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Minimum Epsilon-Kernel Computation for Large-Scale Data Processing

Journal of computer science & technology 2022年第6期37卷 1398-1411页

作者： Hong-Jie Guo Jian-Zhong Li Hong Gao School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Kernel is a kind of data summary which is elaborately extracted from a large *** a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate *** is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive *** this paper,we focus on the minimumε-kernel(MK)computation that asks for a kernel of the smallest size for large-scale data *** the open problem presented by Wang et *** whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its *** to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve *** prove that the MC problem and the MK problem can be Turing-reduced to each ***,we discuss the update of MK under insertion and deletion operations,***,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of *** efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic *** show that the kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,*** speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic ***-SCMK runs up to three times faster than SCMK on synthetic datasets.

关键词： approximate query processing kernel large-scale dataset NP-hard

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Progressive Growth for Point Cloud Completion by Surface-Projection Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-15页

作者： Fei, Ben Zhang, Rui Yang, Weidong Li, Zhijun Chen, Wen-Ming School of Computer Science Fudan University Shanghai China School of Mechanical Engineering Tongji University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China

Point cloud completion concentrates on completing geometric and topological shapes from incomplete 3D shapes. Nevertheless, the unordered nature of point clouds will hamper the generation of high-quality point clouds without predicting structured and topological information of the complete shapes and introducing noisy points. To effectively address the challenges posed by missing topology and noisy points, we introduce SPOFormer, a novel topology-aware model that utilizes surface-projection optimization in a progressive growth manner. SPOFormer consists of three distinct steps for completing the missing topology: (1) Missing Keypoints Prediction. A topology-aware transformer auto-encoder is integrated for missing keypoint prediction. (2) Skeleton Generation. The skeleton generation module produces a new type of representation named skeletons with the aid of keypoints predicted by topology-aware transformer auto-encoder and the partial input. (3) Progressively Growth. We design a progressive growth module to predict final output under Multi-scale Supervision and Surface-projection Optimization. Surface-projection Optimization is firstly devised for point cloud completion, aiming to enforce the generated points to align with the underlying object surface. Experimentally, SPOFormer model achieves an impressive Chamfer Distance-$\ell _{1}$ (CD) score of 8.11 on PCN dataset. Furthermore, it attains average CD-$\ell _{2}$ scores of 1.13, 1.14, and 1.70 on ShapeNet-55, ShapeNet-34, and ShapeNet-Unseen21 datasets, respectively. Additionally, the model achieves a Maximum Mean Discrepancy (MMD) of 0.523 on the real-world KITTI dataset. These outstanding qualitative and quantitative performances surpass previous approaches by a significant margin, firmly establishing new state-of-the-art performance across various benchmark datasets. Our code is available at https://***/kiddoray/SPOFormer IEEE

关键词： Topology

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BDSLL: Biomedical Document Recommendation Using Knowledge-Empowered Semantic Intercepted Large Language Models 4th

BDSLL: Biomedical Document Recommendation Using Knowledge-Em...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aravind Krishnan, A. Deepak, Gerard Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India

ISBN: (纸本)9789819757022

There is a requirement for document recommendation frameworks focusing on certain domains linked to medical sciences and biosciences like biomedical document recommendation in the era of the Web 3.0. This paper proposes a biomedical document recommendation strategy that realizes informative term generation using the encompassment of extraction of terms and categories and the TF-IDF. The framework also generates an ontology from the informative terms harvested from which features are selected to classify the dataset using a random forest classifier. The model also uses very strong knowledge bases that are highly specific to the domain like QIAGEN. It also uses WikiData which is generic but has a full cover of world knowledge to bridge the knowledge present in the datasets and the existing structure web. Metadata generation also increases the quantity of additional knowledge that is encompassed in the model. BioBERT is a strategic large language model that focuses on biosciences and biomedicine as its domain is used to classify the metadata to make it more permeable into the model. Semantic similarity is computed using NPMI, web overlap, SimRank, and Ricklefs and Lau index at different stages in the pipeline to facilitate quantitative reasoning through semantic relatedness computation. The salient semantic analysis also accelerates the overall semantic association into the proposed framework. This framework can demonstrate a precision of 96.56%, recall of 96.93%, and an FDR of 0.04. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ontology

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An Improved Encryption Technique for IPv6 Networks 3rd

An Improved Encryption Technique for IPv6 Networks

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3rd International Conference on Signal and Data Processing, ICSDP 2023

作者： Cherukuru, Pavani Hanji, Bhagyashri R. Information Science of Engineering Dayananda Sagar Academy of Technology and Management Bengaluru India Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bengaluru India

