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arXiv 2024年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixture of experts (MoE) frameworks is becoming increasingly important. Motivated by this, this article studies the contrasting and converging of MAS and MoE in AIGC-enabled networking. First, we discuss the architectural designs, operational procedures, and inherent advantages of using MAS and MoE in generative AI to explore its functionality and applications fully. Next, we review the applications of MAS and MoE frameworks in content generation and resource allocation, emphasizing their impact on networking operations. Subsequently, we propose a novel multi-agent-enabled MoE-proximal policy optimization (MoE-PPO) framework for 3D object generation and data transfer scenarios. The framework uses MAS for dynamic task coordination of each network service provider agent and MoE for expert-driven execution of respective tasks, thereby improving overall system efficiency and adaptability. The simulation results demonstrate the effectiveness of our proposed framework and significantly improve the performance indicators under different network conditions. Finally, we outline potential future research directions. © 2024, CC BY.

关键词： Multi agent systems

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Dual-Level Masked Semantic Inference for Semi-Supervised Semantic Segmentation

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IEEE Transactions on Multimedia 2025年

作者： Ma, Qiankun Zhang, Ziyao Qiao, Pengchong Wang, Yu Ji, Rongrong Liu, Chang Chen, Jie Peking University School of Electronic and Computer Engineering Shenzhen518055 China Chinese Academy of Sciences Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of the Chinese Academy of Sciences Beijing100049 China Peng Cheng Laboratory Shenzhen518055 China Xiamen University Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen361005 China Tsinghua University Department of Automation Beijing100084 China

Semi-supervised semantic segmentation pursues a holistic pixel-wise understanding of unseen images with limited annotation. To this end, existing methods focus on regularizing per-pixel prediction consistency within unlabeled data, while rarely modeling contextual relationships. But in fact, contextual semantics can provide valuable clues for scene understanding like inner-object continuity and spatial relationships' causality. Thus, in this paper, we propose a Dual-level Masked Semantics Inference (DMSI) that takes the initiative to explicitly learn contextual relationships via enforcing our model to infer the semantics of a pixel according to its surrounding contexts. This allows our model to exhaust accurate semantics by incorporating inter-pixel context clues, further leading to comprehensive segmentation. Specifically, DMSI comprises two main components: 1) Dual-level mask consistency regularization (DMCR) that learns the ability of semantics inference by aligning the predictions of masked views with the prediction of the complete view. The masked views here come from both the image level and feature level, where our model captures low-level attributes and high-level representations respectively. 2) AdaMask that provides a proper mask position and ratio for each image, guiding our model to focus on semantic-rich regions while providing balanced training between hard and easy samples. Through learning the ability of semantic inferring, DMSI remarkably enhances the interaction between pixels, further progressively intensifying the understanding of semantics. Extensive experiments under various settings on Cityscapes and Pascal VOC 2012 show that DMSI achieves new state-of-the-art performances. Furthermore, analysis indicates that our method has superiority in mining inter-pixel semantic relationships and improving robustness facing noise corruption. © 1999-2012 IEEE.

关键词： Latent semantic analysis

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Health Data Analytics Using IoT: Transforming Healthcare Management and Treatment Strategies for Improved Patient Outcomes

Health Data Analytics Using IoT: Transforming Healthcare Man...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Dinh Cong Tuan Paravthy Unnikrishnan Bala Dhandayuthapani M. Vennila M. E Choi Sang Long Deepali Rani Sahoo Faculty of Communication Dai Nam University Vietnam Department of Striroga and Prasuti Tantra (Gynaecology and Obstetrics) Amrita School of Ayurveda Amrita Vishwa Vidyapeetham Amritapuri Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Campus Oman Department of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Chengalpattu Faculty of Business Raffles University Malaysia Symbiosis Law School Noida Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The integration of Web of Things (IoT) innovation within the healthcare segment has revolutionized conventional healthcare administration and treatment procedures, driving progressed quiet results. This paper looks at the transformative effect of well-being information analytics utilizing IoT in upgrading healthcare administrations, optimizing understanding care, and advancing preventive measures. The paper analyzes the key components and applications of IoT in healthcare, emphasizing its part in information collection, real-time observation, and examination of understanding imperative signs, action levels, and pharmaceutical adherence. With the help of IoT-enabled gadgets such as wearables, sensors, and keen checking frameworks, healthcare experts can assemble comprehensive, real-time persistent information, encouraging early discovery and opportune mediation for basic well-being issues. Besides, the utilization of IoT in well-being information analytics empowers the improvement of personalized treatment plans custom-fitted to a person's persistent needs, cultivating a patient-centric approach to healthcare conveyance. By leveraging machine learning calculations and prescient analytics, healthcare suppliers can expect potential well-being complications, in this manner minimizing the chance of antagonistic occasions and clinic readmissions. In addition, the execution of IoT-based healthcare arrangements advances further understanding and observing, empowering healthcare suppliers to offer ceaseless, personalized care past conventional healthcare settings.

