Why Do People Watch Multiscreen Videos and Use Dual Screening? Investigating Users’ Polychronicity,Media Multitasking Motivation,and Media Repertoire

ABSTRACT

Despite growing multiscreen video-consumption worldwide, scant research has examined the relationship between users’ personal traits and their behaviors when using various devices for passive viewing and active communicative and social interactions. This study investigates how multiscreen users’ polychronicity, media multitasking motivations, and media repertoire influence passive multiscreen video viewing and active dual screening on video-related interactions. A web survey recruited 600 Singaporean social media users with online video discussion experiences. Results show that passive multiscreen video viewing and dual screening use are both positively associated with respondents having higher polychronicity and larger media repertoire. Polychronicity and media repertoire show relatively stronger effects on dual screening use than passive multiscreen video viewing. As for demographic factors, Chinese ethnicity is associated with passive multiscreen video viewing. Younger respondents tend to use smartphones and computers as second devices for video-related matters, while males use more tablets for dual screening activities than females.     

Video data mining: semantic indexing and event detection from the association perspective

Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.     

Affective video content analysis based on multimodal data fusion in heterogeneous networks

In heterogeneous networks, different modalities are coexisting. For example, video sources with certain lengths usually have abundant time-varying audiovisual data. From the users’ perspective, different video segments will trigger different kinds of emotions. In order to better interact with users in heterogeneous networks and improve their user experiences, affective video content analysis to predict users’ emotions is essential. Academically, users’ emotions can be evaluated by arousal and valence values, and fear degree, which provides an approach to quantize the prediction accuracy of the reaction of the audience and users towards videos. In this paper, we propose the multimodal data fusion method for integrating the visual and audio data in order to perform the affective video content analysis. Specifically, to align the visual and audio data, the temporal attention filters are proposed to obtain the time-span features of the entire video segments. Then, by using the two-branch network structure, matched visual and audio features are integrated in the common space. At last, the fused audiovisual feature is employed for the regression and classification subtasks in order to measure the emotional responses of users. Simulation results show that the proposed method can accurately predict the subjective feelings of users towards the video contents, which provides a way to predict users’ preferences and recommend videos according to their own demand.     

Publicly Private and Privately Public: Social Networking on YouTube

YouTube is a public video-sharing website where people can experience varying degrees of engagement with videos, ranging from casual viewing to sharing videos in order to maintain social relationships. Based on a one-year ethnographic project, this article analyzes how YouTube participants developed and maintained social networks by manipulating physical and interpretive access to their videos. The analysis reveals how circulating and sharing videos reflects different social relationships among youth. It also identifies varying degrees of "publicness" in video sharing. Some participants exhibited "publicly private" behavior, in which video makers' identities were revealed, but content was relatively private because it was not widely accessed. In contrast, "privately public" behavior involved sharing widely accessible content with many viewers, while limiting access to detailed information about video producers' identities.     

一种基于用户播放行为序列的个性化视频推荐策略

本文针对在线视频服务网站的个性化推荐问题,提出了一种基于用户播放行为序列的个性化推荐策略.该策略通过深度神经网络词向量模型分析用户播放视频行为数据,将视频映射成等维度的特征向量,提取视频的语义特征.聚类用户播放历史视频的特征向量,建模用户兴趣分布矩阵.结合用户兴趣偏好和用户观看历史序列生成推荐列表.在大规模的视频服务系统中进行了离线实验,相比随机算法、基于物品的协同过滤和基于用户的协同过滤传统推荐策略,本方法在用户观看视频的Top-N推荐精确率方面平均分别获得22.3%、30.7%和934%的相对提升,在召回率指标上分别获得52.8%、41%和1065%的相对提升.进一步地与矩阵分解算法SVD++、基于双向LSTM模型和注意力机制的Bi-LSTM+Attention算法和基于用户行为序列的深度兴趣网络DIN比较,Top-N推荐精确率和召回率也得到了明显提升.该推荐策略不仅获得了较高的精确率和召回率,还尝试解决传统推荐面临大规模工业数据集时的数据要求严苛、数据稀疏和数据噪声等问题.     

