slack reconstruction mechanism for multimodal continuous emotion recognition

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Multimedia Systems 2025年第2期31卷

作者： Li, Chiqin Xie, Lun Wang, Xinheng Pan, Hang Wang, Zhiliang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

While multimodal signals provide complementary information, they also introduce redundant information, hindering the learning of valuable information. Existing works on emotion recognition have achieved notable progress in multimodal fusion. However, the presence of redundant information remains one of the major challenges in this area. In this paper, we propose a Disentanglement Mamba network with a Temporally Slack Reconstruction Mechanism (DMA-TSM) to mitigate the impact of redundant information. The DMA-TSM employs an encoder-decoder framework. During the encoding phase, it decouples the original multimodal features into modality-common and modality-specific features. With explicit informational properties, these features inherently reduce redundant information at the feature level and alleviate the distributional gap between modalities. In the decoding phase, we present the TSM to avoid redundant information at the temporal level. By imposing a relaxed contextual constraint during the decoding and reconstruction process, the TSM balances preserving contextual information and avoiding redundant information at the temporal level. Additionally, by integrating Mamba with attention mechanisms, the DMA-TSM achieves substantial performance improvements with a relatively low computational cost. Experimental results on the Remote Collaborative and Affective (RECOLA) and the Ulm-Trier Social Stress Test (ULM-TSST) datasets show that our method is superior to the recently proposed methods, emphasizing the importance of reducing redundancy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Decoding

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Improving graph collaborative filtering with network motifs

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Neural Computing and Applications 2025年 1-20页

作者： Zhang, Yuqi Yu, Jian Liu, Zhizhong Wang, Guiling Nguyen, Minh Sheng, Quan Z. Wang, Nancy Department of Computer and Information Sciences Auckland University of Technology Auckland Central1010 New Zealand School of Computer and Control Engineering Yantai University Shandong Yantai264005 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Computing Macquarie University SydneyNSW2109 Australia

Deep learning on graphs, specifically graph convolutional networks (GCNs), has exhibited exceptional efficacy in the domain of recommender systems. Most GCNs have a message-passing architecture that enables nodes to aggregate information from neighbours iteratively through multiple layers. This enables GCNs to learn from higher-order information, but the model does not allow for direct captions of the local structural patterns. Our rationale is to investigate the effectiveness of capturing such local patterns for graph-based collaborative filtering to enhance model’s learning ability per layer. This technique combines lower-order and higher-order interactions during layer-wise propagation. In this paper, we propose MotifGCN to aggregate both lower-order and higher-order information in each graph convolution layer. Specifically, we develop dedicated algorithms of generating motif adjacency matrices. The matrices are then used for motif-enhanced neighbourhood aggregation in each layer. As this paper focuses on recommender systems, MotifGCN is built on the basis of bipartite graphs. Our experiments on four real-world datasets show that MotifGCN has a superior performance compared to various state-of-the-art methods. © The Author(s) 2025.

关键词： Collaborative filtering

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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Carrier-assisted differential detection for short-reach optical communications: Research progress and future trends

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Optical Fiber technology 2025年 93卷

作者： Qin, Peng Huo, Jiahao Zhang, Xiaoying Bai, Haolin Huangfu, Wei Long, Keping School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

Driven by advancements in cloud computing, mobile internet, and virtual reality in the current digital era, extended range (ER) and ze best range (ZR) transmissions over long distances between data centers have created an urgent need for high-speed transmission. However, the traditional intensity modulated direct detection technique limits the transmission distance and data rate due to power degradation caused by chromatic dispersion, while the coherent detection technique is costly due to the need for high-precision local oscillator lasers. Carrier-assisted differential detection (CADD), as an emerging double sideband self-coherent detection technique, combines the advantages of low-cost and optical field recovery capability. We first introduce the fundamentals and challenges of CADD and a variety of modified CADD receivers in detail. To further optimize the performance of the CADD system, explore various SSBI mitigation schemes, including a simplified SSBI iterative algorithm and a guard band-based mitigation scheme. Also, we focus on the development of polarization multiplexing CADD and discuss the solutions based on Jones space field recovery CADD (JSFR-CADD). Compared with JSFR-CADD, we propose a low-cost polarization division multiplexing asymmetric twin-single sideband CADD (PDM-ATSSB CADD) scheme for ER and ZR transmission. Simulations verify the transmission performance of the proposed PDM-ATSSB CADD scheme over single-mode fiber, elucidating the superiority of the scheme. The research results provide an important theoretical basis and technical support for the design of high-performance and low-cost optical communication systems in short-reach ER and ZR scenarios. © 2025 Elsevier Inc.

