Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality

Ranking and recommendation of multimedia content such as videos is usually realized with respect to the relevance to a user query. However, for lecture videos and MOOCs (Massive Open Online Courses) it is not only required to retrieve relevant videos, but particularly to find lecture videos of high quality that facilitate learning, for instance, independent of the video's or speaker's popularity. Thus, metadata about a lecture video's quality are crucial features for learning contexts, e.g., lecture video recommendation in search as learning scenarios. In this paper, we investigate whether automatically extracted features are correlated to quality aspects of a video. A set of scholarly videos from a Mass Open Online Course (MOOC) is analyzed regarding audio, linguistic, and visual features. Furthermore, a set of cross-modal features is proposed which are derived by combining transcripts, audio, video, and slide content. A user study is conducted to investigate the correlations between the automatically collected features and human ratings of quality aspects of a lecture video. Finally, the impact of our features on the knowledge gain of the participants is discussed

GIS and urban data science

With the emergence of new forms of geospatial/urban big data and advanced spatial analytics and machine learning methods, new patterns and phenomena can be explored and discovered in our cities and societies. In this special issue, we presented an overview of nine studies to understand how to use urban data science and GIS in healthcare services, hospitality and safety, transportation and mobility, economy, urban planning, higher education, and natural disasters, spreading across developed countries in North America and Europe, as well as Global South areas in Asia and the Middle East. The embrace of diverse geo-computational methods in this special issue brings forward an outlook to future GIS and Urban Data Science towards more advanced computational capability, global vision and urban-focused research

A Framework for Research in Gamified Mobile Guide Applications using Embodied Conversational Agents (ECAs)

Mobile Guides are mobile applications that provide players with local and location-based services (LBS), such as navigation assistance, where and when they need them most. Advances in mobile technologies in recent years have enabled the gamification of these applications, opening up new opportunities to transfer education and culture through game play. However, adding traditional game elements such as PBLs (points, badges, and leaderboards) alone cannot ensure that the intended learning outcomes will be met, as the player’s cognitive resources are shared between the application and the surrounding environment. This distribution of resources prevents players from easily immersing themselves into the educational scenario. Adding artificial conversational characters (ECAs) that simulate the social norms found in real-life human-to-human guide scenarios has the potential to address this problem and improve the player’s experience and learning of cultural narratives [1]. Although significant progress has been made towards creating game-like mobile guides with ECAs ([2], [3]), there is still a lack of a unified framework that enables researchers and practitioners to investigate the potential effects of such applications to players and how to approach the concepts of player experience, cognitive accessibility and usability in this context. This paper presents a theoretically-well supported research framework consisted of four key components: differences in players, different features of the gamified task, aspects of how the ECA looks, sound or behaves and different mobile environments. Furthermore, it provides based on this framework a working definition of what player experience, cognitive accessibility and usability are in the context of game-like mobile guide applications. Finally, a synthesis of the results of six empirical studies conducted within this research framework is discussed and a series of design guidelines for the effective gamification of mobile guide applications using ECAs are presented. Results show that an ECA can positively affect the quality of the player’s experience, but it did not elicit better player retention of cultural narratives and navigation of routes

Systematic literature review of hand gestures used in human computer interaction interfaces

Gestures, widely accepted as a humans' natural mode of interaction with their surroundings, have been considered for use in human-computer based interfaces since the early 1980s. They have been explored and implemented, with a range of success and maturity levels, in a variety of fields, facilitated by a multitude of technologies. Underpinning gesture theory however focuses on gestures performed simultaneously with speech, and majority of gesture based interfaces are supported by other modes of interaction. This article reports the results of a systematic review undertaken to identify characteristics of touchless/in-air hand gestures used in interaction interfaces. 148 articles were reviewed reporting on gesture-based interaction interfaces, identified through searching engineering and science databases (Engineering Village, Pro Quest, Science Direct, Scopus and Web of Science). The goal of the review was to map the field of gesture-based interfaces, investigate the patterns in gesture use, and identify common combinations of gestures for different combinations of applications and technologies. From the review, the community seems disparate with little evidence of building upon prior work and a fundamental framework of gesture-based interaction is not evident. However, the findings can help inform future developments and provide valuable information about the benefits and drawbacks of different approaches. It was further found that the nature and appropriateness of gestures used was not a primary factor in gesture elicitation when designing gesture based systems, and that ease of technology implementation often took precedence

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care

Mid-Air Gestural Interaction with a Large Fogscreen

Projected walk-through fogscreens have been created, but there is little research on the evaluation of the interaction performance with fogscreens. The present study investigated mid-air hand gestures for interaction with a large fogscreen. Participants (N = 20) selected objects from a fogscreen using tapping and dwell-based gestural techniques, with and without vibrotactile/haptic feedback. In terms of Fitts’ law, the throughput was about 1.4 bps to 2.6 bps, suggesting that gestural interaction with a large fogscreen is a suitable and effective input method. Our results also suggest that tapping without haptic feedback has good performance and potential for interaction with a fogscreen, and that tactile feedback is not necessary for effective mid-air interaction. These findings have implications for the design of gestural interfaces suitable for interaction with fogscreens.Peer reviewe

Feedback Control of Human Stress with Music Modulation

Mental stress has known detrimental effects on human health, however few algorithmic methods of reducing mental stress have been widely explored. While the act of listening to music has been shown to have beneficial effects for stress reduction, and furthermore, audio players have been designed to selectively choose music and other inputs with the intent of stress reduction, limited work has been conducted for real-time stress reduction with feedback control using physiological input signals such as heart rate or Heart Rate Variability (HRV). This thesis proposes a feedback controller that uses HRV signals from wearable sensors to perform real-time (< 1 second) modulations to music through tempo changes with the goal to regulate and reduce stress levels. A standardized, stress inducing test based on the popular Stroop test is also introduced, which has been shown to induce acute stress in subjects and can be used as a testing benchmark for controller design. Ultimately, a controller is presented that when used is not only able to maintain stress levels during stress-inducing inputs to a human but even provides de-stressing effects beyond baseline performance.No embargoAcademic Major: Electrical and Computer Engineerin