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arXiv 2025年

作者： Zhao, Yuming Zhang, Qijian Hou, Junhui Xia, Jiazhi Wang, Wenping He, Ying Department of Computer Science City University of Hong Kong Hong Kong TiMi L1 Studio Tencent Games China School of Computer Science and Engineering Central South University China Department of Computer Science & Engineering Texas A&M University United States College of Computing and Data Science Nanyang Technological University Singapore

Surface parameterization is a fundamental geometry processing task, laying the foundations for the visual presentation of 3D assets and numerous downstream shape analysis scenarios. Conventional parameterization approaches demand high-quality mesh triangulation and are restricted to certain simple topologies unless additional surface cutting and decomposition are provided. In practice, the optimal configurations (e.g., type of parameterization domains, distribution of cutting seams, number of mapping charts) may vary drastically with different surface structures and task characteristics, thus requiring more flexible and controllable processing pipelines. To this end, this paper introduces FlexPara, an unsupervised neural optimization framework to achieve both global and multi-chart surface parameterizations by establishing point-wise mappings between 3D surface points and adaptively-deformed 2D UV coordinates. We ingeniously design and combine a series of geometrically-interpretable sub-networks, with specific functionalities of cutting, deforming, unwrapping, and wrapping, to construct a bi-directional cycle mapping framework for global parameterization without the need for manually specified cutting seams. Furthermore, we construct a multi-chart parameterization framework with adaptively-learned chart assignment. Extensive experiments demonstrate the universality, superiority, and inspiring potential of our neural surface parameterization paradigm. The code will be publicly available at https://***/AidenZhao/FlexPara. Copyright © 2025, The Authors. All rights reserved.

关键词： Photomapping

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Investigation of Environmental Monitoring Based on Three IoT Wireless Sensors 2

Investigation of Environmental Monitoring Based on Three IoT...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Yamini, B. Arthy, M. Priya, S Geetha Kathiresan, S. Jeneetha Jebanazer, J. Uthayakumar, G.S. Srm Institute of Science and Technology Department of Networking and Communication Engineering Tamil Nadu Kattankulathur Chennai India Srm Institute of Science and Technology School of Computing Department of Data Science& Business Systems Tamil Nadu Kattankulathur Chennai India R. M. D Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Environmental monitoring has become more crucial as a result of the recent climatic changes, making it a hot issue and a very active study field. This field uses wireless sensor networks and remote sensing to collect data about the environment. Recent developments, including the Internet of Things (IoT) concept, the cloud computing paradigm, and cyber-physical systems, assist the administration and transmission of enormous volumes of data about changes in environmental parameters. The current work presents three different IoT-based wireless sensors for monitoring the environment and the surrounding area: one uses Wi-Fi User datagram Protocol (UDP)-based communication, one uses Wi-Fi and Hypertext Transfer Protocol (HTTP), and a third one uses Bluetooth Smart. The ability to capture data at distant places and see it from any device with an Internet connection is a feature shared by all of the systems that have been discussed. This makes it possible to monitor vast geographic regions. These systems' specific development details are discussed, along with their main distinctions and affinities. The three created systems were evaluated for their suitability for IoT-based solutions, taking into consideration their energy independence, usability, solution complexity, and Internet connectivity capability. © 2023 IEEE.

关键词： Bluetooth

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Hybrid Semantic/Bit Communication Based Networking Problem Optimization

arXiv

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arXiv 2024年

作者： Xia, Le Sun, Yao Niyato, Dusit Zhang, Lan Zhang, Lei Imran, Muhammad Ali James Watt School of Engineering University of Glasgow Glasgow United Kingdom College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Clemson University United States

This paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a novel and practical next-generation cellular network where two modes of semantic communication (SemCom) and conventional bit communication (BitCom) coexist, namely hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we comprehensively develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with several practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by employing a Lagrange primal-dual method and devising a preference list-based heuristic algorithm. Finally, numerical results validate the performance superiority of our proposed strategy compared with different benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency allocation

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ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks

arXiv

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arXiv 2024年

作者： Chen, Zhaoliang Wu, Zhihao Sadikaj, Ylli Plant, Claudia Dai, Hong-Ning Wang, Shiping Cheung, Yiu-Ming Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou350116 China Department of Computer Science Hong Kong Baptist University Hong Kong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science and with the research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edgedropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Graph Contrastive Learning for Truth Inference

Graph Contrastive Learning for Truth Inference

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International Conference on data Engineering

作者： Hao Liu Jiacheng Liu Feilong Tang Peng Li Long Chen Jiadi Yu Yanmin Zhu Min Gao Yanqin Yang Xiaofeng Hou School of Data Science and Engineering East China Normal University Shanghai China Department of Computer Science and Engineering Chinese University of Hong Kong China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of Aizu Japan School of Computing Science Simon Fraser University Canada

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques.

