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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： Data fusion

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DVO + LCLMF: A web service recommendation mechanism with QoS privacy preservation

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Concurrency and Computation: Practice and Experience 2022年第18期34卷 e7003-e7003页

作者： Li, Kui Ji, Yi-mu Liu, Shang-dong Wu, Fei Yao, Hai-chang He, Jing Liu, Qiang Liu, Yan-lan Shao, Si-si You, Shuai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Nantong Bigdata Research Key Laboratory for Rehabilitation Medicine The Second People's Hospital of Nantong Nantong China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC Australia

QoS-aware based web service recommendation is one of the crucial solutions to help users find high-quality web services. To accurately predict the QoS values of candidate services, it is usually required to collect historical QoS data of users (QoS data for short). If these collected QoS data are improperly processed, QoS data privacy may be threatened. However, how to accurately predict the QoS values of candidate services while protecting QoS data privacy has not been well studied. In response to the situation, we propose a hybrid web service recommendation mechanism, which is divided into three parts. In the first part, the QoS data privacy preservation algorithm, which called DVO, is proposed based on keeping the cosine similarity of QoS data unchanged, that is, to realize the confusion of QoS data while ensuring the availability of QoS data remains unchanged. In the second part, a hybrid matrix factorization model based on location information and service features, which called LCLMF, is proposed to improve the accuracy of QoS values prediction. According to DVO and LCLMF, the DVO + LCLMF is designed in the third part, which can accurately predict QoS values while protecting QoS data privacy. The experimental results show that DVO + LCLMF can accurately predict the QoS values of candidate services on the basis of attaining QoS data privacy protection. © 2022 John Wiley & Sons, Ltd.

关键词： Data privacy

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Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration

arXiv

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arXiv 2022年

作者： Liu, Peixi Zhu, Guangxu Wang, Shuai Jiang, Wei Luo, Wu Poor, H. Vincent Cui, Shuguang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Shenzhen Research Institute of Big Data Shenzhen China The Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen518060 China Department of Electrical and Computer Engineering Princeton University Princeton NJ08544 United States Shenzhen Research Institute of Big Data Shenzhen China Peng Cheng Laboratory China

With the breakthroughs in deep learning and contactless sensors, the recent years have witnessed a rise of ambient intelligence applications and services, spanning from healthcare delivery to intelligent home. Federated edge learning (FEEL), as a privacy-enhancing paradigm of collaborative learning at the network edge, is expected to be the core engine to achieve ambient intelligence. Sensing, computation, and communication (SC2) are highly coupled processes in FEEL and need to be jointly designed in a task-oriented manner for pursuing the best FEEL performance under the stringent resource constraints at the edge devices. However, this remains an open problem as there is a lack of theoretical understanding on how the SC2 resource jointly affect the FEEL performance. In this paper, we address the problem of joint SC2 resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing in ambient intelligence. First, by analyzing the wireless sensing process in human motion recognition, we find that there exists a thresholding value for the sensing transmit power, exceeding which yields sensing data samples with approximately the same satisfactory quality. Then, the joint SC2 resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time, energy supply, and sensing quality of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determine the joint sensing and communication resource allocation that maximizes the total number of samples that can be sensed during the entire training process;the second one concerns the partition of the attained total number of sensed samples over all the communication rounds to determine the batch size at each round for convergence speed maximization. The first subproblem on joint sensing and communication resource allocation is converted to a single-variable optimization problem by exploiting

关键词： Motion estimation

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Disentangled Noisy Correspondence Learning

arXiv

引用

arXiv 2024年

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China SGIT AI Lab State Grid Shaanxi Electric Power Company Limited State Grid Corporation of China Shaanxi China The School of Information Science and Technology University of Science and Technology China United Arab Emirates The Baidu Inc China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of MII and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal input for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL's efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Large margin multi-modal multi-task feature extraction for image classification

arXiv

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arXiv 2019年

作者： Luo, Yong Wen, Yonggang Tao, Dacheng Gui, Jie Xu, Chao School of Electronics Engineering and Computer Science Peking University Beijing China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Centre for Quantum Computation & Intelligent Systems Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We therefore propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization and each sub-problem can be efficiently solved. Experiments on two challenging real-world image datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Experimental characterization of Plastic Optical Fibers for Under- and Over-Filled Injections

Experimental characterization of Plastic Optical Fibers for ...

