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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

Applied Intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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A Comprehensive Review on Deep Learning System Testing 24th

A Comprehensive Review on Deep Learning System Testing

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Ying Shan, Chun Liu, Zhen Liao, Shuyan Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819615476

Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Physiological Information Preserving Video Compression for rPPG

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3563-3575页

作者： Wang, Jieying Shan, Caifeng Liu, Zhaoyang Zhou, Shuwang Shu, Minglei Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao266590 China Nanjing University State Key Laboratory for Novel Software Technology and School of Intelligence Science and Technology Nanjing210023 China Shandong Artificial Intelligence Institute Jinan250014 China

Remote photoplethysmography (rPPG) has recently attracted much attention due to its non-contact measurement convenience and great potential in health care and computer vision applications. Early rPPG studies were mostly developed on self-collected uncompressed video data, which limited their application in scenarios that require long-distance real-time video transmission, and also hindered the generation of large-scale publicly available benchmark datasets. In recent years, with the popularization of high-definition video and the rise of telemedicine, the pressure of storage and real-time video transmission under limited bandwidth have made the compression of rPPG video inevitable. However, video compression can adversely affect rPPG measurements. This is due to the fact that conventional video compression algorithms are not specifically proposed to preserve physiological signals. Based on this, we propose a video compression scheme specifically designed for rPPG application. The proposed approach consists of three main strategies: 1) facial ROI-based computational resource reallocation;2) rPPG signal preserving bit resource reallocation;and 3) temporal domain up- and down-sampling coding. UBFC-rPPG, ECG-Fitness, and a self-collected dataset are used to evaluate the performance of the proposed method. The results demonstrate that the proposed method can preserve almost all physiological information after compressing the original video to 1/60 of its original size. The proposed method is expected to promote the development of telemedicine and deep learning techniques relying on large-scale datasets in the field of rPPG measurement. © 2013 IEEE.

关键词： Image compression

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Discriminative Word Alignment Over Multiple Word Segmentations

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Chinese Journal of Electronics 2025年第2期23卷 263-270页

作者： Ning Xi Xinyu Dai Shujian Huang Jiajun Chen Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Conventional bilingual word alignment is conducted on sentence pairs with single word segmentation for languages such as Chinese, viz. Single-segmentation-based word alignment (SSWA). However, SSWA may run the risk of losing optimal word segmentation granularities or causing data sparseness in word alignment. This paper proposes Multiple-segmentation-based word alignment (MSWA). In MSWA, diverse and complementary knowledge in multiple word segmentations can be employed to lower the above risks in word alignment. Given $k$ word segmentations of a Chinese sentence, a skeleton segmentation is firstly constructed. The alignment between the skele-ton segmentation and the parallel English sentence is log-linearly modeled, where various features defined over multiple word segmentations are incorporated. The Viterbi alignment, the alignment with the highest score, is mapped back to $k$ word alignments based on $k$ segmentations respectively. Experimentally, MSWA outperformed SSWA on all $k$ segmentations in both alignment quality and translation performance.

关键词： Translation Viterbi algorithm Social sciences Education Medical services Skeleton

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VmPTP: Precise Time Protocol for Inter-VM Communication in Embedded Virtualized Systems

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 897-910页

作者： Zhao, Sirong Xie, Guoqi Ma, Wenhong Ren, Wei Li, Kenli Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan Changsha410082 China Huawei Technology Co. Ltd. Central Software Institute Guangdong Shenzhen518129 China

System virtualization techniques are gradually applied to embedded systems with the performance improvement of embedded devices;however, there is a large time offset (i.e., deviation) of hundreds of ms between virtual machines (VMs) due to the drift of the virtualization abstraction layer and the jitter phenomenon. It is urgent to significantly reduce this offset by time synchronization techniques. Unfortunately, the current mainstream time synchronization software (e.g., Chrony and LinuxPTP) cannot be used in the embedded virtualized system connected by the fieldbus, such as controller area network (CAN);meanwhile, drift correction-based time synchronization techniques dedicated to CAN bus cannot be utilized for virtualized systems. In this study, we design a general vmPTP, which is a precise time protocol (PTP) for inter-VM communication in embedded virtualized systems based on Linux kernel-based VM (KVM). This is the first time synchronization work focused on embedded virtualized systems. We implement two vmPTP versions based on VirtIO and inter-VM shared memory (IVSHMEM) according to the four message (i.e., packet) exchanges of PTP. We propose an asymmetry compensation strategy to solve the problem of asymmetric rate of I/O operations in the message exchange process, which optimizes the offset computation. We further produce a convergence stopping approach which can continuously converge the offset until the offset is within the given bound and stop. We conduct experimental evaluations on both X86-64 and ARM64 architectures. For two versions of vmPTP, the experimental results show that: 1) the offsets are always within 1μ s on two architectures;2) the offsets are less than that of LinuxPTP and Chrony;and 3) the vmPTP's CPU and memory utilization on the X86-64 architecture are merely 0.3% and 0.1%, respectively, whereas those on the ARM64 architecture are 3.8% and 0.1%, respectively. We experiment with vmPTP in real-time application and the result demonstrates th

