With a View to the Side: YouTube’s Sidebar and YouTuber’s Linguistic Style as Hints for Trust-Related Evaluations

Based on a heuristic approach to information processing, thematic reference of YouTube’s sidebars and YouTuber’s linguistic style are viewed as cues that should impact viewers’ evaluation of videos. In this study, a 2 × 2 online experiment was conducted wherein these factors were varied systematically for a video about nutrition myths. 147 participants assessed the credibility of information, YouTuber’s trustworthiness, and the self-reported learning gain. Results showed that a sidebar referring to similar (vs. unrelated) videos increased participants’ perceived trustworthiness of a YouTuber who used a YouTube-typical (vs. formal) language. Moreover, participants judged the learning gain to be higher when YouTube’s sidebar referred to similar videos. However, thematic reference of sidebar and linguistic style did not impact participants’ credibility judgments. Since people seem to recognize YouTube’s sidebars when evaluating videos, YouTube’s recommendation criteria might not only mediate videos, but also influence people’s judgments of YouTubers and videos.     

Personal driving diary: Automated recognition of driving events from first-person videos

In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person’s daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important interactions between the driving vehicle and the other actors in videos (e.g., accident, overtaking, etc.), and labels them together with its contextual knowledge on the vehicle (e.g., mean velocity) to construct an event log. A decision tree based activity recognizer is designed, detecting driving events of vehicles and pedestrians from the first-person view videos by analyzing their trajectories and spatio-temporal relationships. The constructed diary enables efficient searching and event-based browsing of video clips, which helps the users when retrieving videos of dangerous situations. Our experiment confirms that the proposed system reliably generates driving diaries by annotating the vehicle events learned from training examples.     

Transformation-invariant clustering using the EM algorithm

Clustering is a simple, effective way to derive useful representations of data, such as images and videos. Clustering explains the input as one of several prototypes, plus noise. In situations where each input has been randomly transformed (e.g., by translation, rotation, and shearing in images and videos), clustering techniques tend to extract cluster centers that account for variations in the input due to transformations, instead of more interesting and potentially useful structure. For example, if images from a video sequence of a person walking across a cluttered background are clustered, it would be more useful for the different clusters to represent different poses and expressions, instead of different positions of the person and different configurations of the background clutter. We describe a way to add transformation invariance to mixture models, by approximating the nonlinear transformation manifold by a discrete set of points. We show how the expectation maximization algorithm can be used to jointly learn clusters, while at the same time inferring the transformation associated with each input. We compare this technique with other methods for filtering noisy images obtained from a scanning electron microscope, clustering images from videos of faces into different categories of identification and pose and removing foreground obstructions from video. We also demonstrate that the new technique is quite insensitive to initial conditions and works better than standard techniques, even when the standard techniques are provided with extra data.     

Effects of video placement and spatial context presentation on path reconstruction tasks with contextualized videos

Many interesting and promising prototypes for visualizing video data have been proposed, including those that combine videos with their spatial context (contextualized videos). However, relatively little work has investigated the fundamental design factors behind these prototypes in order to provide general design guidance. Focusing on real-time video data visualization, we evaluated two important design factors--video placement method and spatial context presentation method--through a user study. In addition, we evaluated the effect of spatial knowledge of the environment. Participants' performance was measured through path reconstruction tasks, where the participants followed a target through simulated surveillance videos and marked the target paths on the environment model. We found that embedding videos inside the model enabled realtime strategies and led to faster performance. With the help of contextualized videos, participants not familiar with the real environment achieved similar task performance to participants that worked in that environment. We discuss design implications and provide general design recommendations for traffic and security surveillance system interfaces.     

Effectiveness of the Step-by-step Discriminatory Analysis at Choosing the Informative Attributes

Consideration was given to the problem of discriminatory analysis and effectiveness of the step-by-step procedure of choosing the informative attributes. Risk as a measure of effectiveness was calculated analytically together with its special related characteristic enabling one to estimate dynamics (variation) of the inter-class distances at passing to the informative attributes and propose a new effective procedure of the step-by-step discriminatory analysis.     

