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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Wu, Peng Wang, Zhicheng Pan, Feiyu Li, Fangkai Hu, Hao Lu, Xiankai Guo, Yiyou School of Software Shandong University Shandong Jinan250000 China School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data-Intensive Computing Fujian University Laboratory of Intelligent Computing and Information Processing Quanzhou Normal University Quanzhou China

ISBN: (纸本)9789819611508

Video object segmentation aims to extract 2D object masks by segmenting video frames into multiple objects, which is crucial in various practical applications such as medical imaging, etc.. However, traditional video object segmentation methods produce 2D masks, which are not suitable for 3D scenarios where depth information is essential, such as in robotic grasping, virtual reality, and autonomous driving, etc.. In this paper, we present a comprehensive review of 3D human mesh reconstruction (HMR) as an extension beyond 2D video object segmentation. We begin by reviewing the mainstream video object segmentation methods, then transition from 2D video object segmentation to 3D HMR. We further categorize recent HMR methods based on key characteristics that define this research field, including the type of model input and the use of statistical models. Finally, we provide detailed information on HMR datasets and evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV systems

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IEEE Transactions on Mobile computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Bibliometric Analysis of Machine Learning Ethics

Bibliometric Analysis of Machine Learning Ethics

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Advancements of Artificial Intelligence in African Context (AAIAC), International Conference on the

作者： Sello Prince Sekwatlakwatla Vusumuzi Malele Unit for Data Science and Computing School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa

automating decision-making on a wide range of aspects are affecting humanity. There is great promise in machine learning (ML) and artificial intelligence (AI) algorithms for assisting with predicted and automated decision-making and transforming both societies and industries to improve African citizens' everyday lives. In a business context, platforms such as cloud computing can benefit a lot from AI and ML. Prediction models could be developed to predict the traffic flow of cloud computing resources, which could ease traffic flow. Although AI and ML cloud computing are powerful and vital, they are unfortunately not immune from ethical challenges. As well as improving the allocation of cloud computing resources with prediction models. In recent years, despite AI’s powerful performance, in order to conduct responsible AI and ML modeling, it is important not to ignore ethical issues that might need serious investigation, particularly if human and classified data are concerned. In this regard, the objective of this paper is to determine the prevalence of AI and ML ethics research in Africa. In this paper, RStudio and the computing-related databases (IEEE Xplore, Web of service and Scopus) were used to conduct a bibliometric analysis on the prevalence of AI and ML ethics that relate to the cloud computing environment. The scope was minimized due to its prevalence in the African context. The findings show that AI and ML ethics are understudied on the African continent compared to the West. In this regard, there is a need to conduct more AI and ML ethics-related research in Africa because failure to do so will result in the West shaping the ethical discussion in areas of importance for African development, such as cloud computing. The latter will allow the West to slowly impose its perspective without considering African perspectives. Of note, this paper contributes to the doctoral study with an objective phrased as "to conduct bibliometric analysis to identify the preval

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VIPNet: Combining Viewpoint information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Importance-Awareness Masking Network for Robust Document Retrieval

Importance-Awareness Masking Network for Robust Document Ret...

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

作者： Liu, Junping He, Jiaqi Hu, Xinrong Yang, Wangli Yang, Jie Guo, Yi School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Computing and Information Technology University of Wollongong Wollongong Australia School of Computer Data and Mathematical Sciences Western Sydney University Australia

ISBN: (纸本)9798350368741

In this paper, we introduce the IMPortance-awaReness maskIng NeTwork (IMPRINT), a novel approach to enhance the robustness of document retrieval systems against query variations, particularly those containing misspellings. Unlike previous models that treat all query components (words/features) equally, IMPRINT prioritizes the most important components while masking out less relevant ones. Specifically, we propose a Mutual information-based measure to quantify component importance and integrate it into a dynamic masking mechanism that adjusts the retention probability of each component. Our method is evaluated on a combination of three benchmark datasets and three types of query variations. The experimental results show substantial performance gains compared to state-of-the-art models, achieving an average improvement of 1.2 absolute MRR@10 points in retrieval accuracy. © 2025 IEEE.