关键词： Linux

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Momentum Posterior Regularization for Multi-hop Dense Retrieval 31

Momentum Posterior Regularization for Multi-hop Dense Retrie...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xia, Zehua Wu, Yuyang Xia, Yiyun Nguyen, Cam-Tu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science Nanjing University China School of Artificial Intelligence Nanjing University China

ISBN: (纸本)9798891761964

Multi-hop question answering (QA) often requires sequential retrieval (multi-hop retrieval), where each hop retrieves missing knowledge based on information from previous hops. To facilitate more effective retrieval, we aim to distill knowledge from a posterior retrieval, which has access to posterior information like an answer, into a prior retrieval used during inference when such information is unavailable. Unfortunately, current methods for knowledge distillation in one-time retrieval are ineffective for multi-hop QA due to two issues: 1) Posterior information is often defined as the response (i.e. the answer), which may not clearly connect to the query without intermediate retrieval;and 2) The large knowledge gap between prior and posterior retrievals makes existing distillation methods unstable, even resulting in performance loss. As such, we propose MoPo (Momentum Posterior Regularization) with two key innovations: 1) Posterior information of one hop is defined as a query-focus summary from the golden knowledge of the previous and current hops;2) We develop an effective training strategy where the posterior retrieval is updated along with the prior retrieval via momentum moving average method, allowing smoother and effective distillation. Experiments on HotpotQA and StrategyQA demonstrate that MoPo outperforms existing baselines in both retrieval and downstream QA tasks. © 2025 Association for Computational Linguistics.

关键词： Question answering

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Can LLMs Clarify? Investigation and Enhancement of Large Language Models on Argument Claim Optimization 31

Can LLMs Clarify? Investigation and Enhancement of Large Lan...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Yiran He, Ben Chen, Xuanang Sun, Le School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761964

In argumentation, the claim is the foundational proposition that underpins the argument, serving as the central pillar upon which the argument is constructed. It guides the subsequent presentation of evidence, reasoning, and analysis, thereby facilitating the audience's understanding of the core issue. Therefore, ensuring that the claim is precise and unambiguous is crucial for constructing a coherent and persuasive argument. While Large Language Models (LLMs) have demonstrated proficiency in text rewriting tasks such as style transfer and query rewriting, their application to claim optimization remains unexplored. Unlike other rewriting tasks, claim clarification requires the model to rewrite ambiguous or unclear segments of the claim, enhance the content by adding omitted key details, and eliminate redundant or verbose elements. Addressing this gap, this paper evaluates the performance of LLMs on the claim clarification task across various settings. While popular rewriting evaluation methods such as BLEU and ROUGE rely on exact word matching, this paper introduces a novel semantic evaluation approach based on a sliding window mechanism. Three distinct LLMs, including LLama2, Mistral, and Qwen2, are assessed for their ability to clarify arguments through zero-shot or few-shot prompting, and supervised fine-tuning (SFT). Additionally, we propose a reinforcement learning-based clarification approach that optimally balances content preservation with claim clarity, thereby augmenting the performance of LLMs on the claim clarification task. © 2025 Association for Computational Linguistics.

