A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission Clusters

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission chains so as to prevent an outbreak of COVID-19 as early as possible. In this article, we study the problem of potential transmission cluster discovery based on the spatio-temporal logs. Given a query of patient user q and a timestamp of confirmed infection tq, the problem is to find all potential infected users who have close social contacts to user q before time tq. We motivate and formulate the potential transmission cluster model, equipped with a detailed analysis of transmission cluster property and particular model usability. To identify potential clusters, one straightforward method is to compute all close contacts on-the-fly, which is simple but inefficient caused by scanning spatio-temporal logs many times. To accelerate the efficiency, we propose two indexing algorithms by constructing a multigraph index and an advanced BCG-index. Leveraging two well-designed techniques of spatio-temporal compression and graph partition on bipartite contact graphs, our BCG-index approach achieves a good balance of index construction and online query processing to fast discover potential transmission cluster. We theoretically analyze and compare the algorithm complexity of three proposed approaches. Extensive experiments on real-world check-in datasets and COVID-19 confirmed cases in the United States validate the effectiveness and efficiency of our potential transmission cluster model and algorithms. © 2023 Association for Computing Machinery.

Computer-Aided System for COVID-19 Using Semi-supervised-based Ensemble Learning and Reinforcement Learning

Coronavirus Disease 2019(COVID-19) has shocked the world with its rapid spread and enormous threat to life and has continued up to the present. In this paper, a computer-aided system is proposed to detect infections and predict the disease progression of COVID-19. A high-quality CT scan database labeled with time-stamps and clinicopathologic variables is constructed to provide data support. To our knowledge, it is the only database with time relevance in the community. An object detection model is then trained to annotate infected regions. Using those regions, we detect the infections using a model with semi-supervised-based ensemble learning and predict the disease progression depending on reinforcement learning. We achieve an mAP of 0.92 for object detection. The accuracy for detecting infections is 98.46%, with a sensitivity of 97.68%, a specificity of 99.24%, and an AUC of 0.987. Significantly, the accuracy of predicting disease progression is 90.32% according to the timeline. It is a state-of-the-art result and can be used for clinical usage. © 2022 IEEE.

Non-Sequential Indexing of Videos using Linguistic Computation

Since the outbreak of Corona Virus Disease(COVID-19), the education sector has seen a drift from traditional in-person teaching methods to virtually-assisted learning. This new trend has paved its path for students to easily gain access to a variety of educational instructors across the globe. But online education comes with its own potential and challenges. Factors like high availability, flexibility, and affordability of the online learning platforms add to the effective deliverance of the content in this progressive present-day online learning. Some key disadvantages are lack of powerful conveyance of content to listeners and sequential navigation of videos. Linearly searching for specific topics through long videos is a common problem that students face, while learning from the internet. This research study proposes a novel approach to promote the application of non-sequential navigation of videos by identifying key-topics and automatically generating timestamps. The model utilizes Natural Language Processing (NLP) and Optical Character Recognition (OCR) techniques for determining the key topics from the video. Timestamps are identified for the keywords before they are uttered, using a novel algorithm for audio indexing. Finally, timestamps are successfully generated for every keyword. Through this study, the objective of non-sequential navigation of videos using a new audio-indexing algorithm is achieved. © 2022 IEEE

Camera-Based Log System for Human Physical Distance Tracking in Classroom

In the pandemic of COVID-19, the indoor physical distancing protocol has been one of the recommendations for people to avoid close contact with each other in order to prevent contagious clusters. This paper proposes an end-to-end camera-based human physical distancing recording system for an indoor environment, specifically, a classroom. The recording system aims to automatically trace the locations of persons and the directions of their movements in a classroom, also with respect to the on- and off-seat activities. No identity of persons is kept in the recording log system, but locations of individual persons at each timestamp are obtained;hence, the spatial and temporal distribution can be studied further. In this paper, we illustrate the overview workflow of the human and seat detection as well as the log system storing human physical distancing actions. © 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).

Techniques for detecting and deterring cheating in home exams in programming

The Covid-19 pandemic led to increased use of home exams, with a perceived increase of cheating. Assessment integrity is a key challenge for higher education. Apart from remote proctoring, what other mitigations may be possible against cheating in home exams, and specifically for programming courses with huge classes? The paper presents our approaches to mitigate cheating, for CS1 based on questions with subtly different variants, for CS2 based on plagiarism detection and timestamps - in sufficient detail that others could use a similar approach. These two approaches can be partially effective against collaboration, but less so against contract cheating where help is acquired from an outside third party. Hence, towards the end of the paper we also outline possible approaches to mitigate such cheating, without or in addition to remote proctoring. © 2022 IEEE.

