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34th Workshop on Network and Operating System Support for Digital Audio and Video, NOSSDAV 2024

作者： He, Dongbiao Lin, Canshu Yu, Xian Westphal, Cedric Ming, Zhongxing Cui, Laizhong Zhou, Xu Garcia-Luna-Aceves, J.J. Sangfor Technologies Inc China College of Computer Science and Software Engineering Shenzhen University China Computer Network Information Center Chinese Academy of Sciences China China Electrical and Computer Engineering Department University of Toronto Canada Futurewei

ISBN: (纸本)9798400706134

We propose a network service as a solution for video conference applications by constructing network layer routing strategies. Our approach takes into account the characteristics of conferencing flows, addresses various self-customized metrics, and proactively ensures a positive user experience by preventing contention. The performance of VCApather is evaluated using a fully-meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather was found to satisfy 69.8% more QoE requirement and to yield an average bitrate improvement of 1.74×. © 2024 ACM.

关键词： Network layers

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A Deep Learning Approach for Prediction of Protein Secondary Structure

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computers, Materials & Continua 2022年第8期72卷 3705-3718页

作者： Muhammad Zubair Muhammad Kashif Hanif Eatedal Alabdulkreem Yazeed Ghadi Muhammad Irfan Khan Muhammad Umer Sarwar Ayesha Hanif Department of Computer Science Government College UniversityFaisalabadPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Software Engineering/Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary *** this reason,it is important to design methods for accurate protein secondary structure *** of the existing computational techniques for protein structural and functional prediction are based onmachine learning with *** deep learning architectures have already been applied to tackle protein secondary structure prediction *** this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were *** input to proposed models is amino acid sequences which were derived from CulledPDB *** tuning with cross validation was employed to attain best parameters for the proposed *** proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

关键词： Convolutional neural network machine learning protein secondary structure deep learning long short-term memory protein secondary structure prediction

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MPC-based Optimization Design for 3D Collision Avoidance of a Mobile Manipulator Based-on Obstacle Velocity Estimation

MPC-based Optimization Design for 3D Collision Avoidance of ...

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2024 International Automatic control Conference, CACS 2024

作者： Song, Kai-Tai Lin, Chih-Hsuan Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University 1001 Ta Hsueh Road Hsinchu Taiwan

ISBN: (纸本)9798350354904

This paper presents an optimization design of 3D dynamic obstacle avoidance for a mobile manipulator based on model predictive control (MPC). The design enables a mobile manipulator to achieve optimized 3D collision avoidance motion with shorter avoidance path and faster avoidance time. A 3D LiDAR is installed onboard the robot to acquire environmental point cloud and estimate obstacle velocity. The MPC is designed to track an initial 3D path of the mobile manipulator and avoid any static and dynamic obstacles in real time. Experimental results show that the proposed method can simultaneously avoid static and dynamic obstacles in 3D space. Compared with baseline algorithms without velocity estimation, the proposed method reduces the avoidance path length by 8.27% and path execution time by 13.79%. © 2024 IEEE.

关键词： Predictive control systems

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Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

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Intelligent Automation & Soft Computing 2023年第8期37卷 2247-2261页

作者： Amani Yahyaoui Jawad Rasheed Shtwai Alsubai Raed M.Shubair Abdullah Alqahtani Buket Isler Rana Zeeshan Haider Department of Software Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Nisantasi UniversityIstanbul34398Turkey Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Electrical and Computer Engineering New York University(NYU)Abu Dhabi129188United Arab Emirates College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Istanbul Topkapi UniversityIstanbul34087Turkey Baqai Institute of Hematology Baqai Medical UniversityKarachi75340Pakistan