Preventing problematic Internet use through video-based interventions: a theoretical model and empirical test

This study relies on the core ideas of the health belief model and suggests that short informational videos on Internet ‘addiction’ can be an effective means towards preventing problematic use of the Internet through their ability to drive changes in viewers’ attitudes towards reducing their Internet use. Building on the heuristic-systematic model of information processing viewpoint, it is further suggested that this attitude change is guided by the information the videos provide, as well as the surprise emotion they generate. To test this model, data were collected at three points in time from 223 participants who were exposed to one of two video interventions. Partial least-square analyses indicated that the videos were efficacious in improving viewers’ attitudes towards reducing their Internet use, after accounting for viewers’ preexisting attitudes, levels of Internet ‘addiction’, demographics and social desirability bias. Consistent with the heuristic-systematic model of information-processing perspective this effect was mobilised simultaneously through the information and surprise induced by the videos.     

The physiological mirror—a system for unconscious control of a virtual environment through physiological activity

This paper introduces a system for real-time physiological measurement, analysis, and metaphorical visualization within a virtual environment (VE). Our goal is to develop a method that allows humans to unconsciously relate to parts of an environment more strongly than to others, purely induced by their own physiological responses to the virtual reality (VR) displays. In particular, we exploit heart rate, respiration, and galvanic skin response in order to control the behavior of virtual characters in the VE. Such unconscious processes may become a useful tool for storytelling or assist guiding participants through a sequence of tasks in order to make the application more interesting, e.g., in rehabilitation. We claim that anchoring of subjective bodily states to a virtual reality (VR) can enhance a person’s sense of realism of the VR and ultimately create a stronger relationship between humans and the VR.     

Preparing pre-service teachers to integrate technology in education: A synthesis of qualitative evidence

This study reviewed qualitative studies that focused on strategies to prepare pre-service teachers to integrate technology into their lessons. A meta-ethnography approach was utilized to locate, critically appraise, and synthesize the results of these studies. Based on an extensive search in the Web of Science, 19 articles were included in this synthesis. The results were divided into two parts: (1) key themes explicitly related to the preparation of pre-service teachers (e.g., using teacher educators as role models, learning technology by design, scaffolding authentic technology experiences), and (2) conditions necessary at the institutional level (e.g., technology planning and leadership, co-operation within and between institutions, training staff). To present how these key themes related to each other, an overarching model was developed. By interpreting the results of the review, recommendations were discussed for pre-service teacher technology training and future research.     

An immersive virtual reality serious game to enhance earthquake behavioral responses and post-earthquake evacuation preparedness in buildings

Enhancing the earthquake behavioral responses and post-earthquake evacuation preparedness of building occupants is beneficial to increasing their chances of survival and reducing casualties after the mainshock of an earthquake. Traditionally, training approaches such as seminars, posters, videos or drills are applied to enhance preparedness. However, they are not highly engaging and have limited sensory capabilities to mimic life-threatening scenarios for the purpose of training potential participants. Immersive Virtual Reality (IVR) and Serious Games (SG) as innovative digital technologies can be used to create training tools to overcome these limitations. In this study, we propose an IVR SG-based training system to improve earthquake behavioral responses and post-earthquake evacuation preparedness. Auckland City Hospital was chosen as a case study to test our IVR SG training system. A set of training objectives based on best evacuation practice has been identified and embedded into several training scenarios of the IVR SG. Hospital staff (healthcare and administrative professionals) and visitors were recruited as participants to be exposed to these training scenarios. Participants’ preparedness has been measured along two dimensions: 1) Knowledge about best evacuation practice; 2) Self-efficacy in dealing with earthquake emergencies. Assessment results showed that there was a significant knowledge and self-efficacy increase after the training. In addition, participants acknowledged that it was easy, helpful, and engaging to learn best evacuation practice knowledge through the IVR SG training system.     

Dynamic stills and clip trailers

We propose a method for generating visual summaries of video. It reduces browsing time, minimizes screen-space utilization, while preserving the crux of the video content and the sensation of motion. The outputs are images or short clips, denoted as dynamic stills or clip trailers, respectively. The method selects informative poses out of extracted video objects. Optimal rotations and transparency supports visualization of an increased number of poses, leading to concise activity visualization. Our method addresses previously avoided scenarios, e.g., activities occurring in one place, or scenes with non-static background. We demonstrate and evaluate the method for various types of videos.     

