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International Joint Conference on Neural Networks (IJCNN)

作者： Yang Wang Ke Lei Chong Zhang Xin Wang Xin Shi Aihua Deng School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China School of Mechanical Engineering Southwest Petroleum University Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Structural Health Monitoring (SHM) monitors the working states of building structures to ensure their reliable operation, but the high cost of SHM sensor nodes limits its largescale deployment. In this paper, we propose a novel computational model that can generate "virtual" sensor nodes with reliable data output at zero deployment costs for cost-efficient SHM sensing. To achieve this, we combine one-dimensional convolutional layer in CNN with Bi-LSTM model to capture temporal and spatial correlations in the data; to improve the quality of the generated data, we design a weighted smoothing algorithm to reduce noise while preserving the integrity of the data; to enhance system robustness, we integrate attention mechanisms at the end of the model to assign different weights to each element, which can capture the limited but crucial information within the data. We evaluate our system on the dataset of the KW51 railroad bridge as an example. The results show that the generated virtual sensor node incurs zero deployment cost, and its data is 94% close to the ground truth, thus making it helpful for facilitating the largescale deployment of SHM systems.

关键词： Costs Smoothing methods Correlation Soft sensors Noise Neural networks Data models

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NUWADYNAMICS: DISCOVERING AND UPDATING IN CAUSAL SPATIO-TEMPORAL MODELING 12

NUWADYNAMICS: DISCOVERING AND UPDATING IN CAUSAL SPATIO-TEMP...

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Kun Wu, Hao Duan, Yifan Zhang, Guibin Wang, Kai Peng, Xiaojiang Zheng, Yu Liang, Yuxuan Wang, Yang China Suzhou Institute for Advanced Research USTC China School of Software Engineering USTC China School of Computer Science USTC China Hong Kong Tongji University China Shenzhen Technology University China National University of Singapore Singapore JD iCity JD Technology

Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as meteorological prediction, urban computing. Adequate high-quality data, coupled with deep models capable of inference, are both indispensable and prerequisite for achieving meaningful results. However, the sparsity of data and the high costs associated with deploying sensors lead to significant data imbalances. Models that are overly tailored and lack causal relationships further compromise the generalizabilities of inference methods. Towards this end, we first establish a causal concept for ST predictions, named NuwaDynamics, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process. Concretely, we initially leverage upstream self-supervision to discern causal important patches, imbuing the model with generalized information and conducting informed interventions on complementary trivial patches to extrapolate potential test distributions. This phase is referred to as the discovery step. Advancing beyond the discovery step, we transfer the data to downstream tasks for targeted ST objectives, aiding the model in recognizing a broader potential distribution and fostering its causal perceptual capabilities (denoted as Update step). Our concept aligns seamlessly with the contemporary backdoor adjustment mechanism in causality theory. Extensive experiments on six real-world ST benchmarks showcase that models can gain outcomes upon the integration of the NuwaDynamics concept. NuwaDynamics also can significantly benefit a wide range of changeable ST tasks like extreme weather and long temporal step super-resolution predictions. Our codes are available at https://***/easylearningscores/NuwaDynamics. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Computation theory

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A Unified Framework and Efficient Computation for Privacy Amplification via Shuffling

arXiv

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arXiv 2025年

作者： Su, Pengcheng Cheng, Haibo Wang, Ping Peking University School of Computer Science Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China

The shuffle model offers significant privacy amplification over local differential privacy (LDP), enabling improved privacy-utility trade-offs. To analyze and quantify this amplification effect, two primary frameworks have been proposed: the privacy blanket (Balle et al., CRYPTO 2019) and the clone paradigm, which includes both the standard clone and stronger clone (Feldman et al., FOCS 2021;SODA 2023). All of these approaches are grounded in decomposing the behavior of local randomizers. In this work, we present a unified perspective—termed the general clone paradigm—that captures all decomposition-based analyses. We identify the optimal decomposition within this framework and design a simple yet efficient algorithm based on the Fast Fourier Transform (FFT) to compute tight privacy amplification bounds. Empirical results show that our computed upper bounds nearly match the corresponding lower bounds, demonstrating the accuracy and tightness of our method. Furthermore, we apply our algorithm to derive optimal privacy amplification bounds for both joint composition and parallel composition of LDP mechanisms in the shuffle model. Copyright © 2025, The Authors. All rights reserved.

