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检索条件"任意字段=Conference on Multimedia Content Access - Algorithms and Systems"

共 2015 条记录，以下是11-20 订阅

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A content-based Viewport Prediction Framework for 360° Video Using Personalized Federated Learning and Fusion Techniques

A Content-based Viewport Prediction Framework for 360° Vide...

引用

IEEE International conference on multimedia and Expo (ICME)

作者： Setayesh, Mehdi Wong, Vincent W. S. Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (纸本)9781665468916

Viewport prediction is a key enabler for 360 degrees video streaming over wireless networks. To improve the prediction accuracy, a common approach is to use a content-based viewport prediction model. Saliency detection based on traditional convolutional neural networks (CNNs) suffers from distortion due to equirectangular projection. Also, the viewers may have their own viewing behavior and are not willing to share their historical head movement with others. To address the aforementioned issues, in this paper, we first develop a saliency detection model using a spherical CNN (SPCNN). Then, we train the viewers' head movement prediction model using personalized federated learning (PFL). Finally, we propose a content-based viewport prediction framework by integrating the video saliency map and the head orientation map of each viewer using fusion techniques. The experimental results show that our proposed framework provides higher average accuracy and precision when compared with three state-of-the-art algorithms from the literature.

关键词： Personalized federated learning saliency detection 360 degrees video viewport prediction

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Scalable Multimodal Learning and multimedia Recommendation 9

Scalable Multimodal Learning and Multimedia Recommendation

引用

IEEE 9th International conference on Collaboration and Internet Computing (CIC)

作者： Shen, Jialie Morrison, Marie Li, Zhu City Univ London London England Univ Bristol Bristol Avon England Univ Missouri Kansas City MO 64110 USA

ISBN: (纸本)9798350339123

Driven by the rapid growth of multimedia big data, multimodal learning (especially multimodal deep learning) has gained its significant importance and achieved biggest success in various multimedia computing related applications. The complexity and scale of modern multimedia recommender system often require much more sophisticated statistical model, learning architecture and data processing algorithms to facilitate effective and efficient content understanding and analysis than ever before. In this work, we discuss several major research challenges of the future multimedia recommender system supported by advanced multimodal learning. We also, 1) introduce why multimodal learning is important for large scale multimedia recommendation, 2) review various limitations of the current generation of learning model and architecture, and 3) review key challenges and technical issues in developing and evaluating modern multimedia recommender systems with multimodal learning under different contexts. We hope that our discussion and prediction provide an impetus for further research on this important direction.

关键词： Multimodal Learning Scalability multimedia Recommendation

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Assessment of the effects of 5G MEC cache on DASH adaptation algorithms

Assessment of the effects of 5G MEC cache on DASH adaptation...

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International Symposium on Broadband multimedia systems and Broadcasting (BMSB)

作者： Viola, Roberto Amendola, Danilo Fernandez, Zaloa Gabilondo, Alvaro Zorrilla, Mikel Casals, Mikel Angueira, Pablo Montalban, Jon Basque Res & Technol Alliance Vicomtech Fdn San Sebastian Spain Univ Navarra Tecnun San Sebastian Spain Univ Basque Country Dept Commun Engn Bilbao 48013 Spain Univ Basque Country Dept Elect Technol Bilbao 48013 Spain

ISBN: (纸本)9781665469012

video streaming services are the major sources of Internet traffic, and their usage is constantly growing. To cope with this increasing video traffic demand, 5G comes with a new Radio access Network (RAN), having increased network data rates, and the Multi-access Edge Computing (MEC) architecture, enabling the deployment of services closer to the end devices. 5G RAN and MEC open to new opportunities to enforce and boost the video content delivery, as the content can be cached locally at the MEC host and served with improved RAN connectivity. However, the presence of a cache located at MEC may affect player adaptation algorithm when employing Adaptive Bitrate (ABR) streaming technologies, such as Dynamic Adaptive Streaming over HTTP (DASH). The cache influences the network measurements performed by the DASH player, which are later used, among other factors, by its adaptation algorithm to select the video representation bitrate. This paper investigates the interactions between MEC cache and DASH adaptation algorithms in a real testbed, including 5G RAN and MEC platform. The effects on different adaptation algorithms are analyzed while caching the DASH video content at the MEC.

关键词： 5G Adaptive Streaming content Caching Multi-access Edge Computing & Quality of Service

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The Effect of Social Networking-Based multimedia Repository on Promoting Open Educational Resources 4th

The Effect of Social Networking-Based Multimedia Repository ...

