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International Conference on computer Supported Cooperative Work in Design

作者： Mengying Xu Hanjiang Lai Jian Yin School of Systems Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P. R. China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Cross-modal hashing has attracted great interest in the past decades. Due to the traditional hashing retrieval model requiring hand-crafted feature extraction, much research on the end-to-end deep hashing models has been explored. However, this training process of deep models is complex and needs a large number of samples. To address the above issues, we propose a lightweight hashing retrieval network with the pre-trained CLIP model, which is called CLIP-Hash. It can obtain better hash features via the pre-training CLIP model and provide a lightweight fine-tune model which is easily trained and only few training samples are required. First, we obtain the powerful feature representations by the visual and textual encodes. Then, we propose a simple hash encoding module, which is a lightweight network and only few parameters are needed to be trained. With the proposed hash encoding module, we can obtain efficient binary hash codes that can align the images and texts. We conduct experiments on MIRFlickr and NUS-WIDE two benchmark datasets. The results show that the proposed simple method can outperform the SOTA hashing methods.

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Multi-variate hybrid modeling for pacific ocean acidification: predicting future pH trends and analyzing key biogeochemical drivers

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CSI Transactions on ICT 2024年第1期13卷 99-116页

作者： Vasanth, K. Kishore, R. K. Sugumaran, Vijayan Krishnamoorthy, Raja Ramdas, Rithvik Tadepalli, Satya Kiranmai Electronics and Communication Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thavapalayam Karur India Department of Decision and Information Sciences School of Business Administration Oakland University Rochester USA Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil India Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad India

Ocean acidification, driven by rising atmospheric carbon dioxide levels, poses a significant threat to the health of marine ecosystems, particularly in the Pacific Ocean. This study employs a multi-variate hybrid machine learning approach to predict future pH trends within the Pacific and to analyze the influence of key biogeochemical drivers on these trends. Hybrid models, strategically combining the strengths of individual algorithms, were developed for predicting several ocean acidification parameters. A performance analysis demonstrated the superior accuracy of hybrid models compared to their counterparts. The predicted pH trends reveal a concerning shift towards increased acidity within the Pacific Ocean, highlighting the urgency of understanding and mitigating its impacts. In-depth analysis was conducted to identify the relative influence of key biogeochemical factors on the changing pH dynamics. This research aims to provide crucial insights for developing targeted mitigation strategies and protecting the vulnerable ecosystems of the Pacific Ocean from the escalating consequences of ocean acidification.

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Attacking Strategy of Multiple Unmanned Surface Vehicles with Improved GWO Algorithm Under Control of Unmanned Aerial Vehicles

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Journal of Shanghai Jiaotong university(Science) 2020年第2期25卷 201-207页

作者： WU Xin PU Juan XIE Shaorong School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Institute for Advanced Communication and Data Science Shanghai 200444China

Unmanned combat system is one of the important means to capture information superiority,carry out precision strike and accomplish special combat tasks in information *** attack strategy plays a crucial role in unmanned combat system,which has to ensure the attack by unmanned surface vehicles(USVs)from *** meet the challenge,we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization(DAGWO).The traditional grey wolf optimization(GWO)algorithm is improved with a distributed auction mechanism(DAM)to constrain the initialization of wolves,which improves the optimization process according to the actual *** addition,one unmanned aerial vehicle(UAV)is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of USV attack *** proposed DAGWO algorithm can not only ensure the diversity of wolves,but also avoid the local optimum *** results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.

关键词： unmanned surface vehicle(USV) attack strategy grey wolf optimization(GWO) task allocation unmanned aerial vehicle(UAV)

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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video datasets

Temporal Transformation Network Based On Scale Sequences for...

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International Conference on computer and Communications (ICCC)

作者： Xiaoke Zhu Bo Zhao Zhiwei Dong Lingyun Dong Danyang Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline.

