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A Collaborative Broadcast Content Recording System Using Distributed Personal Video Recorders

computers, Materials & Continua 2025年第2期82卷 2555-2581页

作者： Eunsam Kim Choonhwa Lee Deparment of Computer Engineering Hongik UniversitySeoul04066Republic of Korea Deparment of Computer Science Hanyang UniversitySeoul04763Republic of Korea

Personal video recorders (PVRs) have altered the way users consume television (TV) content by allowing users to record programs and watch them at their convenience, overcoming the constraints of live broadcasting. However, standalone PVRs are limited by their individual storage capacities, restricting the number of programs they can store. While online catch-up TV services such as Hulu and Netflix mitigate this limitation by offering on-demand access to broadcast programs shortly after their initial broadcast, they require substantial storage and network resources, leading to significant infrastructural costs for service providers. To address these challenges, we propose a collaborative TV content recording system that leverages distributed PVRs, combining their storage into a virtual shared pool without additional costs. Our system aims to support all concurrent playback requests without service interruption while ensuring program availability comparable to that of local devices. The main contributions of our proposed system are fourfold. First, by sharing storage and upload bandwidth among PVRs, our system significantly expands the overall recording capacity and enables simultaneous recording of multiple programs without the physical constraints of standalone devices. Second, by utilizing erasure coding efficiently, our system reduces the storage space required for each program, allowing more programs to be recorded compared to traditional replication. Third, we propose an adaptive redundancy scheme to control the degree of redundancy of each program based on its evolving playback demand, ensuring high-quality playback by providing sufficient bandwidth for popular programs. Finally, we introduce a contribution-based incentive policy that encourages PVRs to actively participate by contributing resources, while discouraging excessive consumption of the combined storage pool. Through extensive experiments, we demonstrate the effectiveness of our proposed collaborativ

关键词： Collaborative recording TV content personal video recorder erasure coding replication

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State-of-the-Art in Multi-faceted Feature Matching for E-Commerce: A Comprehensive Analysis

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Informatica (Slovenia) 2024年第12期48卷 97-106页

作者： Zareena, Noorbasha Rao, B. Tarakeswara Department of Computer Science and Systems Engineering AUTDR HUB Andhra University India Deparment of Computer Science and Engineering Kallam Haranadhareddy Institute of Technology Andhra Pradesh Guntur India

In this paper we provided an insightful exploration into the critical role of feature matching in enhancing the efficacy of e-commerce recommendation systems. By meticulously analyzing user data and product characteristics, feature matching significantly improves the personalization of product suggestions, thereby augmenting user experience and engagement in online shopping platforms. The paper reviews existing methodologies, including collaborative filtering, content-based filtering, hybrid models, and advanced deep learning approaches, which have collectively advanced the domain of personalized recommendations. Despite these advancements, the paper identifies key limitations such as scalability challenges, the cold start problem, over-specialization versus diversity in recommendations, and concerns over user privacy and data security. Through this comprehensive analysis, the survey makes substantial contributions by delineating the current state of the art, pinpointing critical gaps, and suggesting avenues for future research. The paper particularly emphasizes the need for dynamic adaptation in recommendation systems to keep pace with the fast-evolving e-commerce sector and changing consumer behaviors. Future research directions highlighted include the exploration of unsupervised and reinforcement learning models, the development of cross-domain recommendation systems, and the integration of emerging technologies, all aimed at fostering more nuanced, adaptable, and ethically responsible recommendation mechanisms. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Contrastive Learning

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Analysis of effective resource scheduling optimization algorithms in IoT-fog-cloud environment 4th

Analysis of effective resource scheduling optimization algor...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Hritik Goel, Gaurav Ummat, Amandeep Deparment of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

The number of sensors and IoT devices has increased dramatically in last several years. In recent years, the number of IoT devices and sensors has increased significantly. Solving the purpose of fog computing processing requirements of widely distributed IoT devices and sensors in real-time, latency-sensitive applications. In order to effectively distribute resource layers for numerous application IoT networks, it is imperative to optimize fog infrastructure resources, as they are scarce and erratic. Generally speaking, fog computing brings cloud-based networking, processing, and storage closer to IoT device and detection devices. To enhance the capacity to search globally, the Firefly and Crow search algorithms are merged. The proposed hybrid algorithm (FA-CSA) results improved the QoS factor of cost and makespan while compared with PSO, ACO and genetic algorithm. © 2025 the Author(s).

