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arXiv 2024年

作者： Shi, Minjia Liu, Ruowen Solé, Patrick The Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of integrated Service Networks Xidian University Xi’an710071 China Aix Marseille Univ CNRS I2M Marseilles France

We construct a ternary [49,25,7] code from the row span of a Jacobsthal matrix. It is equivalent to a Generalized Quadratic Residue (GQR) code in the sense of van Lint and MacWilliams (1978). These codes are the abelian generalizations of the quadratic residue (QR) codes which are cyclic. The union of the [50,25,8] extension of the said code and its dual supports a 3-(50,14,1248) design. The automorphism group of the latter design is a double cover of the permutation part of the automorphism group of the [50,25,8] code, which is isomorphic to PΣL(2, 49). Other weights in this code, other GQR codes, and other QR codes yield other 3-designs by the same process. A simple group action argument is provided to explain this behaviour of isodual *** Codes 94 B15, 05 B05 Copyright © 2024, The Authors. All rights reserved.

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IBFNet: A Dual Auxiliary Branch Network for Multimodal Hidden Emotion Recognition

IBFNet: A Dual Auxiliary Branch Network for Multimodal Hidde...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jianmeng Zhou Xinyu Liu Shiwei He Mengyan Li Huijie Gu Tong Chen College of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest University Chongqing China Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Micro-expression (ME) is a crucial cue to reveal hidden emotions and helps to diagnose mental illnesses such as depression. However, their rapid and subtle characteristics make them difficult to recognize, and relying on a single ME feature is insufficient to capture comprehensive information about hidden emotions. This paper proposes an inverted bottleneck fusion network (IBFNet), which combines ME and facial tissue oxygen saturation (StO2) for multimodal feature fusion to improve the recognition of hidden emotions. Specifically, IBFNet learns different representations of each modal feature, namely common and individual features. The modalities are then mapped to different subspaces for feature refinement. We design the recurrent cross-modal attention (RCA) module to explore commonalities across modalities, retaining modality-specific information through auxiliary branches to enhance diversity. Finally, the inverted bottleneck structure is used to fuse the common and individual feature of ME and StO2. Experimental results demonstrate a recognition accuracy of 90.47%, which surpasses the limitations of single-modality recognition.

关键词： Emotion recognition Accuracy Fuses Face recognition Redundancy Feature extraction Depression Character recognition Bioinformatics

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A Novel Home Safety IoT Monitoring Method Based on ZigBee Networking

A Novel Home Safety IoT Monitoring Method Based on ZigBee Ne...

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International Conference on Wireless Communications, Networking and Applications, WCNA 2021

作者： An, Ning Li, Peng Wang, Xiaoming Wu, Xiaojun Dang, Yuntong School of Computer Science Shaanxi Normal University Xi’an710119 China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an710119 China School of Music Shaanxi Normal University Xi’an710119 China

ISBN: (纸本)9789811924552

This paper realizes the design of home safety early warning system by studying the wireless communication networking technology of ZigBee and WiFi, as well as sensor communication technology, which is based on taking home safety monitoring as the application background. In this study, CC2530 chip was used as ZigBee wireless communication module. A novel home security IoT monitoring method was proposed through sensor triggering, human activity trajectory perception algorithm design, and wireless networking and communication optimization. Meanwhile, the safety early warning and remote monitoring of home staff can be realized, and home safety can be guaranteed. The system can achieve the purpose of home monitoring and early warning with low software and hardware cost through the experimental design and result analysis. It can not only provide reference for the design of sensor communication system, but also provide technical reference for aging society and response. © 2022, The Author(s).

关键词： Zigbee

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Heuristic Elastic Scaling for Kubernetes Heterogeneous Microservices

Heuristic Elastic Scaling for Kubernetes Heterogeneous Micro...

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IEEE International Conference on Networking, Sensing and Control

作者： Song Wang Sheng He Yishuang Ning Xiang Gao Zhijun Ding Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Tongji University Shanghai China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen China Intelligent Computing Facility Innovation Center ZhejiangLab Hangzhou China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Kubernetes has become the basic platform for building cloud native applications. However, existing horizontal scaling methods based on Kubernetes have problems with resource redundancy. Furthermore, the combined horizontal and vertical scaling based on Kubernetes takes a long time. Regarding the issue above, first, we describe the execution processes of three scaling techniques and compare them in different dimensions; second, we construct a formal elastic scaling model for expansion scenarios, with the goal of minimizing the total cost; and third, we introduce a heuristic expansion algorithm after balancing resource redundancy and execution time as the heuristic information. The experiment results show that the elastic scaling algorithm proposed in this paper achieves better performance than other algorithms.