引用

IEEE Southeastcon

作者： Ali Abdelmoniem Frank Feng Eric Huang P. L. Carro M. Angeles Losada Alicia Lopez J. Mateo A. Ortega D. Ribas E. Lleida N. Antoniades D. Richards Xin J. Jiang Yi Wang Department of Engineering and Environmental Science College of Staten Island CUNY Staten Island NY Communications Networks and Information Technologies (CeNIT) Aragón Institute of Engineering Research (i3A) Universidad de Zaragoza Zaragoza Spain Photonic Technologies Group (GTF) Aragón Institute of Engineering Research (i3A) Universidad de Zaragoza Zaragoza Spain Voice Input Voice Output Laboratory (ViVoLab) Aragón Institute of Engineering Research (i3A) Universidad de Zaragoza Zaragoza Spain Department of Electrical and Computer Engineering Manhattan College Bronx NY

This study investigates the impact of under-filled and over-filled launching configurations on the transmission properties of Plastic Optical Fiber (POF). Experimental analyses on step and graded index fibers reveal differences in spatial and frequency properties under these launching conditions. Results show that the under-filled configuration leads to narrower radial profiles and Encircled Angular Flux (EAF) compared to the over-filled configuration. Additionally, under-filled launching produces better frequency response and larger bandwidth, particularly notable in shorter fibers. Results show that the under-filled launching significantly improves transmission properties, offering potential improvements in POF applications.

关键词：

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A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System

arXiv

引用

arXiv 2022年

作者： Chen, Zhimin Chen, Peng Guo, Ziyu Zhang, Yudong Wang, Xianbin The School of Electronic and Information Shanghai Dianji University Shanghai201306 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China The School of Information Science and Technology Fudan University Shanghai200433 China The State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai200433 China The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle’s direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram’er-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://***/chenpengseu/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Vehicles

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Efficient secret sharing scheme realizing graph-based adversary structures

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2005年第5期42卷 877-882页

作者： Guo, Yuanbo Ma, Jianfeng Wang, Yadi Key Laboratory of Computer Networks and Information Security Xidian University Xi'an 710071 China School of Electronic Technology Information Engineering University of PLA Zhengzhou 450004 China School of Computing and Automatization Tianjin Polytechnic University Tianjin 300160 China

Most of the existing secret sharing schemes are constructed to realize general access structure, which is defined in term of authorized groups of participants, and is unable to be applied directly to the design of an intrusion tolerant system. Instead, the generalized adversary structure, which specifies the corruptible subsets of participants, can be determined directly by exploiting the system setting and the attributes of all participants. An efficient secret sharing scheme realizing graph-based adversary structures is proposed. The scheme requires less computational costs and storage overhead than the existing ones. Furthermore, it is proved that the scheme satisfy both the required properties of the secret sharing scheme, i.e., the reconstruction property and the perfect property.

关键词： Adversary structure Graph-based Secret sharing

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Low-PMEPR preamble sequence design for dynamic spectrum allocation in OFDMA systems

arXiv

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arXiv 2020年

作者： Zhou, Yajing Zhou, Zhengchun Liu, Zilong Fan, Pingzhi Guan, Yong Liang School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China School of Mathematics Southwest Jiaotong University Chengdu611756 China State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Computer Science and Electronic Engineering University of Essex United Kingdom Institute of Mobile Communications Southwest Jiaotong University Chengdu611756 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

Orthogonal Frequency Division Multiple Access (OFDMA) with Dynamic spectrum allocation (DSA) is able to provide a wide range of data rate requirements. This paper is focused on the design of preamble sequences in OFDMA systems with low peak-to-mean envelope power ratio (PMEPR) property in the context of DSA. We propose a systematic preamble sequence design which gives rise to low PMEPR for possibly non-contiguous spectrum allocations. With the aid of Golay-Davis-Jedwab (GDJ) sequences, two classes of preamble sequences are presented. We prove that their PMEPRs are upper bounded by 4 for any DSA over a chunk of four contiguous resource blocks. Copyright © 2020, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Energy-efficient resource allocation in noma heterogeneous networks

arXiv

引用

arXiv 2018年

作者： Zhang, Haijun Fang, Fang Cheng, Julian Long, Keping Wang, Wei Leung, Victor C.M. Engineering and Technology Research Center for Convergence Networks University of Science and Technology Beijing Beijing100083 China School of Engineering University of British Columbia KelownaBCV1X 1V7 Canada College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Non-orthogonal multiple access (NOMA) has attracted much recent attention owing to its capability for improving the system spectral efficiency in wireless communications. Deploying NOMA in heterogeneous network can satisfy users' explosive data traffic requirements, and NOMA will likely play an important role in the fifth-generation (5G) mobile communication networks. However, NOMA brings new technical challenges on resource allocation due to the mutual cross-tier interference in heterogeneous networks. In this article, to study the tradeoff between data rate performance and energy consumption in NOMA, we examine the problem of energy-efficient user scheduling and power optimization in 5G NOMA heterogeneous networks. The energy-efficient user scheduling and power allocation schemes are introduced for the downlink 5G NOMA heterogeneous network for perfect and imperfect channel state information (CSI) respectively. Simulation results show that the resource allocation schemes can significantly increase the energy efficiency of 5G NOMA heterogeneous network for both cases of perfect CSI and imperfect CSI. Copyright © 2018, The Authors. All rights reserved.

关键词： Heterogeneous networks

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