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5th International Conference on Computer Science and Management technology, ICCSMT 2024

作者： Huang, Jie Ma, Kai He, Xiaosong He, Hongjing Institute of Big Data Technology and Application Chongqing Institute of Engineering Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Chongqing China

ISBN: (纸本)9798400709999

Intelligent technologies are driving the development of smart campuses, fostering a dynamic and diverse intelligent ***, the current trend of customizing smart campus solutions often positions campus citizens as mere bystanders or end *** enhance participatory engagement in campus development and increase the autonomy of platform construction, this paper leverages the strengths of big data warehouses, data analytics, the Internet of Things (IoT), and cloud computing-specifically their security, concurrency, reliability, redundancy, and scalability-to propose a campus application creation *** platform aims to streamline the software development process, facilitate the publication of software products, and promote functionality sharing and collaborative *** introduce an architectural model comprising four components: the data lake and warehouse layer, the data ferry layer, the resource service layer, and the platform *** internal operational mechanisms of this architecture are discussed from the perspectives of"applications as data, " self-feedback analysis, application evaluation systems, and system *** advantages of the platform are analyzed, along with application cases from both teacher and student *** also explore the platform's significance within the campus context, including its potential to adjust campus functionality, enhance overall innovation capabilities in education, and increase the autonomy of software *** findings suggest that a modern application creation platform can significantly facilitate the self-improvement of smart campus functionalities, thereby further enhancing the intelligence of the campus. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cloud platforms

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A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

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Computers, Materials & Continua 2025年第4期83卷 1195-1218页

作者： Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and TechnologyChizhou247000China

Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关键词： Fuzzy rough sets feature selection cross-similarity fuzzy relations

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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic Engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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Partial Label Learning with Noisy Labels

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Annals of data Science 2025年第1期12卷 199-212页

作者： Zhao, Pan Tang, Long Pan, Zhigeng School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing210044 China Research Institute of Talent Big Data Nanjing University of Information Science & Technology Nanjing210044 China

Partial label learning (PLL) is a particular problem setting within weakly supervised learning. In PLL, each sample corresponds to a candidate label set in which only one label is true. However, in some practical application scenarios, the emergence of label noise can make some candidate sets lose their true labels, leading to a decline in model performance. In this work, a robust training strategy for PLL, derived from the joint training with co-regularization (JoCoR), is proposed to address this issue in PLL. Specifically, the proposed approach constructs two separate PLL models and a joint loss. The joint loss consists of not only two PLL losses but also a co-regularization term measuring the disagreement of the two models. By automatically selecting samples with small joint loss and using them to update the two models, our proposed approach is able to filter more and more suspected samples with noise candidate label sets. Gradually, the robustness of the PLL models to label noise strengthens due to the reduced disagreement of the two models. Experiments are conducted on two state-of-the-art PLL models using benchmark datasets under various noise levels. The results show that the proposed method can effectively stabilize the training process and reduce the model's overfitting to noisy candidate label sets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Phase locked loops

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Study on the off situ reconstruction of the core neutron field based on dual-task hybrid network architecture

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Nuclear Science and Techniques 2025年第1期36卷 175-191页

作者： Pei Cao Hui Ding Cheng-Long Cao Zi-Hui Yang Guo-Min Sun School of Artificial Intelligence and Big Data Hefei UniversityHefei 230001China College of Electronic Engineering National University of Defense TechnologyHefei 230001China Institute of Nuclear Energy Safety Technology HFIPSChinese Academy of SciencesHefei 230027China

The off situ accurate reconstruction of the core neutron field is an important step in realizing real-time reactor *** existing off situ reconstruction method of the neutron field is only applicable to cases wherein a single region changes at a specified location of the ***,when the neutron field changes are complex,the accurate identification of the individual changed regions becomes challenging,which seriously affects the accuracy and stability of the neutron field ***,this study proposed a dual-task hybrid network architecture(DTHNet)for off situ reconstruction of the core neutron field,which trained the outermost assembly reconstruction task and the core reconstruction task jointly such that the former could assist the latter in the reconstruction of the core neutron field under core complex ***,to exploit the characteristics of the ex-core detection signals,this study designed a global-local feature upsampling module that efficiently distributed the ex-core detection signals to each reconstruction unit to improve the accuracy and stability of *** experiments were performed on the simulation datasets of the CLEAR-I reactor to verify the accuracy and stability of the proposed *** results showed that when the location uncertainty of a single region did not exceed nine and the number of multiple changed regions did not exceed ***,the reconstructed ARD was within 2%,RD_(max)was maintained within 17.5%,and the number of RD≥10%was maintained within ***,when the noise interference of the ex-core detection signals was within±2%,although the average number of RD≥10%increased to 16,the average ARD was still within in 2%,and the average RD_(max)was within 22%.Collectively,these results show that,theoretically,the DTHNet can accurately and stably reconstruct most of the neutron field under certain complex core changes.

关键词： Real-time reactor monitoring Core neutron field reconstruction Dual-task hybrid network architecture Global-local feature upsampling module

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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Science China(Information Sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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Global and Domestic Perspectives on AI in Education: A Knowledge Mapping Analysis Using CiteSpace 24

Global and Domestic Perspectives on AI in Education: A Knowl...