关键词： Costs Heuristic algorithms Redundancy Buildings Microservice architectures Sensors

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Unified output feedback based prescribed performance consensus tracking control of heterogeneous multi-agent systems

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Luo, Dahui Wang, Yujuan Lewis, Frank L. Song, Yongduan Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University The School of Automation Chongqing University Chongqing400044 China Star Institute for Intelligent Systems Chongqing400044 China Uta Research Institute University of Texas at Arlington Fort WorthTX76118 United States

This paper proposes an output-feedback based prescribed performance consensus tracking control methodology for a class of heterogeneous multi-agent systems (HMASs) with inconsistent system structure, where the performance behavior is allowed to be different from that of each other. Both the heterogeneous system structures and the nonidentical performance requirements make the control problem much more challenging than that of MASs with identical structure and performance requirement. This is mainly due to the coupling effect of the system dynamics and performance restriction of each agent in the cooperative control action. The key to solve this problem is to introduce a dual-phase performance-guaranteed method, in which the consensus tracking error is decomposed into auxiliary tracking error and filter tracking error and then the whole performance control is decomposed into two phases. By confining the two errors respectively, the practical tracking error can be proved to be explicitly confined within an arbitrarily given performance envelope by merely adjusting the design parameters rather than modifying control structure. Moreover, the prescribed performance control (PPC) result is not only uniform with any initial conditions and design parameters, allowing it to be global, but also unifying both the global and semi-global result into one frame, distinguishing itself from most existing PPC works where either only global or only semi-global result is guaranteed. Finally, the effectiveness of the proposed control scheme is confirmed by the simulation conducted on a group of tunnel-diode circuits (TDC). © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Levees

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Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings

arXiv

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arXiv 2022年

作者： Wang, Kai Liu, Yu Sheng, Quan Z. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Liaoning Dalian China Intelligent Computing Laboratory School of Computing Macquarie University SydneyNSW Australia

Knowledge graph embedding (KGE) has shown great potential in automatic knowledge graph (KG) completion and knowledge-driven tasks. However, recent KGE models suffer from high training cost and large storage space, thus limiting their practicality in real-world applications. To address this challenge, based on the latest findings in the field of Contrastive Learning, we propose a novel KGE training framework called Hardness-aware Low-dimensional Embedding (HaLE). Instead of the traditional Negative Sampling, we design a new loss function based on query sampling that can balance two important training targets, Alignment and Uniformity. Furthermore, we analyze the hardness-aware ability of recent low-dimensional hyperbolic models and propose a lightweight hardness-aware activation mechanism. The experimental results show that in the limited training time, HaLE can effectively improve the performance and training speed of KGE models on five commonly-used datasets. After training just a few minutes, the HaLE-trained models are competitive compared to the state-of-the-art models in both low- and high-dimensional conditions. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge computing Environment

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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IWRN:A Robust Blind Watermarking Method for Artwork Image Copyright Protection Against Noise Attack 39

IWRN:A Robust Blind Watermarking Method for Artwork Image Co...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kou, Feifei Yao, Yuhan Yao, Siyuan Wang, Jiahao Shi, Lei Li, Yawen Kang, Xuejing School of Computer Science National Pilot School of Software Engineering BUPT Beijing 100876 China Key Laboratory of Trustworthy Distributed Computing and Service BUPT Ministry of Education Beijing 100876 China School of Economics and Management BUPT Beijing 100876 China State Key Laboratory of Media Convergence and Communication CUC Beijing 100024 China State Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang 215400 China

ISBN: (纸本)157735897X

Adding imperceptible watermarks to artwork images, such as paintings and photographs, can effectively safeguard the copyright of these images without compromising their usability. However, existing blind watermarking techniques encounter two major challenges in addressing this task: imperceptibility and robustness, particularly when subjected to various noise attacks. In this paper, we propose a blind watermarking method for artwork image copyright protection, IWRN, which can ensure both the Imperceptibility of the Watermark and Robustness against Noise attacks. For imperceptibility, we design a Learnable Wavelet Network (LWN) to adaptively embed the watermark into the high-frequency region where the watermark has better invisibility. For robustness, we establish a Deform-Attention based Invertible Neural Network (DA-INN) with a decoding optimization, which offers the advantage of computational reversion, and combines the deform-attention mechanism and decoding optimization to enhance the model’s resistance against noises. Additionally, we design a Joint Contrast Learning (JCL) mechanism to improve imperceptibility and robustness simultaneously. Experiments show that our IWRN outperforms other state-of-the-art blind watermarking methods, achieves an average performance of 46.74 PSNR and 99.91% accuracy across three datasets when facing 12 kinds of noise attacks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for Data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy Data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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One-Shot Reference-based Structure-Aware Image to Sketch Synthesis 39

One-Shot Reference-based Structure-Aware Image to Sketch Syn...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Rui Yang, Honghong Zhao, Li Lei, Qin Dong, Mianxiong Ota, Kaoru Wu, Xiaojun Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism School of Computer Science Shaanxi Normal University Xi’an 710119 China College of Computer Science Chongqing University Chongqing 400044 China Muroran Institute of Technology Muroran Hokkaido 0508585 Japan

ISBN: (纸本)157735897X

Generating sketches that accurately reflect the content of reference images presents numerous challenges. Current methods either require paired training data or fail to accommodate a wider range and diversity of sketch styles. While pre-trained diffusion models have shown strong text-based control capabilities for reference-based content sketch generation, state-of-the-art methods still struggle with reference-based sketch generation for given content. The main difficulties lie in (1) balancing content preservation with style enhancement, and (2) representing content image textures at varying levels of abstraction to approximate the reference sketch style. In this paper, we propose a method (Ref2Sketch-SA) that transforms a given content image into a sketch based on a reference sketch. The core strategies include (1) using DDIM Inversion to enhance structural consistency in the sketch generation of content images;(2) injecting noise into the input image during the denoising process to produce a sketch that retains content attributes while aligning with, yet differing in texture from, the reference. Our model demonstrates superior performance across multiple evaluation metrics, including user style preference. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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