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The PSG challenge: towards comprehensive scene understanding

National science Review 2023年第6期10卷 41-44页

作者： Jingkang Yang Zheng Ma Qixun Wang Xiaofeng Guo Haofan Wang Ziwei Liu Wayne Zhang Xing Xu Hai Zhang Sense Time Research S-Lab Nanyang Technological University Xiaohongshu Inc Institute of Optics and Electronics Chinese Academy of Sciences School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Most computer vision tasks currently focus on recognizing objects in *** instance,image classification only needs to identify the main object in an image [1],while object detection and image segmentation only require models to locate objects in images [2].However,these tasks are insufficient to achieve a comprehensive and in-depth understanding of a *** example,in ***(i),a model that only detects people,elephants,fences and trees would not have an understanding of the scene, making it unable to provide safety reminders such asnot to feed the *** many realworld AI applications like smart cities,autonomous driving and smart manufacturing,it is crucial to not only localize targets in the scene but also reason and predict their *** example,in autonomous driving,it is important to analyze whether pedestrians on the side of the road are pushing or riding *** smart factories,it is necessary to judge whether operators are working *** these relations is vital for decision-making.

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Precise Leg Abduction Hydrotherapy Exercise Pose Analysis for Real Time Monitoring and Feedback 5

Precise Leg Abduction Hydrotherapy Exercise Pose Analysis fo...

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5th International Conference on Smart Sensors and Application, ICSSA 2024

作者： Dawood, Aly Khaled Mekki, Amr Emad Khairuddin, Uswah Eissa, Noureldin S. Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Department of Computer Engineering Arab Academy for Science Technology and Maritime Transport Cairo2033 Egypt

ISBN: (纸本)9798350354294

This paper presents a novel technique for obtaining precise leg abduction hydrotherapy pose analysis in real-time. It aims to enhance hydrotherapy through the application of AI-powered pose estimation technology. The proposed system addresses the limitations of traditional subjective assessments by providing precise, real-time monitoring of gait and posture using computer vision techniques. The system tracks movements underwater using YOLO-NAS Pose, offering immediate feedback. The methodology involves capturing underwater movements with a high degree of accuracy, processing these movements to calculate precise joint angles, counting repetitions, and providing real-time insights. The experiments focused on a single exercise, leg abduction, performed by one individual. The results showed a joint extraction accuracy rate of 98.3%. Additionally, during live webcam testing on land, the system achieved an average frame rate of 4 frames per second (fps). These results highlight the system’s potential to significantly improve the effectiveness of hydrotherapy rehabilitation by offering precise and real time feedback. © 2024 IEEE.

关键词： Subjective testing

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Leveraging Edge Computing for Enhancing Next Generation Network Services 15

Leveraging Edge Computing for Enhancing Next Generation Netw...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Naveena, S.Sathiya Goyal, Prakhar Narmadha Verma, Devika A. Balaji, N.V. Rampal, Sourav Prince Shri Venkateshwara Padmavathy Engineering College Department of Business Administration Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350370249

Aspect computing is quickly turning into a possible answer for augmenting and streamlining the overall performance of next era community services. Edge computing gives a disbursed computing architecture that allows computation and records storage competencies to are living closer to the edge network, rather than in, valuable records middle. This enables applications to enjoy faster reaction times and reduced latency, growing the rate of facts shipping. Side computing also presents greater scalability, simplifying the combination of IoT gadgets, permitting statistics streaming from more than one asset. As networks pass to 5G, area computing will offer the important flexibility and stability to deal with big amounts of statistics visitors securely and successfully. By leveraging the power of edge computing, corporations can create an agile and resilient service structure, imparting more advantageous safety and a variety of customizable abilities that permit them to capitalize on the success of the 5 G community. © 2024 IEEE.

关键词： Abilities Advantageous Capitalize Customizable Permit Streaming Success Variety

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Efficient transformation capabilities of single database private block retrieval

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International Journal of Information technology (Singapore) 2022年第3期14卷 1415-1423页

作者： Bhat, Radhakrishna Sunitha, N.R. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur India

Private information retrieval (PIR) is one of the promising techniques to preserve user privacy in the presence of trusted-but-curious servers. The information theoretically private query construction assures the highest user privacy over curious and unbounded computation servers. Therefore, the need for information-theoretic private retrieval was fulfilled by various schemes in a variety of PIR settings. But, there is a lack of efficient encryption switching scheme which supports efficient switching between information-theoretic to/from computationally bounded PIRs. We propose a combination of new bit connection methods called rail-shape and signal-shape and new quadratic residuosity assumption based family of trapdoor functions for generic single database private block retrieval (PBR). The main goal of this work is to show that the possibility of mapping from computationally bounded privacy to information theoretic privacy or vice-versa in a single database setting using newly constructed bit connection and trapdoor function combinations. Notably, the proposed schemes are single round, memoryless and plain database schemes (at their basic constructions). © 2022, The Author(s).

关键词： Information-theoretic privacy Oblivious transfer Private Block Retrieval Private information retrieval Probabilistic encryption User privacy

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Failed Yet Stored: A First Look at DNS Negative Caching

Failed Yet Stored: A First Look at DNS Negative Caching

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Zeng, Meng Zhu, Yujia Li, Baiyang Zhang, Yuedong Liu, Qingyun Fang, Binxing Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China National Computer Network Emergency Response Technical Team/Coordination of China China School of Computer Science and Technology ShenZhen China

ISBN: (纸本)9798350367942

Caching is a critical method for enhancing the efficiency and the security of the Domain Name System (DNS). Initially, only successful domain name resolution results were cached. To mitigate failures in DNS transactions (e.g. NXDomain), the IETF proposed standards, further developed into RFC 9520 as of December 2023. In addition to the basic implementation, RFC 9520 standardizes more sophisticated forms of negative caching. This new standard aims to reduce redundant query retries in DNS traffic and protect resolvers from Denial of Service (DoS) *** this study, we present a comprehensive examination of the specific implementations of Negative Caching in resolvers. We designed and validated a method for measuring negative caching and conducted experiments on 44 public resolvers, including their Do53, DoH, and DoT interfaces. Our findings indicate that while public resolvers generally implement various types of negative caching, some exhibit unexpected cache handling behaviors when encountering specific negative responses. Additionally, we discovered that most public resolvers modify the TTL value of negative responses before returning them to clients. Despite the lack of explicit TTL values for newly specified negative responses, we devised a method to approximate the default TTL values used by public resolvers. © 2024 IEEE.

