182,804 research outputs found

    Bringing Video Communication to the Community: Opportunities and Challenges

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    The rise of online social networks, the wide availability of video communication technology and the deployment of high-speed broadband networks together provide the opportunity for video to become a medium for mass social communication among communities. However, current solutions provide poor support for ad hoc social interactions among multiple groups of participants. This position paper summarises the results of more than 5 years’ research to make communication and engagement easier between groups of people separated in space. It shows how communication can be effectively combined with different shared activities, and how the technical capabilities of Communication Orchestration and Dynamic Composition work together to improve the quality of human interactions. The paper also describes ongoing work to develop the Service-Aware Network as a means of optimising the quality of a user’s communication experience while making most efficient use of network resources. We believe these developments could enable video-mediated communication to become an effective and accepted enabler for social communication within community groups globall

    Facial Expression Recognition using Residual Convnet with Image Augmentations

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    During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID 19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model. The code is available at https://github.com/yusufrahadika-facial-expressions-essay

    Student support networks in online doctoral programs: Exploring nested communities

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    Aim/Purpose: Enrollment in online doctoral programs has grown over the past decade. A sense of community, defined as feelings of closeness within a social group, is vital to retention, but few studies have explored how online doctoral students create community. Background: In this qualitative case study, I explore how students in one online doctoral program created a learning community. Methodology: Data for the study was drawn from 60 hours of video footage from six online courses, the message boards from the six courses, and twenty interviews with first and second-year students. Contribution: Findings from this study indicate that the structure of the social network in an online doctoral program is significantly different from the structure of learning communities in face-to-face programs. In the online program, the doctoral community was more insular, more peer-centered, and less reliant on faculty support than in in-person programs. Findings: Utilizing a nested communities theoretical framework, I identified four subgroups that informed online doctoral students’ sense of community: cohort, class groups, small peer groups, and study groups. Students interacted frequently with members of each of the aforementioned social groups and drew academic, social, and emotional support from their interactions. Recommendations for Practitioners: Data from this study suggests that online doctoral students are interested in making social and academic connections. Practitioners should leverage technology and on-campus supports to promote extracurricular interactions for online students. Recommendation for Researchers: Rather than focus on professional socialization, students in the online doctoral community were interested in providing social and academic support to peers. Researchers should consider how socialization in online doctoral programs differs from traditional, face-to-face programs. Impact on Society: As universities increase online offerings, it is important to consider the issues that impact retention in online programs. By identifying the social structures that support online community, this study helps build knowledge around retention and engagement of online students. Future Research: Future research should continue to explore the unique social networks that support online students

    Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

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    TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world's second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science Conference (WebSci 2020). Please cite the WebSci version; Second version includes corrected typo
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