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Deep learning Algorithm for Person Re-Identification based on Dual Network Architecture

computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MsMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of computer science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-based Reinforcement learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Fine extraction of multi-crop planting area based on deep learning with sentinel- 2 time-series data

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Environmental science and Pollution research 2025年第19期32卷 11931-11949页

作者： Jiang, Jingmin Zhang, Jiahua Wang, Xue Zhang, shichao Kong, Delong Wang, Xiaopeng Ali, shawkat Ullah, Hidayat Space Information and Big Earth Data Research Center School of Computer Science and Technology Qingdao University Qingdao266071 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challenges. Therefore, this study aims to provide an effective model for multi-crop classification using high-resolution remote sensing time-series data. We designed two deep learning models based on convolutional neural network-long short-term memory (CNN-LsTM) and bidirectional long short-term memory (Bi-LsTM). The monthly synthetic time series of the normalized difference vegetation index (NDVI) from sentinel-2 data will be used as input features to extract the multi-crop planting area in shandong province’s northwestern, southwestern, and eastern regions. The results showed that deep learning models achieved higher accuracy compared to the random forest (RF) and extreme gradient boosting (XGBoost) models, with CNN-LsTM achieving the highest overall accuracy of 96.48%. At the county level, the coefficients of determination (R2) for the CNN-LsTM model were 0.91 for wheat, 0.88 for maize, and 0.73 for spring cotton. This study demonstrates that the CNN-LsTM model combined with monthly synthetic time-series NDVI provides a feasible approach for accurately mapping high-resolution multi-crop planting areas and also contributes significantly to decision support and resource management in agricultural production. © The Author(s), under exclusive licence to springer-Verlag GmbH Germany, part of springer Nature 2025.

关键词： Deep learning High-resolution Monthly synthetic time-series NDVI Multi-crop classification Remote sensing sentinel- 2

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A deep transfer learning model for the deformation of braced excavations with limited monitoring data

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Journal of Rock Mechanics and Geotechnical Engineering 2025年第3期17卷 1555-1568页

作者： Yuanqin Tao shaoxiang Zeng Tiantian Ying Honglei sun sunjuexu Pan Yuanqiang Cai College of Civil Engineering Zhejiang University of TechnologyHangzhou310023China Engineering Research Center of Ministry of Education for Renewable Energy Infrastructure Construction Technology Hangzhou310023China

The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training *** address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(s2sCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been *** the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping *** illustrate the proposed methodology,an excavation project in Hangzhou,China is *** proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning *** the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent *** results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.

关键词： Braced excavation Wall deflections Transfer learning Deep learning Finite element simulation

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Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods

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Journal of Artificial Intelligence research 2025年 82卷 2233-2278页

作者： Fraser, Kathleen C. Dawkins, Hillary Kiritchenko, svetlana National Research Council Canada 1200 Montreal Road Ottawa Canada

Large language models (LLMs) have advanced to a point that even humans have difficulty discerning whether a text was generated by another human, or by a computer. However, knowing whether a text was produced by human or artificial intelligence (AI) is important to determining its trustworthiness, and has applications in many domains including detecting fraud and academic dishonesty, as well as combating the spread of misinformation and political propaganda. The task of AI-generated text (AIGT) detection is therefore both very challenging, and highly critical. In this survey, we summarize state-of-the art approaches to AIGT detection, including watermarking, statistical and stylistic analysis, and machine learning classification. We also provide information about existing datasets for this task. synthesizing the research findings, we aim to provide insight into the salient factors that combine to determine how "detectable" AIGT text is under different scenarios, and to make practical recommendations for future work towards this significant technical and societal challenge. ©2025 National research council Canada.

关键词： Adversarial machine learning

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A Comprehensive survey on Federated learning Applications in Computational Mental Healthcare

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computer Modeling in Engineering & sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain saxena Amit Pundir sanjeev singh Akshat Gaurav savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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BMWP: the first Bengali math word problems dataset for operation prediction and solving

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Discover Artificial Intelligence 2025年第1期5卷 1-15页

作者： Mondal, sanchita Khatua, Debnarayan Mandal, sourav Prasad, Dilip K. sekh, Arif Ahmed School of Medical Science and Technology IIT Kharagpur West Bengal Kharagpur721302 India Department of Mathematics and Statistics Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur512213 India School of Computer Science and Engineering XIM University Odisha Puri752050 India Department of Computer Science Uit the Arctic University of Norway Tromsø9017 Norway

solving math word problems of varying complexities is one of the most challenging and exciting research questions in artificial intelligence (AI), particularly in natural language processing (NLP) and machine learning (ML). Foundational language models such as GPT must be evaluated for intelligence, and solving word problems is a key method for this assessment. These problems become especially difficult when presented in low-resource regional languages such as Bengali. Word problem solving integrates the cognitive domains of language processing, comprehension, and transformation into real-world solutions. During the past decade, advances in AI and machine learning have significantly progressed in addressing this complex issue. Although researchers worldwide have primarily utilized datasets in English and some in Chinese, there has been a lack of standard datasets for low-resource languages such as Bengali. In this pioneering study, we introduce the first Bengali Math Word Problem Benchmark Data set (BMWP), comprising 8653 word problems. We detail the creation of this dataset and the benchmarking methods employed. Furthermore, we investigate operation prediction from Bengali word problems using state-of-the-art deep learning (DL) techniques. We implemented and compared various standard DL-based neural network architectures, achieving an accuracy of 92±2%. The data set and the code will be available at https://***/sanchitaMondal/BMWP. © The Author(s) 2025.