The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole *** then,research has accelerated and varied to find practical solutions for the early detection and correct identification of this *** researchers have focused on using the potential of Artificial Intelligence(AI)techniques in disease diagnosis to diagnose and detect the *** paper developed deep learning(DL)and machine learning(ML)-based models using laboratory findings to diagnose *** different methods are used in this study:K-nearest neighbor(KNN),Decision Tree(DT)and Naive Bayes(NB)as a machine learning method,and Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Long-term memory(LSTM)as DL *** approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo,*** data consists of 5644 laboratory results from different patients,with 10%being Covid-19 positive *** dataset includes 18 attributes that characterize *** used accuracy,f1-score,recall and precision to evaluate the different developed *** obtained results confirmed these approaches’effectiveness in identifying COVID-19,However,ML-based classifiers couldn’t perform up to the standards achieved by DL-based *** all,NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56%and 85%accuracies,*** these,DL models attained better performance as CNN,DNN and LSTM secured more than 90%*** LTSM outperformed all by achieving an accuracy of 96.78%and an F1-score of 96.58%.

关键词： Artificial intelligence COVID-19 deep learning diagnosis machine learning

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Moving Object Recognition and Tracking Based on Image-fusion Frame Differencing and Differential Translational Transform 4

Moving Object Recognition and Tracking Based on Image-fusion...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Chen, Minghui Ji, Yingjun Wang, Aoyuan Wang, Jiacun Liaoning University College Of Information Shenyang China Monmouth University Department Of Computer Science And Software Engineering West Long BranchNJ United States

ISBN: (纸本)9798350315790

To address the issue of sensitivity to external interference and noise in traditional moving object recognition and tracking algorithms, this paper proposes a novel algorithm that combines Image Fusion-based Frame Difference (IFD) with Differential Translation Transformation (DTT). Firstly, image enhancement and noise reduction are applied to adjust contrast and brightness in the images of moving objects. Subsequently, the IFD algorithm is employed to adjust the blending of new frame with the background, smoothly fusing them. Pixel differences between the fusion frame and the background are calculated to identify moving objects in the image. Matching is performed between the target areas obtained by the IFD algorithm based on pixel displacement differences between consecutive frames and the target displacements obtained by the DTT algorithm. This allows for recognition and tracking of the targets. To validate the effectiveness of the algorithm, experiments are conducted on two sets of moving-object image data, resulting in accuracies of 90.12% and 90.87%. © 2024 IEEE.

关键词： Image fusion

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Low-confidence Heterogeneous Pixel-Prototype Contrastive Learning for Semi-supervised Medical Image Segmentation

Low-confidence Heterogeneous Pixel-Prototype Contrastive Lea...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Shihuan Lai, Zhihui Kong, Heng Shenzhen University College of Computer Science and Software Engineering Shenzhen China Baoan Central Hospital of Shenzhen Department of Breast and Thyroid Surgery Shenzhen China

ISBN: (纸本)9798350386226

Medical image segmentation is a challenging task especially when dealing with unlabeled data. It has been proven that the use of complementary information for co-training is effective for medical image segmentation. We find that high-confidence regions are easy to segment, but low-confidence regions are difficult to correctly segment, which results in poor segmentation performance. Therefore, accurate segmentation in low-confidence regions is the effective way to enhance the performance of medical image segmentation. To achieve this goal, we propose a novel co-training strategy named low-confidence heterogeneous pixel-prototype contrastive learning with uncertainty-guide cross supervision for semi-supervised medical image segmentation. Specifically, an uncertainty-guided cross supervision module is firstly designed to estimate the confidence score of predictions from multiple outputs and then perform cross supervision between high-confidence regions. Moreover, we design a low-confidence heterogeneous prototype contrastive learning, which builds the relationship between low-confidence pixel and heterogeneous prototype to mine discriminative information in low-confidence regions so as to improve the segmentation performance. Extensive experiments on medical image datasets demonstrate that our method outperforms state-of-the-art methods in segmentation results. © 2024 IEEE.

关键词： Contrastive Learning

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A Survey on the Emerging Trend of Human Microchip Implants And Their Potential

A Survey on the Emerging Trend of Human Microchip Implants A...