关键词： Fiber to the x

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of computer science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 144-158页

作者： Zhang, Dengyong He, Ruiyi Liao, Xin Li, Feng Chen, Jiaxin Yang, Gaobo Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation the School of Computer and Communication Engineering Changsha410114 China Hunan University Changsha410082 China

Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works. © 2024 IEEE.

关键词： Frequency domain analysis

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Adaptive VDI Session Placement via User Logoff Prediction

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Machine Intelligence Research 2025年第1期22卷 189-200页

作者： Wenping Fan Puhui Meng Yu Tian Min-Ling Zhang Yao Zhang School of Computer Science and Engineering Southeast UniversityNanjing210096China VMware Information Technology(China)Limited Beijing100190China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing210096China

After the global pandemic,DaaS(desktop as a service)has become the first choice of many companies’remote working *** the desktops are usually deployed in the public cloud when using DaaS,customers are more cost-sensitive which boosts the requirement of proactive power *** researches in this area focus on virtual desktop infrastructure(VDI)session logon behavior modeling,but for the remote desktop service host(RDSH)-shared desktop pools,logoff optimization is also *** systems place sessions by round-robin or in a pre-defined order without considering their logoff ***,these approaches usually suffer from the situation that few left sessions prevent RDSH servers from being powered-off which introduces cost *** this paper,we propose session placement via adaptive user logoff prediction(SODA),an innovative compound model towards proactive RDSH session ***,an ensemble machine learning model that can predict session logoff time is combined with a statistical session placement bucket model to place RDSH sessions with similar logoff time in a more centralized manner on RDSH ***,the infrastructure cost-saving can be improved by reducing the resource waste introduced by those RDSH hosts with very few hanging sessions left for a long *** on real RDSH pool data demonstrate the effectiveness of the proposed proactive session placement approach against existing static placement techniques.

关键词： Virtual desktop infrastructure(VDI)resource management remote desktop service logoff prediction adaptive modeling session placement

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Flexible Spectrum Sensing in NOMA System for LEO Satellite-Terrestrial Uplink communications

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IEEE Internet of Things Journal 2025年第11期12卷 18439-18442页

作者： Zhu, Jie Xu, Tianheng Wang, Chao Ying, Kai Zhou, Ting Zhang, Haijun Hu, Honglin Chinese Academy of Sciences Shanghai Advanced Research Institute Shanghai201210 China Tongji University Department of Information and Communication Engineering Shanghai200092 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Shanghai University School of Microelectronics Shanghai200444 China Shanghai Frontier Innovation Research Institute Shanghai201100 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

With the sixth-generation mobile communication technology (6G) experiencing a shift of spatial expansion, the integration of terrestrial architecture and satellites has become one core feature of 6G. While the scarcity of spectrum resources has become a significant challenge hindering the advancement of integrated satellite-terrestrial networks, it is crucial to push the development of effective strategies to optimize spectrum utilization. This letter introduces a flexible spectrum sensing technique in the Non-Orthogonal Multiple Access (NOMA) uplink system for Low Earth Orbit (LEO) satellite-terrestrial communications, striking an optimal balance between under-sensing and over-sensing. Confronted with diverse complicated scenarios of multi-satellite coverage, we conceive the operational principles and derive the sensing thresholds, exploiting spectrum holes and mitigating the fluctuation of false alarms. Notably, numerical outcomes showcase that the proposed technique outperforms the benchmarks, achieving a maximum throughput gain of 61.5% in dynamic communication environments. © 2014 IEEE.

关键词： Satellite communication systems

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