The global COVID-19 pandemic has strained health-care systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1-score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible.

Project-based learning (PjBL) is a desirable form of active learning that facilitate student engagement, team work and problem solving. Current literature in PjBL have studied the merits, demerits, implementation strategies and the impact of PjBL on student performance. However, PjBL has not been properly studied from the lens of Industry Standard Tools and Practices (IST&Ps). Currently, the specific learning effectiveness of PjBL vis-a-vis IST&Ps are largely unknown. To provide insight into the effectiveness of PjBL in relation to IST&Ps, we implemented PjBL in our class using 5 popular IST (SQL, Atlassian Jira, GitHub, Jenkins, and planning poker) and 3 most common Agile Development practices as ISP. We collected data from 120 students juniors and seniors using RIMMS as our instrument. Data was analyzed both qualitatively and quantitatively. Our preliminary results shows that IST&P has significantly positive impact over learning effectiveness, and students' engagement.

This research to practice full paper presents our investigation into the use of gamification in teaching cybersecurity awareness to students. Although there are evidences of increasing research activities in cybersecurity, cyberattacks continue to be pervasive. Academic institutions are largely responsible for educating and producing skilled professionals with cybersecurity competences. However, cybersecurity education can be challenging, especially to smaller institutions, usually characterized by meagre resources. In literature, one of the beneficial pedagogical methods for teaching cybersecurity awareness is gamification, which is based on constructivist theoretical framework. While this method has proved very useful, one major challenge is that gamification platforms can be costly to develop and difficult to maintain. This may discourage smaller institutions from using gamification to teach cybersecurity awareness. Freemium gamified platforms e.g., Kahoot! can offer an alternative that is affordable, easy to use and requires very little to no overhead cost. The research questions under investigation are: what is the impact of gamification (as an instructional method) on students’ learning of cybersecurity awareness?, which game elements and what aspect of gamification best motivate students?. Using questionnaire, we asked the students, from a small university in Northwestern Pennsylvania, USA, to rate their knowledge and awareness of cyberattacks. Afterwards, we taught a cyber awareness module with 5 learning objectives to these students using gamification in Kahoot! platform. At the end of the class, we administered another questionnaire to the students and asked them to rate their knowledge and awareness of those same cyberattacks. Our analysis and statistical results show that gamification is an effective technique for knowledge acquisition in cybersecurity awareness. Furthermore, students are mostly motivated by game elements that give them a sense of achievement. Finally we found that students are more interested in the knowledge acquisition aspect of gamification rather than the entertainment and winning aspects. The results of our study may be beneficial for instructional design of introductory cybersecurity awareness courses.

This Full Paper (Research) presents an analysis on the competences in software testing for the fresh graduates in computer science. Software Testing education (ST) is receiving increasing attention in literature, recent studies have evaluated instructional methods used in ST education. However, analysis of competences (skills, knowledge, and ability) required in ST education are lacking in literature. Competences play critical roles in curriculum development e.g., they inform the design of student learning outcomes, learning objectives and program outcomes. This full paper in the research category aims to analyze competences in ST education and then examine the gap between these competences and the current ST curriculum. Using natural language processing (NLP) techniques, we collect 2033 job descriptions from three popular job portals (indeed, monster, and career builder) in the USA and Canada. Also, we collected course syllabi from 20 universities offering ST courses and use these to assess the current curriculum in ST. We analyzed the data using thematic analysis and found that the current software testing curricula do not always teach or equip students with some of the soft skills they require to be successful in software testing career. For instance, our result shows that soft skills such as teamwork, communication, leadership, which are often required by software testing employers are not always taught in ST courses.

Multiple choice questions are at the heart of many standardized tests and examinations at academic institutions allover the world. In this paper, we argue that recent advancements in sensing and human-computer interaction expose these types of questions to highly effective attacks that today’s proctor’s are simply not equipped to detect. We design one such attack based on a protocol of carefully orchestrated wrist movements combined with haptic and visual feedback mechanisms designed for stealthiness. The attack is done through collaboration between a knowledgeable student (i.e., a mercenary) and a weak student (i.e., the beneficiary) who depends on the mercenary for solutions. Through a combination of experiments and theoretical modeling, we show the attack to be highly effective. The paper makes the case for an outright ban on all tech gadgets inside examination rooms, irrespective of whether their usage appears benign to the plain eye.

Mobile devices are increasingly relied upon in user's daily lives. This dependence supports a growing network of mobile device charging hubs in public spaces such as airports. Unfortunately, the public nature of these hubs make them vulnerable to tampering. By embedding illicit power meters in the charging stations an attacker can launch power side-channel attacks aimed at inferring user activity on smartphones (e.g., web browsing or typing patterns). In this paper, we present three power side-channel attacks that can be launched by an adversary during the phone charging process. Such attacks use machine learning to identify unique patterns hidden in the measured current draw and infer information about a user's activity. To defend against these attacks, we design and rigorously evaluate two defense mechanisms, a hardware-based and software-based solution. The defenses randomly perturb the current drawn during charging thereby masking the unique patterns of the user's activities. Our experiments show that the two defenses force each one of the attacks to perform no better than random guessing. In practice, the user would only need to choose one of the defensive mechanisms to protect themselves against intrusions involving power draw analysis.

Despite decades of research on automated handwriting authentication, there is yet to emerge an automated handwriting authentication application that breaks into the mainstream. In this paper, we argue that the burgeoning wearables market holds the key to a practical handwriting authentication app. With potential applications in online education, standardized testing and mobile banking, we present Handwriting Watcher, a mechanism which leverages a wrist-worn sensor-enabled device to authenticate a user's free handwriting. Through experiments capturing a wide range of writing scenarios, we show Handwriting Watcher attains mean error rates as low as 6.56% across the population. Our work represents a promising step towards a market-ready, generalized handwriting authentication system.