User Intention towards a Music Streaming Service: A Thailand Case Study

This paper presents a novel acceptance model for an online music streaming scenario of Thailand. The music streaming industry has been gaining in popularity in the recent times.  This research has been conducted in order to measure the user attitude towards the use of this relatively new service using a modified version of the popular Technology Acceptance Model. We try to identify the most popular music-streaming service of Thailand and also the factors that affect the use of such a service. Data has been collected in the form of an online questionnaire survey from more than 300 participants for the purpose of model building and validation. A subsequent regression analysis carried out on the proposed model explains more than 60 percent of the variance of the dependent variable i.e. Behavioral Intention in our case to the predictor variables Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment and Perceived Satisfaction Level. The results show that Perceived Enjoyment and Perceived satisfaction are the two strongest predictors for Behavioral Intention which is quite different from that of the utilitarian type of information systems.Keywords: Music streaming, TAM, hedonic information systems, regressio

Big Data in Smart-Cities: Current Research and Challenges

Smart-cities are an emerging paradigm containing heterogeneous network infrastructure, ubiquitous sensing devices, big-data processing and intelligent control systems. Their primary aim is to improve the quality of life of the citizens by providing intelligent services in a wide variety of aspects like transportation, healthcare, entertainment, environment, and energy. In order to provide such services, the role of big-data and its analysis is extremely important as it enables to obtain valuable insights into the large data generated by the smart-cities.  In this article, we investigate the state-of-art research efforts directed towards big-data analytics in a smart-city context. Specifically, first we present a big-data centric taxonomy for the smart-cities to bring forth a generic overview of the importance of big-data paradigm in a smart-city environment. This is followed by the presentation of a top-level snapshot of the commonly used big-data analytical platforms. Due to the heterogeneity of data being collected by the smart-cities, often with conflicting processing requirements, suitable analytical techniques depending upon the data type are also suggested. In addition to this, a generic four-tier big-data framework comprising of the sensing hub, storage hub, processing hub and application hub is also proposed that can be applied in any smart-city context. This is complemented by providing the common big-data applications in a smart-city and presentation of ten selected case studies of smart-cities across the globe. Finally, the open challenges are highlighted in order to give future research directions

RCEA: Real-time, Continuous Emotion Annotation for collecting precise mobile video ground truth labels

Collecting accurate and precise emotion ground truth labels for mobile video watching is essential for ensuring meaningful predictions. However, video-based emotion annotation techniques either rely on post-stimulus discrete self-reports, or allow real-time, continuous emotion annotations (RCEA) only for desktop settings. Following a user-centric approach, we designed an RCEA technique for mobile video watching, and validated its usability and reliability in a controlled, indoor (N=12) and later outdoor (N=20) study. Drawing on physiological measures, interaction logs, and subjective workload reports, we show that (1) RCEA is perceived to be usable for annotating emotions while mobile video watching, without increasing users' mental workload (2) the resulting time-variant annotations are comparable with intended emotion attributes of the video stimuli (classification error for valence: 8.3%; arousal: 25%). We contribute a validated annotation technique and associated annotation fusion method, that is suitable for collecting fine-grained emotion annotations while users watch mobile videos

A Survey of Standardized Approaches towards the Quality of Experience Evaluation for Video Services: An ITU Perspective

Over the past few years there has been an exponential increase in the amount of multimedia data being streamed over the Internet. At the same time, we are also witnessing a change in the way quality of any particular service is interpreted, with more emphasis being given to the end-users. Thus, silently there has been a paradigm shift from the traditional Quality of Service approach (QoS) towards a Quality of Experience (QoE) model while evaluating the service quality. A lot of work that tries to evaluate the quality of audio, video, and multimedia services over the Internet has been done. At the same time, research is also going on trying to map the two different domains of quality metrics, i.e., the QoS and QoE domain. Apart from the work done by individual researchers, the International Telecommunications Union (ITU) has been quite active in this area of quality assessment. This is obvious from the large number of ITU standards that are available for different application types. The sheer variety of techniques being employed by ITU as well as other researchers sometimes tends to be too complex and diversified. Although there are survey papers that try to present the current state of the art methodologies for video quality evaluation, none has focused on the ITU perspective. In this work, we try to fill up this void by presenting up-to-date information on the different measurement methods that are currently being employed by ITU for a video streaming scenario. We highlight the outline of each method with sufficient detail and try to analyze the challenges being faced along with the direction of future research

User Intention Towards A Music Streaming Service: A Thailand Case Study

This paper presents a novel acceptance model for an online music streaming scenario of Thailand. The music streaming industry has been gaining in popularity in the recent times. This research has been conducted in order to measure the user attitude towards the use of this relatively new service using a modified version of the popular Technology Acceptance Model. We try to identify the most popular music-streaming service of Thailand and also the factors that affect the use of such a service. Data has been collected in the form of an online questionnaire survey from more than 300 participants for the purpose of model building and validation. A subsequent regression analysis carried out on the proposed model explains more than 60 percent of the variance of the dependent variable i.e. Behavioral Intention in our case to the predictor variables Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment and Perceived Satisfaction Level. The results show that Perceived Enjoyment and Perceived satisfaction are the two strongest predictors for Behavioral Intention which is quite different from that of the utilitarian type of information systems