关键词： Fast Fourier transforms

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AI-Enhanced threat analysis and risk assessment for connected and autonomous vehicles

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software Quality Journal 2025年第3期33卷 1-19页

作者： Ahmad, Usman Han, Mu Mahmood, Shahid School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China The University of Faisalabad Faisalabad Pakistan Intelligent Mobility and Software FEV Iberia Barcelona Spain

Threat Analysis and Risk Assessment (TARA) methodologies aid in the early detection of potential threats during the concept and design phases of automotive security engineering process. Their importance is underscored by the relevant industry standards such ISO/SAE 21434 and SAE J3061. While human expertise and involvement are vital for conducting effective TARA, Artificial Intelligence (AI) can enhance the process significantly by automating certain aspects and thus streamlining operations. This study introduces an AI-driven approach to enhance the implementation of TARA. The proposed method leverages machine learning and evolutionary computing algorithms to identify cybersecurity threats in Connected and Autonomous Vehicles (CAVs) through the STRIDE framework. A structured and preprocessed dataset, containing various threat scenarios and characteristics specific to CAVs, facilitates the application of these AI techniques. The automation focuses on the threat identification and assessment phases of TARA. Machine learning algorithms KNN and Naïve Bayes are evaluated using metrics like accuracy, precision, recall, F1 score, and confusion matrix, achieving 87.5% accuracy. The Ant Colony Optimization (ACO) algorithm predicts attack feasibility ratings, confirming the model’s effectiveness. These metrics demonstrate the AI models’ ability to identify and analyze cybersecurity threats. The results show AI’s potential to transform automotive cybersecurity by automating key TARA stages and providing an efficient framework for threat analysis and risk evaluation.

关键词： Artificial intelligence Connected and autonomous vehicles (CAVs) Cybersecurity Evolutionary algorithms ISO/SAE 21434 Machine learning Threat analysis and risk assessment (TARA)

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Application of Data Analysis to Medical Manpower Adjustment 2021

Application of Data Analysis to Medical Manpower Adjustment

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5th International Conference on E-Society, E-Education and E-Technology, ICSET 2021

作者： Peng, Shih Jung School of Computer Science and Software Engineering Zhaoqing University 86-*** China

ISBN: (纸本)9781450390156

In today's environment of longer human life and higher average age, the general family does not have enough ability and economic conditions to apply for professional nursing staff to assist in the patient of the elderly in need of long-term care in the home. To maintain the family's income, they have to continue the existing work and send the elderly to the care center. However, there is still a shortage problem of nursing staff, even in nursing centers, because each nurse needs to take care of several patients with dealing the related medical matters at the same time. We found that the physical condition of each patient is different. In this paper, the data function and algorithm propose as the basis of data analysis. We can turn the physical condition of each patient into relevant data and analyze the corresponding data. In this way, we can find out the abnormal condition of the patient's body early, which is helpful for the prevention of accidents. After data analysis, if the patient's BFI continues in the stable area in the past month, some detection of Body Function can be omitted. This approach can not only appropriately improve work efficiency but also reduce the workload of nursing staff. Finally, we establish the management mode of medical manpower scheduling then the medical manpower-shortage problem would adjust appropriately. © 2021 ACM.

关键词： Nursing

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Towards combating frequency simplicity-biased learning for domain generalization 24

Towards combating frequency simplicity-biased learning for d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xilin He Jingyu Hu Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Muhammad Haris Khan Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and Guangdong Provincial Key Laboratory of Intelligent Information Processing University of Exeter Mohamed bin Zayed University of Artificial Intelligence

ISBN: (纸本)9798331314385

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at https://***/C0notSilly/AdvFrequency.