Actively open-minded thinking is key to combating fake news: A multimethod study

The fake news phenomenon has exposed the vulnerability of individuals and societies to information manipulation in social media. We conducted two studies to understand why people believe in fake news and propose a simple IT intervention method that can help in detecting disinformation. In Study 1, we designed a laboratory experiment using behavioral and neurophysiological tools to test two competing theories in the disinformation literature. Both behavioral and neurophysiological evidence support the classical reasoning account hypotheses and reject the motivated reasoning predictions, suggesting that the lack of actively open-minded thinking (AOT) is linked to the belief in fake news. An intervention method was designed (i.e., performance feedback) that reduces individuals’ overconfidence in their ability to detect fake news and encourages more analytical thinking. In Study 2, we conducted an online survey presenting participants with their performance feedback halfway through the survey. The results show that the intervention increased participants’ performance by 14%. Our study contributes to the research on fake news by providing behavioral and neurophysiological evidence in support of the classical reasoning account. It also offers a simple and practical method that increases users’ ability to detect fake news.     

Efficient indexing and retrieval of large-scale geo-tagged video databases

We are witnessing a significant growth in the number of smartphone users and advances in phone hardware and sensor technology. In conjunction with the popularity of video applications such as YouTube, an unprecedented number of user-generated videos (UGVs) are being generated and consumed by the public, which leads to a Big Data challenge in social media. In a very large video repository, it is difficult to index and search videos in their unstructured form. However, due to recent development, videos can be geo-tagged (e.g., locations from GPS receiver and viewing directions from digital compass) at the acquisition time, which can provide potential for efficient management of video data. Ideally, each video frame can be tagged by the spatial extent of its coverage area, termed Field-Of-View (FOV). This effectively converts a challenging video management problem into a spatial database problem. This paper attacks the challenges of large-scale video data management using spatial indexing and querying of FOVs, especially maximally harnessing the geographical properties of FOVs. Since FOVs are shaped similar to slices of pie and contain both location and orientation information, conventional spatial indexes, such as R-tree, cannot index them efficiently. The distribution of UGVs’ locations is non-uniform (e.g., more FOVs in popular locations). Consequently, even multilevel grid-based indexes, which can handle both location and orientation, have limitations in managing the skewed distribution. Additionally, since UGVs are usually captured in a casual way with diverse setups and movements, no a priori assumption can be made to condense them in an index structure. To overcome the challenges, we propose a class of new R-tree-based index structures that effectively harness FOVs’ camera locations, orientations and view-distances, in tandem, for both filtering and optimization. We also present novel search strategies and algorithms for efficient range and directional queries on our indexes. Our experiments using both real-world and large synthetic video datasets (over 30 years’ worth of videos) demonstrate the scalability and efficiency of our proposed indexes and search algorithms.     

Agent-based Cloud service composition

Service composition in multi-Cloud environments must coordinate self-interested participants, automate service selection, (re)configure distributed services, and deal with incomplete information about Cloud providers and their services. This work proposes an agent-based approach to compose services in multi-Cloud environments for different types of Cloud services: one-time virtualized services, e.g., processing a rendering job, persistent virtualized services, e.g., infrastructure-as-a-service scenarios, vertical services, e.g., integrating homogenous services, and horizontal services, e.g., integrating heterogeneous services. Agents are endowed with a semi-recursive contract net protocol and service capability tables (information catalogs about Cloud participants) to compose services based on consumer requirements. Empirical results obtained from an agent-based testbed show that agents in this work can: successfully compose services to satisfy service requirements, autonomously select services based on dynamic fees, effectively cope with constantly changing consumers’ service needs that trigger updates, and compose services in multiple Clouds even with incomplete information about Cloud participants.     

