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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Bingqing Du Uddin Md. Borhan Tianxing Chen Jianyong Chen Jianqiang Li Jie Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Unmanned Aerial Vehicles (UAVs) have emerged as pivotal tools for infrastructure inspection, mitigating risks to human life and enhancing operational efficiency. Despite advancements, achieving autonomous surface coverage inspection in GPS-denied environments remains a significant challenge. This paper presents a novel approach to enhance UAV policy learning for surface screening of urban infrastructure in environments lacking GPS. Utilizing Deep Reinforcement Learning (DRL), the methodology enables UAVs to autonomously conduct surface coverage inspections more robustly and efficiently. A key innovation is the Optimal Speed Decision (OSD) model, which allows UAVs to adjust flight speed dynamically based on scene perception, enhancing efficiency and safety. Additionally, an Off-Policy Critic-Based Reward Shaping (OPCBRS) method is introduced to improve learning efficiency and performance by addressing the issue of sparse rewards using offline data. Simulation results confirm the effectiveness of these methods, with the OSD model demonstrating significant improvements in navigating complex scenarios and maintaining a balance between efficiency and robustness over traditional uniform speed policies. This research offers a robust framework for UAV-based infrastructure inspection in GPS-denied environments, advancing autonomous UAV applications.

关键词： Technological innovation Navigation Simulation Inspection Autonomous aerial vehicles Deep reinforcement learning Robustness Real-time systems Safety Robots

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Intelligent Blocking and Prevention of SARS-CoV-2 Based on Evolutionary Reinforcement Learning

Intelligent Blocking and Prevention of SARS-CoV-2 Based on E...

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Congress on Evolutionary Computation

作者： Chengwei He Huaping Hong Lijia Ma Qingling Zhu Yuan Bai College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In recent years, the global repercussions of SARS-CoV-2 and its variants have posed significant challenges to various areas, including the economic order, transportation, healthcare, and education, and the mitigation and prevention of SARS-CoV-2 and other infectious diseases have received much attention. This paper tries to develop a rational and effective intervention strategy in response to the dynamic evolution of the SARS-CoV-2 epidemic. To address this issue, we first use the SEIR model to analyze the population distribution under the SARS-CoV-2 epidemic, and then integrate a deep Q-neural network (DQN) to reflect an economic quantification model contextualized within the SARS-CoV-2 scenario. Next, we propose an evolutionary deep reinforcement learning to generate an optimal and adaptive intervention strategy, by optimizing the weight sequence of the DQN. The experimental results show the effectiveness of the proposed method in maximizing economic benefits, while ensuring the uninterrupted functionality of medical institutions. Importantly, the learned intervention strategy exhibits the applicability to other infectious diseases.

关键词： Economics Epidemics Infectious diseases Prevention and mitigation Heuristic algorithms Sociology Loss measurement

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VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting

arXiv

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arXiv 2024年

作者： Yang, Yingnan Zhu, Qingling Chen, Jianyong College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Multivariate time series (MTS) forecasting has been extensively applied across diverse domains, such as weather prediction and energy consumption. However, current studies still rely on the vanilla point-wise self-attention mechanism to capture cross-variable dependencies, which is inadequate in extracting the intricate cross-correlation implied between variables. To fill this gap, we propose Variable Correlation Transformer (VCformer), which utilizes Variable Correlation Attention (VCA) module to mine the correlations among variables. Specifically, based on the stochastic process theory, VCA calculates and integrates the cross-correlation scores corresponding to different lags between queries and keys, thereby enhancing its ability to uncover multivariate relationships. Additionally, inspired by Koopman dynamics theory, we also develop Koopman Temporal Detector (KTD) to better address the non-stationarity in time series. The two key components enable VCformer to extract both multivariate correlations and temporal dependencies. Our extensive experiments on eight real-world datasets demonstrate the effectiveness of VCformer, achieving top-tier performance compared to other state-of-the-art baseline models. Code is available at this repository: https://***/CSyyn/VCformer. Copyright © 2024, The Authors. All rights reserved.

关键词： Random processes

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Secure Audio Steganography with Reversible data Hiding and Encryption 6

Secure Audio Steganography with Reversible Data Hiding and E...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Arthy, P.S. Sanjai Kumar, K. Vinson Joshua, S. Sahaana, G. Kalpana, R. Ponkumar, D. David Neels Siamala Devi, S. Jeya Lakshmi, S. Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Tamil Nadu Chennai India Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331522667

This research study enhances secure data transmission by embedding sensitive information within audio files. Using Python for the frontend and PHP with MySQL as the backend, this approach employs reversible data hiding (RDH) to ensure that the original content of the audio file can be fully restored. Audio steganography is used to conceal encrypted messages within a WAV file, leveraging its varying compression rates for optimal steganographic performance. The Least Significant Bit (LSB) technique, a widely used method, embeds the hidden message into the audio stream without noticeable distortion. In this system, the secret message is scrambled, divided into segments, and embedded in even-value bytes of the audio file, with the first bit position chosen randomly for enhanced security. A permutation codebook is employed to rearrange and decrypt the message during recovery, ensuring both data confidentiality and complete reversibility of the audio file. This method provides a secure, efficient solution for transmitting sensitive information through audio channels. © 2025 IEEE.

