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Enhancing peer-to-peer live multicast quality using helpers

Enhancing peer-to-peer live multicast quality using helpers

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IEEE International conference on Image Processing

作者： Jiajun Wang Kannan Ramchandran Department of EECS University of California Berkeley USA

Peer-to-peer (P2P) video streaming over the Internet plays an important role in reshaping today's Internet traffic. the performance of a P2P system is typically bottlenecked by the limited upload bandwidth of the participating peers, which typically have asymmetric upload/download bandwidth connections to the Internet. Increasing the streaming bitrate in P2P systems beyond what is sustainable with participating peers' upload bandwidths therefore places extra burden on the content server, clearly not a desirable solution in terms of scalability. Motivated by this, we explore here an alternative architecture based on the notion of "helpers", who are online users with spare upload capacity. We show how efficient utilization of helpers' spare upload capacity can lead to significantly improved streaming bitrate in a P2P network without incurring additional server burden. For instance, a 2000 node P2P system with an average upload bandwidth of 512 kbps can sustain streaming rate of 512 kbps with very little server usage. But with an additional 533 helpers, the system can stream at 640 kbps without incurring any additional server load.

关键词： Peer to peer computing Bandwidth Streaming media Bit rate Web server Network servers Web and internet services Scalability Protocols multimedia communication

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Compression-Based Image Retrieval for Efficient Networked Applications

Compression-Based Image Retrieval for Efficient Networked Ap...

引用

International conference on computing and networking Technology (ICCNT)

作者： T. Kuppuraj Paramjit Baxi N.T. Velusudha Samaksh Goyal P.S Pavan Jayashree V. Bagade Department of Computer Applications Karpagam Academy of Higher Education Coimbatore India Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Quantum University Research Center Quantum University School of Engineering and Technology Civil Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Information Technology Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Compression-based total photograph retrieval (CBIR) is a method that is used to retrieve pictures from big datasets correctly. Unlike traditional photo retrieval methods, CBIR utilizes lossless or loss compression algorithms to lessen the scale of the image statistics for transmission over a network connection. that is particularly useful in programs wherein transmitting cumbersome image statistics is only sometimes viable. With the aid of compressing the facts related to a photo and sending only the compressed version, transmission instances are significantly decreased, even though nonetheless maintaining the visual first-rate and integrity of the photo. this makes CBIR a possible choice for packages consisting of streaming video, medical imaging, far-flung sensing, and massive-scale surveillance systems. CBIR also has packages in multimedia databases and different networked structures that require efficient image retrieval and viewing.

关键词： Visualization Image coding Accuracy Surveillance Image retrieval Streaming media multimedia databases Propagation losses Software Sensors

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Evaluation of Graph-based Algorithms for Guessing User Recommendations of the Social Network Instagram

Evaluation of Graph-based Algorithms for Guessing User Recom...

引用

International conference on Semantic computing

作者： Frank Martin Mehlhose Michael Petrifke Christoph Lindemann University of Leipzig Germany

ISBN: (数字)9781728188997

ISBN: (纸本)9781728189000

In this paper, we present an analysis of the user recommendation algorithm of the popular social network Instagram. First, we introduce a framework for the analysis and evaluation of user recommendations in any social network. then, we build a neighborhood graph for selected active users on Instagram, containing more than 609,000 users and 747,000 relationships. For these users, we also create a dataset of not publicly available personalized recommendation lists. Finally, we apply several graph-based algorithms to this graph to identify Instagram's user recommendation system. We show that an algorithm based on Twitter's Who-to-Follow algorithm is most similar to Instagram's unknown algorithm when recommending users connected to the followees of the target users. Moreover, this algorithm is very cost-effective due to requiring only a relatively small, bipartite graph, compared to the other candidates. Furthermore, we give an analysis of the composition of the user suggestions based on the different reasons for the suggestions.

关键词： Social networking (online) conferences multimedia Web sites Blogs Semantics Approximation algorithms Bipartite graph

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Future Direction of Traffic Classification in SDN from Current Patents Point-of-view

Future Direction of Traffic Classification in SDN from Curre...

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International Computer Engineering conference (ICENCO)

作者： Fatimah Audah Tan Saw Chin Rizaluddin Kapsin Nazaruddin Omar Ahmad Tajuddin Faculty of Computing and Informatics Multimedia University Cyberjaya Selangor Malaysia Telekom Malaysia Research & Development Cyberjaya Selangor Malaysia Samsudin Telekom Malaysia Kuala Lumpur Selangor Malaysia

ISBN: (数字)9781728151465

ISBN: (纸本)9781728151472

the variety and complexity of current Internet traffic has challenge the Internet Service Providers in providing quality service to the subscribers. Traffic classifications are devised in order to efficiently manage the traffic according to the demand and available resources. In contrast to traditional network, Software Defined networking (SDN) has a centralized controller in which traffic classifier can take advantage. this paper has identified the state-of-the-art inventions that have been granted patents in the field of network traffic classification, both in traditional network and SDN. the implementation of traffic classification need to adapt; as more traditional network is migrating to SDN. therefore, the limitation of the current traffic classification has been investigated in order to suggest for its future direction.