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4th International conference on Novel and Intelligent Digital systems, NiDS 2024

作者： Krouska, Akrivi Troussas, Christos Papakostas, Christos Stavrianakou, Efi Mylonas, Phivos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9783031733437

In recent years, the Open Education movement has emerged as an innovative approach in the field of education. Closely connected to it are the Open Educational Resources (ΟΕRs), which are hosted in Digital Repositories. Despite their unique advantages, global recognition, and increasing numbers, OERs have not yet become fully established. Teachers encounter various challenges in using OERs, including a lack of pedagogical knowledge about their value, usage, production, and integration into teaching. At the same time, many digital repositories are considered inadequate, facing issues such as difficulty in content search, incomplete content, and insufficient assistance for users. As such, this research aims to maximize OERs potential and utilization, introducing a social networking-based multimedia repository. This repository fosters interaction, communication, and collaboration, educating teachers about OERs. The main goal of the research is to determine whether incorporating social networking features into a repository could positively influence users’ experience, access frequency, and the utilization of OERs in teaching. A quantitative research method was employed, showing the positive impact of social networking features on teacher attitude and intention to use OERs in their instruction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Digital repository multimedia content Open Education Open educational resources Social network

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A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios 24

A Multimodal Single-Branch Embedding Network for Recommendat...

引用

18h ACM conference on Recommender systems (RecSys)

作者： Ganhoer, Christian Moscati, Marta Hausberger, Anna Nawaz, Shah Schedl, Markus Johannes Kepler Univ Linz Inst Computat Percept Linz Austria

ISBN: (纸本)9798400705052

Most recommender systems adopt collaborative filtering (CF) and provide recommendations based on past collective interactions. Therefore, the performance of CF algorithms degrades when few or no interactions are available, a scenario referred to as cold-start. To address this issue, previous work relies on models leveraging both collaborative data and side information on the users or items. Similar to multimodal learning, these models aim at combining collaborative and content representations in a shared embedding space. In this work we propose a novel technique for multimodal recommendation, relying on a multimodal Single-Branch embedding network for Recommendation (SiBraR). Leveraging weight-sharing, SiBraR encodes interaction data as well as multimodal side information using the same single-branch embedding network on different modalities. This makes SiBraR effective in scenarios of missing modality, including cold start. Our extensive experiments on large-scale recommendation datasets from three different recommendation domains (music, movie, and e-commerce) and providing multimodal content information (audio, text, image, labels, and interactions) show that SiBraR significantly outperforms CF as well as state-of-the-art content-based RSs in cold-start scenarios, and is competitive in warm scenarios. We show that SiBraR's recommendations are accurate in missing modality scenarios, and that the model is able to map different modalities to the same region of the shared embedding space, hence reducing the modality gap.

关键词： Cold-start Recommendation Recommender systems Hybrid Recommender System content-based Recommender System Multimodal Models Single-Branch Network Weight Sharing Missing Modality Collaborative Filtering multimedia Recommendation

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Exploring the Potential of Chaotic Maps for multimedia Encryption: A Survey 11

Exploring the Potential of Chaotic Maps for Multimedia Encry...

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11th International conference on Intelligent systems and Embedded Design, ISED 2023

作者： Munjal, Harshit Sharma, Madhu Cse Dit University Uttarakhand Dehradun India School of Computing Dit University Uttarakhand Dehradun India

ISBN: (纸本)9798350318159

In the era of the internet, where exponential growth exponential expansion in data transmission and storage has generated worries about data security and privacy, this paper provides a thorough examination of the emerging potential of chaotic maps as a solution for improving multimedia encryption approaches. Chaotic maps, known for their capacity to generate complex and unpredictable patterns, are poised to play a critical role in bolstering multimedia data security against a wide ran8ge of threats. This paper examines recent advances in the field of chaotic map-based encryption algorithms, shining light on their advantages, disadvantages, and prospective applications for protecting multimedia content. It provides significant insights into the potential and avenues for further exploration in this constantly expanding subject, where data protection has become paramount, by an in-depth analysis of previous research © 2023 IEEE.

关键词： Cryptography

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Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation 23

Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendati...

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23rd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Soumm, Michael Popescu, Adrian Delezoide, Bertrand Amanda 34 Ave Champs Elysees F-75008 Paris France Univ Paris Saclay CEA List F-91120 Palaiseau France

ISBN: (纸本)9781665493468

Most recommendation datasets for tourism are restricted to one world region and rely on explicit data such as checkins. However, in reality, tourists visit various places worldwide and document their trips primarily through photos. These images contain a wealth of raw information that can be used to capture users' preferences and recommend personalized content. Visual content was already used in past works, but no large-scale publicly-available dataset that gives access to users' personal images exists for recommender systems. As such a resource would open-up possibilities for new image-based recommendation algorithms, we introduce Vis2Rec, a new dataset based on visit data extracted from users' Flickr photographic streams, which includes over 7 million photos, 36k recognizable points of interest, and 14k user profiles. Google Landmarks v2 is used as an auxiliary dataset to identify points of interest in users' photos, using a state-of-the-art image-matching deep architecture. Image-based user profiles are then constituted by aggregating the points of interest detected for each user. In addition, ground truth visits were determined for the test subset in order to enable accurate evaluation. Finally, we benchmark Vis2Rec using various existing recommender systems, and discuss the possibilities opened up by the availability of user images, as well as the societal issues that come with them. Following good practice in dataset sharing, Vis2Rec is created using only freely distributable content, and additional anonymization is performed to ensure the privacy of users. The raw dataset and the preprocessed user profiles will be publicly available at https://***/MSoumm/Vis2Rec.