关键词： Steganography

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Smart water grid technology based on deep learning: a review

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International Journal of Wireless and Mobile Computing 2022年第3-4期22卷 338-349页

作者： Wu, Huan Peng, Lin Jiang, Feng Cheng, Shuiping Chen, Jie Yan, Linda College of Environmental Science and Engineering Tongji University Shanghai China Co. Ltd. Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Finance and Management Chongqing Business Vocational College Chongqing China College of Environment and Ecology Chongqing University Chongqing China School of Engineering University of Portsmouth Hampshire PortsmouthPO1 2UP United Kingdom

In recent years, the development of deep learning technology has made breakthroughs in computer vision, natural language processing and other fields. The Smart Water Grid (SWG) technology based on deep learning has also been a hot area of research in recent years. It has achieved better performance in the related detection and prediction of urban pipe networks. Therefore, this survey paper presents an extensive review of the application of deep learning to several different issues related to the SWG. This paper emphasises feasibility studies and summarises the state-of-the-art development in this field from a technical point of view, which consists of pipeline leakage and burst detection, contamination source identification and water demand forecasting. Furthermore, this paper also proposes challenges and future directions in these key research areas, demonstrating that deep learning-based SWG technology is still an emerging and encouraging research field. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Forecasting

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PAIR: protein-aptamer interaction prediction based on language models and contrastive learning framework

PAIR: protein-aptamer interaction prediction based on langua...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jun Zhang Zhiqiang Yan Hao Zeng Zexuan Zhu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Aptamers are single-stranded DNA or RNA oligonucleotides that selectively bind to specific targets, making them valuable for drug design and diagnostic applications. Identifying the interactions between aptamers and target proteins is crucial for these applications. The systematic evolution of ligands by exponential enrichment process, traditionally used for this purpose, is challenging and time-consuming. The resulting aptamers often suffer from limitations in stability and diversity. Computational approaches have shown promise in aiding the discovery of high-performance aptamers, but existing methods are usually constrained by insufficient training data and limited generalizability. Recently, advancements in pre-training large language models have offered a new avenue to mitigate the dependency on large datasets. In this study, we propose a novel method to predict aptamer-protein interactions using large language models within a contrastive learning framework. Experimental results demonstrate that our method exhibits superior generalization and outperforms existing approaches. This method holds promise as a powerful tool for predicting aptamer-protein interactions.

关键词： Proteins Drugs Protein engineering Biological system modeling Large language models Computational modeling Training data Contrastive learning Predictive models Few shot learning

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Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Generating Targeted Universal Adversarial Perturbation again...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yujun Chen, Yanqu Wang, Jiakai Hu, Jin Tao, Renshuai Liu, Xianglong State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China

ISBN: (纸本)9798350368741

There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constrained by inefficient exploitation of audio features, demonstrating insufficient attack ability and robustness in the physical world. To solve this problem, we propose a phoneme-tailored attack (PTA) to improve the quality of the generated AEs. Specifically, to improve attack ability, we propose a Diverse Audio Composition Enrichment method, which enhances the utilization of audio features through phoneme-level slicing and recombination. To adapt AEs to complex environments, we propose a Natural Noise Pattern Guidance method to align AEs with natural noise patterns to improve their robustness. Experiments show that our method achieves an average accuracy of more than 72.34% and 98% with and without a norm constraint, and also demonstrates excellent performance in terms of generalization across datasets and resilience to MP3 compression. © 2025 IEEE.

关键词： Automatic speech recognition natural noise pattern phoneme-level targeted universal adversarial examples

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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Dynamic Bi-Elman Attention Networks (DBEAN): Dual-Directional Context-Aware Representation Learning for Enhanced Text Classification

arXiv

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arXiv 2025年

作者： Liao, Mengyao Lai, Zhenglin Xu, Dong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Text classification, a fundamental task in natural language processing (NLP), aims to categorize textual data into predefined labels. Traditional methods struggled with complex linguistic structures and semantic dependencies. The advent of deep learning, particularly recurrent neural networks (RNNs) and Transformer-based models, has significantly advanced the field by enabling nuanced feature extraction and context-aware predictions. Despite improvements, existing models exhibit limitations in balancing interpretability, computational efficiency, and long-range contextual understanding. This paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN), which integrates bidirectional temporal modelling with self-attention mechanisms. DBEAN dynamically assigns weights to critical segments of input, improving contextual representation while maintaining computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Elman neural networks

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Visual Explanations: Activation-Based Acute Lymphoblastic Leukemia Cell Classification

Visual Explanations: Activation-Based Acute Lymphoblastic Le...

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IEEE International Conference on Development and Learning, ICDL

作者： Muhammad Umair Raza Adil Nawaz Xintong Liu Zhuangzhuang Chen Victor C.M. Leung Jie Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

The saliency methods are widely used for generating heatmaps that emphasize the important portions of an input image for deep networks on a specific classification task. Interpretability is crucial for deploying deep neural networks in real-world applications. However, the heatmaps produced by current visual explainable methods may contain or visualize particulars differently. To analyze and compare the visualization of different methods, such as Gradient-based, Activation-based, Perturbation-based, and Region-based methods, we empirically evaluated them on the acute lymphoblastic leukemia (cancer cell) classification task using state-of-the-art convolutional neural networks. We also visualized the essential pathological features (salient parts) that are the reasons for the classification results on the classification of normal versus malignant cell (CNMC) dataset.

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