scholarly journals Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7328
Author(s):  
Sidney Loyola de Sá ◽  
Antonio A. de A. Rocha ◽  
Aline Paes
Keyword(s):  
Video Streaming ◽  
State Of The Art ◽  
Representation Learning ◽  
Random Forest Algorithm ◽  
Web Content ◽  
Content Popularity ◽  
Streaming Services ◽  
Textual Features ◽  
Video Prediction

The Internet’s popularization has increased the amount of content produced and consumed on the web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged, focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract the attention of most users. For example, in the data explored in this article, only 6% of the most popular videos account for 85% of total views. Finding out in advance which videos will be popular is not trivial, especially given many influencing variables. Nevertheless, a tool with this ability would be of great value to help dimension network infrastructure and properly recommend new content to users. In this way, this manuscript examines the machine learning-based approaches that have been proposed to solve the prediction of web content popularity. To this end, we first survey the literature and elaborate a taxonomy that classifies models according to predictive features and describes state-of-the-art features and techniques used to solve this task. While analyzing previous works, we saw an opportunity to use textual features for video prediction. Thus, additionally, we propose a case study that combines features acquired through attribute engineering and word embedding to predict the popularity of a video. The first approach is based on predictive attributes defined by resource engineering. The second takes advantage of word embeddings from video descriptions and titles. We experimented with the proposed techniques in a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of engineering features and embeddings using the Random Forest algorithm achieved the best result, with an accuracy of 87%.

scholarly journals Netflix and L2 learning: A case study

2018 ◽  
Vol 26 (2) ◽  
pp. 30 ◽  
Author(s):  
Gilbert Dizon
Keyword(s):  
Language Learning ◽  
Video Streaming ◽  
Video On Demand ◽  
L2 Motivation ◽  
Video Viewing ◽  
L2 Learning ◽  
Efl Students ◽  
Streaming Services ◽  
Streaming Service

<p>The evolving video viewing habits of consumers combined with advances in mobile technology have resulted in the growth of video on demand (VOD) services. While these video streaming services potentially offer several benefits for L2 learners, little is known about them in the context of language learning. Thus, this study fills this gap in the literature by examining EFL students’ views of Netflix, the leading provider of subscription VOD. The study also investigates the extent to which EFL learners use Netflix for L2 learning and the means by which they access the service. Nine Japanese EFL students participated in the study and were given three months to use Netflix. The participants were then interviewed to obtain their opinions of the video streaming service. Four themes were extracted from the interview data: (1) enhanced learning effectiveness, (2) increased L2 motivation, (3) better access to L2 knowledge, and (4) hindered convenience. It was also found that the learners watched primarily through mobiles devices rather than PCs, and viewed more L2 titles than L1 programming. These results illustrate that subscription VOD has the potential to foster L2 learning and underscore the necessity for more research into their use for language learning.</p>

Evaluasi Aksesibilitas Learning Object Berdasarkan Web Content Accessibility Guidelines 2.0

2017 ◽  
Vol 9 (2) ◽  
pp. 67-71
Author(s):  
Herru Darmadi ◽  
Yan Fi ◽  
Hady Pranoto
Keyword(s):  
Learning Objects ◽  
Authoring Tool ◽  
Learning Object ◽  
Web Content ◽  
Web Browser ◽  
E Learning ◽  
Index Terms ◽  
Accessibility Guidelines ◽  
Content Accessibility

Learning Object (LO) is a representation of interactive content that are used to enrich e-learning activities. The goals of this case study were to evaluate accessibility and compatibility factors from learning objects that were produced by using BINUS E-learning Authoring Tool. Data were compiled by using experiment to 30 learning objects by using stratified random sampling from seven faculties in undergraduate program. Data were analyzed using accessibility and compatibility tests based on Web Content Accessibility Guidelines 2.0 Level A. Results of the analysis for accessibility and compatibility tests of Learning Objects was 90% better than average. The result shows that learning objects is fully compatible with major web browser. This paper also presents five accessibility problems found during the test and provide recommendation to overcome the related problems. It can be concluded that the learning objects that were produced using BINUS E-learning Authoring Tool have a high compatibility, with minor accessibility problems. Learning objects with a good accessibility and compatibility will be beneficial to all learner with or without disabilities during their learning process. Index Terms—accessibility, compatibility, HTML, learning object, WCAG2.0, web

Centrality Measures

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
State Of The Art ◽  
Centrality Measures ◽  
Eigenvector Centrality ◽  
Multilayer Networks ◽  
Comprehensive Description ◽  
Multilayer Network ◽  
Suitable Definition ◽  
Katz Centrality ◽  
Pagerank Centrality

Defining the centrality of nodes and layers in multilayer networks is of fundamental importance for a variety of applications from sociology to biology and finance. This chapter presents the state-of-the-art centrality measures able to characterize the centrality of nodes, the influences of layers or the centrality of replica nodes in multilayer and multiplex networks. These centrality measures include modifications of the eigenvector centrality, Katz centrality, PageRank centrality and Communicability to the multilayer network scenario. The chapter provides a comprehensive description of the research of the field and discusses the main advantages and limitations of the different definitions, allowing the readers that wish to apply these techniques to choose the most suitable definition for his or her case study.

scholarly journals Photovoltaics Enabling Sustainable Energy Communities: Technological Drivers and Emerging Markets

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1862
Author(s):  
Alexandros-Georgios Chronis ◽  
Foivos Palaiogiannis ◽  
Iasonas Kouveliotis-Lysikatos ◽  
Panos Kotsampopoulos ◽  
Nikos Hatziargyriou
Keyword(s):  
State Of The Art ◽  
Research Question ◽  
Economic Benefits ◽  
Energy Market ◽  
Small Scale ◽  
Local Energy ◽  
Community Members ◽  
Open Research ◽  
Energy Community

In this paper, we investigate the economic benefits of an energy community investing in small-scale photovoltaics (PVs) when local energy trading is operated amongst the community members. The motivation stems from the open research question on whether a community-operated local energy market can enhance the investment feasibility of behind-the-meter small-scale PVs installed by energy community members. Firstly, a review of the models, mechanisms and concepts required for framing the relevant concepts is conducted, while a clarification of nuances at important terms is attempted. Next, a tool for the investigation of the economic benefits of operating a local energy market in the context of an energy community is developed. We design the local energy market using state-of-the-art formulations, modified according to the requirements of the case study. The model is applied to an energy community that is currently under formation in a Greek municipality. From the various simulations that were conducted, a series of generalizable conclusions are extracted.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

scholarly journals Efficient Rank-Based Diffusion Process with Assured Convergence

2021 ◽  
Vol 7 (3) ◽  
pp. 49
Author(s):  
Daniel Carlos Guimarães Pedronette ◽  
Lucas Pascotti Valem ◽  
Longin Jan Latecki
Keyword(s):  
Diffusion Process ◽  
Learning Strategies ◽  
State Of The Art ◽  
Representation Learning ◽  
Theoretical Background ◽  
High Dimensional ◽  
Visual Features ◽  
Learning Approaches ◽  
Previous Decade ◽  
Asymptotic Complexity

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art.

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