关键词：

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Contextual Graph Reconstruction and Emotional Variation Learning for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Rao, Yujing Cao, Min Ye, Mang Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan430072 China Soochow University School of Computer Science and Technology Suzhou215006 China

Conversational Emotion Recognition (CER) significantly benefits from the integration of multiple modalities. However, real-world scenarios are often plagued by hardware malfunctions and network failures that lead to missing modalities and incomplete emotional representations. Existing methods primarily focus on modeling inter-modal relationships within isolated utterances to generate missing data, so they do not adequately capture conversational context and dynamic emotional evolution. In conversations, emotional expressions are inherently context-dependent and dynamic. Insufficient modeling of these properties can substantially degrade performance. To address these challenges, we propose Contextual Graph Reconstruction and Emotional Variation Learning (CGR-EVL). Our approach flexibly constructs an utterance graph with diverse coverage based on speaker activity and modality availability, thereby capturing more contextual information. To enhance the handling of missing modalities, we further integrate temporal relations through a relational graph convolutional network to reconstruct missing features and introduce a speaker emotion-aware constraint to ensure emotional coherence. Additionally, we propose the concept of emotional entropy to quantify variation patterns and develop a novel loss function that aligns predicted and actual variations. Experiments on the IEMOCAP and MELD datasets show that CGR-EVL outperforms state-of-the-art methods, particularly under conditions of incomplete modalities. © 2010-2012 IEEE.

关键词： Graph neural networks

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Frontiers of computer science 2020年第1期14卷 191-207页

作者： Huiping LIU Cheqing JIN Aoying ZHOU School of Data Science and Engineering School of Computer Science and Software EngineeringEast China Normal UniversityShanghai 200062China

With the increasing number of GPS-equipped vehicles,more and more trajectories are generated continuously,based on which some urban applications become feasible,such as route *** general,popular route that has been travelled frequently is a good choice,especially for people who are not familiar with the road ***,accurate estimation of the travel cost(such as travel time,travel fee and fuel consumption)will benefit a wellscheduled trip *** this paper,we address this issue by finding the popular route with travel cost *** this end,we design a system consists of three main ***,we propose a novel structure,called popular traverse graph where each node is a popular location and each edge is a popular route between locations,to summarize historical trajectories without road network ***,we propose a self-adaptive method to model the travel cost on each popular route at different time interval,so that each time interval has a stable travel ***,based on the graph,given a query consists of source,destination and leaving time,we devise an efficient route planning algorithmwhich considers optimal route concatenation to search the popular route from source to destination at the leaving time with accurate travel cost ***,we conduct comprehensive experiments and implement our system by a mobile App,the results show that our method is both effective and efficient.

关键词： location-based services route planning travel cost estimation minimum description length optimal road concatenation

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Towards Understanding Third-party Library Dependency in C/C++ Ecosystem 7

Towards Understanding Third-party Library Dependency in C/C+...

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37th IEEE/ACM International Conference on Automated software engineering, ASE 2022

作者： Tang, Wei Xu, Zhengzi Liu, Chengwei Wu, Jiahui Yang, Shouguo Li, Yi Luo, Ping Liu, Yang School of Software Tsinghua University Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450396240

Third-party libraries (TPLs) are frequently reused in software to reduce development cost and the time to market. However, external library dependencies may introduce vulnerabilities into host applications. The issue of library dependency has received considerable critical attention. Many package managers, such as Maven, Pip, and NPM, are proposed to manage TPLs. Moreover, a significant amount of effort has been put into studying dependencies in language ecosystems like Java, Python, and JavaScript except C/C++. Due to the lack of a unified package manager for C/C++, existing research has only few understanding of TPL dependencies in the C/C++ ecosystem, especially at large scale. Towards understanding TPL dependencies in the C/C++ ecosystem, we collect existing TPL databases, package management tools, and dependency detection tools, summarize the dependency patterns of C/C++ projects, and construct a comprehensive and precise C/C++ dependency detector. Using our detector, we extract dependencies from a large-scale database containing 24K C/C++ repositories from GitHub. Based on the extracted dependencies, we provide the results and findings of an empirical study, which aims at understanding the characteristics of the TPL dependencies. We further discuss the implications to manage dependency for C/C++ and the future research directions for software engineering researchers and developers in fields of library development, software composition analysis, and C/C++ package manager. © 2022 ACM.

关键词： Ecosystems

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