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4th Asia-Pacific artificial intelligence and big data Forum, AIBDF 2024

作者： Pan, Shengwu Zeng, Qi Li, Mingjiang Yan, Na Qiannan Normal University for Nationalities Guizhou Duyun China Institute of Big Data Application and Artificial Intelligence Qiannan Normal University for Nationalities Guizhou Duyun China

ISBN: (纸本)9798400710865

This study utilized CiteSpace to perform an extensive knowledge mapping analysis of 826 Chinese articles and 488 international publications on artificial intelligence in education, obtained from the China National Knowledge Infrastructure (CNKI) and Web of Science databases, spanning the years 2014 to 2024. The research employed visualization methods, such as co-citation analysis and network algorithms, to comprehensively assess research hotspots, key topics, and emerging trends in the field, both domestically and internationally. Research indicates that overseas studies prioritize the incorporation of AI technology in education and the development of theoretical frameworks, whereas domestic research focuses on the practical use of AI in curriculum development and educational administration. Despite these divergent perspectives, both international and domestic research entities share a mutual interest in the use and advancement of AI in education, especially in relation to personalized learning and educational equity. To promote AI in global education, Future research should prioritize AI-driven digital transformation and standardization in education, Interdisciplinary collaboration and leveraging big data analytics will enhance innovation and decision-making, Addressing ethical issues, privacy, and societal acceptance is crucial for equitable and sustainable educational development. © 2024 Copyright held by the owner/author(s).

关键词： artificial intelligence in Education

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Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4595-4609页

作者： Weng, Juanjuan Luo, Zhiming Li, Shaozi Jinan University College of Information Science and Technology Guangzhou510632 China Xiamen University Department of Artificial Intelligence Xiamen361005 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyishan354300 China

This paper aims to enhance the transferability of adversarial samples in targeted attacks, where attack success rates remain comparatively low. To achieve this objective, we propose two distinct techniques for improving the targeted transferability from the loss and feature aspects. First, in previous approaches, logit calibrations used in targeted attacks primarily focus on the logit margin between the targeted class and the untargeted classes among samples, neglecting the standard deviation of the logit. In contrast, we introduce a new normalized logit calibration method that jointly considers the logit margin and the standard deviation of logits. This approach effectively calibrates the logits, enhancing the targeted transferability. Second, previous studies have demonstrated that mixing the features of clean samples during optimization can significantly increase transferability. Building upon this, we further investigate a truncated feature mixing method to reduce the impact of the source training model, resulting in additional improvements. The truncated feature is determined by removing the Rank-1 feature associated with the largest singular value decomposed from the high-level convolutional layers of the clean sample. Extensive experiments conducted on the ImageNet-Compatible, CIFAR-10 and ImageNet-1k datasets demonstrate the individual and mutual benefits of our proposed two components, which outperform the state-of-the-art methods by a large margin in black-box targeted attacks. © 2005-2012 IEEE.

关键词： Calibration Closed box Training Standards Translation Perturbation methods Optimization Predictive models Feature extraction Industries

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Graph Augmentation Empowered Contrastive Learning for Recommendation

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ACM Transactions on Information Systems 2025年第2期43卷 1-27页

作者： Xu, Lixiang Liu, Yusheng Xu, Tong Chen, Enhong Tang, Yuanyan School of Artificial Intelligence and Big Data Hefei University Hefei China State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology University of Science and Technology of China Hefei China Zhuhai UM Science and Technology Research Institute FST University of Macau China

The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the Graph Augmentation Empowered Contrastive Learning (GAECL) for recommendation framework, which uses graph augmentation based on topological and semantic dual adaptation and global co-modeling via structural optimization to co-create contrasting views for better augmentation of the CF paradigm. Specifically, we strictly filter out unimportant topologies by reconstructing the adjacency matrix and mask unimportant attributes in nodes according to the PageRank centrality principle to generate an augmented view that filters out noisy data. Additionally, GAECL achieves global collaborative modeling through structural optimization and generates another augmented view based on the PageRank centrality principle. This helps to filter the noisy data while preserving the original semantics of the data for more effective data augmentation. Extensive experiments are conducted on five datasets to demonstrate the superior performance of our model over various recommendation models. © 2025 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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SFANet: Visible-Infrared Person Re-Identification with Shape-Guided Feature Aggregation

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IEEE Sensors Journal 2025年第10期25卷 18452-18461页

作者： Cheng, Zhuxuan Zhang, Zhijia Fan, Huijie Ding, Wu Shenyang University of Technology College of Artificial Intelligence Shenyang110870 China Key Laboratory of Optical Information and Simulation Technology Shenyang110016 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110169 China Shenyang Huadun Institute of Big Data Research Shenyang110166 China

Retrieving persons for visible-infrared person re-identification (VI-ReID) task is challenging, owing to the huge modality discrepancy induced by the unique imaging principle of RGB and infrared sensors. Existing approaches rely on seeking modality-shared information within unified visual feature space, ignoring the identity-related clues brought by shape information. To facilitate the increasing of inter-class distance and the decreasing of intra-class distance so that achieve robust retrieval performance, this paper propose a novel Shape-guided Feature Aggregation Network (SFANet) to aggregate parsing masks with global-local features. Specifically, we first extract modality-shared and parsing mask features using a parameter-shared backbone and a mask encoder. Then, a part generator (PG) is proposed to refine the implicit information within the parsing masks by separating them into several local pattern maps, then shape-guided feature aggregation module (SFAM) is designed to aggregate the local pattern maps with the group part features through a matching and distributing strategy. Finally, we introduce a part alienation loss (PAL) provides diversity of feature space for local pattern maps and group part features, a modality consistency mining loss (MCML) is further introduced for reducing the impact of modality-specific features and maximizing the utilization of modality-invariant features. Our SFANet achieves significant improvements over the state-of-the-art methods on two mainstream datasets. © 2001-2012 IEEE.

关键词： Aggregates

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