Learner Engagement with YouTube Videos in Informal Online Learning: An Investigation of the Effects of Segmenting, Signaling, and Weeding

Millions of educational videos available on YouTube offer unprecedented opportunities for online learning. As it invites open-ended and self-paced exploration of almost any topic, YouTube has emerged as an important platform for informal online learning that occurs outside the formal classroom. A considerable number of studies have been directed toward YouTube educational videos. However, research on learner engagement with YouTube educational videos is limited, despite the central role of engagement in learning and the increasing popularity of YouTube videos in informal online learning. This paper addresses this research gap. We adopt the conceptualization that learner engagement has three dimensions – behavioral, emotional, and cognitive - and investigate how the features of segmenting, signaling, and weeding (SSW), the three multimedia learning principles, in YouTube educational video presentations collectively affect learner engagement in informal online learning. Our analysis shows that different SSW features have various associations with the three dimensions of learner engagement. These findings substantiate the empirical knowledge of learner engagement with YouTube educational videos. Our study corroborates extant video engagement research and extends its relevance to informal learning on social media. It also informs video designers and developers on adding video presentation features to optimize video engagement on social media

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

Endocytic trafficking is required for neuron cell death through regulating TGF-beta signaling in \u3ci\u3eDrosophila melanogaster\u3c/i\u3e

Programmed cell death (PCD) is an essential feature during the development of the central nervous system in Drosophila as well as in mammals. During metamorphosis, a group of peptidergic neurons (vCrz) are eliminated from the larval central nervous system (CNS) via PCD within 6-7 h after puparium formation. To better understand this process, we first characterized the development of the vCrz neurons including their lineages and birth windows using the MARCM (Mosaic Analysis with a Repressible Cell Marker) assay. Further genetic and MARCM analyses showed that not only Myoglianin (Myo) and its type I receptor Baboon is required for neuron cell death, but also this death signal is extensively regulated by endocytic trafficking in Drosophila melanogaster. We found that clathrin-mediated membrane receptor internalization and subsequent endocytic events involved in Rab5-dependent early endosome and Rab11-dependent recycling endosome differentially participate in TGF-β [beta] signaling. Two early endosome-enriched proteins, SARA and Hrs, are found to act as a cytosolic retention factor of Smad2, indicating that endocytosis mediates TGF-β [beta] signaling through regulating the dissociation of Smad2 and its cytosolic retention factor

Learning Audio Sequence Representations for Acoustic Event Classification

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC

Impact of Unified Communications on Communication, Relationship Building and Performance

Unified Communications (UC) integrates multiple communication and multimedia services such as voice, email, fax, voice messaging, video conferencing and chat into a unified user experience. In this research, we study the use of UC in global virtual teams in a multinational corporation. Specifically, we conduct a case study to examine how the use of UC enhances individuals’ ability to communicate, helps them create and build relationship, and then how such relationship building in turn improves their performance. This research will contribute to the theoretical understanding of the use of UC in organizational settings. This research also has practical significance as it can help organizations to make better decisions in regards to their investments in and usage of communication technologies in a global environment