This paper evaluates the influence of an additional visual aesthetic layer on the experience of concert goers during a live event. The additional visual layer incorporates musical features as well as bio-sensing data collected during the concert, which is coordinated by our audience engagement monitoring technology. This technology was used during a real Jazz concert. The collected measurements were used in an experiment with 32 participants, where two different forms of visualization were compared: one factoring in music amplitude, audience engagement collected by the sensors and the dynamic atmosphere of the event, the other one purely relying on the beat of the music. The findings indicate that the visual layer could add value to the experience if used during a live concert, providing a higher level of immersion and feeling of togetherness among the audience.

Objective video quality metrics are designed to be as reliable as the subjective quality assessments on which they are calibrated and validated. However, existing standard methodologies for subjective video quality assessment provide low reliable results for some conditions. We investigate whether an extension of the quality ruler experimental methodology, originally defined for images and shown to be more reliable than, e.g., standard single stimulus (SS) methods, can be adapted to reliably assess the quality of videos. The video quality ruler methodology allows subjects to assess video quality using a set of reference anchor images (the ruler), spanning a wide range of quality altogether, but closely spaced in function of quality one from the other. Subjects are asked to compare the quality of the displayed test video with the quality of these anchor images, displayed on a tablet, and indicate which of the reference images matches in quality the test video. As a result, the video quality assessment task is reduced to a set of visual comparisons between video and reference image quality. We describe how to adapt the original quality ruler methodology to video quality assessment, and we compare the proposed methodology with two other, widely used experimental methodologies: the single stimulus (SS) and the double stimulus (DS) method. Our results show that video quality ruler is a reliable method to assess video quality according to a multitude of criteria.

This paper presents an effective method based on Vector Regression and Object oriented Pooling (VROP) for blind image quality assessment (BIQA). Unlike previous models which map the extracted features directly to a quality score, the proposed vector regression framework yields a vector of belief scores for the input image. We explore the uncertainty factors in quality assessment and design the belief scores to measure the confidences of an image to be assigned to the corresponding quality grades. Moreover, we propose an object oriented pooling strategy to further improve the performance by incorporating semantic information of image contents. According to this strategy, regions occupied by objects will be assigned more weights in the pooling phase, leading to a more accurate quality assessment. Extensive experiments on benchmark datasets demonstrate that our approach achieves state-of-the-art performance and shows a great generalization ability.

Video-conferencing is becoming an essential part in everyday life. The visual channel allows for interactions which were not possible over audio-only communication systems such as the telephone. However, being a de-facto over-the-top service, the quality of the delivered video-conferencing experience is subject to variations, dependent on network conditions. Video-conferencing systems adapt to network conditions by changing for example encoding bitrate of the video. For this adaptation not to hamper the benefits related to the presence of a video channel in the communication, it needs to be optimized according to a measure of the Quality of Experience (QoE) as perceived by the user. The latter is highly dependent on the ongoing interaction and individual preferences, which have hardly been investigated so far. In this paper, we focus on the impact video quality has on conversations that revolve around objects that are presented over the video channel. To this end we conducted an empirical study where groups of 4 people collaboratively build a Lego® model over a video-conferencing system. We examine the requirements for such a task by showing when the interaction, measured by visual and auditory cues, changes depending on the encoding bitrate and loss. We then explore the impact that prior experience with the technology and affective state have on QoE of participants. We use these factors to construct predictive models which double the accuracy compared to a model based on the system factors alone. We conclude with a discussion how these factors could be applied in real world scenarios.

As crowdsourcing gains popularity, organisations seek ways to systematically and reliably involve their workforce with data processing pipelines. Mobile crowdsourcing allows for opportunistic task executions and thus, potentially, for higher throughput. However, how to engage and to retain employees in enterprise crowdsourcing campaigns is still an open research topic. .is paper discusses the results of a study performed in IBM Benelux. We surveyed 93 employees to discover the factors that might a.ect engagement in mobile enterprise crowdsourcing. .e survey informed the design of an experiment that aimed at investigating the e.ectiveness of di.erent task noti€cation strategies. We studied how factors such as time and context of noti€cation can a.ect the participation and retention of employees. Results show that break times are the most suitable for crowd work, and that "aggressive" noti€cation strategies act as deterrent for participation, while moderate yet regular nudges are the most likely to retain contributors.

The driving force behind digital crowdsourcing are its workers: working, hidden behind the scenes, churning out data in experiments, participating in research studies, completing little tasks to accomplish HITs online. Understanding workers and crowdwork better is therefore key to develop a more effective and fair use of crowdsourcing for research. This chapter attempts to help develop an understanding of the various aspects of the crowd by drawing parallels between workers of different platforms (AMT, Microworkers and Crowdee) through quantitative and qualitative analysis of current and newly collected data. A picture of the crowd is drawn by uncovering their motivations, workplaces, skills and infrastructure, issues and perspectives about the design of microtasks, the employers and the microtask-based platforms. Legal and ethical perspectives on crowdwork are also discussed, and online resources are reviewed that researchers can use as a primer to employ crowdworkers in an ethical and fair way. The chapter provides information, a review of internationally recognised ethical principles and practical advice to those who would like to use crowdsourcing for experiments and to carry out research studies as an informed researcher and crowd employer.

This article showcases our effort to explore the music club of the future. We present the development and results of an end-to-end system which enhances the club-going experience through the use of wearable technology. Each party guest wearing one of the wristbands actively contributes to the overall experience with their movement and location patterns. The system collects acceleration data from each of the attendees in real-time and feeds it into a pluggable network infrastructure, which processes the data, affecting the environment via data visualization or controlling of the light and sound system of a curated space within the club. Finally we describe the results of a two night, 450 person per night deployment.