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A Transformer-Based Approach for 3D Human Pose Estimation in Rehabilitation Exercise Movements

A Transformer-Based Approach for 3D Human Pose Estimation in...

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computer Vision, Image and Deep Learning (CVIDL), international conference on

作者： Naichen Kang Gang Chen Cong Zhang Yubo Xue School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

The proportion of elderly patients with chronic diseases, represented by stroke, continues to rise. More and more elderly patients in the rehabilitation stage are facing serious challenges such as long rehabilitation periods, difficulty perceiving the effects of the rehabilitation stage, and limited mobility leading to difficulties in offline visits. These issues undoubtedly create significant psychological pressure and economic burden on patients. In response to this situation, this paper proposes a multistage 3D human pose estimation method based on a monocular camera. The model is specifically designed to address common environmental occlusion and inter-limb occlusion issues, aiming to improve the measurement accuracy and evaluation effectiveness of the model. The network structure is based on a combination model of HRNet+OCNet to achieve high-precision prediction of 3D keypoints. By incorporating a correction module based on PAF, the coordinates of target keypoints under “traversal occlusion” and “ full occlusion of end joint points “ scenes are corrected, thereby obtaining a 3D human pose sequence closer to reality.

关键词： Training Solid modeling Three-dimensional displays Pose estimation Psychology Stroke (medical condition) Predictive models

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Research on Deep Learning Network Sample Generation Method Based on Abnormal Examples

Research on Deep Learning Network Sample Generation Method B...

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Software Engineering and computer Science (CSECS), international conference on

作者： Xuan Xian Tiantian Du Zihan Deng Liuzhou City Vocational College School of computer science and technology Liuzhou Guangxi P.R.China Shenzhen University School of Economics and Management Shenzhen Guangdong Harbin Engineering University School of computer science and technology Harbin Heilongjiang

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The complexity and uncertainty of deep neural networks (DNN) necessitate comprehensive behavior testing for model quality assurance. Current studies generating samples via coverage criteria and fuzz testing often neglect sample diversity and error-detection ability. The surprise adequacy metric, quantifying neuron output differences between samples and the training set, lacks a specific generation method. Herein, a novel method is introduced. Initially, key decision-influencing neurons are identified to refine the surprise adequacy metric. Subsequently, seed samples with error-revealing potential are filtered by this metric. Finally, integrating coverage-guided fuzz testing, with the surprise adequacy value and class probability difference as joint optimization targets, the gradient ascent algorithm computes perturbations for iterative sample generation. Five DNN models and four image datasets were used for validation. Results confirm that the enhanced metric swiftly captures abnormal samples and saves computation time. In sample generation, compared to DeepGini and RobOT, the derived test sets' surprise coverage rises by up to 5.9% and 15.9% respectively; versus DLFuzz and DeepXplore, the proposed method achieves a boost of up to 26.5% and 33.7%, validating its efficacy.

关键词： measurement Training Uncertainty Computational modeling Neurons Artificial neural networks Fuzzing Optimization Robots Software engineering

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Ensemble Learning Based Employment Recommendation Under Interaction Sparsity for College Students 9th

Ensemble Learning Based Employment Recommendation Under Inte...

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19th international conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhu, Haiping Zhao, Yifei Wu, Yuchen Chen, Yan Li, Wenhao Zheng, Qinghua Tian, Feng Department of Computer Science and Technology Xi’an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9783031466632

Recommendation systems play a crucial role in helping college students find job opportunities. However, the sparsity of interactions in employment recommendation for college students poses a challenge for models based on historical user preferences. To address this issue, we propose a novel model called Ensemble Learning based Employment Recommendation under Interaction Sparsity for College Students (EERIS). The model comprises two components: a similarity information component that uses pooled users to determine the nearest neighbor in user similarity measurement, and a global interaction component that uses interaction vectors of user groups to enhance interactions. To evaluate the missing interactions, we propose a loss function called CellLoss. These components are combined based on ensemble learning to improve the model’s generalization and scalability. Our experiments on two real-world datasets demonstrate the superior performance of the EERIS model. Ablation experiments further confirm that each component positively contributes to the model’s performance. Additionally, we design a revised metric for better model testing. Overall, the proposed EERIS model effectively addresses the interaction sparsity in employment recommendation for college students and provides satisfactory recommendations to students. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Characterization Model for Irregular MPI Applications Based on PAS2P

Characterization Model for Irregular MPI Applications Based ...