关键词： Technological innovation Data analysis Medical services Machine learning Real-time systems Internet of Things Wearable devices Mediation Wearable sensors Pharmaceuticals

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A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

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Chinese Control Conference (CCC)

作者： Weidong Yan Zhaoliang Xu Yang Li The Department of Automation Science and Electrical Engineering Beihang University Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively.

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Test-Time Training-Free Domain Adaptation

Test-Time Training-Free Domain Adaptation

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yongxiang Feng Weihua He Kaichao You Bing Liu Ziyang Zhang Yaoyuan Wang Minglei Li Yihang Lou Jiawei Li Guoqi Li Jianxing Liao Advanced Computing and Storage Lab Huawei Technologies Co. Ltd. School of Electronics Engineering and Computer Science Peking University China Language & Speech Innovation Lab Huawei Technologies Co. Ltd. GoTen AI Lab Department of Intelligent Vision Huawei Technologies Co. Ltd. China Department of Production Automation Development Huawei Technologies Co. Ltd. China Institute of Automation Chinese Academy of Sciences China

Deploying deep learning models to new environments is very challenging. Domain adaptation (DA) is a promising paradigm to solve the problem by collecting and adapting to unlabeled data in new environments. Though research efforts have led to steady performance improvement over the past decade, DA algorithms are still hard to deploy, as training on unlabeled new data makes tuning difficult and not feasible for inference-only devices. To make DA practical, in this paper we study a new problem named Test-time Training-Free Domain Adaptation (TTDA), where trained models must adapt to a single input (mimicking the test-time scenario) without training. By exploiting spatial activation that was previously overlooked and simply averaged out, we propose a simple method based on Feature Statistics Transformation (FST) on-the-fly for each test example. The proposed algorithm is tested in the TTDA setting on two standard DA benchmarks. Surprisingly, it surpasses or performs on par with state-of-the-art DA methods, even though they require additional training. We envision that this training-free paradigm has the potential to bring DA to embedded devices and would be of interest to audience of community.

关键词： Training Performance evaluation Adaptation models Transfer learning Signal processing algorithms Statistical distributions Classification algorithms

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A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition

A Multi-Head Convolution Network with Attention Consistency ...

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Chinese Control Conference (CCC)

作者： Wenkang Liu Mingyi Sun Yang Li the Department of Automation Science and Electrical Engineering Beihang University Beijing China the Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus.

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MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition

arXiv

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arXiv 2023年

作者： Guo, Jingcai Xu, Yuanyuan Xu, Wenchao Zhan, Yufeng Sun, Yuxia Guo, Song Department of Computing The Hong Kong Polytechnic University Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518057 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Automation Beijing Institute of Technology Beijing100081 China Department of Computer Science Jinan University Guangzhou510632 China Guangdong Provincial Key Laboratory of High-Performance Computing Guangzhou510275 China

Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achieve the MOSR. However, our observation reveals that the feature distributions of malware samples are extremely similar to each other even between known and unknown families. Thus the obtained classifier may produce overly high probabilities of testing unknown samples toward known families and degrade the model performance. In this paper, we propose the Multi-modal Dual-Embedding Networks, dubbed MDENet, to take advantage of comprehensive malware features from different modalities to enhance the diversity of malware feature space, which is more representative and discriminative for down-stream recognition. Concretely, we first generate a malware image for each observed sample based on their numeric features using our proposed numeric encoder with a re-designed multi-scale CNN structure, which can better explore their statistical and spatial correlations. Besides, we propose to organize tokenized malware features into a sentence for each sample considering its behaviors and dynamics, and utilize language models as the textual encoder to transform it into a representable and computable textual vector. Such parallel multi-modal encoders can fuse the above two components to enhance the feature diversity. Last, to further guarantee the open-set recognition, we dually embed the fused multi-modal representation into one primary space and an associated subspace, i.e., discriminative and exclusive spaces, with contrastive sampling and ρ-bounded enclosing sphere regularizations, which resort to classification and detection, respectively. Moreover, we also enrich our previously proposed large-scaled malware dataset MAL-100 with multi-modal characteristics and cont

关键词： Modal analysis

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MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services

arXiv

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arXiv 2024年

作者： Wu, Hongjia Zeng, Hui Xiong, Zehui Kang, Jiawen Cai, Zhiping Chan, Tse-Tin Niyato, Dusit Han, Zhu Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Automation Guangdong University of Technology China College of Computer National University of Defense Technology China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Updates of extensive Internet of Things (IoT) data are critical to the immersion of vehicular metaverse services. However, providing high-quality and sustainable data in unstable and resource-constrained vehicular networks remains a significant challenge. To address this problem, we put forth a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models trained by their latest local data for augmented reality (AR) services in the vehicular metaverse, while preserving their privacy through federated learning. To comprehensively evaluate the contribution of locally trained learning models provided by MUs to AR services, we design a new immersion metric that captures service immersion by considering the freshness and accuracy of learning models, as well as the amount and potential value of raw data used for training. We model the trading interactions between metaverse service providers (MSPs) and MUs as an equilibrium problem with equilibrium constraints (EPEC) to analyze and balance their costs and gains. Moreover, considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Then, a fully distributed dynamic reward method based on deep reinforcement learning is presented, which operates without any private information about MUs and other MSPs. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets compared to benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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Dynamics and resonance fluorescence from a superconducting artificial atom doubly driven by quantized and classical fields

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Physical Review Research 2024年第3期6卷 033064-033064页

作者： Xinhui Ruan Jia-Heng Wang Dong He Pengtao Song Shengyong Li Qianchuan Zhao L. M. Kuang Jaw-Shen Tsai Chang-Ling Zou Jing Zhang Dongning Zheng O. V. Astafiev Yu-xi Liu Zhihui Peng Department of Automation Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications School of Integrated Circuits School of Physical Sciences Center for Quantum Computing Graduate School of Science CAS Key Laboratory of Quantum Information Hefei National Laboratory Hefei 230088 China School of Automation Science and Engineering MOE Key Lab for Intelligent Networks and Network Security Nobel Street 3 143026 Moscow Russia Institutskiy Pereulok 9 141701 Dolgoprudny Russia Egham Surrey TW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anticrossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak-coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations, and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks.

关键词： Light-matter interaction Optical pumping Quantum circuits Quantum coherence & coherence measures Quantum-to-classical transition Strong electromagnetic field effects

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GAN-based Facial Attribute Manipulation

arXiv

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arXiv 2022年

作者： Liu, Yunfan Li, Qi Deng, Qiyao Sun, Zhenan Yang, Ming-Hsuan The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The National Laboratory of Pattern Recognition Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences Beijing100190 China The Department of Computer Science and Engineering University of California MercedCA95340 United States

Facial Attribute Manipulation (FAM) aims to aesthetically modify a given face image to render desired attributes, which has received significant attention due to its broad practical applications ranging from digital entertainment to biometric forensics. In the last decade, with the remarkable success of Generative Adversarial Networks (GANs) in synthesizing realistic images, numerous GAN-based models have been proposed to solve FAM with various problem formulation approaches and guiding information representations. This paper presents a comprehensive survey of GAN-based FAM methods with a focus on summarizing their principal motivations and technical details. The main contents of this survey include: (i) an introduction to the research background and basic concepts related to FAM, (ii) a systematic review of GAN-based FAM methods in three main categories, and (iii) an in-depth discussion of important properties of FAM methods, open issues, and future research directions. This survey not only builds a good starting point for researchers new to this field but also serves as a reference for the vision community. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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