A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the Internet of Things (IoT) can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Chest X-ray (CXR) images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a discrete social learning particle swarm optimization algorithm (DSLPSO) is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a Support Vector Machine (SVM) for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.

Designing IDE Interventions to Promote Social Interaction and Improved Programming Outcomes in Early Computing Courses

As in other STEM disciplines, early computing courses tend to stress individual assignments and discourage collaboration. This can lead to negative learning experiences that compel some students to give up. According to social learning theory, one way to improve students’ learning experiences is to help them form and participate actively in vibrant social learning communities. Building on social learning theory, we have designed a set of software interventions (scaffolds and prompts) that leverage automatically collected learning process data to promote increased social interactions and better learning outcomes in individual programming assignments, which are a key component of early undergraduate computing courses. In an empirical study, we found that students’ interaction with the interventions was correlated with increased social activity, improved attitudes toward peer learning, more closely coupled social networks, and higher performance on programming assignments. Our work contributes a theoretically motivated technological design for social programming interventions; an understanding of computing students’ willingness to interact with the interventions; and insights into how students’ interactions with the interventions are associated with their social behaviors, attitudes, connectedness with others in the class, and their course outcomes.

Understanding the Effects of Structured Note-taking Systems for Video-based Learners in Individual and Social Learning Contexts

Video-based learning is widely adopted by online learners, yet, learning experience and quality may be negatively affected by asynchronous and remote natures of video-based learning. As note-taking is a common practice employed by video-based learners and is known to be an effective way to trigger active construction and processing of knowledge, yet as a meta-skill, it is challenging to most learners. In this study, we aim to approach the goal of providing cognitive and social scaffolds to video-based learners by structuring their note-taking process. We presented and evaluated structured note-taking systems designed for learners in two contexts, namely, individual learning context and social learning context. With an online controlled study involving 43 participants, we compared the structured note-taking systems with two baseline systems (for individual learning and social learning contexts respectively) and found that structured note-taking significantly improved certain aspects of video-based learning such as and higher cognitive engagement and lower distraction. We discussed our results to inform the design, iteration, and adoption of note-taking tools in video-based learning.

How social learning shapes the efficacy of preventative health behaviors in an outbreak

The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior.

Factors Influencing Social-Learning Experience in Nearby Pocket Parks On Campus Ground

Nearby pockets on campus grounds have become necessary learning sustainable settings to improve the academic experience by promoting outdoor social and learning activities. However, many universities still focus mainly on formal indoor learning and lack outdoor education that meets modern academic outcomes. Therefore, the current study aimed to identify the factors affecting students' social-learning experience in nearby pocket parks on campus ground, focusing on the tropical regions. The current study employed a questionnaire survey conducted in three Malaysian universities to collect data from 408 participants. The results showed various types of influencing factors that affect the social-learning experience in nearby pockets on campus ground, including landscape elements and activities, environmental factors, and access to these spaces. The results also indicated that students' demographics, including gender, education status, and university, influenced the outdoor social-learning experience. The current study contributed information to the development of on-campus sustainable settings for integrating nearby pockets in social interaction and learning activities in order to improve the academic social-learning experience.

Models of Identity Signaling

Identity signals inform receivers of a signaler’s membership in a subset of individuals, and in doing so shape cooperation, conflict, and social learning. Understanding the use and consequences of identity signaling is therefore critical for a complete science of collective human behavior. And, as with all complex social systems, this understanding is aided by the use of formal mathematical and computational models. Here I review some formal models of identity signaling. I divide these models into two categories. The first concerns models that assert how identity functions as a signal and test the consequences of those assertions, with a focus on public health behavior and disease transmission. The second concerns models used to understand how identity signals operate strategically in different social environments, with a focus on covert or encrypted communication.