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computer Science Society (SCCC) international conference Chilean

作者： Felipe Tirado Alvaro Wong Dolores Rexachs Emilio Luque Departamento de Computación e Industrias Universidad Católica del Maule Talca Chile Computer Architecture and Operating System Department Universidad Autónoma de Barcelona Barcelona Spain

ISBN: (数字)9798331527891

ISBN: (纸本)9798331527907

Performance modeling of parallel applications is essential for optimizing resource usage in high-performance computing (HPC) systems. However, some scientific applications exhibit irregular performance behaviors, which complicates cre-ating accurate characteristic models. This irregularity is mainly due to these applications' nondeterministic computational and communication patterns. Tools such as PAS2P (Parallel Application Signatures for Performance Prediction) are used to extract detailed information about parallel applications. PAS2P is based on the repetitive behavior of the application to analyze and predict the application's performance, using the same resources that the parallel application uses for its execution. This paper presents a characterization model based on the PAS2P methodology for irregular applications that groups the repeatability patterns of all the processes running the application into a single characteristic model. To achieve this, we consolidate the different characterizations performed by each process independently, using metrics such as the number of instructions, the execution time of relevant sections, and the topological characteristics of the application. By grouping these repeatability patterns of all processes, we can obtain a concise and accurate representation of the behavior of irregular applications, thus improving predictability and performance optimization in HPC systems.

关键词： measurement computer science Analytical models Accuracy Computational modeling High performance computing Predictive models Data mining Optimization

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Revolutionizing Heart Disease Prediction with Quantum-Enhanced Support Vector Machines

Revolutionizing Heart Disease Prediction with Quantum-Enhanc...

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Computational Intelligence and Network systems (CINS), international conference on

作者： Sharon Jessika S Harsh Mantri Angad Chaudhry Jehan Sharukh Bhathena Syed Rayan Alam Gautham sai SA Natarajan B School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamilnadu India School of CSE VIT Vellore Campus Vellore Tamilnadu India School of CSE VIT Chennai Campus Chennai Tamilnadu India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

Heart diseases are encountered as a leading root for higher death cases around the globe. However, the death rate can be reduced by taking proper treatment procedures and medical inspections. The early identification of heart diseases poses huge challenges to the physicians due to the complexity of the root causes. The emergence of intelligent decision-making support systems aids in predicting the diseases at the earliest. The recent innovations handle a huge amount of earlier data and produce accurate results as expected. The introduction of quantum computing-based machine learning approaches produces outstanding results in prediction and classification. The proposed research work aims to improve the heart disease prediction rate by incorporating Quantum Support Vector Machine Algorithms (QSVM). The outcomes of this research work shows the improved results in terms of prediction accuracy. Further, the additional evaluation metrics state the significance of the proposed work.

关键词： Heart Support vector machines measurement Technological innovation Accuracy Computational modeling Qubit Vectors Integrated circuit modeling Diseases

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The Tale of Errors in Microservices: Extended Abstract 25

The Tale of Errors in Microservices: Extended Abstract

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Abstracts of the 2025 ACM SIGMETRICS international conference on measurement and modeling of computer systems

作者： I-Ting Angelina Lee Zhizhou Zhang Abhishek Parwal Milind Chabbi Department of Computer Science & Engineering Washington University in St. Louis St. Louis USA Programming Systems Group Uber Technologies Inc. Sunnyvale USA Marketplace Configurations Uber Technologies Inc. Sunnyvale USA

ISBN: (纸本)9798400715938

Microservice architectures have become the de facto paradigm for building scalable, service-oriented systems. Although their decentralized design promotes resilience and rapid development, the inherent complexity leads to subtle performance challenges. In particular, non-fatal errors - internal failures of remote procedure calls that do not cause top-level request failures - can accumulate along the critical path, inflating latency and wasting *** this work, we analyze over 11 billion RPCs across more than 6,000 microservices at Uber. Our study shows that nearly 29% of successful requests experience non-fatal errors that remain hidden in traditional monitoring. We propose a novel latency-reduction estimator (LR estimator) to quantify the potential benefit of eliminating these errors. Our contributions include a systematic study of RPC error patterns, a methodology to estimate latency reductions, and case studies demonstrating up to a 30% reduction in tail latency.

关键词： RPC errors

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Decentralized Cognitive Swarm-Based Control systems: A Novel Approach for Autonomous Decision-Making and Adaptation

Decentralized Cognitive Swarm-Based Control Systems: A Novel...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Pavel A. Panilov Tatiana Y. Tsibizova Computer science and control systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The emergence of decentralized systems has precipitated a paradigm shift in the realm of autonomous control across diverse domains, ranging from robotics to critical infrastructure management. One such approach that is gaining traction is the cognitive swarm-based control system (CSBCS), which applies principles from cognitive science and swarm intelligence to decentralized decision-making processes. The present paper explores the design and application of CSBCS, emphasizing its ability to foster adaptability, robustness, and scalability in dynamic environments. The CSBCS framework integrates cognitive models that simulate human-like thinking, enabling individual agents within the swarm to operate autonomously, collaborate effectively, and adapt to changing conditions without centralized oversight. The experimental findings, derived from simulations and real-world applications demonstrate the potential of CSBCS in augmenting both individual and collective performance across a spectrum of domains, ranging from multi-robot coordination to critical infrastructure protection. CSBCS provides an framework for addressing complex issues that traditional centralized approaches are unable to tackle effectively.