Relationship of Students' Engagement with Learning Management System and their Performance- An Undergraduate Programming Course Perspective

The Covid-19 pandemic forced the closures of universities across the United States, resulting in multiple modes of instruction. These transitions required both students and instructors to adequately use educational technology tools and applications. Most instructors used a learning management system (e.g., Canvas, Blackboard) and an online conference tool (e.g., Zoom, Teams) to ensure students' access to course material, class participation, and engagement. In the new normal time, although the in-person classes started in many universities, the hybrid of Hyflex mode (i.e., students in both in-person and on zoom sessions) is more prevalent. Students and instructors find educational technology tools as an easier way to disseminate the course information (e.g., videos), material (e.g., course videos, study guides, and notes), and assessments (e.g., quizzes). Considering the reliance on technology tools, it is crucial to understand the relationships between students' application engagement and performance. This paper examined the relationship between students' engagement with an educational Learning Management System (LMS) and their performance. In addition, we also evaluated the way students' engagement with the LMS changed over time during a semester (15 weeks). For this purpose, we collected the data from two sections, 84 students of the introductory engineering programming (MATLAB) course. For students' engagement with the LMS (Canvas in this case), we collected the timestamps each week, indicating the number of hours spent by each student on the LMS. As the timestamps were cumulative, we collected the data at the end of each week at the same time and calculated the weekly time spent by each student on the LMS. We used students' performance scores in two exams for students' performance. We used Pearson correlation and multiple regression analysis for this semester-long study to understand the relationship between students' engagement with the LMS and students' performance. We also conducted the repeated measures ANOVA to understand the trends of students' engagement with the LMS. The study results bring an interesting perspective indicating a significant relationship between students' app engagement in three weeks and programming parts of exam1 and four weeks on the programming part of exam2. Although instructor-based variations were significant in PartII of both exams, app engagement significantly predicted exam2 and PartII of exam1. The paper discusses these results with course content, limitations, and future directions. © American Society for Engineering Education, 2022.

Timestamp analysis of mental health tweets of Twitter users along with COVID-19 confirmed cases

Twitter users post tweets on many topics, emotions, and events. The technological advancement and ease of tweeting quicken people's interaction with social network sites. Engagement with tweets led to product promotion in many corporate companies. Many studies focused on understanding tweeting patterns for marketing, retweeting, getting noticed, and receiving feedback. The time of a tweet was used for marketing strategies. Domain-based tweet timestamp patterns helped corporates in their tweet schedules and attracted more customers for their products. We collected 2.3 million depressive, anti-depressive, and COVID-19 tweets for one year. Our analysis of these tweets results in detailed tweet patterns in different timings in a day and days in a week. The depressive tweets follow the diurnal pattern, whereas the anti-depressive tweets follow a similar trend with intermediate aberrations. We also classified the tweet keywords into three different types with their frequency and amplitude of tweet patterns. Analyzing multi-domain tweets to discover time series patterns related to human health will be helpful for the planning and execution of medical disaster preparedness and emergency teams. © 2022 ACM.

Forensic Analysis of Microsoft Teams: Investigating Memory, Disk and Network

Videoconferencing applications have seen a jump in their userbase owing to the COVID-19 pandemic. The security of these applications has certainly been a hot topic since millions of VoIP users’ data is involved. However, research pertaining to VoIP forensics is still limited to Skype and Zoom. This paper presents a detailed forensic analysis of Microsoft Teams, one of the top 3 videoconferencing applications, in the areas of memory, disk-space and network forensics. Extracted artifacts include critical user data, such as emails, user account information, profile photos, exchanged (including deleted) messages, exchanged text/media files, timestamps and Advanced Encryption Standard encryption keys. The encrypted network traffic is investigated to reconstruct client-server connections involved in a Microsoft Teams meeting with IP addresses, timestamps and digital certificates. The conducted analysis demonstrates that, with strong security mechanisms in place, user data can still be extracted from a client’s desktop. The artifacts also serve as digital evidence in the court of Law, in addition to providing forensic analysts a reference for cases involving Microsoft Teams. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Analyzing Medical Data with Process Mining: A COVID-19 Case Study

The recent increase in the availability of medical data, possible through automation and digitization of medical equipment, has enabled more accurate and complete analysis on patients’ medical data through many branches of data science. In particular, medical records that include timestamps showing the history of a patient have enabled the representation of medical information as sequences of events, effectively allowing to perform process mining analyses. In this paper, we will present some preliminary findings obtained with established process mining techniques in regard of the medical data of patients of the Uniklinik Aachen hospital affected by the recent epidemic of COVID-19. We show that process mining techniques are able to reconstruct a model of the ICU treatments for COVID patients. © 2022, Springer Nature Switzerland AG.