关键词： Semantics

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Quantum Pseudorandom Scramblers 22nd

Quantum Pseudorandom Scramblers

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22nd Theory of Cryptography Conference, TCC 2024

作者： Lu, Chuhan Qin, Minglong Song, Fang Yao, Penghui Zhao, Mingnan Computer Science Department Portland State University Portland United States State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Hefei National Laboratory Hefei230088 China

ISBN: (纸本)9783031780165

Quantum pseudorandom state generators (PRSGs) have stimulated exciting developments in recent years. A PRSG, on a fixed initial (e.g., all-zero) state, produces an output state that is computationally indistinguishable from a Haar random state. However, pseudorandomness of the output state is not guaranteed on other initial states. In fact, known PRSG constructions provably fail on some initial states. In this work, we propose and construct quantum Pseudorandom State Scramblers (PRSSs), which can produce a pseudorandom state on an arbitrary initial state. In the information-theoretical setting, we obtain a scrambler which maps an arbitrary initial state to a distribution of quantum states that is close to Haar random in total variation distance. As a result, our scrambler exhibits a dispersing property. Loosely, it can span an ϵ-net of the state space. This significantly strengthens what standard PRSGs can induce, as they may only concentrate on a small region of the state space provided that the average output state approximates a Haar random state. Our PRSS construction develops a parallel extension of the famous Kac’s walk, and we show that it mixes exponentially faster than the standard Kac’s walk. This constitutes the core of our proof. We also describe a few applications of PRSSs. While our PRSS construction assumes a post-quantum one-way function, PRSSs are potentially a weaker primitive and can be separated from one-way functions in a relativized world similar to standard PRSGs. © International Association for Cryptologic Research 2025.

关键词： Distribution functions

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Efficient and Privacy-Preserving Authentication for Federated Learning in Industrial Internet of Things Data Sharing Application

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IEEE Internet of Things Journal 2025年第9期12卷 11652-11663页

作者： Huang, Yongfei Xu, Guangxia Wang, Qiuyan Song, Xiaoling Wang, Xinhui Hengyang Normal University College of Computer Science and Technology Hengyang421002 China Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China Hunan Institute of Technology School of Electrical and Information Engineering Hengyang421002 China

Federated learning (FL) has emerged as a promising approach for collaborative machine learning while respecting data privacy. However, directly aggregating local models during FL training can expose sensitive information, as adversaries may analyze model parameters to infer the underlying raw data. To address this concern, implementing a robust identity authentication mechanism can verify the integrity of local models and confirm their sources. However, many existing authentication schemes are computationally intensive, which can hinder performance. In this article, we present a lightweight authentication scheme for FL called EPPAFL. This innovative approach is compatible with all machine learning algorithms, maintains data utility, and ensures strong privacy protection. Additionally, it can be integrated with detection mechanisms to defend against data poisoning and free-rider attacks. Specifically, we first design a certificateless signature scheme based on the elliptic curve cryptography (ECC) and then propose an anonymous and efficient authentication protocol based on the Chinese remainder theorem (CRT), which supports batch verification, dynamic updates, and revocations of group keys. Moreover, participants interact anonymously to achieve secure communication, and trusted authority (TA) can track and recover the real identities of participants in case of malicious activities. We provide formal security proofs and analyses to demonstrate the achieved security properties. Performance evaluations demonstrate that our proposed protocol significantly reduces the costs associated with signature generation and verification, outperforming the existing schemes in both communication and computational efficiency. © 2014 IEEE.

关键词： Federated learning

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引用

25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Tu, Longchuan Meng, Shunmei Liu, Xiao Liu, Guanfeng Beheshti, Amin Zhang, Xuyun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjin China State Key Laboratory for Novel Software Technology Nanjing University Nanjin China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819605699

Recently, graph neural networks(GNNs) have played a key crucial in many recommendation situations. In particular, contrastive learning-based hypergraph neural networks (HGNNs) are gradually becoming a research focus for addressing issues of data sparsity and noise. Despite many studies proving their outstanding performance, there are still some shortcomings: i) Most contrastive learning-based HGNNs primarily rely on cross-view contrastive learning, while neglecting contrastive learning on the interaction graph. ii) Utilizing node embedding for hypergraph structure learning is susceptible to the influence of low-quality representations, thereby constraining the learning capability of HGNNs. To address these issues, we offer a Semantic Similarity-based Graph Contrastive Learning framework (SSGCL), which aims to jointly learn representations with rich semantic information through within-view and cross-view contrastive learning. Specifically, we first introduce consistency contrastive learning, which captures self-supervised signals through semantic similarity between nodes and their neighborhoods. It better captures the unique features of nodes through the connections between nodes and their neighborhoods. Then, we utilize interaction graph learning hypergraph structure, promoting it to extract potential node connections and thus improving its ability to describe homogeneous node relationships. Experimental evaluations on three actual datasets show that SSGCL performs much better than the current baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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