关键词： Adaptation models Scalability Robot kinematics Heuristic algorithms Computational modeling Decision making computer architecture Distance measurement Critical infrastructure Resource management

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Retinal Vascular System Segmentation based on Non-Linear Map-Based Estimation of joint MGRF Model

Retinal Vascular System Segmentation based on Non-Linear Map...

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IEEE international Symposium on Biomedical Imaging

作者： Ahmed Alksas Ahmed Sharafeldeen Hossam Magdy Balaha Mohamed Ghazal Marah Alhalabi Ali Mahmoud Rasha Hassan Jawad Yousaf Harpal Sandhu Ayman El-Baz Department of Bioengineering University of Louisville Louisville KY USA Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

We introduce a novel, fully automated, unsupervised segmentation technique for extracting the vascular system from retinal Optical Coherence Tomography Angiography (OCTA) images, a crucial component in computer-aided diagnostic systems for retinal disease detection. Our approach integrates a two-level joint Markov-Gibbs Random Field (MGRF) framework, featuring a new probabilistic model—the Linear Combination of Discrete Gaussian (LCDG). The first level addresses OCTA image appearance and their spatially smoothed counterparts through LCDG, with parameters refined by an enhanced Expectation Maximization (EM) algorithm. The second level captures vascular and retinal tissue maps via MGRF, utilizing parameters analytically derived from images. A novel application of modified self-organizing maps, acting as a MAP-based optimizer, enables unsupervised, nonlinear optimization-driven segmentation, marking a departure from traditional methods. Evaluated on a dataset of 204 subjects, our framework achieves significant metrics: a Dice similarity coefficient of 0.90±0.04, a 95-percentile bidirectional Hausdorff distance of 0.79 ± 0.28, and an accuracy of 0.86 ± 0.06, underscoring its effectiveness.

关键词： Self-organizing feature maps measurement Image segmentation Optical coherence tomography Computational modeling Biological system modeling Estimation

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Enhanced Graph Convolutional Networks with Adaptive Aggregation

Enhanced Graph Convolutional Networks with Adaptive Aggregat...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Vinod Joshi R.K. Somani Department of Computer Science Sangam University Bhilwara Rajasthan India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Graph neural networks represent the latest promising advances in deep learning models to analyze graph-structured data, but traditional models, such as the Graph Convolutional Network, have scalability issues such as oversmoothing and lack of generalization. This work extends the standard GCN by proposing adaptive aggregation functions, scalable normalization techniques, and robust regularization methods. The proposed change dynamically selects aggregation strategies based on graph properties, improves the computational efficiency at optimality, and enhances the model generalization, especially on large complex graphs. The improved GCN was tested in relation to the major metrics: accuracy, cross-entropy loss, and F1 score. Experimental results show a 6.5% increase in accuracy and a 20% reduction in runtime compared with baselines like GAT and GraphSAGE. This makes the model more apt for real-time applications and scalable datasets. However, the approach brought out the shortcomings of handling highly dynamic, sparse graphs. Future developments would include joint integration of attention mechanisms with recurrent neural networks, hybrid architectures that capture dynamic graph structures, and better fitting into evolving topologies.

关键词： Adaptation models Analytical models Adaptive systems Accuracy Graph convolutional networks Computational modeling Data models Agriculture Topology Standards

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Complex Recurrent Variational Autoencoder for Speech Resynthesis and Enhancement

Complex Recurrent Variational Autoencoder for Speech Resynth...

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international joint conference on Neural Networks (IJCNN)

作者： Yuying Xie Thomas Arildsen Zheng-Hua Tan Department of Electronic Systems Aalborg University Aalborg Denmark

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Aiming at learning a probabilistic distribution over data, generative models have been actively studied with broad applications. This paper proposes a complex recurrent variational autoencoder (VAE) framework, for modeling time series data, particularly speech signals. First, to account for the temporal structure of speech signals, we introduce complex-valued recurrent neural network in the framework. Then, inspired by recent advancements in speech enhancement and separation, the reconstruction loss in the proposed model is L1-based loss, considering penalty on both complex and magnitude spectrograms. To exemplify the use of the complex generative model, we choose speech resynthesis first and then enhancement as the specific application in this paper. Experiments are conducted on the VCTK, TIMIT, and VoiceBank+DEMAND datasets. The results show that the proposed method can resynthesize complex spectrogram well, and offers improvements on objective metrics in speech intelligibility and signal quality for enhancement.

关键词： measurement Representation learning Recurrent neural networks Time series analysis Speech enhancement Probabilistic logic Data models

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