A review and framework of laser-based collaboration support

New technologies are emerging to enable and support physical, implicit and explicit collaborations. They are essential for dealing with increasingly complex systems in unstructured, dynamic environments. The purpose of this article is to review the role of laser technology in enabling better, more precise interactions and their control, and to identify opportunities and challenges in this area. While the most common applications of laser technology are found in medical and health care, manufacturing, and communication, other domains such as safety, quality assurance, agriculture, construction, entertainment, defense, transportation, and law enforcement also benefit from it. In spite of the rapid dissemination of this technology, its role in support of collaboration and discovery is still in its infancy. Research activities concerning new ways of using lasers as a collaboration supporting technology that may strengthen new areas have been relatively limited. Nevertheless, the translation to this domain of collaboration support has been recognized as vital for activities that demand increasingly more coordinated effort among interacting agents (e.g., humans, machines, particles) and digital, possibly also photonic agents. Recent advances in laser technology in a number of application domains are reviewed in this article, focusing primarily on lasers’ role for supporting different forms of precision interactions and collaboration. In addition, a framework with five collaboration support functions and five collaboration dimensions is defined for this review. The taxonomy framework is useful for enabling better understanding of the existing and emerging opportunities that laser-based technology offers for collaboration support, its advantages and several research gaps.     

A Study of Gimlet Use in Reference Transactions

The focus of this research study is a web-based reference tracking tool called Gimlet. Surveys were sent to State University of New York at Oswego (SUNY Oswego) librarians and to librarians and staff in the United States and abroad. Results indicated that SUNY Oswego librarians find Gimlet a useful and convenient tool that helps them feel productive while generating informative statistics. Respondents outside SUNY Oswego found Gimlet easy to administer and customize, but required improved functionality to generate reports. The reports are valuable to librarians and administrators for evaluating programs. Although Gimlet is user friendly, a library's needs vary according to customizable options and in the generating of reports.     

A content search system considering the activity and context of a mobile user

People routinely carry mobile devices in their daily lives and obtain a variety of information from the Internet in many different situations. In searching for information (content) with a mobile device, a user’s activity (e.g., moving or stationary) and context (e.g., commuting in the morning or going downtown in the evening) often change, and such changes can affect the user’s degree of concentration on his or her mobile device’s display and information needs. Therefore, a search system should provide the user with an amount of information suitable for the current activity and a type of information suitable for the current context. In this study, we present the design and implementation of a content search system that considers a mobile user’s activity and context, with the goal of reducing the user’s operation load for content search. The proposed system switches between two kinds of content search systems according to the user’s activity: the location-based content search system is activated when the user is stationary (e.g., standing and sitting), while a menu-based content search system is activated when the user is moving (e.g., walking). Both systems present information according to user context. The location-based system presents detailed information via menus and a map according to location-based categories. The menu-based system presents only a few options to enable users to get content easily. Through user experiments, we confirmed that participants could get desired information more easily with this system than with a commercial search system.     

Life in the Bubble: Examining the forwarding of political videos

We examined whether participants’ political beliefs significantly predicted likelihood of forwarding political videos and the characteristics of the targets of these forwards. Participants viewed one of four political advertisements that varied in terms of the candidate’s political party (Democrat or Republican) and the emotion that the advertisement evoked (Positive or Negative). Democrats were more likely to forward advertisements when they experienced positive emotional arousal, and the targets of the forwards were not especially similar to Democrat participants in terms of political orientation or personality. Conversely, Republicans were more likely to forward advertisements when they experienced negative emotional arousal, and the targets of the forwards were highly similar to the Republican participants in terms of political orientation and personality. This is consistent with previous research (e.g., Jost, Glaser, Kruglanski, & Sulloway, 2003) indicating that conservatism is associated with greater negative affect sensitivity and insularity in communication.     

Systematic Review: Trust-Building Factors and Implications for Conversational Agent Design

ABSTRACT

Off-the-shelf conversational agents are permeating people’s everyday lives. In these artificial intelligence devices, trust plays a key role in users’ initial adoption and successful utilization. Factors enhancing trust toward conversational agents include appearances, voice features, and communication styles. Synthesizing such work will be useful in designing evidence-based, trustworthy conversational agents appropriate for various contexts. We conducted a systematic review of the experimental studies that investigated the effect of conversational agents’ and users’ characteristics on trust. From a full-text review of 29 articles, we identified five agent design-themes affecting trust toward conversational agents: social intelligence of the agent, voice characteristics and communication style, look of the agent, non-verbal communication, and performance quality. We also found that participants’ demographic, personality, or use context moderate the effect of these themes. We discuss implications for designing trustworthy conversational agents and responsibilities around on stereotypes and social norm building through agent design.