ISBN: (纸本)9789819795772

Encryption method for IPv6 networks provides new features and security concepts with a lot of profoundness likely Authentication Header and Encapsulating Security Payload Header (ESP). This paper shows the method of preventing certain types of network attacks over the internet and open issues of IPv6 security. It mainly deals with the Authentication and Encryption methods such as RSA and DES that are being used for years, and their drawbacks in IPv6 environment to enhance the multilevel security. There are three levels of encryption for security enhancement. The first level includes the conversion of a string of characters into corresponding ASCII values and then encrypt these ASCII values into random prime factors so that uniqueness is maintained. It has repeated patterns that is removed in the second level, where it uses two private keys which are prime for even and odd numbers and perform the addition with the first level results and convert into a binary form. In the third level, 2’s complement is applied on binary form and sent it through ESP packet of IPv6. On the other side, all the levels are repeated in reverse to get the original message. The experimental result shows the higher level of security for IPV6. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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A Deep Learning Model to Detect Fake News about COVID-19

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Recent Advances in computer science and Communications 2023年第9期16卷 58-66页

作者： Shanmugavel, Selva Birunda Rangaswamy, Kanniga Devi Muthukannan, Muthiah Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Chennai India Department of Civil Engineering KCG College of Technology Chennai India

Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19. Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks. Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks. Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%. © 2023 Bentham science Publishers.

关键词： COVID-19

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Skyway:Accelerate Graph Applications with a Dual-Path Architecture and Fine-Grained Data Management

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Journal of computer science & technology 2024年第4期39卷 871-894页

作者： Mo Zou Ming-Zhe Zhang Ru-Jia Wang Xian-He Sun Xiao-Chun Ye Dong-Rui Fan Zhi-Min Tang Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Institute of Information Engineering Chinese Academy of SciencesBeijing 100045China Department of Computer Science Illinois Institute of TechnologyChicagoIL 60616U.S.A.

Graph processing is a vital component of many AI and big data ***,due to its poor locality and complex data access patterns,graph processing is also a known performance killer of AI and big data *** this work,we propose to enhance graph processing applications by leveraging fine-grained memory access patterns with a dual-path architecture on top of existing software-based graph *** first identify that memory accesses to the offset,edge,and state array have distinct locality and impact on *** then introduce the Skyway architecture,which consists of two primary components:1)a dedicated direct data path between the core and memory to transfer state array elements efficiently,and 2)a data-type aware fine-grained memory-side row buffer hardware for both the newly designed direct data path and the regular memory hierarchy data *** proposed Skyway architecture is able to improve the overall performance by reducing the memory access interference and improving data access efficiency with a minimal *** evaluate Skyway on a set of diverse algorithms using large real-world *** a simulated fourcore system,Skyway improves the performance by 23%on average over the best-performing graph-specialized hardware optimizations.

关键词： graph application computer architecture memory hierarchy

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Integrating the Internet of Things to Protect Electric Vehicle Control Systems from Cyber Attacks

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 433-440页

作者： Bhukya, Baddu Naik Venkataiah, V. Kuchibhatla, S. Mani Koteswari, S. Kumari, R.V.S. Lakshmi Raju, Yallapragada Ravi Department of Electrical and Electronics Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Department of Computer Science Engineering CMR College of Engineering & Technology Telangana Hyderabad India Department of Electrical and Electronics Engineering ACE Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering Pragati Engineering College Kakinada India Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering Gayatri Vidya Parishad College of Engineering for Women Andhra Pradesh Visakhapatnam India Department of Computer Science and Technology Madanapalle Institute of Technology and Science Andhra Pradesh India

The Internet of Things (IoT) facilitates the delivery of intelligent services by sensing, gathering, processing, and exchanging data from millions of linked smart devices. The Internet of Things (IoT), which is based on communication infrastructure, provides real-time cyber-physical device monitoring, control, and sensing, such as electric autos. Traditional communication infrastructure's vulnerability to cyberattacks hampered IoT's capacity to investigate these potential applications. This study proposes an algorithm for monitoring and controlling electric vehicles through the Internet of Things communication network to prevent fake data injection attacks. A state-space model depicts a fully autonomous electric car equipped with a vision system. Intelligent sensors and actuators from the Internet of Things are utilized to monitor and change system conditions, compensating for the large distance between the electric car and the control center. Sensing data is delivered from the vehicle to the central command center via an insecure, attack-prone communication path. The mean square error approach is used to derive states in the most effective state estimation system for comprehending and showing autos. A semi-definite programming approach is used to create an optimal control algorithm to manage the vehicle states. The simulation results show how precisely and successfully the proposed algorithms can foresee and regulate a vehicle's state. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Internet of things