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2023 Advances in Science and engineering Technology International Conferences, ASET 2023

作者： Al Mansouri, Ltifa Mohammed Khalifa Alnajjar, Fady College of It United Arab Emirates University Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665454742

Human microchip implants are small devices that are implanted into the body for various purposes including storing and transmitting information, monitoring health or location, and providing access to facilities or systems. Although human microchip implants offer numerous advantages, their adoption remains low in the public sector. This article presents a comprehensive survey on the emerging trend of human microchip implants and their potential uses in various fields. In addition to introducing the general concept of microchip implants, the work discusses their current and potential future applications. To better understand the direction of this trend, recent case studies of existing applications of human microchip implants are analyzed and presented. The article also explores the barriers to the adoption of human microchip implants and discusses their potential impact on humans, whether positive or negative. © 2023 IEEE.

关键词： Near field communication

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Path Planning and control of a 6-DOF Manipulator for Freeform Surface Polishing Based-on a Point Cloud Model

Path Planning and Control of a 6-DOF Manipulator for Freefor...

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2024 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2024

作者： Tsai, Fu-Yu Song, Kai-Tai Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University 1001 Ta Hsueh Road Hsinchu Taiwan

ISBN: (纸本)9798350362572

This paper presents a novel path generation method for robotic surface polishing based on point cloud data. In this method, an initial path is first planned to create waypoints via the point cloud. The method estimates the contact area to dynamically adjust initial path points to ensure uniform trajectory coverage on the surface. In this paper, the bilateral tool path generation (BTPG) algorithm is proposed based on a dual-thread strategy to expedite polishing path generation. Further, we introduce a method that generates smooth postures to enhance the stability of the robotic polishing pose. In this work, hybrid force/velocity control scheme is adopted to track polishing path and apply the assigned force on the workpiece freeform surface. Practical experimental results show that the proposed method achieves 95% improvement in path planning time and 85% in smoothness of the robot fourth joint's posture compared with the baseline slicing method. © 2024 IEEE.

关键词： Robot programming

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Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation 28th

Self-Sampling Training and Evaluation for the Accuracy-Bi...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Liu, Dugang Qiao, Yang Tang, Xing Chen, Liang He, Xiuqiang Pan, Weike Ming, Zhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China FIT Tencent Shenzhen China

ISBN: (纸本)9783031306778

Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most of the existing methods require some specific data, architectures and training methods. In this paper, we first argue through an online study that arbitrarily removing all the biases in industrial recommendation may not consistently yield a desired performance improvement. For the situation that a randomized dataset is not available, we propose a novel self-sampling training and evaluation (SSTE) framework to achieve the accuracy-bias tradeoff in recommendation, i.e., eliminate the harmful biases and preserve the beneficial ones. Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data. A self-training module infers the beneficial biases and learns better tradeoff based on these subsets, and a self-evaluation module aims to use these subsets to construct more plausible references to reflect the optimized model. Finally, we conduct extensive offline experiments on two datasets to verify the effectiveness of our SSTE. Moreover, we deploy our SSTE in homepage recommendation of a famous financial management product called Tencent Licaitong, and find very promising results in an online A/B test. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Smart Scheduling in Cloudlet Federation

Smart Scheduling in Cloudlet Federation

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2023 International Conference on Energy, Power, Environment, control, and Computing, ICEPECC 2023

作者： Asim, Sherrjel Nayyer, Muhammad Ziad Ahmed, Usama Faheem, Muhammad GIFT University Department of Computer Science Gujranwala Pakistan Government College University Department of Software Engineering Faisalabad Pakistan

ISBN: (纸本)9781665476010

Rapidly increasing data requires a lot of time to execute different tasks by the systems at Cloudlet Federation (CF). The demand of today's computing is to exploit resource utilization up to maximum. Efficient resource allocation and scheduling of tasks are the significant characteristics of Cloudlet Federation (CF). Different tasks have diverse execution time. Some tasks require greater execution time whereas some need just a couple of seconds for their execution. Scheduling offers a feasible solution for handling these tasks with the help of different scheduling algorithms. To address task scheduling issue, this paper proposes a scheduling technique named Least Busy Time Scheduling (LBTS) for smart scheduling of tasks in Cloudlet Federation (CF). The proposed technique assists the scheduler to assign task to a system considering the parameter of least busy time to reduce waiting time and optimize the overall performance of the system. © 2023 IEEE.

关键词： Scheduling algorithms

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