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Implicit Neural Representation in Medical Imaging: A Comparative Survey

Implicit Neural Representation in Medical Imaging: A Compara...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Amirali Molaei Amirhossein Aminimehr Armin Tavakoli Amirhossein Kazerouni Bobby Azad Reza Azad Dorit Merhof School of Computer Engineering Iran University of Science and Technology Iran School of Electrical Engineering Iran University of Science and Technology Iran Department of Electrical Engineering and Computer Science South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functions, INRs offer several benefits. Recognizing the potential of INRs beyond these domains, this survey aims to provide a comprehensive overview of INR models in the field of medical imaging. In medical settings, numerous challenging and ill-posed problems exist, making INRs an attractive solution. The survey explores the application of INRs in various medical imaging tasks, such as image reconstruction, segmentation, registration, novel view synthesis, and compression. It discusses the advantages and limitations of INRs, highlighting their resolution-agnostic nature, memory efficiency, ability to avoid locality biases, and differentiability, enabling adaptation to different tasks. Furthermore, the survey addresses the challenges and considerations specific to medical imaging data, such as data availability, computational complexity, and dynamic clinical scene analysis. It also identifies future research directions and opportunities, including integration with multi-modal imaging, real-time and interactive systems, and domain adaptation for clinical decision support. To facilitate further exploration and implementation of INRs in medical image analysis, we have provided a compilation of cited studies along with their available open-source implementations on ${\color{Magenta}GitHub}$. Finally, we aim to consistently incorporate the most recent and relevant papers regularly.

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PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining

PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pi...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Shaohuai Shi Xinglin Pan Xiaowen Chu Bo Li School of Computer Science and Technology Harbin Institute of Technology Shenzhen Department of Computer Science Hong Kong Baptist University Data Science and Analytics Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Computer Science and Engineering The Hong Kong University of Science and Technology

Large models have attracted much attention in the AI area. The sparsely activated mixture-of-experts (MoE) technique pushes the model size to a trillion-level with a sub-linear increase of computations as an MoE layer can be equipped with many separate experts, but only one or two experts need to be trained for each input data. However, the feature of dynamically activating experts of MoE introduces extensive communications in distributed training. In this work, we propose PipeMoE to adaptively pipeline the communications and computations in MoE to maximally hide the communication time. Specifically, we first identify the root reason why a higher pipeline degree does not always achieve better performance in training MoE models. Then we formulate an optimization problem that aims to minimize the training iteration time. To solve this problem, we build performance models for computation and communication tasks in MoE and develop an optimal solution to determine the pipeline degree such that the iteration time is minimal. We conduct extensive experiments with 174 typical MoE layers and two real-world NLP models on a 64-GPU cluster. Experimental results show that our PipeMoE almost always chooses the best pipeline degree and outperforms state-of-the-art MoE training systems by 5%-77% in training time.

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The Case of Transparent Cache Invalidation in Web Applications

arXiv

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arXiv 2023年

作者： Ji, Yunhong Zhou, Xuan Zhou, Yongluan Wang, Ke School of Information Renmin University of China Beijing100872 China School of Data Science and Engineering East China Normal University Shanghai200062 China Department of Computer Science University of Copenhagen CopenhagenDK-2100 Denmark

Application-level caches are widely adopted by web applications to minimize the response time of user requests as well as to reduce the burden on the system backend, such as the database servers. In the state of practice, developers have to take care of the data freshness of application-level caches manually. Given the growing complexities of today's web applications, it becomes increasingly challenging for developers to understand, reason about, and implement cache invalidation methods. Furthermore, according to our survey of open-source web application projects and engineers, it is indeed challenging to map database updates with cache entries at the application level. Therefore, we propose a design to handle data validity in a transparent and precise manner, without requiring any intervention from developers. Its main idea is to modify the DBMS to provide necessary information for cache management and enhance the cache with an invalidation index to identify and invalidate outdated data automatically and efficiently. Based on the design, we further provide two specific solutions. Our preliminary experiments indicate that our solutions could effectively achieve transparent cache invalidation while maintaining cost-effectiveness. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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Robust Indoor Person Re-Identification with Multimodal Training

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IEEE Internet of Things Journal 2025年

作者： Su, Can Xue, Xinlei Ma, Lei Zhang, Xiaolong Yan, Wei Bian, Kaigui Peking University School of Computer Science AI Innovation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing100871 China Peking University School of Computer Science Beijing100871 China Peking University Beijing Academy of Artificial Intelligence National Biomedical Imaging Center College of Future Technology National Key Laboratory for Multimedia Information Processing Beijing100871 China Beihang University Beijing100191 China