Personalising live zooming using the ePlayer

An innovative Internet streaming video player, called ePlayer, oriented to live events, has been researched, developed and evaluated. ePlayer supports a personalised zoomable user interface which enables a new user view experience. The main novelty of the system is first that it is designed to optimise the zoomed video quality when viewing life events, via adaptation of the streamed video quality, across multi-video streams with a variable network quality of service. Second, it also personalises the live video zooming with respect to users’ zooming preferences, easing the user interaction needed for the zooming task. The experimental results indicate that the system is not only able to zoom effectively, but that it can also maintain the visual quality of the video at the same time. The ePlayer is also able to infer a user’s zooming preferences via dynamically clustering a user’s zooming regions of interest when viewing live sports video content.     

Time,engagement and video games: How game design elements shape the temporalities of play in massively multiplayer online role-playing games

Researchers and developers constantly seek novel ways to create engaging applications that are able to retain their users over the long term, make them desire to spend time using the application or go back to using it after a break. With this aim, video games can be an insightful source of inspiration, as they are specifically designed to maximise playing time, increase players' intentions of playing during the day or enhance their willingness to replay. In a gaming context, ‘time’ is an important factor for engagement because game designers can design the game time to retain players in the game environment. Drawing on social practice theory, which is increasingly used in Information Systems (IS) research, I conducted an ethnographic study in World of Warcraft (WoW) to understand how various temporalities are produced within a video game and the effects that they have on players' engagement. The findings show that game temporalities stem from the complex interaction between the design features of the game and the norms, routines and expectations that are part of the game practices. Moreover, these temporalities can engender temporal experiences that may stimulate engagement in various ways. The study contributes to IS literature by proposing a novel understanding of how time can be intentionally designed to sustain user engagement. Finally, it suggests that ‘time design’ in video games could inspire designs in broader IS contexts, such as in the gamification of online communities, crowdsourcing platforms and crowd working systems.     

Piles across space: Breaking the real-estate barrier on small-display devices

We describe an implementation that has users ‘flick’ notes, images, audio, and video files onto virtual, imaginary piles beyond the display of small-screen devices. Multiple sets of piles can be maintained in persistent workspaces. Two user studies yielded the following: Participants developed mental schemes to remember virtual pile locations, and they successfully reinstated pile locations after several days, while situated in varying environments. Alignment of visual cues on screen with surrounding physical cues in situ accelerated a sorting task when compared to other, non-aligned visual cues. The latter, however, yielded better long-term retention.     

Edge Video CDN: A Wi-Fi Content Hotspot Solution

The emergence of smart edge-network content item hotspots, which are equipped with huge storage space (e.g., several GBs), opens up the opportunity to study the possibility of delivering videos at the edge network. Different from both the conventional content item delivery network (CDN) and the peer-to-peer (P2P) scheme, this new delivery paradigm, namely edge video CDN, requires up to millions of edge hotspots located at users’ homes/offices to be coordinately managed to serve mobile video content item. Specifically, two challenges are involved in building edge video CDN, including how edge content item hotspots should be organized to serve users, and how content items should be replicated to them at different locations to serve users. To address these challenges, we propose our data-driven design as follows. First, we formulate an edge region partition problem to jointly maximize the quality experienced by users and minimize the replication cost, which is NP-hard in nature, and we design a Voronoi-like partition algorithm to generate optimal service cells. Second, to replicate content items to edge-network content item hotspots, we propose an edge request prediction based replication strategy, which carries out the replication in a server peak offloading manner. We implement our design and use trace-driven experiments to verify its effectiveness. Compared with conventional centralized CDN and popularity-based replication, our design can significantly improve users’ quality of experience, in terms of users’ perceived bandwidth and latency, up to 40%.     

A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations.     

Multiscreen Social TV System: A Mixed Method Understanding of Users’ Attitudes and Adoption Intention

Past social TV (STV) system user studies usually tested a small number of trialists in lab settings in Western countries. This Asian research took a mix method approach (interview, observation, and web survey) to examine the attitudes of 76 young adults’ experiences of using a multiscreen STV system. The results show that the majority of participants preferred shifting and complementary patterns when using STV. Their favorite feature was multiscreen video teleportation via QR codes. Nearly half of participants agreed that sociability features of the STV system could facilitate their perceived social presence and sense of community, which increased adoption intention. Additionally, text messaging was selected as the preferred mode for social interactions during video viewing. Although most participants liked the user-friendly design to upload and share videos conveniently, age has a negative association with users’ receptiveness of the user generated video channel creation and aggregation feature.     