关键词： Telecommunication traffic Internet Reliability Cryptography Data privacy Patents

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Cloud-Native CDN Monitoring Using CI/CD

Cloud-Native CDN Monitoring Using CI/CD

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International conference on computing and networking Technology (ICCNT)

作者： Richa Vivek Savant S Navin Sunder Samarth Seshadri Niharika Panda Shinu M Rajagopal Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In a time where digital content consumption reigns, it is essential to efficiently deliver multimedia resources. this research seeks to transform how content is delivered by implementing and overseeing a secure, monitored Content Delivery Network (CDN) on a scalable Kubernetes cluster, managed through a secure CI/CD pipeline built on Jenkins, using the Amazon Web Services (AWS) and essential DevOps tools like Docker, SonarQube, Trivy, Prometheus, Grafana, Argo CD and Helm. the results obtained in this study are derived from an emulation of a CDN. the SonarQube analysis confirms whether the system meets all code quality gates, Trivy identifies and resolves critical container vulnerabilities and OWASP vulnerability checks identify and mitigate security risks in software dependencies. the system is monitored by analyzing the performance metrics visualized in Grafana dashboards, which offer detailed insights into metrics including and not limited to: active jobs, job queue duration, queued rate, memory usage, CPU usage, executor health metrics, system load, RAM usage, root filesystem usage, disk space usage, disk I/O activities, time synchronized drift and time synchronized status. Additionally, SMTP e-mail notifications are configured to improve responsiveness to anomalies identified via monitoring. this study emphasizes making content delivery mechanisms more robust, providing advanced tools for developers, and enabling easy access to digital resources for the community.

关键词： Chaos Codes Content distribution networks Web services Pipelines Random access memory Security Synchronization Monitoring Queueing analysis

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Fake Social Media Detection using Machine Learning

Fake Social Media Detection using Machine Learning

引用

International conference on computing and networking Technology (ICCNT)

作者： R thangamani M. Vimaladevi J Mohammed Rifath G Harini S Hari Department of Artificial Intelligence Kongu Engineering College Tamil Nadu India Artificial Intelligence And Machine Learning Kongu Engineering College Perundurai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Social media platforms such as Facebook, Twitter, Instagram and others have become an integral part of our lives, facilitating global connectivity. But the proliferation of fake profiles on these platforms poses serious risks, including the spread of false information, phishing attacks and identity theft. this research project focuses on identifying fake Twitter profiles using machine learning techniques to distinguish them from genuine accounts. In the dataset used in this study, Twitter profiles are classified into genuine accounts (TFP and E13) and fake accounts (INT, TWT and FSF).. the main objective is to evaluate the effectiveness of the ensemble learning technique Random Forest algorithm in classifying social media profiles. In-depth discussions include algorithm architecture, hyperparameters, and selection of critical features such as follower and friend counts, status changes, and other relevant features. Additionally, alternative classifiers including neural networks, LSTM, XG Boost and others are briefly discussed in the summary. the Random Forest algorithm has a commendable 95.54% accuracy after rigorous training. this high accuracy underlines its ability to effectively distinguish between real and fake Twitter profiles. By assigning a value of 0 to genuine profiles and 1 to fake profiles, the algorithm provides a practical solution to detect and mitigate fake accounts, thereby solving the cyber security issues associated with social media. this research contributes to ongoing efforts to create a safer online environment by identifying and removing fake profiles.

关键词： Training Accuracy Machine learning algorithms Social networking (online) Blogs Real-time systems Classification algorithms Web sites multimedia communication Random forests

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Image Denoising using Multiscale Transforms

Image Denoising using Multiscale Transforms

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International conference on computing and networking Technology (ICCNT)

作者： Itreddy Shyam Sundar Reddy Kakarla Bhanu Prakash Panyam Hemanth Reddy Tripty Singh Adirath Mandal Suman Chatterjee Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Graphic Era Dehradun Uttarakhand India Dept of Mechanical Engineering Kongju National University Cheonan South Korea

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

A crucial step in multimedia processing is image and video denoising, which aims to eliminate undesirable noise while keeping significant image properties. this research investigates the use of four distinct multiscale transforms for picture denoising: ridgelets, shearlets, curvelets, and wavelets. To capturing and displaying visual structures at different scales and orientations, each transform offers special features. through studies on artificial and real-world noisy images, examine each method’s denoising performance and compare its capacity to reduce noise while maintaining image information. the outcomes demonstrate the advantages and disadvantages of each strategy and shed light on how well they work with various kinds of noise and image content. this study investigates the effects of four different multiscale transforms (ridgelets, shearlets, curvelets, and wavelets) on the Signal-to-Noise Ratio (SNR) in addition to examining their denoising capabilities. SNR values are important measurements for assessing how well denoising works since they offer measurable information on how to strike a balance between noise reduction and detail preservation in the image. through extensive experiments on synthetic and real-world noisy photos, this study evaluates the effectiveness of every denoising technique over a range of noise kinds and image contents. the abstract also emphasizes the distinct qualities that every multiscale transform offers in terms of capturing and expressing visual structures at different scales and orientations. By means of a thorough examination, the research reveals the benefits and drawbacks of every technique, providing significant understanding regarding their appropriateness for various denoising situations.