关键词： Training Crowdsourcing Limiting Accuracy Annotations Contrastive learning Nearest neighbor methods

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A Framework Optimization in Social Media using Xampp: A Systematic Approach 4

A Framework Optimization in Social Media using Xampp: A Syst...

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International Conference on 4th Industrial Revolution Based Technology and Practices, ICFIRTP 2022

作者： Joshi, Kapil Kumar, Rajiv Kumar, Anil Reshi, Jagdeep Sharma, Aditi Dumka, Ankur Uttaranchal University Uttaranchal Institute of Technology Department of CSE Dehradun India Uttaranchal University Uttaranchal School of Computing Sciences Dehradun India DIT University Data Science Research Group School of Computing Dehradun India School of Engineering and Digital Sciences Nazarbayev University Kazakhstan Women Institute of Technology Department of CSE Dehradun India Graphic Era Deemed to Be University Department of CSE Dehradun India

ISBN: (纸本)9798350345919

XAMPP is an acronym that stands for Cross-Platform, Apache, MySQL, PHP, and Perl, with the Ps standing for PHP and Perl, respectively. This article investigates the various helpful assets and procedures which are utilized in site creation. In a discussion, chiefly dependent on a nearby host named Xampp stage, we likewise address the procedure that follows. Next, we break down different web application creation frameworks. Furthermore,, We're discussing life cycle model and web application engineering creation. Frequently remembered for this article is the result of various research articles for buyers to consider the issues they that experience. This paper discusses the innovation utilized in this turn of events, PHP and with screen captures clarified its highlights with Xampp subsequently. It is normal that it would have significant criticism context for the strategy. © 2022 IEEE.

关键词： FACENET Map reading MTCNN Xampp

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Multiplication of Sparse Matrices and their Transpose Using Compressed Sparse Diagonals

Multiplication of Sparse Matrices and their Transpose Using ...

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IEEE Conference on High Performance Extreme computing (HPEC)

作者： Sardar Anisul Haque Mohammad Tanvir Parvez Shahadat Hossain School of Computing and Data Science Oryx Universal College in Partnership with LJMU (UK) Doha Qatar Department of Computer Engineering College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science University of Northern British Columbia Prince George BC Canada

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

Matrix-matrix multiplication is one of the most important kernel in linear algebra operations with a multitude of applications in scientific and engineering computing. Sparse matrix computation on modern High-performance computing (HPC) architecture presents challenges such as load balancing, data locality optimization, and computational scalability. data structures to store sparse matrices are designed to minimize overhead information as well as to optimize the operations count and memory access. In this study, we present a new data structure, “compressed sparse diagonal” (CSD), to efficiently store and compute with general sparse matrices. The CSD builds upon the previously developed diagonal storage - an orientation-independent uniform scheme to compute with “structured” matrices [1]. Compared with the widely used compressed sparse row/column (CSR/CSC), the CSD scheme avoids explicit transposition operation when multiplying a matrix with its transpose. The results from preliminary numerical experiments with the aforementioned types of matrices demonstrate the CSD scheme's effectiveness in matrix-transposed matrix multiplication.

关键词： Scalability High performance computing Engineering computing Linear algebra data structures Load management Sparse matrices Kernel Optimization

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Basketball Win Percentage Prediction using Ensemble-based Machine Learning

Basketball Win Percentage Prediction using Ensemble-based Ma...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Dhruv Sikka Rajeswari D Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9781665482721

Basketball is a prominent team sport played on a rectangular court between two teams of five players each. The goal is to shoot the ball through the defender's hoop, which is high on a backboard at each end of the court, while stopping the other team from shooting through its own hoop. Predicting how sports will turn out is an interesting sports analytics problem because it gives valuable data for running the sports market. In this work, data from NBA (National Basketball Association) basketball teams is analyzed, and a machine learning model is made that uses advanced statistical measures and performance data to evaluate and predict the winning percentage of different teams. Voting Regression is used to put together algorithms like Random Forest, Decision Trees, Linear Regression, Gaussian Regression, and Gradient Boosting into a single unit that can predict a team's win percentage over the course of a season with 93.3% accuracy.