关键词： Recurrent neural networks

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Detecting coagulation time in cheese making by means of computer vision and machine learning techniques

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computers IN INDUsTRY 2025年 164卷

作者： Loddo, Andrea Di Ruberto, Cecilia Armano, Giuliano Manconi, Andrea Univ Cagliari Dept Math & Comp Sci I-09124 Cagliari Italy CNR Inst Biomed Technol Natl Res Council Via Fratelli Cervi 93 I-20054 Milan Italy

Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper, we present several key practical contributions to the field, including the introduction of CM-IDB, the first publicly available image dataset related to the cheese-making process. Also, we propose an innovative artificial intelligence-based approach to automate the detection of curd-firming time during cheese production using a combination of computer vision and machine learning techniques. The proposed method offers real-time insights into curd firmness, aiding in predicting optimal cutting times. Experimental results show the effectiveness of integrating sequence information with single image features, leading to improved classification performance. In particular, deep learning-based features demonstrate excellent classification capability when integrated with sequence information. The study suggests the suitability of the proposed approach for integration into real-time systems, especially within dairy production, to enhance product quality and production efficiency.

关键词： Image processing computer vision Machine learning Food industry Curd-firming time detection

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Exploring the vulnerability of black-box adversarial attack on prompt-based learning in language models

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Neural Computing and Applications 2025年第3期37卷 1457-1473页

作者： Tan, Zihao Chen, Qingliang Zhu, Wenbin Huang, Yongjian Liang, Chen Department of Computer Science Jinan University Guangdong Guangzhou510632 China Guangzhou Xuanyuan Research Institute Co. Ltd. Guangdong Guangzhou510006 China Department of Computing The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

Prompt-based learning has been proved to be an effective way in pre-trained language models (PLMs), especially in low-resource scenarios like few-shot settings. However, the trustworthiness of PLMs is of paramount significance and potential vulnerabilities have been shown in prompt-based templates that could mislead the predictions of language models, causing serious security concerns. In this paper, we will shed light on some vulnerabilities of PLMs, by proposing a prompt-based adversarial attack on manual templates in black-box scenarios. First of all, we design character-level and word-level heuristic approaches to break manual templates separately. Then we present a greedy algorithm for the attack based on the above heuristic destructive approaches and further combine it with negative words. Finally, we evaluate our approach with the classification tasks on three variants of BERT series models and eight datasets. And comprehensive experimental results justify the effectiveness of our approach in terms of attack success rate and attack speed. On average, it achieves an attack success rate of close to 90% and a query time of around 3000, which is significantly better than the compared baseline methods. Further experimental studies indicate that our proposed method also displays good capabilities in scenarios with varying shot counts, template lengths and query counts, exhibiting good generalizability. © The Author(s), under exclusive licence to springer-Verlag London Ltd., part of springer Nature 2024.

关键词： Adversarial machine learning

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Machine learning for the Evaluation of the Nephrops norvegicus Population 10th

Machine Learning for the Evaluation of the Nephrops norv...

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10th International Conference on Machine learning, Optimization, and Data science, LOD 2024

作者： Reggiannini, Marco Martinelli, Michela Papini, Oscar Zacchetti, Lorenzo Domenichetti, Filippo Pieri, Gabriele National Research Council of Italy Via G. Moruzzi 1 Pisa56124 Italy National Research Council of Italy Largo Fiera della Pesca 2 Ancona60125 Italy Alma Mater Studiorum – University of Bologna Piazza di Porta S. Donato 1 Bologna40126 Italy

ISBN: (纸本)9783031824838

This paper introduces computer vision methods for detecting, recognising, and estimating Nephrops norvegicus (Norway lobster) burrow density via Underwater Television surveys. The current manual approach involves human operators visually assessing videos, which is prone to errors and subjectivity. Automated machine learning systems show promise in identifying and counting burrows, potentially standardising recognition and reducing operator errors. However, challenges exist in implementing computer vision techniques. An automated system aims to process video streams, detect seabed openings, extract visual features, and classify N. norvegicus burrows, significantly advancing the automation of underwater video reading. The primary processing presented in the paper lies in a boosting algorithm capable of extending the original annotated ground truth and assessing the improved performance of the extended data set with respect to the original one. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Deep learning

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