关键词： Genetic algorithms

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Enhanced 360 Degree Cognitive Awareness for Heavy Vehicle Drivers Through Haptic Feedback 4

Enhanced 360 Degree Cognitive Awareness for Heavy Vehicle Dr...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Chowdhury, Shah Fahim Rabbi, Abid Mahamud Saif, Mushfiq Qayyum Khan, Nafisa Anjum Hasan, Khandaker Tabin Deparment of Computer Science and Engineering Dhaka Bangladesh Faculty of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9798331530938

This research explores the use of haptic technology in heavy vehicle driving safety, focusing on its potential to enhance driver assistance systems and improve driving experience. The study addresses driver-induced causes of traffic accidents, including drivers' inability to see around the vehicle due to its length and height. The proposed system provides 360-degree cognitive awareness through haptic feedback, which can improve road safety and mitigate challenges in diverse driving conditions. By using haptic feedback, the driver will be able to get a tangible sense of the presence of objects surrounding the vehicle. Through a comprehensive review of existing literature and experimental studies, the research demonstrates the potential of haptic feedback in revolutionizing the driving experience. The findings highlight the optimum mapping between sensors and vibration motors positions on the human body © 2025 IEEE.

关键词： Vehicle safety

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Cloud-Native Countinous Integration/Continous Deployment (CI/CD) Pipeline 2

Cloud-Native Countinous Integration/Continous Deployment (CI...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Kumar, Shivansh Bala, Er. Neetu Singh, Arun Prakash Raj, Yash Chandigarh University Deparment of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

This paper explores the design and implementation of a cloud-native Continuous Integration/Continuous Deployment (CI/CD) pipeline for automating the deployment of machine learning models. Leveraging modern technologies like Docker, Kubernetes, Jenkins, and cloud platforms such as AWS, Google Cloud, and Azure, the pipeline enhances the efficiency, consistency, and reliability of the development process. Key challenges such as scalability, security, and model drift are addressed, offering solutions to ensure smooth operations in dynamic production environments. Performance evaluations demonstrate the benefits of a cloud-native approach, highlighting improvements in deployment speed and resource optimization. The paper concludes by discussing future directions for further automation and advancements in cloud-native CI/CD processes for machine learning. © 2024 IEEE.

关键词： Cloud platforms

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computer Vision and Image Segmentation: LBW Automation Technique 3rd

Computer Vision and Image Segmentation: LBW Automation Techn...

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Nayak, Jeebanjyoti Jena, Jyotsnarani Pradhan, Hrushikesh Das, Jyotiprakash Bhagat, Surendra N. Deparment of Computer Science and Engineering Balasore College of Engineering and Technology Sergarh Balasore India

ISBN: (纸本)9789819939312

Cricket is globally a famous game in which different technologies are being used to help the umpire in correct decision management. The bowl delivery is whether a fair one or LBW which is generally a matter of concern. Sometimes the decision of third umpire also goes wrong. This kind of error in making the decision can change the dimension of the game so, it is essential to make the decision precisely. This paper proposes computer vision and image subtraction technique. At first, it must figure out that whether a delivery is no-ball or the fair one, for that image processing method is used on popping crease by detecting the pixel size. If the delivery is a fair one then, it will track the movement of the ball by using the colour segmentation method, video processing on the pitch. Then by the help of ultra-edge detection technique it can identify whether the ball has touched the edge of the bat or not. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision

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Digital twinning of vertical centrifugal casting

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Concurrent engineering Research and Applications 2024年第1-4期32卷 78-89页

作者： Anadkat, Dhaval Dave, Anjali Sata, Amit Shukla, Minal Jarboui, Slaheddine Department of Mechanical Engineering Marwadi University Rajkot India Department of Mechanical Engineering Lukhdhirji Engineering College Morbi India MGL Group Rajkot India Deparment of Computer Engineering College of Computer Science King Khalid University KSA Abha Saudi Arabia

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation. © The Author(s) 2024.