关键词： Document Retrieval Model Robustness Query Variations Word/Feature Importance

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Identify Assessment Indicators in Online Learning to Increase Student Performance Using Neural Network Approach 9

Identify Assessment Indicators in Online Learning to Increas...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Rabiha, Sucianna Ghadati Dewi, Meta Amalya Murad, Dina Fitria Kurniadi, Felix Indra Romli, Awanis Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universiti Malaysia Pahang Faculty of Computing Pahang Malaysia

ISBN: (纸本)9798350383010

The main point of predicting student performance is to identify students at-risk as early as possible to enable instructors to implement educational interventions and facilitate student achievement. In this study, we have used Neural Network aproach to identify assement indicator in Online Learning. The dataset used is Distance Learning Student Value data extracted through Learning Management systems The highest accuracy value obtained was 99.61% with a precision value of 99.37%, a recall of 98.87% and a relatively low RMSE value of 5.5%. The best accuracy results from the training data distribution ratio of 70% and 30%, while the proposed number of hidden layers is 2 hidden layers with the number of neurons in each layer H1(Hidden layer 1) and H2(Hidden layer 2) is 12. And the value of learning rate is 0.04. This study contributes to the selection of Neural Network architectures in determining student performance predictions. In addition, it has an impact on the analysis of which variables affect student passing grades. So that it can be a reference in determining content improvements and further learning models in Online Learning. © 2024 IEEE.

关键词： Contrastive Learning

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Stochastic Block Models for Complex Network Analysis: A Survey

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ACM Transactions on Knowledge Discovery from data 2025年第3期19卷 1-35页

作者： Liu, Xueyan Song, Wenzhuo Musial, Katarzyna Li, Yang Zhao, Xuehua Yang, Bo College of Computer Science and Technology Jilin University Changchun China School of Information Science and Technology Northeast Normal University Changchun China Complex Adaptive Systems Lab Data Science Institute University of Technology Sydney Sydney Australia Aviation University of the Air Force Changchun China School of Digital Media Shenzhen Institute of Information Technology Shenzhen China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Ministry of Education Changchun China

Complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Complex Networks Structural Pattern Discovery Stochastic Block models Learning Methods of SBMs

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Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach 38

Replay-and-Forget-Free Graph Class-Incremental Learning: A T...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Niu, Chaoxi Pang, Guansong Chen, Ling Liu, Bing AAII University of Technology Sydney Australia School of Computing and Information Systems Singapore Management University Singapore Department of Computer Science University of Illinois Chicago United States

Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., node classification in a graph). The key characteristic of CIL lies in the absence of task identifiers (IDs) during inference, which causes a significant challenge in separating classes from different tasks (i.e., inter-task class separation). Being able to accurately predict the task IDs can help address this issue, but it is a challenging problem. In this paper, we show theoretically that accurate task ID prediction on graph data can be achieved by a Laplacian smoothing-based graph task profiling approach, in which each graph task is modeled by a task prototype based on Laplacian smoothing over the graph. It guarantees that the task prototypes of the same graph task are nearly the same with a large smoothing step, while those of different tasks are distinct due to differences in graph structure and node attributes. Further, to avoid the catastrophic forgetting of the knowledge learned in previous graph tasks, we propose a novel graph prompting approach for GCIL which learns a small discriminative graph prompt for each task, essentially resulting in a separate classification model for each task. The prompt learning requires the training of a single graph neural network (GNN) only once on the first task, and no data replay is required thereafter, thereby obtaining a GCIL model being both replay-free and forget-free. Extensive experiments on four GCIL benchmarks show that i) our task prototype-based method can achieve 100% task ID prediction accuracy on all four datasets, ii) our GCIL model significantly outperforms state-of-the-art competing methods by at least 18% in average CIL accuracy, and iii) our model is fully free of forgetting on the four datasets. Code is available at https://***/mala-lab/TPP. © 2024 Neural information processing syste

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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STAGGER: Enabling All-in-One Subarray Sensing for Efficient Module-level Processing in Open-Bitline ReRAM 24

STAGGER: Enabling All-in-One Subarray Sensing for Efficient ...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Chengning Feng, Dan Hu, Yuchong Tong, Wei Liu, Jingning Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage Systems Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400706011

Emerging resistive RAM (ReRAM) devices can in-situ execute vector-matrix-multiplication (VMM) and is able to achieve energy-efficient in-memory scientific computing. However, the peripheral separated S&Hs and ADCs for row buffering and sensing in conventional designs are the system bottleneck. We propose an ADC-less all-in-one processing-in-ReRAM design that enables the precharge once, readout multiple-bits (PORM) functionality for overlapping the tRCD latencies of different-significance result bits sensed out on the same bitline. Specifically, to support PORM sensing, we propose a cascaded-feedback bitline sensing architecture for VMM and a buffering-and-sensing-collocated sense amplifier elemental design with the bitline and the storage node fully decoupled for enabling conflict-free column accesses. We further propose cross-level inter-leaving mechanism for successive column VMM accesses to reduce the overall latency through improving the hardware spatiotemporal utilization. Experimental results show that our proposed design achieves 297% overall performance improvement and 85.8% energy reduction, compared with an aggressive baseline. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Feedback amplifiers

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