关键词： Denial-of-service attack

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Enhancing Financial Sentiment Analysis: Integrating the LoughranMcDonald Dictionary with BERT for Advanced Market Predictive Insights

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Procedia computer science 2025年 258卷 2244-2257页

作者： Sheetal R Prakash K. Aithal Department of Computer Science & Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India Member of IEEE Department of Computer Science & Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India

One critical aspect of financial markets is understanding investor sentiment to facilitate effective decision-making. This study integrates traditional sentiment analysis methods, such as the Loughran-McDonald (LM) dictionary— designed for financial sentiment—with advanced Natural Language Processing (NLP) techniques using Bidirectional Encoder Representations from Transformers (BERT). The LM dictionary provides domain-specific word lists to label sentiment, whereas BERT enhances this by capturing nuanced meanings and semantic relationships in financial texts. It involves pre-processing Financial NewsHeadlines, applying the LM dictionary for sentiment scoring, and finetuning a pretrained BERT model to classify sentiment. A PyTorch dataset was created, tokenized using BERT, and processed through the model using techniques like dropout regularization and cross-entropy loss for optimization. The hybrid approach yields promising results: a classification accuracy of 97%, precision of 0.98, recall of 0.93, and an F1 score of 0.95, confirming its effectiveness in capturing sentiment polarity. In addition, comparisons between dictionary-labelled and pre-annotated datasets demonstrate the model’s improved generalization ability. The results also show that our hybrid model outperformed various other existing models. This hybrid approach attempts to improve accuracy in capturing sentiment polarity by implementing methods to overcome imbalanced dataset, thereby facilitating a better understanding of sentiment in financial reports and facilitating informed decision-making. The integration of Named Entity Recognition (NER) with sentiment analysis based on sentiment polarity (positive, negative, or neutral) enables a more granular view of how specific companies are perceived in financial reports by highlighting the entities that are most affected by market sentiment.

关键词： Lexicon-based Analysis Sentiment Analysis NER Financial Reports Loughran-McDonald BERT Natural Language Processing

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High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

High-Fidelity Document Stain Removal via A Large-Scale Real-...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Li, Mingxian Sun, Hao Lei, Yingtie Zhang, Xiaofeng Dong, Yihang Zhou, Yilin Li, Zimeng Chen, Xuhang School of Computer Science and Engineering Huizhou University China The Ohio State University Department of Computer Science and Engineering United States School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Electronic and Communication Engineering Shenzhen Polytechnic University China

ISBN: (纸本)9798331510831

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained details. To address this challenge, we construct StainDoc, the first large-scale, high-resolution (2145 x 2245) dataset specifically designed for document stain removal. StainDoc comprises over 5,000 pairs of stained and clean document images across multiple scenes. This dataset encompasses a diverse range of stain types, severities, and document backgrounds, facilitating robust training and evaluation of document stain removal algorithms. Furthermore, we propose StainRestorer, a Transformer-based document stain removal approach. StainRestorer employs a memory-augmented Transformer architecture that captures hierarchical stain representations at part, instance, and semantic levels via the DocMemory module. The Stain Removal Transformer (SRTransformer) leverages these feature representations through a dual attention mechanism: an enhanced spatial attention with an expanded receptive field, and a channel attention captures channel-wise feature importance. This combination enables precise stain removal while preserving document content integrity. Extensive experiments demonstrate StainRestorer's superior performance over state-of-the-art methods on the Stain-Doc dataset and its variants *** and ***, establishing a new benchmark for document stain removal. Our work highlights the potential of memory-augmented Transformers for this task and contributes a valuable dataset to advance future research. © 2025 IEEE.

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A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

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SN computer science 2024年第1期5卷 135页

作者： Jatain, Divya Niranjanamurthy, M. Dayananda, P. Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of AI and ML BMS Institute of Technology and Management Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

With more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching–learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110 K learner comments (referred to as Educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too. © 2023, The Author(s).

关键词： Bat algorithm Bio-inspired metaheuristic optimisation Data science applications in education Earthworm algorithm Fuzzy sets Modified inverse class frequency Opinion mining

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Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model

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Petroleum science 2023年第5期20卷 2951-2966页

作者： Shu-Yi Du Xiang-Guo Zhao Chi-Yu Xie Jing-Wei Zhu Jiu-Long Wang Jiao-Sheng Yang Hong-Qing Song School of Civil and Resource Engineering University of Science and Technology BeijingBeijing100083China National&Local Joint Engineering Lab for Big Data Analysis and Computer Technology Beijing100190China CNPC International Hong Kong Limited Abu DhabiUnited Arab Emirates Computer Network Information Center Chinese Academy of SciencesBeijing065007China China National Petroleum Corporation Langfang065007HebeiChina

Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir *** physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production *** establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production *** the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front *** experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for *** addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate *** proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints.

关键词： Production optimization Random forest The Bayesian algorithm Ensemble learning Particle swarm optimization

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