Streaming Applications Research Articles

Turkish Radio and TV Broadcasting Corporation’s new streaming application and the continuity of music policy as an educational institution

While numerous studies focus on streaming platforms as new business models or as novel elements in the supply chain, there is a notable gap in research addressing how streaming is operationalized within the context of state cultural policies. This study aims to fill this gap by conducting a preliminary examination of Turkish Radio and Television Broadcasting Corporation’s (TRT’s) digital transformation through its music streaming platform, TRT Listen. This research contributes to the international literature by highlighting various potential approaches to the digitalization of public radio broadcasting. This study, which examines the purpose and realization of TRT Listen, first provides a historical overview of TRT’s radio broadcasting and thus reveals the continuity in TRT’s cultural policy, which is shaped as a mission to educate the public and inculcate a national cultural identity, from radio broadcasting to the platform app. Two different research methods were used in this study: walk-through technique for the app and in-depth interviews with TRT’s staff and music industry representatives. The basic argument suggests that TRT Listen represents a continuation of historical music policies and nation state cultural agenda inherited from TRT’s radio era. However, at the operational level, this strategic continuity seems to disappear in the audience engagement techniques of digital platforms. The three main discussion sections of this article – TRT Listen’s strategic orientation, content curation and copyright management – reveal the platform’s mission to educate the public culturally in Turkey.

Public perceptions of synthetic cooling agents in electronic cigarettes on Twitter.

Amid a potential menthol ban, electronic cigarette (e-cigarette) companies are incorporating synthetic cooling agents like WS-3 and WS-23 to replicate menthol/mint sensations. This study examines public views on synthetic cooling agents in e-cigarettes via Twitter data. From May 2021 to March 2023, we used Twitter Streaming Application Programming Interface (API), to collect tweets related to synthetic cooling agents with keywords such as 'WS-23,' 'ice,' and 'frozen.' The deep learning RoBERTa (Robustly Optimized BERT-Pretraining Approach) model that can be optimized for contextual language understanding was used to classify attitudes expressed in tweets about synthetic cooling agents and identify e-cigarette users. The BERTopic (a topic modeling technique that leverages Bidirectional Encoder Representations from Transformers) deep-learning model, specializing in extracting and clustering topics from large texts, identified major topics of positive and negative tweets. Two proportion Z-tests were used to compare the proportions of positive and negative attitudes between e-cigarette users (vapers) and non-e-cigarette-users (non-vapers). Of 6,940,065 e-cigarettes-related tweets, 5,788 were non-commercial tweets related to synthetic cooling agents. The longitudinal trend analysis showed a clear upward trend in discussions. Vapers posted most of the tweets (73.05%, 4,228/5,788). Nearly half (47.87%, 2,771/5,788) held a positive attitude toward synthetic cooling agents, which is significantly higher than those with a negative attitude (19.92%,1,153/5,788) with a P-value < 0.0001. The likelihood of vapers expressing positive attitudes (60.17%, 2,544/4,228) was significantly higher (P < 0.0001) than that of non-vapers (14.55%, 227/1,560). Conversely, negative attitudes from non-vapers (30%, 468/1,560) were significantly (P < 0.0001) higher than vapers (16.2%, 685/4,228). Prevalent topics from positive tweets included "enjoyment of specific vape flavors," "preference for lush ice vapes," and "liking of minty/icy feelings." Major topics from negative tweets included "disliking certain vape flavors" and "dislike of others vaping around them." On Twitter, vapers are more likely to have a positive attitude toward synthetic cooling agents than non-vapers. Our study provides important insights into how the public perceives synthetic cooling agents in e-cigarettes. These insights are crucial for shaping future U.S. Food and Drug Administration (FDA) regulations aimed at safeguarding public health.

Assessing the public discourse on Twitter: Reactions to the JUUL e-cigarettes ban in the United States.

JUUL is a high-nicotine pod-based vaping device that is popular among adolescents and young adults. On 23 June 2022, the US Food and Drug Administration (FDA) denied authorization to market JUUL, and ordered JUUL Labs to remove products from the US market. The next day, a US federal appeals court temporarily suspended the ban. The mixed public discourse surrounding the FDA ban warrants further investigation. This study examined Twitter data to describe public reaction to these announcements. Posts containing terms 'JUUL' and/or '#JUUL' (N=97548 unique tweets) were collected from 23 June to 3 July 2022, from Twitter's Streaming Application Programming Interface (API). After removing retweets, we used an inductive approach to become familiar with the data, generated a codebook, and conducted a content analysis on a random sample of n=4000 tweets. A total of 2755 (68.9%) tweets discussed JUUL in the context of the FDA ban. News (n=1425/2755; 51.7%) about the JUUL ban, government distrust (n=588; 21.3%), and individual rights (n=253; 9.2%) were the most prevalent themes. Less commonly discussed themes included inconsistencies between policies (n=174; 6.3%), mentions of switching to other products (n=162; 5.9%), smoking cessation (n=99; 3.6%), and craving for JUUL (n=94; 3.4%). Sentiment analysis of JUUL ban-related posts (n=2755) demonstrated that 1989 (72.2%) tweets were categorized as neutral, while anti-ban posts (n=566; 20.5%) were more prevalent than pro-ban posts (n=200; 7.3%). Besides straightforward announcements of the JUUL ban and its suspension, Twitter posts discussed government distrust, individual rights, and policy inconsistencies. While most posts conveyed neutral sentiments, anti-ban posts were almost three times more prevalent than pro-ban posts. Our findings suggest that text-based social media platforms like Twitter may be an effective instrument to understand opinions, attitudes, and beliefs regarding the FDA's JUUL ban.

TV Broadcast Piracy Through Illegal Live Streaming Applications: Challenges and Legal Protection for Copyright Holders

The copyright infringement that is currently on the rise is the emergence of illegal TV streaming apps. The application is now very annoying to official broadcasting rights holders because the presence of such illegal streaming applications will cause losses. This paper aims to find out the form of legal protection as well as legal action for television stations that suffer losses from the piracy of such broadcasting activities.This study is a normative legal study with normative jurisprudence approach to critically analyse the criminal and civil law norms against piracy TV broadcasts through the Live Streaming application aimed at digging and revealing the protection and enforcement of the law for TV stations suffered losses as a result of such piracy.If a person is found to conduct broadcasting or distribution without the permission of the owner who violates the provisions of Article 25 paragraph (2) of the Copyright Act with the criminal punishment of Article 118 of the copyright Act. Whereas the perpetrator of illegal live streaming application that violates Article 32 paragraph (1) of the ITE Act, can be subject to criminal sanctions as referred to in Article 48 paragraph 1 of the Act. The resolution of copyright disputes can be carried out through litigation and non-litigation. It would be better for the public to choose to watch legally and subscribe through an official service despite having to pay for the safety and convenience of the user.

Server Redundancy: Performa Jaringan Mengunakan DNS Failover MikroTik pada Kasus Private Server dan Public Server

The digitization of user services has increased, in line with the need for VPS and cloud computing services, which are rampant among application and platform developers. Quite a few companies that create applications or application users have servers to handle user needs. Testing is carried out using the ICMP Protocol to get real-time results and can be measured. From Scenario 1, carrying out 20 test requests, we get a packet loss of 5% with RTT Avarage of 195,838ms and Mdev 4,103ms. If you apply DNS failover in scenario 2, the client will likely access the web a little slower, as evidenced by the packet loss being 25% greater in value. Compared to scenario 1, having a high standard deviation (Mdev) of round-trip times is not desirable. This variation is also known as jitter. Increased jitter can cause a bad user experience, especially in real-time audio and video streaming applications. However, this is still understandable because it only has a 1-5 second effect on the service. Next, in scenario 3, we can see that private and public servers have relatively high gab with 0% packet loss, which has a small Mdev value of 0.309ms. Therefore, the DNS failover method is a solution for network administrators who have problems related to server migration between public servers and private servers so that services can run even if a server is maintaining or downlinking.

Performance evaluation of mpeg-5 part 2 (lcevc): impact of packet loss

This paper presents an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for video streaming applications using best effort protocols. LCEVC is a new video standard by MPEG, which enhances any base codec through an additional low bitrate stream, improving both video compression efficiency and and transmission. However, there is an interplay between packetization, packet loss visibility, choice of codec and video quality, which implies that prior studies with other codecs may be not as relevant. The contributions of this paper is, therefore in twofold: It evaluates the compression performance of LCEVC and then the impact of packet loss on its video quality when compared to H.264 and HEVC.The results from this evaluation suggest that, regarding compression, LCEVC outperformed its base codecs, overall in terms average encoding bitrate savings when using the constant rate factor (CRF) rate control. For example at a CRF of 19, the average encoding bitrate was reduced by 18.7% and 15.8% when compared with the base H.264 and HEVC codecs respectively. Furthermore, LCEVC produced better visual quality across the packet loss range compared to its base codecs and the quality only started to decrease once packet loss exceeded 0.8-1%, and decreases at a slower pace compared to its equivalent base codecs. This suggests that the LCEVC enhancement layer also provides error concealment. The results presented in this paper will be of interest to those considering the LCEVC standard and expected video quality in error-prone environments

Research on the Business Model and Development of NetEase Cloud Music

With the rapid development of the internet in today's world, there is a growing demand for online music streaming, accompanied by the formation of a substantial user base. According to statistical data, the majority of users belong to the 90s and 00s generations. It is precisely these younger customer segments that have transformed the characteristics of music products from a singular playback format to a more mobile and social experience. Presently, the market features prominent music streaming applications such as NetEase Cloud Music, QQ Music, and Xiami Music, each offering similar functionalities along with their unique preferences. This paper focuses on the business model, marketing strategies, and potential cross-industry offline operational models of NetEase Cloud Music, a subsidiary of NetEase. The paper employs SWOT and 4Ps analysis methods to explore the strengths, weaknesses, and growth prospects of this application as well as its marketing model. Additionally, this paper envisions a cross-industry offline development model for NetEase Cloud Music that integrates the lifestyle needs of a youthful user base and adheres to the operational dynamics of modern urbanization.

A Multi-Channel Packet Scheduling Approach to Improving Video Delivery Performance in Vehicular Networks

When working with the Wireless Access in Vehicular Environment (WAVE) protocol stack, the multi-channel operation mechanism of the IEEE 1609.4 protocol may impact the overall network performance, especially when using video streaming applications. In general, packets delivered from the application layer during a Control Channel (CCH) time slot have to wait for transmission until the next Service Channel (SCH) time slot arrives. The accumulation of packets at the beginning of the latter time slot may introduce additional delays and higher contention when all the network nodes try, at the same time, to obtain access to the shared channel in order to send the delayed packets as soon as possible. In this work, we have analyzed these performance issues and proposed a new method, which we call SkipCCH, that helps the MAC layer to overcome the high contention produced by the packet transmission bursts at the beginning of every SCH slot. This high contention implies an increase in the number of packet losses, which directly impacts the overall network performance. With our proposal, streaming video in vehicular networks will provide a better quality of reconstructed video at the receiver side under the same network conditions. Furthermore, this method has particularly proven its benefits when working with Quality of Service (QoS) techniques, not only by increasing the received video quality but also because it avoids starvation of the lower-priority traffic.

Quality control on oil palm RNA samples for efficient genomic downstream applications.

Quality control (QC) is primordial for determining the efficiency in any downstream genomic applications. There are several steps in the verification of the quality of RNA samples destined for genomic studies. The aim of this research was to determine whether RNA should be discarded at the level of the field lab if it fails preliminary quality control using Optical Density (OD) measurements. In this study, all samples were submitted to rigorous quality control in every stage of work. RNA samples showing poor OD values still gave excellent results in downstream QC and genomic applications. At the end of the quality control exercise, it was observed that the original samples were the same and had not undergone any deterioration along the different stages of handling and manipulation. This paper shows the different and most important stages of quality control on RNA samples (RIN) for an effective down stream application in genomic studies. RNA samples should not be discarded based on preliminary QC from our field labs.

Sustainable application of industrial side streams as alternative fine aggregates for cement mortar

Increase in industrialization has led to the production of huge volume of side-stream materials that need safe disposal solutions. The present study proposes the use of local industrial side streams such as ferrochrome slag, phyllite dust and mine tailings as secondary raw materials for construction, mainly as fine aggregates. Four different cement mortar mixtures, with a combination of selected side streams as a sole, binary, and ternary blends were investigated. Workability, strength, and durability properties of the derived mortar mixtures were compared with mortar produced using standard sand as reference. Mortar mixtures with a ternary blend of side-stream fine aggregates resulted in a compressive strength of 68–72 MPa at 28 days, which is 30–40% higher than that of control mix. The addition of industrial side streams resulted in a denser microstructure and enhanced the mechanical properties. The durability performance of the mortar with alternative fine aggregates is comparable with those of standard sand.

A Hierarchical Multiscenario H.265/HEVC Video Encryption Scheme

With the pervasive application of video streaming, the security of streaming media information continually faces new challenges. Most existing encryption methods employ uniform criteria for encrypting all scenarios, leading to unnecessary mutual inhibition of algorithm security and coding efficiency. Furthermore, several encryption algorithms are inadequate for high-resolution videos. A novel, independently hierarchical video cryptosystem for H.265/high efficiency video coding (HEVC) is proposed that develops a scene-adaptive encryption strategy tailored for multiscenario videos. Additionally, we fully consider the seeds of pseudo-random number generators, syntax components in compressed codes, and scenario indicators. We analyze the visibility of encrypted videos in different scenarios and the encryption performance of various syntax parameters based on the integration of encryption for three distinct scenario categories to further enhance encryption efficiency and security. The method’s versatility is demonstrated using a diverse array of videos with significant and insignificant inter-frame motion information across varying resolutions. The experimental results from the video datasets indicate that our scheme effectively balances security and coding efficiency. Furthermore, the scene-adaptive approach can be tailored flexibly according to subscriber needs.

THORIN: an Efficient Module for Federated Access and Threat Mitigation in Big Stream Cloud Architectures

In order to make cloud services attractive for several IT organizations, it is necessary to provide access control and to implement safe and reliable mechanisms of Identity and Access Management (IAM). In this work, we focus on security issues and challenges in the design and implementation of cloud architectures and, in particular, for the management of Big Stream applications in Internet of Things (IoT) scenarios. The proposed work introduces a new set of modules allowing a federated access control policy for cloud users. An analysis of possible threats and attacks against the proposed Big Stream platform is presented, investigating the system performance in terms of detection and elimination of malicious nodes. In particular, we propose a new module, denoted as Traffic Handler Orchestrator & Rapid Intervention (THORIN), which is very efficient in counteracting botnet-based threats.

Assessment of Optimization Parameters for Video Transmission over Optical Fiber using an H265/HEVC Encoder in Video Streaming Applications

Assessment of Optimization Parameters for Video Transmission over Optical Fiber using an H265/HEVC Encoder in Video Streaming Applications

An emotion-driven and topic-aware dialogue framework for human–robot interaction

ABSTRACT Developing dialogue services for robots has been promoted nowadays for providing natural human–robot interactions to enhance user experiences as conversation is a key instrument for creating and maintaining their mutual relationships. In this work, we present a trainable framework for modeling context-aware human–robot dialogues, and apply it to a live streaming application to demonstrate the need for social robots. Our social chatting robot framework takes into account both emotional context and topic-focused content information to generate appropriate responses. This framework has some unique features. It adopts a multimodal deep learning model to recognize the participants’ emotions. Moreover, our work includes a topic-aware neural model that enables the social robot to follow certain conversation topics to fulfill the specific dialoguing goals and to enhance the chatting coherence. Most importantly, we design a strategy able to take emotions as the drive to control over the neural method of generating utterances. To evaluate the proposed approach, we have conducted several sets of quantitative and quantitative experiments. The results highlight the importance of multimodal emotion recognition and topic-awareness in dialoguing and confirm the feasibility and effectiveness of our framework for human–robot interaction.

How patients evaluate physicians in live Video: An empirical study from a dual process perspective

Live diagnosis is an application of live streaming in online health communities, through which physicians share knowledge and interact with patients in real time. Live videos enable patients to evaluate physicians comprehensively before a paid consultation. By applying dual-process theory, we examine the relationships between physicians’ speech features, demographic characteristics, the information quality of live content in live videos and patients’ subscription and consultation behavior. Data from 992 physicians who provided live diagnoses were collected from an online health community. Results show that physicians’ speech features and live content quality are significantly associated with patients’ subscription behavior, which is further associated with their online consultation behavior. This study provides a new perspective for investigating patients’ consultation behavior in the online health community context. It enriches the application of dual-process theory, and offers implications for physicians and online health community administrators in providing better live diagnosis services.

Evaluation of consumer usage behavior for interactive entertainment: A Netflix case study

The streaming business has been continuously rising in the market and movie streaming applications are trying to break boundaries. Netflix, one of the top streaming platforms hence created the interactive entertainment like Bandersnatch and Kaleidoscope to compete with the close call streaming platform market. As interactive entertainment continues to gain popularity, questions regarding how it affects user acceptance and satisfaction are being raised. Using the UTAUT2 model, this study analyzed the usage behavior of 847 valid respondents. Analyzing the latent variables simultaneously, structural equation modeling was considered to completely assess the usage behavior among users towards interactive entertainment. Results showed that facilitating conditions aside from behavioral intentions prompted the most significant factor, followed by hedonic motivation, habit, performance expectancy, and social influence. It was also seen that price value played a significant role with the account sharing capabilities provided by Netflix. Results have shown that watching interactive entertainment is a pleasant experience, wherein consumers would engage with the type of movies or series frequently when available and will spend more time in watching it. This presented a positive purchase intention for an account on Netflix when they are provided with more interactive entertainment among users. With Netflix’s establishment of the new way of entertainment, this study would provide a benchmark analysis of how likely people would consider interactive entertainment. Moreover, the findings of this study could be utilized by other streaming platforms, especially the companies that wanted to consider the path of future entertainment through interactive entertainment.

Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet

Video streaming applications (VSAs) are increasingly being deployed on large-scale edge platforms, which have the potential to significantly improve the quality of service (QoS) and end-user experience (QoE), ultimately maximizing business outcomes. However, there is currently very little understanding of how QoS, QoE, and the impact of QoS on QoE for VSAs on edge platforms in the wild and at scale. To close the knowledge gap, we collect SNESet, an active measurement dataset comprising QoS and QoE telemetry metrics of 8 VSAs over four months, covering end-users from 798 edge sites,30 cities, and 3 ISPs in one country.We characterize and compare the QoS and QoE metrics in SNESet with existing publicly available datasets, highlighting that SNESet includes a significantly greater number of metrics (horizontal diversity and vertical hierarchy) and provides more comprehensive coverage of specific metrics.Moreover, we qualitatively and quantitatively analyze the impact of QoS on QoE in both domain-general and domain-specific scenarios. Our findings can inform the system design decisions that different entities in the video ecosystem (content providers, video player designers, third-party optimizers, edge vendors) make to maximize end-users experience and ultimately maximize the business outcomes. We hope SNESet can attract more research efforts in the data management community, computer network community, and beyond.

DEĞER AKIŞ HARİTALAMA YÖNTEMİ VE BİR İMALAT İŞLETMESİNDE UYGULANMASI

Teknolojinin gelişimi, birçok sektörün daha geniş bir kitleye hitap etmesine olanak sağlamaktadır. Bu durum, firmalar arasındaki rekabeti artırmakta ve daha fazla müşteri ile etkileşime geçmelerini gerektirmektedir. Çeşitli sektörlerde faaliyet gösteren şirketler, kaliteyi artırmak, maliyeti düşürmek, üretimi verimli hale getirmek, israfı azaltmak, hurda miktarını düşürmek ve müşteri taleplerini zamanında karşılamak için yalın kavramını benimsemektedir. Yalın üretim teknikleri, aynı hedefe hizmet etmelerine rağmen uygulanmaları açısından farklılık göstermektedir. Yalın üretim yöntemlerinden biri olan Değer Akışı Haritalama (DAH) yöntemi, üretim sürecinde akışı bozan darboğazları ve tüm süreçle ilgili gerekli bilgileri görsel olarak sunmaktadır. Ayakkabı yan sanayisinde üretim yapan bir firma tarafından yapılan bu çalışmada; değer yaratmayan faaliyetleri ortadan kaldırarak üretim süresini kısaltmak ve müşteri taleplerini daha hızlı karşılamak amaçlanmaktadır. Bu amaç doğrultusunda ürün ailesi seçimi için ABC analizi kullanılmış ve sonrasında DAH yöntemi uygulanmıştır. Sünger ana malzemesiyle üretilen ürünler için mevcut durum haritası çizilmiş ve iyileştirme fırsatları belirlenmiştir. İyileştirmelerin yapılabilmesi için gelecek durum haritası ve spagetti diyagramı çizilmiştir. Gerçekleştirilen iyileştirmeler sonucunda üretim akışı 32,25 günden 23,5 güne düşürülmüştür. Ayrıca, malzeme dolaşımı 262,25 metreden 233,8 metreye indirilmiştir.

Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology Study.

Traditionally, surveys are conducted to answer questions related to public health but can be costly to execute. However, the information that researchers aim to extract from surveys could potentially be retrieved from social media, which possesses data that are highly accessible and lower in cost to collect. This study aims to evaluate whether attitudes toward COVID-19 vaccines collected from the Household Pulse Survey (HPS) could be predicted using attitudes extracted from Twitter (subsequently rebranded X). Ultimately, this study aimed to determine whether Twitter can provide us with similar information to that observed in traditional surveys or whether saving money comes at the cost of losing rich data. COVID-19 vaccine attitudes were extracted from the HPS conducted between January 6 and May 25, 2021. Twitter's streaming application programming interface was used to collect COVID-19 vaccine tweets during the same period. A sentiment and emotion analysis of tweets was conducted to examine attitudes toward the COVID-19 vaccine on Twitter. Generalized linear models and generalized linear mixed models were used to evaluate the ability of COVID-19 vaccine attitudes on Twitter to predict vaccine attitudes in the HPS. The results revealed that vaccine perceptions expressed on Twitter performed well in predicting vaccine perceptions in the survey. These findings suggest that the information researchers aim to extract from surveys could potentially also be retrieved from a more accessible data source, such as Twitter. Leveraging Twitter data alongside traditional surveys can provide a more comprehensive and nuanced understanding of COVID-19 vaccine perceptions, facilitating evidence-based decision-making and tailored public health strategies.

Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming Quality

Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate. By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.