关键词： Centrifugal casting

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EMIX: A Data Augmentation Method for Speech Emotion Recognition 48

EMIX: A Data Augmentation Method for Speech Emotion Recognit...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Dang, An Vu, Toan H. Dinh Nguyen, Le Wang, Jia-Ching National Central University Deparment of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781728163277

In the last few years, many deep learning (DL) models have been developed to improve the accuracy of speech emotion recognition (SER). However, as SER datasets are generally small and insufficient due to their difficult and expensive collection, the DL models are prone to overfitting, so their performance is limited. In this paper, we introduce a novel data augmentation (DA) method for the SER problem, namely EMix, which is simple but effective. The method creates new data by mixing pairs of selective samples from the original data. The generated mixtures will be noisier or less ambiguous than their constructive ones. To verify the effectiveness of the proposed DA, we develop a transformer-based network for the SER task, and experiment with the two public datasets including IEMOCAP and Crema-D. The experimental results demonstrate the superiority of EMix over other DA methods. In comparison with state-of-the-art methods, our approach shows competitive performance. © 2023 IEEE.

关键词： Emotion Recognition

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Impact of COVID-19 Pandemic in India Challenges and Future Directions 2

Impact of COVID-19 Pandemic in India Challenges and Future D...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Kaushal, Rajanish Kumar Ahamad, Isarar Sharma, Rohit Vishwakarma, Satyanand Kushwaha, Ritesh Kumar Kubba, Akshaya Chandigarh University Deparment of Electrical Engineering Punjab Mohali India Integral University Deparment of Electrical Engineering Lucknow India Punjab Engineering College Deparment of Electrical Engineering Chandigarh India Chandigarh University Department of Ece Punjab Mohali India Chandigarh University Deparment of Computer Science Punjab Mohali India

ISBN: (纸本)9798350364699

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the infectious disease that is causing the worldwide coronavirus disease 2019 (COVID-19) pandemic. Novel coronavirus (nCoV) cases were initially reported in China in December 2019, and the virus has since spread quickly to other nations throughout the globe. As a result, on January 30, 2020, the World Health Organization (WHO) declared a Public Health Emergency of International Concern (PHEIC), and on March 11, 2020, it classified the outbreak as a pandemic (Source: WHO). Normal daily life has been disrupted by the COVID-19 epidemic. Various actions are being taken by nations to break the COVID-19 virus from spreading. The Indian government imposed a nationwide lockdown to hearten social distancing. Because of these unfavorable circumstances, the nation suffered in a number of areas, including healthcare, education, and social systems. The current study discusses the numerous effects of COVID-19 on the social life of citizens, the healthcare system, the school system, and the economy. The most noteworthy effects that were mentioned were better family dynamics, financial loss, and disruptions in the schooling of the children. Few respondents mentioned negative effects on their health, such as worsening symptoms and skipping hospital visits. COVID-19 had a serious negative impact on tourism as well. For the sample of people who are still making a living from this industry, the predictable loss in total income ranges between 3.12 and 6.39%, while the estimated decline in wages runs between 3.21 and 5.98%. © 2024 IEEE.

关键词： COVID-19

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Federated Learning for Image Captioning: A Comprehensive Review of Privacy-Preserving Collaborative Model Training in Distributed Environments 2

Federated Learning for Image Captioning: A Comprehensive Rev...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Padate, Roshni Kalla, Mukesh Gupta, Ashutosh Sharma, Arvind Sir Padampat Singhania University Deparment of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798350347579

This study presents a comprehensive review of the use of federated learning in the context of image captioning in distributed environments. It focuses on key aspects such as privacy preservation, data locality, and collaborative model training. The evolution of federated learning and its unique characteristics are explored, along with an examination of available open-source frameworks specific to image captioning. The study categorizes different approaches to federated learning for image captioning and showcases recent applications in diverse domains, including medical imaging, edge computing, autonomous vehicles, social media, and cross-domain image analysis. Additionally, optimization techniques, security analysis, and research challenges are discussed, encompassing data heterogeneity, privacy preservation, communication efficiency, limited labeling, scalability, and robustness against adversarial attacks. This comprehensive review contributes to a deeper understanding of federated learning for image captioning and highlights areas for further research and advancement in the field. © 2023 IEEE.

关键词： Privacy-preserving techniques

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