Existing person re-identification (ReID) methods mainly rely on images and videos to match persons across cameras, yet visual data captured by cameras are vulnerable to environmental interferences (e.g. illumination and occlusion) or personal appearance changes, leading to performance degradation under such scenes. Meanwhile, the popularization of Wi-Fi networks has allowed probe requests to be captured for mobile sensing applications such as crowd counting and trajectory estimation. However, the MAC address randomization technique adopted by modern devices breaks the association of probe requests and adversely affects the functionality of these applications. In this paper, we propose MaRPA, the first multimodal training approach that incorporates both videos and Wi-Fi probe requests to simultaneously promote tasks of probe requests association and person ReID. MaRPA first distinguishes among pairwise probe request frames through a contrastive learning model. It then matches video and probe request sequences by exploring their similarities from the position and the vision aspects. Matched videos and probe requests provide complementary information and generate more robust features for both tasks. To evaluate MaRPA, we contribute a new dataset containing synchronous videos and probe requests data for probe requests association and person ReID. Experimental results demonstrate the effectiveness of our approach. For probe requests association, it achieves > 85% discrimination accuracy and > 0.90 V-measure score;for person ReID, it achieves 75.8% mAP and 90.6% Rank-1, improving state-of-the-art video-based ReID methods by over 40% © 2014 IEEE.

关键词： Wi-Fi

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

作者： Zhicheng Chen Shibo Feng Zhong Zhang Xi Xiao Xingyu Gao Peilin Zhao Shenzhen International Graduate School Tsinghua University and Tencent AI Lab School of Computer Science and Engineering Nanyang Technological University Tencent AI Lab Shenzhen International Graduate School Tsinghua University and Key Laboratory of Data Protection and Intelligent Management (Sichuan University) Ministry of Education Institute of Microelectronics Chinese Academy of Sciences

ISBN: (纸本)9798331314385

The superior generation capabilities of Denoised Diffusion Probabilistic Models (DDPMs) have been effectively showcased across a multitude of domains. Recently, the application of DDPMs has extended to time series generation tasks, where they have significantly outperformed other deep generative models, often by a substantial margin. However, we have discovered two main challenges with these methods: 1) the inference time is excessively long; 2) there is potential for improvement in the quality of the generated time series. In this paper, we propose a method based on discrete token modeling technique called Similarity-driven Discrete Transformer (SDformer). Specifically, SDformer utilizes a similarity-driven vector quantization method for learning high-quality discrete token representations of time series, followed by a discrete Transformer for data distribution modeling at the token level. Comprehensive experiments show that our method significantly outperforms competing approaches in terms of the generated time series quality while also ensuring a short inference time. Furthermore, without requiring retraining, SDformer can be directly applied to predictive tasks and still achieve commendable results.

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AI in Medicine: CNN Techniques for Pneumonia and Pandemic Pathogen Identification

AI in Medicine: CNN Techniques for Pneumonia and Pandemic Pa...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Ganji Ramanjaiah Nagendar Yamsani Smritilekha Das Balajee Maram Ananda Babu Rudrubati Katakam Venkateswara Rao Dept. of Computer Science & Engineering (Data Science) R VR & JC College of Engineering Chowdavaram Guntur Andhra Pradesh India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Dept. Of Computer Science & Engineering (AI & DS) Kallam Haranadhareddy Institute of Technology Chowdavaram Guntur Andhra Pradesh India Dept of Computer Science and Engineering Koneru lakshmaiah Education Foundation Vaddeswaram Guntur DT Andhra Pradesh India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

The current paper deals with the applicability of Convolutional Neural Networks in the detection of pneumonia and pandemic pathogens using medical imaging data. Leveraging the transformative power of AI in our study, we have placed a focus on the development and evaluation of a CNN model that will be trained on a heterogeneous dataset of chest X-rays and CT scans. This paper will propose a method for developing a CNN to classify images from a diverse dataset of chest X-rays and CT scans, ensuring diagnostic accuracy and speed to meet the critical challenges involved in disease diagnosis in a timely manner. Our results show that the CNN model has high accuracy, sensitivity, and specificity for the identification of pneumonia and differentiation of pandemic pathogens from others, surpassing conventional diagnostic techniques. What was more important to underline in this study was that the model demonstrated strong performance across multiple datasets and, more importantly, can easily be transplanted to clinical workflows. Improvement in the early detection capacity made this research emblematic of the huge role AI is expected to play in redefining health care, especially with respiratory diseases and pandemic preparedness. Future work will extend to the augmentation of the dataset and the fine-tuning of the model for its full clinical application.

关键词： Pathogens Pneumonia Accuracy Pandemics Computed tomography Sensitivity and specificity Convolutional neural networks Artificial intelligence Medical diagnostic imaging X-ray imaging

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