A Scottish freedom of information regime for a denationalised environment: rhetorical or authentically practical?

This paper provides an evaluation of the Scottish freedom of information regime in the modern denationalised environment. The authors conducted a pragmatic investigation by means of a real-world compliance inquiry which involved, among other things, the electronic submission of standardised requests for information to those local authority arm's-length external organisations which find themselves now subject to the provisions of the Freedom of Information (Scotland) Act 2002. A compliance matrix, with several response measures, recorded the progression and outcome of the requests sent to each named ‘public authority’. The article is also furnished with a contextual overview of the interaction between the home nation FOI regimes and private enterprise with a nod to contemporaneous events. In turn, the paper reveals several quagmires for the operational practicality of FOI in the privatised arena: a pronounced reminder of the difficulties associated with maintaining a functioning and practical FOI regime in light of a myriad of public service delivery models in the denationalised epoch.     

Inferring contextual preferences using deep encoder-decoder learners

ABSTRACT

Context-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the category of the item liked in a specific contextual situation, by applying encoder-decoder learners (long short-term memory networks and auto encoders) on mobile sensor data. In these approaches, the encoder-decoder learners reduce the dimensionality of the contextual features to a latent representation which is learned over time. Given new contextual sensor data from a user, the latent patterns discovered from each deep learner is used to predict the liked item’s category in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual situations with the items’ categories. Empirical results utilising a real world data set of contextual situations derived from mobile phones sensors log show a significant improvement (up to 73% improvement) in prediction accuracy compared with state of the art classification methods.     

The mass age of internet law

This article addresses the topical question of how the increased availability of (and popular interest in) ‘user-generated content’ (UGC) influences the development of cyberlaw. While recognising that the Web has been ‘user-generated’ since its creation, it is argued that the commercial and critical interest in small-scale and individual contributions to the audio/video Web, and the use of such material by established media providers (online and offline) presents a challenge to those who are interested in Internet regulation and the evolution of Web media law. Differing perspectives on the future of Internet regulation are considered in the light of how they relate to understanding the UGC challenge. UGC does not necessarily ‘create’ novel issues or problems, but it brings questions of law, regulation and control that traditionally have assumed a small number of regulatory subjects into contact with a wide, non-legal audience. This article has a particular focus on the role of the familiar ‘platforms’ for creating and sharing content, including social networking sites. Possible responses to the threat of private censorship and control range from community pressure to the application of self-regulation; each response, though, could stifle the creativity and freedom that is at the heart of the shift from mass media to UGC. The author concludes by arguing that a critical understanding of the role of the private gatekeeper is particularly important in order to protect the rights of the diverse, apolitical users that form a large part of the audience for and membership of user-generated and social networking websites.     

Trailfinders—curating an interactive city promenade experience with mobile phones

Abstract

With a growing number of embedded digital experiences in arts and heritage settings, creating a broad global field, this article focuses on the use of mobile phones in the engagement of humans with the cultural heritage of public spaces, specifically those that require a promenade from one outdoor space to another. This article discusses findings from a recent promenade initiative, commissioned for a UK arts festival, along with a review of literature, to critically engage with growing evidence of the diverse ways in which technology is integrated at direct user level. Concepts of ‘hybrid spaces’ and ‘user agency’ are discussed, along with the ‘performativity’ of spaces. Central to the discussion is the function of narrative in engaging the participant to construct new perspectives, which are facilitated by the mediating role of personal media devices between subject, narrative, space and community.     

Energy optimization for mobile video streaming via an aggregate model

Wireless video streaming on smartphones drains a significantly large fraction of battery energy, which is primarily consumed by wireless network interfaces for downloading unused data and repeatedly switching radio interface. In this paper, we propose an energy-efficient download scheduling algorithm for video streaming based on an aggregate model that utilizes user’s video viewing history to predict user behavior when watching a new video, thereby minimizing wasted energy when streaming over wireless network interfaces. The aggregate model is constructed by a personal retention model with users’ personal viewing history and the audience retention on crowd-sourced viewing history, which can accurately predict the user behavior of watching videos by balancing “user interest” and “video attractiveness”. We evaluate different users streaming multiple videos in various wireless environments and the results illustrate that the aggregate model can help reduce energy waste by 20 % on average. In addition, we also discuss implementation details and extensions, such as dynamically updating personal retention, balancing audience and personal retention, categorizing videos for accurate model.     

