A Mathematics Pipeline to Student Success in Data Analytics through Course-Based Undergraduate Research

This paper reports on Data Analytics Research (DAR), a course-based undergraduate research experience (CURE) in which undergraduate students conduct data analysis research on open real- world problems for industry, university, and community clients. We describe how DAR, offered by the Mathematical Sciences Department at Rensselaer Polytechnic Institute (RPI), is an essential part of an early low-barrier pipeline into data analytics studies and careers for diverse students. Students first take a foundational course, typically Introduction to Data Mathematics, that teaches linear algebra, data analytics, and R programming simultaneously using a project-based learning (PBL) approach. Then in DAR, students work in teams on open applied data analytics research problems provided by the clients. We describe the DAR organization which is inspired in part by agile software development practices. Students meet for coaching sessions with instructors multiple times a week and present to clients frequently. In a fully remote format during the pandemic, the students continued to be highly successful and engaged in COVID-19 research producing significant results as indicated by deployed online applications, refereed papers, and conference presentations. Formal evaluation shows that the pipeline of the single on-ramp course followed by DAR addressing real-world problems with societal benefits is highly effective at developing students\u27 data analytics skills, advancing creative problem solvers who can work both independently and in teams, and attracting students to further studies and careers in data science

Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission

Design and Evaluation of User-Centered Explanations for Machine Learning Model Predictions in Healthcare

Challenges in interpreting some high-performing models present complications in applying machine learning (ML) techniques to healthcare problems. Recently, there has been rapid growth in research on model interpretability; however, approaches to explaining complex ML models are rarely informed by end-user needs and user evaluations of model interpretability are lacking, especially in healthcare. This makes it challenging to determine what explanation approaches might enable providers to understand model predictions in a comprehensible and useful way. Therefore, I aimed to utilize clinician perspectives to inform the design of explanations for ML-based prediction tools and improve the adoption of these systems in practice. In this dissertation, I proposed a new theoretical framework for designing user-centered explanations for ML-based systems. I then utilized the framework to propose explanation designs for predictions from a pediatric in-hospital mortality risk model. I conducted focus groups with healthcare providers to obtain feedback on the proposed designs, which was used to inform the design of a user-centered explanation. The user-centered explanation was evaluated in a laboratory study to assess its effect on healthcare provider perceptions of the model and decision-making processes. The results demonstrated that the user-centered explanation design improved provider perceptions of utilizing the predictive model in practice, but exhibited no significant effect on provider accuracy, confidence, or efficiency in making decisions. Limitations of the evaluation study design, including a small sample size, may have affected the ability to detect an impact on decision-making. Nonetheless, the predictive model with the user-centered explanation was positively received by healthcare providers, and demonstrated a viable approach to explaining ML model predictions in healthcare. Future work is required to address the limitations of this study and further explore the potential benefits of user-centered explanation designs for predictive models in healthcare. This work contributes a new theoretical framework for user-centered explanation design for ML-based systems that is generalizable outside the domain of healthcare. Moreover, the work provides meaningful insights into the role of model interpretability and explanation in healthcare while advancing the discussion on how to effectively communicate ML model information to healthcare providers

Defense in Depth of Resource-Constrained Devices

The emergent next generation of computing, the so-called Internet of Things (IoT), presents significant challenges to security, privacy, and trust. The devices commonly used in IoT scenarios are often resource-constrained with reduced computational strength, limited power consumption, and stringent availability requirements. Additionally, at least in the consumer arena, time-to-market is often prioritized at the expense of quality assurance and security. An initial lack of standards has compounded the problems arising from this rapid development. However, the explosive growth in the number and types of IoT devices has now created a multitude of competing standards and technology silos resulting in a highly fragmented threat model. Tens of billions of these devices have been deployed in consumers\u27 homes and industrial settings. From smart toasters and personal health monitors to industrial controls in energy delivery networks, these devices wield significant influence on our daily lives. They are privy to highly sensitive, often personal data and responsible for real-world, security-critical, physical processes. As such, these internet-connected things are highly valuable and vulnerable targets for exploitation. Current security measures, such as reactionary policies and ad hoc patching, are not adequate at this scale. This thesis presents a multi-layered, defense in depth, approach to preventing and mitigating a myriad of vulnerabilities associated with the above challenges. To secure the pre-boot environment, we demonstrate a hardware-based secure boot process for devices lacking secure memory. We introduce a novel implementation of remote attestation backed by blockchain technologies to address hardware and software integrity concerns for the long-running, unsupervised, and rarely patched systems found in industrial IoT settings. Moving into the software layer, we present a unique method of intraprocess memory isolation as a barrier to several prevalent classes of software vulnerabilities. Finally, we exhibit work on network analysis and intrusion detection for the low-power, low-latency, and low-bandwidth wireless networks common to IoT applications. By targeting these areas of the hardware-software stack, we seek to establish a trustworthy system that extends from power-on through application runtime

