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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

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A Legal News Summarisation Model Based on RoBERTa, T5 and Dilated Gated CNN

A Legal News Summarisation Model Based on RoBERTa, T5 and Di...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Weijian Qin Xudong Luo School of Computer Science and Engineering Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining and Security Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education of PRC Guilin China

Summarising legal news is crucial for monitoring judicial sentiments and managing emergencies, by distilling accurate, concise and comprehensive insights from extensive, complex legal texts to enhance the efficiency of managing public opinions. However, current methods are limited in handling long-form texts. To address this, we propose a novel two-phase sentence-level summarisation model. It first converts paragraphs into sentence vectors using RoBERTa and average pooling layer, then selects the more relevant sentences using a Dilated Gated CNN layer. These extracted sentences are treated as a corpus for the T5 PEGASUS model to generate refined summaries. Experiments show our model significantly outperforms baselines on ROUGE metrics by effectively capturing relevant information from lengthy, intricate legal opinions to produce high-quality summaries.

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FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks

arXiv

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arXiv 2023年

作者： Xian, Youquan Gan, Xiaoyun Yao, Chuanjian Li, Dongcheng Wang, Peng Liu, Peng Zhao, Ying Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin54104 China School of Computer Science and Engineering Guangxi Normal University Guilin54104 China School of Business Guilin University of Electronic Technology Guilin54104 China

Federated Learning (FL), as a privacy-preserving machine learning paradigm, trains a global model across devices without exposing local data. However, resource heterogeneity and inevitable stragglers in wireless networks severely impact the efficiency and accuracy of FL training. In this paper, we propose a novel Dynamic Cross-Tier Federated Learning framework (FedDCT). Firstly, we design a dynamic tiering strategy that dynamically partitions devices into different tiers based on their response times and assigns specific timeout thresholds to each tier to reduce single-round training time. Then, we propose a cross-tier device selection algorithm that selects devices that respond quickly and are conducive to model convergence to improve convergence efficiency and accuracy. Experimental results demonstrate that the proposed approach under wireless networks outperforms the baseline approach, with an average reduction of 54.7% in convergence time and an average improvement of 1.83% in convergence accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Federated learning

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Roland: Robust In-band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

Roland: Robust In-band Parallel Communication for Magnetic M...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Wangqiu Zhou Hao Zhou Xiang Cui Xinyu Wang Xiaoyan Wang Zhi Liu LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Graduate School of Science and Engineering Ibaraki University Ibaraki Japan Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

In recent years, receiver (RX) feedback communication has attracted increasing attention to enhance the charging performance for magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems. People prefer to adopt the in-band implementation with minimal overhead costs. However, the influence of RX-RX coupling couldn’t be directly ignored like that in the RFID field, i.e., strong couplings and relay phenomenon. In order to solve these two critical issues, we propose a Robust layer-level in-band parallel communication protocol for MIMO MRC-WPT systems (called Roland). Technically, we first utilize the observed channel decomposability to construct group-level channel relationship graph for eliminating the interference caused by strong RX-RX couplings. Then, we generalize such method to deal with the RX dependency due to relay phenomenon. Finally, we conduct extensive experiments on a prototype testbed to evaluate the effectiveness of the proposed scheme. The results demonstrate that our Roland could provide ≥95% average decoding accuracy for concurrent feedback communication of 14 devices. Compared with the state-of-the-art solution, the proposed protocol Roland can achieve an average decoding accuracy improvement of 20.41%.

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DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models 39

DG-Mamba: Robust and Efficient Dynamic Graph Structure Learn...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yuan, Haonan Sun, Qingyun Wang, Zhaonan Fu, Xingcheng Ji, Cheng Wang, Yongjian Jin, Bo Li, Jianxin SKLCCSE School of Computer Science and Engineering Beihang University China National Superior College for Engineers Beihang University China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China The Third Research Institute of Ministry of Public Security China