ViMediaNet: an emulation system for interactive multimedia based telepresence services

The telepresence service (TPS) leverages advanced communication, virtual reality, and cooperative web technologies to provide remote engaged users an immersive experience of being fully present, which are widely used in teleconferencing, remote surgery, and hazardous environment exploration. The common feature of TPS-based applications lies in that the users’ experiences heavily depend on the quality of interactive multimedia services, that is, most deliveries of multimedia contents are mission-critical, as well as time-critical, to ensure the control and operation accuracy for telerobots. Therefore, the delivery performance of multimedia contents over heterogeneous networks becomes the primary concern in the TPS system design, especially for those delay sensitive applications, like remote surgery and environment exploration. In this paper, we incorporate video processing tools with the network simulator to design and implement a joint emulation system, named as the virtual media network (ViMediaNet), through which, the TPS designer can readily deploy the TPS system over a heterogeneous network virtualized by the network emulator, investigate the critical network performance metrics in the multimedia content delivery, such as transmission delay and packet jitter, and observe control results in real time. To verify the capability of ViMediaNet in emulating the real-setting TPS system, we take a mobile device based vision navigation system as an example to evaluate performance impacts of the rate adaptation technology in the telerobot control. The experimental results reveal that the adaptive video streaming is a promising solution to significantly enhance the control accuracy of telerobots in TPS systems.     

A Multimodal Scheme for Program Segmentation and Representation in Broadcast Video Streams

With the advance of digital video recording and playback systems, the request for efficiently managing recorded TV video programs is evident so that users can readily locate and browse their favorite programs. In this paper, we propose a multimodal scheme to segment and represent TV video streams. The scheme aims to recover the temporal and structural characteristics of TV programs with visual, auditory, and textual information. In terms of visual cues, we develop a novel concept named program-oriented informative images (POIM) to identify the candidate points correlated with the boundaries of individual programs. For audio cues, a multiscale Kullback-Leibler (K-L) distance is proposed to locate audio scene changes (ASC), and accordingly ASC is aligned with video scene changes to represent candidate boundaries of programs. In addition, latent semantic analysis (LSA) is adopted to calculate the textual content similarity (TCS) between shots to model the inter-program similarity and intra-program dissimilarity in terms of speech content. Finally, we fuse the multimodal features of POIM, ASC, and TCS to detect the boundaries of programs including individual commercials (spots). Towards effective program guide and attracting content browsing, we propose a multimodal representation of individual programs by using POIM images, key frames, and textual keywords in a summarization manner. Extensive experiments are carried out over an open benchmarking dataset TRECVID 2005 corpus and promising results have been achieved. Compared with the electronic program guide (EPG), our solution provides a more generic approach to determine the exact boundaries of diverse TV programs even including dramatic spots.     

MetaCDN: Harnessing ‘Storage Clouds’ for high performance content delivery

Content delivery networks (CDNs) such as Akamai and Mirror Image place web server clusters in numerous geographical locations to improve the responsiveness and locality of the content it hosts for end-users. However, their services are priced out of reach for all but the largest enterprise customers. An alternative approach to content delivery could be achieved by leveraging existing infrastructure provided by ‘Storage Cloud’ providers, who offer internet accessible data storage and delivery at a fraction of the cost. In this paper, we introduce MetaCDN, a system that exploits ‘Storage Cloud’ resources, creating an integrated overlay network that provides a low cost, high performance CDN for content creators. MetaCDN removes the complexity of dealing with multiple storage providers, by intelligently matching and placing users’ content onto one or many storage providers based on their quality of service, coverage and budget preferences. MetaCDN makes it trivial for content creators and consumers to harness the performance and coverage of numerous ‘Storage Clouds’ by providing a single unified namespace that makes it easy to integrate into origin websites, and is transparent for end-users. We then demonstrate the utility of this new approach to content delivery by showing that the participating ‘Storage Clouds’ used by MetaCDN provide high performance (in terms of throughput and response time) and reliable content delivery for content consumers, whilst the MetaCDN system itself introduces minimal overhead compared to using these ‘Storage Clouds’ directly.