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Human Gender and Age Estimator Using Local and Global Features with Machine Learning 1

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3rd International Conference on Information technology, InCITe 2023

作者： Hiremath, Jayaprada S. Patıl, Shantakumar B. Department of Computer Science and Engineering Visvesvaraya Technological University Belgaum590018 India Computer Science and Engineering Sai Vidya Institute of Technology Bengaluru560 064 India

ISBN: (数字)9789819959976

ISBN: (纸本)9789819959969

The study focuses on face processing and seeks to provide a solid framework for face, age, and gender recognition. Although most previous methods rely on a visual descriptor that frequently captures a single property of picture areas, our approach incorporates various feature types, HOG, and LBP descriptors. To do this, feature selection generates more accurate and comprehensive face descriptions. Following the computation of descriptors, a compact collection of features is selected, allowing face image processing for age and gender analysis. The experimental findings show that our age and gender outperform previously suggested approaches tested on three demanding datasets, including face objects with varying expressions and degrees of lighting. This accomplishment indicates the usefulness of our suggested technique and enables us to attain more precision than current state-of-the-art technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Eco-Detect: A Sustainable Solution to Advanced Waste Sorting using AI and IoT 4

Eco-Detect: A Sustainable Solution to Advanced Waste Sorting...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Nandini Prasad, K.S. Manjula, G. Aishwarya, K.J. Chetankumar Prakash, K. N. Bhanu Chandan, V Sai Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering India Dayananda Sagar Academy of Technology and Management Department of Computer Science and Design India Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798331529635

The traditional waste management system, prevalent in many regions, relies heavily on manual sorting processes carried out by human workers. These processes are often labor- intensive and time-consuming, leading to inefficiencies in waste management operations. Manual sorting methods struggle to cope with the increasing volume and complexity of waste generated, resulting in challenges in accurately categorizing and classifying different types of waste materials. This limitation not only hampers the effectiveness of waste sorting but also impedes efforts to maximize recycling rates and minimize environmental impact. In response to the limitations of traditional waste management approaches, the proposed Eco Detect Advanced Waste Sorting system presents a transformative solution. Leveraging the advancements in artificial intelligence and computer vision, this system introduces the utilization of the YOLOv7 algorithm for real-time interference. YOLOv7, renowned for its exceptional accuracy and speed in object detection, is integrated into the waste sorting process to enable rapid and precise identification and classification of diverse waste materials. By employing deep learning methodologies, the system is capable of recognizing a wide array of waste categories, including plastics, paper, glass, metals, and organic materials, with remarkable efficiency. The integration of the YOLOv7 algorithm into the proposed waste sorting system represents a significant advancement in waste management technology. By enabling swift and accurate identification of various waste types, the system facilitates the optimization of recycling practices, promotes the establishment of a circular economy, and contributes to the overall sustainability agenda. Its adaptability and scalability make it well-suited for widespread implementation, addressing the pressing need for sustainable waste management for clean energy solutions on a global scale. Ultimately, the paper envisions a future where te

关键词： Waste utilization

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VISION-PF: A computer Vision Approach to Injury Prevention in Physical Fitness

VISION-PF: A Computer Vision Approach to Injury Prevention i...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ouf, Mohamed Ashraf Amin, Rowan Mohamed Sabry, Moustafa Mamdouh Ali, Abdulrahman Hussien Hamdan, Yehia Ahmed Saleh, Sherine Nagy Arab Academy for Science Technology and Maritime Transport Computer Engineering Department Alexandria Egypt

ISBN: (纸本)9798350351408

Ensuring accurate and effective exercise performance monitoring is essential for sports-focused injury prevention. Maintaining perfect form is crucial since incorrect training increases the chance of injuries dramatically. This article focuses on the popular push-up exercise and introduces a new approach that uses a carefully curated dataset for analysis. The proposed model can accurately discriminate between correct and wrong push-up forms by employing LSTM networks and sophisticated computer vision algorithms. This improved method enables focused interventions, tailored feedback, and the detection of form deviations, providing insights into typical errors. This research aims to improve athlete safety, maximize athletic performance, and establish a framework for using the concept in other significant sports-related injury prevention initiatives. The proposed model significantly improved performance, achieving F1 scores of 0.9 and 0.85 on the training and testing sets, respectively. © 2024 IEEE.

关键词： Machine vision

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