"Tricky to get your head around": Information work of people managing chronic kidney disease in the UK

People diagnosed with a chronic health condition have many information needs which healthcare providers, patient groups, and resource designers seek to support. However, as a disease progresses, knowing when, how, and for what purposes patients want to interact with and construct personal meaning from health-related information is still unclear. This paper presents findings regarding the information work of chronic kidney disease patients. We conducted semi-structured interviews with 13 patients and 6 clinicians, and observations at 9 patient group events. We used the stages of the information journey – recognizing need, seeking, interpreting, and using information – to frame our data analysis. We identified two distinct but often overlapping information work phases, ‘Learning’ and ‘Living With’ a chronic condition to show how patient information work activities shift over time. We also describe social and individual factors influencing information work, and discuss technology design opportunities including customized education and collaboration tools

A time for change: improving occupational performance for clients with stress-related conditions using biofeedback and self- regulation to enhance function in adult roles

Traditional health care systems have a long history of marginalizing clients experiencing stress-related conditions with complex symptoms. Stress symptoms fall into the gap between physical or psychological diagnoses from providers who cannot identify their etiology and generate effective treatment plans. Whether stress is a predisposing factor contributing to a health condition or the sequelae of a health event, failure to address its impact on clients’ overall physical, emotional, and cognitive abilities contributes to their decreased function and quality of life. Occupational therapists (OTs) often treat individuals with stress- and anxiety-related conditions. Unfortunately, few studies provide measurable, objective data indicating improvement from evidence-based interventions for this population. Biofeedback and self-regulation, also called psychophysiology, can provide objective data in the form of feedback (e.g., lights, sounds, numbers, or graphs) to demonstrate individuals’ real-time status, improving their self-regulation and sense of control over their stress response. These techniques are used with the model of human occupation (MOHO), a client-centered framework that pairs well with integrative approaches such as biofeedback. The proposed “A Time for Change: Improving Occupational Performance for Clients With Stress-Related Conditions Using Biofeedback and Self-Regulation to Enhance Function in Adult Roles” program is an online education program for OTs using self-regulation and two types of biofeedback (temperature and heart rate variability) as part of an integrative treatment protocol for clients with stress-related conditions. The program includes the Functional Continuum Questionnaire (based on the MOHO), a program protocol, recommendations for a companion workbook, and a postimplementation evaluation plan

Detecting Stressful Social Interactions Using Wearable Physiological and Inertial Sensors

Stress is unavoidable in everyday life which can result in several health related short and long-term adverse consequences. Previous research found that most of the stress events occur due to interpersonal tension followed by work related stress. Enabling automated detection of stressful social interactions using wearable technology will help trigger just-in-time interventions which can help the user cope with the stressful situation. In this dissertation, we show the feasibility of differentiating stressful social interactions from other stressors i.e., work and commute.However, collecting reliable ground truth stressor data in the natural environment is challenging. This dissertation addresses this challenge by designing a Day Reconstruction Method (DRM) based contextual stress visualization that highlights the continuous stress inferences from a wearable sensor with surrounding activities such as conversation, physical activity, and location on a timeline diagram. This dissertation proposes a Conditional Random Field, Context-Free Grammar (CRF-CFG) model to detect conversation from breathing patterns to support the visualization. The advantage of breathing signal is that it does not capture the content of the conversation and hence, is more privacy preserving compared to audio. It proposes a framework to systematically analyze the breathing data collected in the natural environment. However, it requires wearing of chest worn sensor. This dissertation aims to determine stressful social interaction without wearing chest worn sensor or without requiring any conversation model which is privacy sensitive. Therefore, it focuses on detecting stressful social interactions directly from stress time-series only which can be captured using increasingly available wrist worn sensor.This dissertation proposes a framework to systematically analyze the respiration data collected in the natural environment. The analysis includes screening the low-quality data, segmenting the respiration time-series by cycles, and develop time-domain features. It proposes a Conditional Random Field, Context-Free Grammar (CRF-CFG) model to detect conversation episodes from breathing patterns. This system is validated against audio ground-truth in the field with an accuracy of 71.7\%.This dissertation introduces the stress cycle concept to capture the cyclical patterns and identifies novel features from stress time-series data. Furthermore, wrist-worn accelerometry data shows that hand gestures have a distinct pattern during stressful social interactions. The model presented in this dissertation augments accelerometry patterns with the stress cycle patterns for more accurate detection. Finally, the model is trained and validated using data collected from 38 participants in free-living conditions. The model can detect the stressful interactions with an F1-score of 0.83 using stress cycle features and enable the delivery of stress intervention within 3.9 minutes since the onset of a stressful social interaction

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged