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Out-of-school science education is important for students' emotional outcomes, such as their interest in science. Not all students, however, have access to real outdoor environments for hands-on learning. To address this issue, live streaming technology allows for real-time interaction and video-based instruction, which can enhance students' interest in science and connect in-school and out-of-school learning. The effects of live streaming programs, however, are not well understood. In this study, we used the experiential learning theory to design and implement an activity on seed dispersal and conservation using live streaming technology at a botanical garden. The combined findings from both quantitative and qualitative data showed that primary school students in the urban area significantly increased their situational interest in science through triggering by question-evoked information gaps and engaging with vicarious interactive experiences based on the live streaming technology, indicating the effectiveness of the live streaming activity in the botanical garden. This case study demonstrates the potential of live streaming technology as a remote strategy to share educational resources beyond traditional classrooms and support high-quality education. [ABSTRACT FROM AUTHOR]

The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research. [ABSTRACT FROM AUTHOR]

Over the past decade, the concept and practice of shopstreaming (also known as livestream shopping) have grown significantly within the e-business world, as it integrates live streaming technology with e-commerce. However, the relationship between the perceived benefits of this shopping mode and the intention to use it is not fully understood. This research seeks to enhance the current understanding of this relationship by studying the association in the context of fashion and personal care (FPC) goods. Uniquely, the study bases its core model on a combination of the theory of planned behaviour (TPB) and some elements of the enhanced stimulus–organism–response (ESOR) theory, which incorporates cognitive, emotional and physiological processes within the organism component. This enables the development of a framework which facilitates the examination of the relationship between perceived benefits and intention to purchase within a shopstreaming environment, moderated by attitude (organism). The uniqueness of the study is further enhanced by the inclusion and analysis of perceived platform quality and the streamer's (seller's) influence as moderating constructs. These analyses were carried out using data from 901 respondents to a structured questionnaire, collected over a 4-month period. The results of the study showed that the seller has a significant moderating effect on the mediation of intention to purchase by attitude, though the mediation between perceived benefits and attitude was not affected by perceived platform quality. The study therefore offers significant insights to Saudi FPC brands, streamers and marketing agencies to develop and optimise sales and content strategy. [ABSTRACT FROM AUTHOR]

In the contemporary era, the prevailing trend among individuals is the widespread utilization of streaming technology to indulge in audio or video content emanating from a server. This innovative approach negates the necessity for downloading the content beforehand, allowing users to seamlessly revel in the material through the streaming mechanism. However, this cuttingedge technology mandates a substantial bandwidth to uphold its Quality of Service (QoS) at an optimal level. Challenges may arise, such as packet loss, attributed to the restricted bandwidth provided by the network service provider, thereby compromising the delivered content's quality. To address this issue, one viable solution is the integration of CDN (Content Delivery Network), a technology proven to enhance the quality of content delivered through streaming services. This research endeavors to elevate the quality of live video streaming by leveraging CDN. The HTTP Live Streaming (HLS) format for content streaming, the study empirically demonstrates that CDN significantly improves the Quality of Service. As a result, the proposed method Content Delivery Network (CDN) using H.265 plays vital role in live video streaming. The result shows that live video streaming with CDN outperforms without CDN. Specifically, the live video streaming experience with CDN showcases a remarkable throughput average of 5899.7 kbps, coupled with a minimal packet loss ratio average of 0.05%. This stands in stark contrast to the version without CDN, which exhibits a comparatively diminished throughput average of 5287.3 kbps and a higher packet loss ratio average of 0.22%. These empirical findings strongly emphasize the effectiveness of CDN in elevating the Quality of Service for live video streaming. The tangible enhancements in throughput and the reduction of packet loss achieved through CDN implementation contribute significantly to an enriched user experience. The evidence supports the notion that integrating CDN technology not only optimizes the technical performance of live video streaming but also plays a pivotal role in enhancing the overall satisfaction and engagement of users with the content delivery system. [ABSTRACT FROM AUTHOR]

Nowadays, the majority of media Internet traffic consists of H.264-encoded streaming videos due to its high compatibility. One of the most popular streaming technology used to deliver videos over Internet is Dynamic Adaptive Streaming over HTTP (DASH). It transmit the video as a sequence of independent short video segments tailored to the receiver's limitations (related to several factors such as available bandwidth or resolution on reception), aiming to enhance the Quality of Experience. In this paper, we present two types of datasets created from 4065 video segments of 2 seconds. The first type consists of extracting features related to color, space, and time from the segments across different vertical resolutions (240, 360, 480, 720, 1080, 1440 and 4K). The second one includes several quality metrics obtained from the same segments when they are encoded with different compression levels in the different resolutions. [ABSTRACT FROM AUTHOR]

The article focuses on the evolution of the streaming industry towards an oligopolistic structure driven by consumer data, leading to anticompetitive practices that disadvantage independent creators. Topics include the historical context of antitrust in the entertainment sector, the modern dominance of streaming giants, and proposals for updating antitrust laws to foster competition and protect both consumers and smaller content creators in the digital entertainment market.

Technological progress has led to significant advancements in Earth observation and satellite systems. However, some services associated with remote sensing face issues related to timeliness and relevance, which affect the application of remote sensing resources in various fields and disciplines. The challenge now is to help end-users make precise decisions and recommendations for relevant resources that meet the demands of their specific domains from the vast array of remote sensing resources available. In this study, we propose a remote sensing resource service recommendation model that incorporates a time-aware dual LSTM neural network with similarity graph learning. We further use the stream push technology to enhance the model. We first construct interaction history behavior sequences based on users' resource search history. Then, we establish a category similarity relationship graph structure based on the cosine similarity matrix between remote sensing resource categories. Next, we use LSTM to represent historical sequences and Graph Convolutional Networks (GCN) to represent graph structures. We construct similarity relationship sequences by combining historical sequences to explore exact similarity relationships using LSTM. We embed user IDs to model users' unique characteristics. By implementing three modeling approaches, we can achieve precise recommendations for remote sensing services. Finally, we conduct experiments to evaluate our methods using three datasets, and the experimental results show that our method outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop and validate a procedure known as the Information Vortex Indicator (IVI) for its effectiveness, designed to detect the timing of information vortex formation in textual data streams. Research has established that the formation of this vortex coincides with the onset of the dissemination of fake news (FN) concerning a particular object (such as a person, organization, company, event, etc.). The primary aim of this detection is to minimize the time required for an appropriate response or defense against the adverse effects of information turbulence caused by the spread of fake news. Methodology: The study used Big Data information resources analysis instruments (Gogołek, 2019, 2022), including selected statistical and artificial intelligence techniques and tools, to automatically detect vortex occurrence in real time. Experimental validation of the efficacy of these tools has been conducted, enabling a reliable assessment of the timing of vortex emergence. This assessment is quantified using the V-function, procedure, or test, which formally describes the IVI procedure. The V-function's parameters are derived from the distribution patterns of letter pair clusters within the textual information stream. Conclusions: A comparison of manual (reference) and automatic detection of vortex emergence times confirmed an accuracy rate of over 80% in detecting the appearance of fake news. These results underscore the effectiveness of the IVI procedure and the utility of the selected tools for rapidly automating the detection of information vortices, which herald the propagation of fake news. Furthermore, the study demonstrates the applicability of IVI for the continuous monitoring of information with significant media value across multiple multilingual data streams. Originality: This research introduces a novel approach utilizing the distribution of letter pair clusters within information streams to detect the onset of information vortices, coinciding with the emergence of fake news. This methodology represents a unique contribution to the field, as prior research on this subject is limited. [ABSTRACT FROM AUTHOR]

Mass media companies can be elaborated as organizations that manipulate technological components for their various departments such as movie studios, publishing houses, radio and television station management teams that impact a large range of audience via vast communication strategies. The companies have also been referred to as media conglomerates, media groups, or media houses further illustrating their grasp over the global markets and their revenue structures. The largest media companies such as Apple, Disney, and Comcast among others, offer products and services to users that are diverse individuals as well as large organizations leading to significant revenues as well as challenges that have been further explored in a comprehensive manner in the study. IoT utilizes wireless networks that are without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental conditions. If you're wanting to integrate WSN into your business, our highly driven and experienced engineers can give you an all-encompassing solution. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Erciyes Communication / Erciyes İletişim Dergisi is the property of Erciyes University, Faculty of Communication / Erciyes Universitesi Iletism Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In an ever-evolving technological landscape, addressing the performance challenges of real-time communication protocols is crucial. Real-time communication, facilitated by streaming media protocols, utilizes peer-to-peer or client-server models to enhance Quality of Service (QoS). WebRTC (Web Real-Time Communication) stands as a widely adopted, browser-based, opensource, peer-to-peer protocol, offering real-time media transmission through JavaScript APIs without third-party plugins. This paper presents an in-depth performance evaluation of a WebRTC-based video conferencing system using Socket.io services on a Node.js server. Our research expands on recent studies by introducing a comprehensive set of performance parameters, including Processing delay, CPU Utilization, Latency, Jitter, and Packet Loss, and packet delay. Our findings indicate that WebRTC performs exceptionally well within specific latency thresholds. However, scalability concerns emerge when a large number of clients are introduced, especially in bandwidth-constrained environments. [ABSTRACT FROM AUTHOR]

Many network devices, such as a smartphones and tablets, and streaming services, such as YouTube and Netflix, have become widespread. This causes network‐bandwidth congestion, so users require stable and consistent communication quality. Call admission control (CAC) has been proposed to solve these bandwidth resource problems. Some CACs have been proposed in order to maintain QoS. However, these CAC does not assume user behaviour. If CAC assumes some user behaviour, optimal control parameter derived conventional CAC may change because traffic load change by considering user behaviour. This article assumes that arriving types of flows are of three types: narrowband flows, broadband flow without seek‐bar operation, and broadband flows with seek‐bar operation. Under this assumpution, two CAC methods with single threshold or two thresholds are proposed. With these methods, it is assumed that all arriving flows result in the same user satisfaction when accommodated in a network. Under this assumption, the maximum of each user satisfaction is the same as the maximum number of accommodated flows. Using the call loss probability characteristics of Method 1, the objective function in Method 2 is modified. By using the modified objective function, CAC can be realised while properly accommodating broadband users who have been excessively rejected. [ABSTRACT FROM AUTHOR]

Automated live video stream analytics has been extensively researched in recent times. Most of the traditional methods for video anomaly detection is supervised and use a single classifier to identify an anomaly in a frame. We propose a 3-stage ensemble-based unsupervised deep reinforcement algorithm with an underlying Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). In the first stage, an ensemble of LSTM-RNNs are deployed to generate the anomaly score. The second stage uses the least square method for optimal anomaly score generation. The third stage adopts award-based reinforcement learning to update the model. The proposed Hybrid Ensemble RR Model was tested on standard pedestrian datasets UCSDPed1, USDPed2. The data set has 70 videos in UCSD Ped1 and 28 videos in UCSD Ped2 with a total of 18560 frames. Since a real-time stream has strict memory constraints and storage issues, a simple computing machine does not suffice in performing analytics with stream data. Hence the proposed research is designed to work on a GPU (Graphics Processing Unit), TPU (Tensor Processing Unit) supported framework. As shown in the experimental results section, recorded observations on framelevel EER (Equal Error Rate) and AUC (Area Under Curve) showed a 9% reduction in EER in UCSD Ped1, a 13% reduction in ERR in UCSD Ped2 and a 4% improvement in accuracy in both datasets. [ABSTRACT FROM AUTHOR]

Copyright of Acta Economica is the property of University of Banja Luka, Faculty of Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Video viewing is currently the primary form of entertainment for modern people due to the rapid development of mobile devices and 5G networks. The combination of pervasive edge devices and adaptive bitrate streaming technologies can lessen the effects of network changes, boosting user quality of experience (QoE). Even while edge servers can offer near-end services to local users, it is challenging to accommodate a high number of mobile users in a dynamic environment due to their restricted capacity to maximize user long-term QoE. We are motivated to integrate user allocation and bitrate adaptation into one optimization objective and propose a multiagent reinforcement learning method combined with an attention mechanism to solve the problem of multiedge servers cooperatively serving users. Through comparative experiments, we demonstrate the superiority of our proposed solution in various network configurations. To tackle the edge user allocation problem, we proposed a method called attention-based multiagent reinforcement learning (AMARL), which optimized the problem in two directions, i.e., maximizing the QoE of users and minimizing the number of leased edge servers. The performance of AMARL is proved by experiments. [ABSTRACT FROM AUTHOR]

This paper examines the role streaming networks played in transforming the world of book-to-film adaptation. In 2013, Netflix debuted the first ever television show commissioned by a streaming service, House of Cards: a story inspired by Michel Dobb's 1989 novel. From then on streaming services became a go-to source for backlist books looking for a second life and another chance at the bestsellers list. An analysis of book adaptations produced by streaming services alongside the subsequent New York Times bestsellers list shows that the freedom and creative liberties these companies have allowed in their adaptations has produced wildly successful projects with the potential to launch lesser-known books into prominence, often years after their initial publication. [ABSTRACT FROM AUTHOR]

In this paper, Fe3O4 powder was prepared by the chemical precipitation method using impinging stream technology. The influences of feed flow rate, L/D, reactant concentration, ratio of iron ion concentration, reaction temperature, and impact time on the size and distribution of particles were investigated by the orthogonal experiment method. The microstructure and morphology of Fe3O4 powders were characterized by scanning electron microscopy, X‐ray diffractometer, and granulometer. The results showed that when ratio of iron ion concentration c(Fe2+):c(Fe3+) = 0.75, reactant concentration c(Fe3+) = 0.4 mol · L−1, feed flow rate Q = 800 L · h−1, L/D = 3, reaction temperature T = 20°C, impact time t = 50 min, the prepared Fe3O4 had an average particle size of 1.815 μm and the most uniform distribution. The influence of ultrasonic enhancement on the mixing process and powder preparation in an impinging stream reactor was investigated. The size and distribution of the powders were significantly affected by ultrasonic enhancement. With the increase in ultrasonic power, the particle size of the powders decreased and the distribution became narrower. The particle size was reduced by 68.78%, and the particle distribution range was narrowed by 84.34% under ultrasound enhancement. This study promised the effective utilization of ultrasonic cavitation in the optimization of experimental equipment and the preparation of ultrafine powder, which provides a basis for process optimization of powder preparation. [ABSTRACT FROM AUTHOR]

With the development and popularization of remote sensing earth observation technology and the remote sensing satellite system, the problems of insufficient proactiveness, relevance and timeliness of large-scale remote sensing supporting services are increasingly prominent, which seriously restricts the application of remote sensing resources in multi-domain and cross-disciplinary. It is urgent to help terminal users make appropriate decisions according to real-time network environment and domain requirements, and obtain the optimal resources efficiently from the massive remote sensing resources. In this paper, we propose a recommendation algorithm using fusion of attention and multi-perspective (MRS_AMRA). Based on MRS_AMRA, we further implement an active service recommendation model (MRS_ASRM) for massive multi-source remote sensing resources by combining streaming pushing technology. Firstly, we construct value evaluation functions from multi-perspective in terms of remote sensing users, data and services to enable the adaptive provision of remote sensing resources. Then, we define multi-perspective heuristic policies to support resource discovery, and fusion these policies through the attention network, to achieve the accurate pushing of remote sensing resources. Finally, we implement comparative experiments to simulate accurate recommendation scenarios, compared with state-of-the-art algorithms, such as DIN and Geoportal. Furthermore, MRS_AMRA achieves an average improvement of 10.5% in the recommendation accuracy NDCG@K, and in addition, we developed a prototype system to verify the effectiveness and timeliness of MRS_ASRM. [ABSTRACT FROM AUTHOR]

In the process of modern industrial production equipment developing towards the direction of structure, automation, and intelligence, motor is still the main power output equipment. If the data flow classification occurs during the operation of the motor, it will lead to problems such as the reduction of its operation efficiency and the increase in system energy consumption. In serious cases, it will even cause motor damage, and the overall system equipment will be shut down for maintenance for a long time, resulting in serious economic losses. Therefore, the research on intelligent multilabel data stream classification technology of motor is of great significance to ensure the stability and reliability of efficient operation of production equipment. In order to improve the recognition efficiency and accuracy of ms-1dcnn's multilabel data stream classification method in the environment of variable motor conditions and strong noise interference, a multiscale feature fusion framework is constructed based on the residual network structure. The implementation principles of two kinds of attention mechanism algorithms, squeeze and excitation module and convolution attention module, are studied, respectively. The attention module suitable for one-dimensional residual network is designed and embedded into the residual module to build a multiscale attention residual network model. Finally, the effectiveness and superiority of the proposed model are verified by using the experimental platform data. [ABSTRACT FROM AUTHOR]

Background: The use of bedside cameras in neonatal units facilitates livestreaming of infants to support parental and family bonding when they are unable to be physically present with their baby. This study aimed to explore the experiences of parents of infants previously admitted for neonatal care and who used live video streaming to view their baby in real-time. Methods: Qualitative semi-structured interviews were conducted after discharge with parents of infants admitted for neonatal care on a tertiary level neonatal unit in the UK in 2021. Interviews were conducted virtually, transcribed verbatim and uploaded into NVivo V12 to facilitate analysis. Thematic analysis by two independent researchers was undertaken to identify themes representing the data. Results: Seventeen participants took part in sixteen interviews. Thematic analysis identified 8 basic themes which were grouped into 3 organizational themes: (1) family integration of the baby including parent-infant, sibling-infant, and wider family-infant attachment facilitated through livestreaming, (2) implementation of the livestreaming service including communication, initial set up of the livestreaming service, and areas for improvement, and (3) parental control including emotional, and situational control. Conclusions: The use of livestreaming technology can provide parents with opportunities to integrate their baby into their wider family and friendship community and gain a sense of control over their baby's admission for neonatal care. On-going parental education around how to use, and what to expect from, livestreaming technology is required to minimise any potential distress from viewing their baby online. [ABSTRACT FROM AUTHOR]

The availability of geodata with high spatial and temporal resolution is increasing steadily. Often, these data are continuously generated by distributed sensor networks and provided as geodata streams. Geostatistical analysis methods, such as spatiotemporal autocorrelation, have thus far been applied primarily to historized data. As such, the advantages of continuous and up-to-date acquisition of geodata have not yet been transferred to the analysis phase. At the same time, open-source frameworks for distributed stream processing have been developed into powerful real-time data processing tools. In this paper a methodology is developed to apply analyses of spatiotemporal autocorrelation directly to geodata streams through a distributed streaming process using open-source software frameworks. For this purpose, we adapt the extended Moran's I index for continuous and up-to-date computation, then apply it to simulated geospatial data streams of recorded taxi trip data. Various application scenarios for the developed methodology are tested and compared on a distributed computing cluster. The results show that the developed methodology can provide geostatistical analysis results in real time. This research demonstrates how modern datastream processing technologies have the potential to significantly advance the way geostatistical analysis can be performed and used in the future. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and wireless communications, as well as the popularization of personal communication systems, the security of real-time communications is demanded. The efficient technology of stream ciphers can satisfy this requirement of security. In this paper, to enhance the security strength of stream ciphers, we design a key-based multi-mode clock-controlled stream cipher for real-time secure communications of the Internet of things (IoT). The proposed stream cipher is equipped with a multi-mode depending on the key. The different working modes are shipped with different encrypting circuits depending on the user's key. We analyze the period, the linear complexity, and use known attacks to verify the security strength of the proposed cipher. Compared with existing dual mode clock-controlled stream ciphers, the merits of our proposed cipher are its long period, high linear complexity, low hardware complex, low initialization clock, and simplicity in mode switching. Furthermore, the proposed cipher passes the FIPS PUB 140-1 and SP800-22 tests, obtaining at least 97.00%. [ABSTRACT FROM AUTHOR]

Copyright of Consumer Behavior Review (CBR) is the property of Consumer Behavior Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, platforms in many industries offer content for a (monthly) flat rate (e.g., music streaming). While flat rates are efficient in reducing transaction costs for administering customers, platforms' rules for remunerating content right holders are crucial for royalty allocation and, as a result, heavily discussed in several industries. The music industry's business practices could be on the verge of their next disruption. There is an ongoing heated debate with respect to how the income of flat rates through streaming services should be allocated to right holders (labels and artists). This research investigates aspects of the supply and demand side effects as well as the resulting monetary consequences of changing the currently applied proportional-to-usage remuneration policy (pro rata) to a user-centric policy. Using individual-level data from 3,326 participants and data from Spotify's API, we empirically quantify the monetary consequences of this change for the music industry. Depending on the remuneration system, we find a substantial reallocation of nearly 170 million € per year at Spotify. We discuss demand and supply-side consequences that may change the way music is currently produced and consumed. We conclude with a research agenda on the impact of business conventions for users, platforms, and artists in the music streaming industry. [ABSTRACT FROM AUTHOR]

The following essay is part of a body of work titled Signal to Noise: sound and fury in (post)apartheid South Africa. These are a collection of creative nonfiction essays set against the backdrop of my involvement with a small, independent mobile recording studio based in Johannesburg between 1983 and 1997. The metaphor of a drowning signal, pushing through and making itself heard above the noise, resonates throughout the collection. The complexities of the political versus artistic nature of what we were involved with provide a setting for an anecdotal approach to what is part history, part biography, part memoir and part theoretical sonic exploration. The following essay falls into this approach and is constructed from memories enhanced by diaries, scrapbooks, shards of notes, lyrics, photos and conversations. These have been employed in reconstructing a narrative arc that covers the recording of the first album made by the band Sankomota, who were banned from entry into South Africa and were based in Maseru, mostly playing to audiences at one of the leading hotels. Sankomota, then called Uhuru, experienced extraordinary, almost metaphysical, peaks and troughs throughout their nearly thirty-year existence hence the hauntological device in the title. The record was also the first made in our fledgling mobile studio using newly affordable equipment that kickstarted many such do-it-yourself projects worldwide. This was the first in a steady stream of technologies that would eventually break the hegemony of mainstream record companies. In apartheid South Africa, this was hugely significant, as being able to sideline the censorship of state-owned media enterprises meant immense freedom in the kind of projects one came to consider. Savage incidents of force and brutality were still common then, and our small venture has to be seen in the context of broader unrest and suffering. Frank Leepa was an uncompromising survivor. His words and melodies still move and inspire a younger generation. [ABSTRACT FROM AUTHOR]

This research explores for the first time the application of machine learning to detect emotional responses in video game streaming channels, specifically on Twitch, the most widely used platform for broadcasting content. Analyzing sentiment in gaming contexts is difficult due to the brevity of messages, the lack of context, and the use of informal language, which is exacerbated in the gaming environment by slang, abbreviations, memes, and jargon. First, a novel Spanish corpus was created from chat messages on Spanish video game Twitch channels, manually labeled for polarity and emotions. It is noteworthy as the first Spanish corpus for analyzing social responses on Twitch. Secondly, machine learning algorithms were used to classify polarity and emotions offering promising evaluations. The methodology followed in this work consists of three main steps: (1) Extracting Twitch chat messages from Spanish streamers' channels related to gaming events and gameplays; (2) Processing and selecting the messages to form the corpus and manually annotating polarity and emotions; and (3) Applying machine learning models to detect polarity and emotions in the created corpus. The results have shown that a Bidirectional Encoder Representation from Transformers (BERT) based model excels with 78% accuracy in polarity detection, while deep learning and Random Forest models reach around 70%. For emotion detection, the BERT model performs best with 68%, followed by deep learning with 55%. It is worth noting that emotion detection is more challenging due to the subjective interpretation of emotions in the complex communicative context of video gaming on platforms such as Twitch. The use of supervised learning techniques, together with the rigorous corpus labeling process and the subsequent corpus pre-processing methodology, has helped to mitigate these challenges, and the algorithms have performed well. The main limitations of the research involve category and video game representation balance. Finally, it is important to stress that the integration of machine learning in video games and on Twitch is innovative, by allowing the identification of viewers' emotions on streamers' channels. This innovation could bring benefits such as a better understanding of audience sentiment, improving content and audience retention, providing personalized recommendations and detecting toxic behavior in chats. [ABSTRACT FROM AUTHOR]

The presented dataset EU-MOHP v013.1.1 provides multiscale information on the hydrologic position (MOHP) of a geographic point within its respective river network and catchment as gridded maps. More precisely, it comprises the three measures "divide to stream distance" (DSD) as sum of the distances to the nearest stream and catchment divide, "lateral position" (LP) as a relative measure of the position between the nearest stream and divide and "stream distance" (SD) as the distance to the nearest stream. These three measures are calculated for nine hydrologic orders to reflect different spatial scales from local to continental. Its spatial extent covers major parts of the European Economic Area (EEA39) which also largely coincides with physiographical Europe. Although there are multiple potential use cases, this dataset serves predominantly as valuable static environmental descriptor or predictor variable for hydrogeological and hydrological modelling such as mapping or forecasting tasks using machine learning. The generation of this dataset uses free open source software only and therefore can be transferred to other regions or input datasets. Measurement(s) divide to stream distance • lateral position • stream distance Technology Type(s) remote sensing Sample Characteristic - Environment drainage basin • groundwatershed • catchment Sample Characteristic - Location Europe [ABSTRACT FROM AUTHOR]

The twenty-first century has seen the biggest breakthrough as regards the production and distribution of audiovisual culture. Slow cinema and serials--in particular those coming to us via streaming platforms as full seasons to binge-watch--are a special case of such a breakthrough for they offer us a new dynamics of experiencing culture; dynamics that relies on a meticulous orchestration of boredom to stimulate our creative participation in culture. This article will demonstrate, on the basis of selected Mubi, HBO, and Netflix productions, how the clever playing out of (serial) boredom can affect viewers' mental, emotional, and even physical capacity and how important it is for a more effective perception of the twenty-first-century--digital--reality to be able to actively manage our "boredom-activated" culture compass. [ABSTRACT FROM AUTHOR]

In recent years, digital music is becoming more and more popular as mobile Internet and streaming media technology advance. Traditional music indexing technology mainly uses keywords to query. To find their favorite music, people must search through the vast amount of music available on the Internet nowadays, much like hunting for a needle in a haystack. In the era of mobile Internet, people's pace of life is very fast. Devices can access the network anytime and anywhere. Users have the habit of listening to music in their daily work, study, or sports. Facing the vast music library, personalized music recommendation can help users quickly and accurately find music tracks that meet their interests, which is also the focus of current music recommendation technology. According to the characteristics of Tchaikovsky music, in this paper, we establish and build an approach that can understand situations and recommend by using the additional information of labels to describe Tchaikovsky music and realize a structure on this foundation. Through user involvement, the system can deliver services akin to network radio and complete the evaluation of the Tchaikovsky music recommendation algorithm's efficacy. [ABSTRACT FROM AUTHOR]

Drawing on the search experience, the study investigates the impact of Over-the-top Services on their intention to continuous subscription. It investigates the mediation of search experience on the linkage between Over-the-top Services (OTT) and intention to continuous subscription. Moreover, study examines the Moderated Mediation effect of screen watching time (first moderator) and device (second moderator) in the relationship of Over-the-top Services, Intention to Continuous Subscription, and Search Experience. An online survey was used to collect the responses and 405 responses were considered for the study. The analysis shows that OTT services has a significant impact on its user search experience which in turn also has strong and positive impact on the intention to continuous subscription by its users. It was also discovered that the screen watching time as its role of two way interaction has significant moderating effect between OTT service and search experience relationship and search experience and intention to continuous subscription relationship. The three-way interaction between device and screen watching time in the mediation model was analysed & found strong support in the relationship between OTT services and search experience and search experience & intention to continuous subscription. This is one of the few paper to check the inter-relationship between OTT services, intention to continuous subscription and search experience. The three-way interaction between mediation model and screen watching time (first moderator) and device (second moderator) is studied for the first time in the literature therefore the findings will originate the value with novelty and important implications to the OTT consumers, and content providers. [ABSTRACT FROM AUTHOR]

Aiming at the problems that students in the traditional music teaching system seldom participate in the system interaction and the relatively few types of system transportable files, this research designs and researches the music teaching system in school through artificial intelligence technology, streaming media technology, and webcast technology. The system mainly designs specific online classroom teaching modules according to the main characteristics of college music teaching. The system mainly adopts the b/s framework. Through the system performance test, the system response time is less than 2.5 when 200 people visit. Even when 500 people visit, the response time exceeds 3S, but it also maintains a certain performance level. [ABSTRACT FROM AUTHOR]

Featured Application: In modern television systems based on adaptive streaming technology, an assessment of customer contentment might be necessary to deliver the highest possible audio and video quality. Data-driven 'quality of experience' methods based on continuous clustering can be a solution for the problem of service level assessment from the perspective of customers. The quality of experience (QoE) assessment of adaptive video streaming may be crucial for detecting degradations impacting customer satisfaction. In a telecommunication environment, eliminating failure points may be the highest priority. This study aims to assess the QoE level of the video played by the STB device connected to the production TV system. The evaluation has been based on the stalling effects, video quality changes, and the time related to the last decreased bitrate change occurrence. The two-phase continuous clustering approach has been studied to assess the QoE level based on the ACR scale. The number of devices with grades 1 or 2 is relatively low, but those devices generate significantly more events than adequately functioning devices. STBs try to play the highest possible bitrate, and there is no possibility of setting the intermediate bitrate level. The STB player does not have the button to set the quality level, usually available in pure over-the-top applications. Hence the bitrate fluctuations that can annoy customers appear for the lowest grades. The boundary cases can be easily assessed. The outcome should be challenged by the customers' opinions to find the proper QoE threshold. Continuous clustering may allow telecom operators to assess customer satisfaction with their TV service. [ABSTRACT FROM AUTHOR]