关键词： Wavelet transforms Visualization Noise reduction Streaming media Noise measurement Signal to noise ratio Image denoising

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AMOSI:Attention-Based Multimodal Sentiment Intensity Analysis with LSTM-CNN Architecture for Online Opinion Videos

AMOSI:Attention-Based Multimodal Sentiment Intensity Analysi...

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International conference on computing and networking Technology (ICCNT)

作者： V V V Bhagya Sree G Bharathi Mohan R Prasanna Kumar M Rithani Gundala Pallavi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

through online video sharing websites, people share their thoughts, experiences, and reviews on a daily basis. Researching the subjectivity and sentiment in these opinion videos is becoming more and more popular among academics and industry professionals. Although sentiment analysis works well for text, there isn’t much research done on it when it comes to videos and other multimedia. the largest obstacles to research in this area are the absence of appropriate methodology, baselines, datasets,and statistical analysis of the relationships between data from various modality sources. the Multimodal Opinionlevel Sentiment Intensity dataset (MOSI), the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos, is presented to the scientific community in this paper. With labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, the dataset is meticulously annotated. User-generated opinion videos are increasingly prevalent, necessitating advanced sentiment analysis techniques. We present an attention-based LSTM-2D CNN model for multimodal sentiment intensity prediction. Our contributions are three-fold: 1) We collect new annotations of visual gestures and acoustic effects in online videos to augment the MOSI dataset. 2) We design an end-to-end neural architecture to model textual, visual and acoustic modalities. Bi-directional LSTMs capture temporal dependencies in text and audio. 2D CNNs extract spatial visual features. A hierarchical attention mechanism identifies important utterances and frames. 3) Extensive experiments demonstrate 74.7% accuracy, outperforming prior BC-LSTM models by 1.7%. Ablation studies validate the impact of multimodal fusion and attention. Our work provides an improved neural baseline on MOSI, with detailed analysis of multi-view sequential modelling for opinion videos. the additional annotations and strong results will aid future research in this emerging domain.

关键词： Visualization Sentiment analysis Attention mechanisms Annotations Computer architecture Predictive models Streaming media Feature extraction Acoustics Data models

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the Application of Time Series Forecasting to Financial Risk Management

The Application of Time Series Forecasting to Financial Risk...

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International conference on computing and networking Technology (ICCNT)

作者： Manish Dakhore R.Delecta Jenifer Mohamed Dawood Shamout Nilesh Anute Wanda Gema Prasadio Akbar Hidayat Hendy Tannady Mechanical Engineering Department Yeshwantrao Chavan college of Engineering Nagpur Maharashtra India Department of Management Studies Saveetha Engineering College Chennai India Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Management ASM’s Institute of Business Management and Research Pune India Department of Management Universitas Multimedia Nusantara Indonesia

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Time collection forecasting (TSF) is an essential predictive modeling device for plenty of choice-making contexts, particularly those regarding economic change control. Time collection forecasting involves analyzing a given variable’s past observations or ancient information to expect destiny values or traits in the equal variable. It’s far more and more being used in monetary markets to higher apprehend and expect price moves and different monetary trends, as well as within the monetary chance management context to detect rising dangers and assume destiny marketplace moves. there is an expansion of tactics used in TSF, starting from simple linear models to more significant advanced machine-gaining knowledge of strategies. In this paper, we offer a top-level view of the software of TSF for monetary danger management, with particular emphasis on methods for assessing monetary market dangers and strategies for predicting stock fee moves. We describe the processes available to economic practitioners, including the most famous ones, such as autoregressive shifting standard models, synthetic neural networks, and guide vector machines. ultimately, we speak of the challenges associated with applying TSF in the monetary chance control context and touch upon the modern-day and destiny traits within the region. Time series forecasting is a critical device for financial hazard management. It is a statistical device used to investigate and expect future values of monetary time collection, along with stock expenses, trade charges, interest prices, commodity expenses, stock market indices, etc. Time series forecasting uses quantitative strategies, linear regression, autoregression, and moving averages. It can also rent extra complex strategies, including autoregressive transferring averages and neural networks, to extract and interpret styles in the underlying records. the principle cause of this approach is to offer measures of economic danger and go back, which can be utilized

关键词： Economics Biological system modeling Time series analysis Neural networks Predictive models Hazards Vectors Forecasting Portfolios Context modeling

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