关键词： Schedules Machine learning algorithms Games Forestry Predictive models Prediction algorithms Reliability

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GarbageNet: A Unified Learning Framework for Robust Garbage Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第4期2卷 372-380页

作者： Yang, Jianfei Zeng, Zhaoyang Wang, Kai Zou, Han Xie, Lihua The School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore The School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China The Institutes of Data Science School of Computing National University of Singapore Singapore119077 Singapore The Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States

The recyclability of domestic waste plays a crucial role in the modern society, which helps reduce multiple types of pollution and brings economic effect. To achieve this goal, garbage classification is one of the most important steps during the recycling process. Prevailing deep learning techniques empower high-performance visual recognition models and can benefit the automation of garbage classification task. Nevertheless, there exist three challenges when directly leveraging deep recognition models for this task, referring to lack of sufficient data, high cost of category increment, and noisy data quality. In this article, we present a novel incremental learning framework, GarbageNet, to address the aforementioned challenges. First, weakly-supervised transfer learning guarantees the capacity of feature extractor. Second, for new categories of garbages, GarbageNet embeds them as anchors for reference and classifies the test samples by finding their nearest neighbors in the latent space. Third, an attentive mixup of training data is utilized for suppressing the negative effect of mislabeled data. We evaluate our method on real-world datasets, and the empirical results demonstrate that GarbageNet achieves the state-of-the-art performance with regard to accuracy, robustness, and extendability. The proposed method won the first place in the HUAWEI Cloud Garbage Classification Challenge in 2019. Impact Statement-This article contributes to a vision-based garbage recognition system for the recyclability of domestic waste. To the best of our knowledge, it is the first article that explores a realistic vision-based solution for the garbage recognition with a real-world dataset and a comprehensive benchmark on current state-of-the-art visual recognition models. The proposed method addresses several challenges and achieves state-of-the-art performance. We believe that this interdisciplinary research contributes to the AI for the environment, which helps promote environmenta

关键词： Convolutional neural networks

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Mixture of Expert System for IESS Detection Based on EEG Signal

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Journal of Shanghai Jiaotong University (science) 2024年 1-9页

作者： Wang, Zong Fu, Lijun Yang, Guang Wan, Lin Chang, Zhijun Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China University of Chinese Academy of Sciences Beijing100049 China R.C. of Big Data and Artificial Intelligence Technology Shandong University Jinan250100 China Senior Department of Pediatrics the Seventh Medical Center of Chinese PLA General Hospital Beijing100700 China Department of Pediatrics the First Medical Center of Chinese PLA General Hospital Beijing100853 China The Second School of Clinical Medicine Southern Medical University Guangzhou510280 China The National Science Library Chinese Academy of Sciences Beijing100190 China

Childhood epilepsy presents significant challenges in terms of medical diagnosis and treatment, particularly when it comes to infantile epileptic spasm syndrome (IESS). This condition is characterized by electroencephalogram (EEG) signals with short seizure duration and insignificant waveform, which complicates manual efforts to detect IESS seizures. To address these challenges, we introduce an innovative model called the IESS mixture of experts (MOE) model to assist EEG technologists in their work. The model utilizes a group of subdata experts operating through a 3D ResNet network to enhance the detection of seizure signals. Specifically, we propose an unsupervised clustering method for EEG signal partitioning into some balanced sub-data domains, addressing the imbalance between IESS seizure and non-seizure data. The IESS MOE model consists of a navigation network that controls input and output, as well as some chosen sub-expert networks that make judgments on sub-expert data domains. We use the PLA IESS dataset for unsupervised learning-based data partitioning among sub-models and subsequent expert modeling. Additionally, we establish a comparison between the performance of the IESS MOE model and six human experts from different levels of medical centers using external validation on 19 cross-individual patient data sets. Finally, test results demonstrate that the sensitivity of the IESS MOE model is 0.539 3, surpassing the average level achieved by human experts at 0.514 3 while maintaining high specificity at 0.905 0 and achieving a high F1 score at 0.675 8, confirming its superior generalization ability. Our proposed model can assist human experts in monitoring episodes related to IESS. © Shanghai Jiao Tong University 2024.

关键词： Electroencephalography

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