关键词： Visualization Data privacy Image databases Semantics multimedia Web sites Benchmark testing Streaming media

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DeePref: Deep Reinforcement Learning For Video Prefetching In content Delivery Networks 33

DeePref: Deep Reinforcement Learning For Video Prefetching I...

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33rd IEEE International conference on Computer Communications and Networks (ICCCN)

作者： Alkassab, Nawras Huang, Chin-Tser Botran, Tania Lorido Univ South Carolina Columbia SC 29208 USA Roblox San Mateo CA USA

ISBN: (纸本)9798350348439;9798350384611

content Delivery Networks carry the majority of Internet traffic, and the increasing demand for video content as a major IP traffic across the Internet highlights the importance of caching and prefetching optimization algorithms. Prefetching aims to make data available in the cache before the requester places its request to reduce access time and improve the Quality of Experience on the user side. Traditional prefetching techniques are well adapted to a particular access pattern, but fail to adapt to sudden variations or randomization in workloads. This paper explores the use of deep reinforcement learning to tackle the changes in users' access patterns and automatically adapt over time to predict future requests. We propose DEEPREF, an auto-aggressive video prefetcher that is sensitive to different storage capacities at edge networks. DEEPREF is agnostic to hardware design, operating systems, and applications in which it utilizes only the video ID to make prefetching decisions online. DEEPREF outperforms baseline approaches that use video content popularity as a building block to statically or dynamically make prefetching decisions. Our results show that DEEPREF, using a real-world dataset, achieves 17% increase in terms of prefetching accuracy and 28% increase in prefetching coverage. We also study the possibility of transfer learning of statistical models from one edge network into another, where unseen user requests from unknown distribution are observed. Our results indicate that DEEPREF effectively adapts to distribution shifts where the increase in prefetching accuracy and prefetching coverage are [30%, 10%], respectively.

关键词： Deep reinforcement learning

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Exploring WeTV Application with Naïve Bayes, Decision Tree, and Random Forest Classifiers for Sentiment Analysis

Exploring WeTV Application with Naïve Bayes, Decision Tree,...

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2024 International Visualization, Informatics and Technology conference, IVIT 2024

作者： Mareby, Yeremia Steven Putra Desanti, Ririn Ikana Universitas Multimedia Nusantara Information System Study Program Jakarta Indonesia

ISBN: (纸本)9798350354447

In 2023, Indonesia has experienced a notable increase in internet usage, particularly in the realm of digital content payments. This rise has consequently led to a substantial growth in the use of streaming applications, which are vital for providing entertainment to various communities by enabling content streaming on their devices. This research focuses on analyzing the general sentiments expressed in user reviews of WeTV, a popular streaming platform in Indonesia. To achieve this, the study employs three machine learning algorithms: Naïve Bayes, Decision Tree, and Random Forest. Among these algorithms, Naïve Bayes demonstrates the highest performance, achieving an accuracy of 79.1%, followed by Random Forest and Decision Tree. The results indicate that a significant portion of user sentiments towards the WeTV application are predominantly negative. The high accuracy of the Naïve Bayes model will be utilized for deployment on a basic website accessible to users, aiming to provide insights into user opinions and improve the overall user experience on the platform. © 2024 IEEE.

关键词： Adversarial machine learning

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FLoRaSat 2: Simulating Cross-Linked Direct-to-Satellite IoT LEO Constellations 12

FLoRaSat 2: Simulating Cross-Linked Direct-to-Satellite IoT ...

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12th Advanced Satellite multimedia systems conference and the 18th Signal Processing for Space Communications Workshop, ASMS/SPSC 2025

作者： Choquenaira-Florez, Alexander Y. Ohs, Robin Fraire, Juan A. Rivano, Hervé Saarland University Saarbrücken66123 Germany Inria INSA Lyon CITI Villeurbanne69621 France Universidad Nacional de Córdoba CONICET Córdoba Argentina

ISBN: (纸本)9798331522353

Direct-to-Satellite IoT (DTS-IOT) represents a promising solution for data transmission in remote regions where terrestrial infrastructure deployment is unfeasible. In DTS-IOT scenarios, Low-Earth Orbit (LEO) satellites function as in-orbit gateways. Addressing the need for practical simulation tools, we present FLORASAT 2, an open-source, event-driven, end-to-end simulation tool leveraging OMNET++. The original FLO-RASAT met many DTS-IOT Medium access Control (MAC) requirements. Still, this enhanced version introduces advanced Inter-Satellite Link (ISL) communication modules, including a helper for constellation creation, dynamic ISL topology control, routing, and analytics, facilitating the thorough evaluation of constellation-grade DTS-IOT networks. These new features allow detailed simulation scenario configuration, flexible support for developing and including diverse routing algorithms, and the tooling to perform automated data analysis from parametric simulations. Overall, the simulator enables the analysis of complex behaviors in DTS-IOT environments, optimizing performance and enhancing connectivity and efficiency in large-scale satellite IoT constellation networks. © 2025 IEEE.

关键词： access control

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