Impact of Social Media on TV Content Consumption: New Market Strategies, Scenarios and Trends

The mass adoption of Social Media together with the proliferation and widely usage of multi-connected companion devices have tremendously transformed the TV/video consumption paradigm, opening the door to a new range of possibilities. This Special Issue has aimed at analyzing, from different point of views, the impact of Social Media and social interaction tools on the TV/video consumption area. The targeted topics of this Special Issue and a general overview of the accepted articles are provided in this Guest Editorial

Indexing of fictional video content for event detection and summarisation

This paper presents an approach to movie video indexing that utilises audiovisual analysis to detect important and meaningful temporal video segments, that we term events. We consider three event classes, corresponding to dialogues, action sequences, and montages, where the latter also includes musical sequences. These three event classes are intuitive for a viewer to understand and recognise whilst accounting for over 90% of the content of most movies. To detect events we leverage traditional filmmaking principles and map these to a set of computable low-level audiovisual features. Finite state machines (FSMs) are used to detect when temporal sequences of specific features occur. A set of heuristics, again inspired by filmmaking conventions, are then applied to the output of multiple FSMs to detect the required events. A movie search system, named MovieBrowser, built upon this approach is also described. The overall approach is evaluated against a ground truth of over twenty-three hours of movie content drawn from various genres and consistently obtains high precision and recall for all event classes. A user experiment designed to evaluate the usefulness of an event-based structure for both searching and browsing movie archives is also described and the results indicate the usefulness of the proposed approach

Computer-supported collaborative inquiry learning and classroom scripts

This study examined the influence of classroom-script structure (high vs. low) during computer-supported collaborative inquiry learning on help-seeking processes and learning gains in 54 student pairs in secondary science education. Screen- and audio-capturing videos were analysed according to a model of the help-seeking process. Results show that the structure of the classroom script substantially affects patterns of student help seeking and learning gain in the classroom. Overall, students in the high-structured classroom-script condition sought less help but learnt more than those in the low-structured classroom-script condition

Reliable and Energy-Efficient Hybrid Screen Mirroring Multicast System

This paper presents a reliable and energy-efficient hybrid screen mirroring multicast system for sharing high-quality real-time multimedia service with adjacent mobile devices over WiFi network. The proposed system employs overhearing-based multicast transmission scheme with Raptor codes and NACK-based retransmission to overcome well-known WiFi multicast problems such as low transmission rate and high packet loss rate. Furthermore, to save energy on mobile devices, the proposed system not only shapes the screen mirroring traffic, but also determines the target sink device and Raptor encoding parameters such as the number of source symbols, symbol size, and code rate while considering the energy consumption and processing delay of the Raptor encoding and decoding processes. The proposed system is fully implemented in Linux-based single board computers and examined in real WiFi network. Compared to existing systems, the proposed system can achieve good energy efficiency while providing a high-quality screen mirroring service.11Nsciescopu

Advances in Pattern Recognition Algorithms, Architectures, and Devices

Over the last decade, tremendous advances have been made in the general area of pattern recognition techniques, devices, and algorithms. We have had the distinct pleasure of witnessing this remarkable growth as evidenced through their dissemination in the previous Optical Engineering special sections we have jointly edited— January 1998, March 1998, May 2000, and January 2002. Twenty-six papers were finally accepted for this latest special section, encompassing the recent trends and advancements made in many different areas of pattern recognition techniques utilizing algorithms, architectures, implementations, and devices. These techniques include matched spatial filter based recognition, hit-miss transforms, invariant pattern recognition, joint transform correlator JTC based recognition, morphological processing based recognition, neural network based recognition, wavelet based recognition, fingerprint and face recognition, data fusion based recognition, and target tracking, as well as other techniques. These papers summarize the work of 70 researchers from eight countries

The Flipped Classroom Model as Applied to an Augmentative and Alternative Communication Course

The Flipped Classroom Model (FCM) is an andragogical approach where students complete content-related work outside of the class and engage in activities related to this content during the class period. This approach has garnered recent attention in the field of speech-language pathology, but its implementation has not been studied in an augmentative and alternative communication (AAC) course and there is limited information on student perspectives of the experience. This study presents the results of a qualitative investigation designed to investigate the preferences and experiences of preservice speech-language pathology graduate students in an AAC course utilizing the FCM. Semi-structured interviews with eight students were transcribed and analyzed utilizing a phenomenological framework. The themes that emerged from the data included course design, course delivery, instructor characteristics, student preferences, student characteristics, online versus in-person learning, and career relevance. Recommendations for implementing the FCM in teaching AAC are discussed in light of the results presented

A machine learning driven solution to the problem of perceptual video quality metrics

The advent of high-speed internet connections, advanced video coding algorithms, and consumer-grade computers with high computational capabilities has led videostreaming-over-the-internet to make up the majority of network traffic. This effect has led to a continuously expanding video streaming industry that seeks to offer enhanced quality-of-experience (QoE) to its users at the lowest cost possible. Video streaming services are now able to adapt to the hardware and network restrictions that each user faces and thus provide the best experience possible under those restrictions. The most common way to adapt to network bandwidth restrictions is to offer a video stream at the highest possible visual quality, for the maximum achievable bitrate under the network connection in use. This is achieved by storing various pre-encoded versions of the video content with different bitrate and visual quality settings. Visual quality is measured by means of objective quality metrics, such as the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF), and others, which can be easily computed analytically. Nevertheless, it is widely accepted that although these metrics provide an accurate estimate of the statistical quality degradation, they do not reflect the viewer’s perception of visual quality accurately. As a result, the acquisition of user ratings in the form of Mean Opinion Scores (MOS) remains the most accurate depiction of human-perceived video quality, albeit very costly and time consuming, and thus cannot be practically employed by video streaming providers that have hundreds or thousands of videos in their catalogues. A recent very promising approach for addressing this limitation is the use of machine learning techniques in order to train models that represent human video quality perception more accurately. To this end, regression techniques are used in order to map objective quality metrics to human video quality ratings, acquired for a large number of diverse video sequences. Results have been very promising, with approaches like the Video Multimethod Assessment Fusion (VMAF) metric achieving higher correlations to useracquired MOS ratings compared to traditional widely used objective quality metrics