ISBN: (纸本)157735897X

Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy result in poor robustness for Dynamic Graph Neural Networks (DGNNs). Dynamic Graph Structure Learning (DGSL) offers a promising way to optimize graph structures. However, aside from encountering unacceptable quadratic complexity, it overly relies on heuristic priors, making it hard to discover underlying predictive patterns. How to efficiently refine the dynamic structures, capture intrinsic dependencies, and learn robust representations, remains under-explored. In this work, we propose the novel DG-Mamba, a robust and efficient Dynamic Graph structure learning framework with the Selective State Space Models (Mamba). To accelerate the spatio-temporal structure learning, we propose a kernelized dynamic message-passing operator that reduces the quadratic time complexity to linear. To capture global intrinsic dynamics, we establish the dynamic graph as a self-contained system with State Space Model. By discretizing the system states with the cross-snapshot graph adjacency, we enable the long-distance dependencies capturing with the selective snapshot scan. To endow learned dynamic structures more expressive with informativeness, we propose the self-supervised Principle of Relevant Information for DGSL to regularize the most relevant yet least redundant information, enhancing global robustness. Extensive experiments demonstrate the superiority of the robustness and efficiency of our DG-Mamba compared with the state-of-the-art baselines against adversarial attacks. Copyright © 2025, Association for the Advancement of Artificial n Intelligence (***). All rights reserved. g.

关键词： Graph neural networks

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Semi-Supervised Clustering Framework for Fine-grained Scene Graph Generation 39

Semi-Supervised Clustering Framework for Fine-grained Scene ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Jiarui Wang, Chuan Zhang, Jun Wu, Shuyi Jinjing, Zhao Liu, Zeming Yang, Liang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Institute of Information Engineering CAS China School of Computer Science and Technology Beijing JiaoTong University China Guangdong Provincial Key Lab of Intell. Info. Processing & Shenzhen Key Lab of Media Security Shenzhen University China Information Research Center of Military Science PLA Academy of Military Science China National Key Laboratory of Science and Technology on Information System Security China School of Computer Science and Engineering Beihang University China School of Artificial Intelligence Hebei University of Technology China

ISBN: (纸本)157735897X

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships existing in the scene. Considering the substantial human labor costs, existing scene graph annotations are often sparse and biased, which result in confusion training with low-frequency predicates. In this work, we design a Semi-Supervised Clustering framework for Scene Graph Generation (SSC-SGG) that uses the sparse labeled data to guide the generation of effective pseudo-labels from unlabeled object pairs, thus enriching the labeled sample space, especially for low-frequency interaction samples. We approach from the perspective of clustering, reducing the problem of confirmation bias in a self-training manner. Specifically, we first enhance the model’s robustness to feature extraction via prototype-based clustering, aggregating different relationship augmented features onto the same prototype. Secondly, we design a dynamic pseudo-label assignment algorithm based on a mini-batch, which adjusts the detection sensitivity to different frequency samples from the historical assignment. Finally, we conduct joint training on the pseudo-labels and the labeled data. We conduct experiments on various SGG models and achieve substantial overall performance improvements, demonstrating the effectiveness of SSC-SGG. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Time-Frequency Analysis-Based Transient Harmonic Feature Extraction for Load Monitoring

Time-Frequency Analysis-Based Transient Harmonic Feature Ext...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Peng Xia Hao Zhou Shenyao Jiang Fan Deng Zhi Liu Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou Zhejiang China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

Feature extraction is important for non-intrusive appliance load monitoring (NILM), because it includes crucial steps which altogether transform raw signals into distinct features of appliances. Although numerous studies have achieved good results using transient harmonic features, their performances degrade in practical scenarios. In this paper, a time-frequency analysis-based framework for transient harmonic feature extraction is proposed, which takes all factors that would eventually influence NILM system into account. More specffically, the framework proposes a new qualitative transient harmonic feature, along with current retrieval and data augmentation methods. The proposed framework is evaluated on two well-known datasets (i.e., Controlled On/Off Loads Library and Home Equipment laboratory Dataset). As compared with the other state-of-art methods, the proposed framework achieves similar accuracy (i.e., more than 96%) on single-load data, and more than 33% accuracy improvement over multi-load data (e.g., from 59.4% to 93% when six devices are active at the same time).

关键词： Load monitoring Home appliances Time-frequency analysis Transforms Harmonic analysis Feature extraction Libraries

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Tomographic SAR imaging with large elevation aperture: a P-band small UAV demonstration

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science China(Information sciences) 2022年第3期65卷 211-224页

作者： Tao ZENG Minkun LIU Yan WANG Zegang DING Linghao LI Zhen WANG Yangkai WEI Jianping WANG Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Yangtze Delta Region Academy of Beijing Institute of Technology Faculty of Electrical Engineering Mathematics and Computer Science (EEMCS) Delft University of Technology

Elevation resolution is an important indicator in tomographic SAR imaging as it represents the ability to discriminate closed targets in elevation. In general, the elevation resolution is proportional to the length of the elevation aperture. However, as the elevation aperture increases, the geometric consistency of the image will undesirably deteriorate and hence fails the image coregistration approach required by the traditional super-resolution tomographic imaging. In this paper, a new super-resolution tomographic imaging method is proposed to overcome the inconsistency problem caused by the large elevation aperture. The core strategy is to get rid of two-dimensional image coregistration by applying a three-dimensional(3 D) back projection like imaging manner: the 3 D space is firstly divided into a 3 D imaging grid, each of which is individually imaged via compressive sensing for super-resolution. The effectiveness of the proposed approach is evaluated by both computer simulations and real P-band UAV SAR data.

关键词： UAV SAR tomography large elevation aperture super-resolution P-band

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Gold-Price Forecasting Method Using Long Short-Term Memory and the Association Rule

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Journal of Mobile Multimedia 2023年第1期19卷 165-186页

作者： Boongasame, Laor Viriyaphol, Piboonlit Tassanavipas, Kriangkrai Temdee, Punnarumol Department of Mathematics Faculty of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok 10520 Thailand Business Innovation and Investment Laboratory: B2I-Lab Faculty of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok 10520 Thailand Gold Research Center Gold Traders Association Bangkok Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi 11120 Thailand Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai 57100 Thailand

Since gold prices influence international economic and monetary systems, numerous studies have been conducted to forecast gold prices. Nonetheless, studies employing the linear relationship method usually fail to explain the change in the pattern of the gold price. This study introduces a new paradigm that incorporates association rules and long short-term memory (LSTM) as a nonlinear-based method. For simulation, the proposed method was analyzed with data from Yahoo Finance from January 2010 to December 2020. The association rule was used to choose features relevant to the gold spot (GS) in the US Dollar Index (DXY). The LSTM forecast the gold price with a range of hyperparameter settings. The simulation results showed that the proposed method—the LSTM with GS and DXY, or LSTM-GS-DXY—resulted in low mean absolute percentage error (MAPE) metrics. In addition, the proposed LSTM-GS-DXY system outperformed the simple moving average (SMA), weight moving average (WMA), exponential moving average (EMA), and auto-regressive integrated moving average (ARIMA). © 2022 River Publishers.

关键词： artificial neural network association rule gold forecasting LSTM

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Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning

Strong Transferable Adversarial Attacks via Ensembled Asympt...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhengwei Fang Rui Wang Tao Huang Liping Jing School of Computer Science and Technology Beijing Jiaotong University Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Strong adversarial examples are crucial for evaluating and enhancing the robustness of deep neural networks. However, the performance of popular attacks is usually sensitive, for instance, to minor image transformations, stemming from limited information ─ typically only one input example, a handful of white-box source models, and undefined defense strategies. Hence, the crafted adversarial examples are prone to overfit the source model, which hampers their transferability to unknown architectures. In this paper, we propose an approach named Multiple Asymptotically Normal Distribution Attacks (MultiANDA) which explicitly characterize adversarial perturbations from a learned distribution. Specifically, we approximate the posterior distribution over the perturbations by taking advantage of the asymptotic normality property of stochastic gradient ascent (SGA), then employ the deep ensemble strategy as an effective proxy for Bayesian marginalization in this process, aiming to estimate a mixture of Gaussians that facilitates a more thorough exploration of the potential optimization space. The approximated posterior essentially describes the stationary distribution of SGA iterations, which captures the geometric information around the local optimum. Thus, MultiANDA allows drawing an unlimited number of adversarial perturbations for each input and reliably maintains the transferability. Our proposed method outperforms ten state-of-the-art black-box attacks on deep learning models with or without defenses through extensive experiments on seven normally trained and seven defense models.

关键词： Deep learning Image transformation Perturbation methods Computational modeling Stochastic processes Gaussian distribution Robustness

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