Motivating students to engage with a course, encouraging positive behavior, and inspiring them to take an active role in their educational process - particularly at the beginning of the course - are universal challenges in education. In this article, we share our experience implementing an early submission incentive policy in a Machine Organization and Assembly Language Programming course. This policy encourages students to complete and submit their weekly lab work early in exchange for bonus points. We examine the impact of this positive behavior reinforcement on overall student performance (final grade) and performance in specific components such as exams and programming assignments. Our results, based on data collected over four years and involving more than 1,400 students, indicate that students who participate in early submissions achieve a higher final grade and perform better on other assessments, such as programming assignments.

The ubiquitous use of online tools, contractors and homework sites, has made plagiarism a concerning topic in computer science education. With the introduction of ChatGPT, it poses a threat now more than ever. Many cheating detection tools, such as similarity checkers and style anomaly checkers, help instructors decide whether a student has plagiarized. However, these are not scalable to large classes. Similarity tools can produce high rates of suspected cheating and thus ineffectively use an instructor's time in weeding out the actual cheating cases, especially in the early weeks of CS courses where programs can be small and student solutions can be very similar. We developed a new approach using outlier detection to filter inconsistent performers based on their lab scores throughout the course and their midterm exam scores. Instructors can then manually analyze a manageable amount of students even with large class sizes. We performed our experiment on two large course offerings of CS1 (a total of 177 students) using our algorithm and compared it to a manual analysis performed by an experienced CS1 instructor. The detection approach identified 11 students in the first offering (Winter 2019) and 12 students in the second offering (Spring 2023). With an average precision of 83%, our tool produces a list of concerning students with high precision. This significantly helps teachers efficiently allocate their time and pursue cheating early in the term in order to address and prevent further issues.

Capstone or senior design courses are a key feature of most Computer Science programs as they provide students with authentic project experiences. Capstone courses usually focus on technical skill development, but we recognize that students will greatly benefit from developing communication skills. Within Computer Science, mastering such skills is essential for explaining and documenting system design, tackling development as a team, or leading large projects. Additionally, computer scientists need to be able to communicate their expertise to outside audiences such as funders, policymakers, customers, etc. However, studies have shown that students lack experience-based training to communicate functionally and practically. In this paper, we describe our experience with a targeted intervention within a capstone course in Computer Science. During the course, students complete a senior project while receiving training on team and project management, public speaking, and leadership. We evaluated the impact of the intervention on improving students' speaking skills and confidence by deploying surveys before and after the intervention. We observed a significant improvement in confidence and mastery of technical and non-technical oral communication skills. We also highlight how this training was particularly beneficial for underrepresented students within the discipline.

With the recent surge in interest in Computer Science, many challenges have emerged, ranging from faculty turnover to providing adequate resources and an inclusive environment for students throughout their academic journey. As part of our ongoing efforts, we introduce the concept of "Undergraduate Learning Assistants" (ULAs) - undergraduates carefully selected and trained by our department to serve as peer tutors for core Computer Science courses, such as CS1, CS2, and CS3. This program, driven not only by technical considerations but also by the goal of fostering a more inclusive and accessible learning community, seeks to enhance students' sense of belonging and encourage undergraduates to explore potential teaching roles. In this article, we share our experiences and present our assessments of this program.

Machine learning (ML), and deep learning in particular, have become a critical workload as they are becoming increasingly applied at the core of a wide range of application spaces. Computer systems, from the architecture up, have been impacted by ML in two primary directions: (1) ML is an increasingly important computing workload, with new accelerators and systems targeted to support both training and inference at scale; and (2) ML supporting computer system decisions, both during design and run times, with new machine learning based algorithms controlling systems to optimize their performance, reliability and robustness. In this paper, we will explore the intersection of security, ML and computing systems, identifying both security challenges and opportunities. Machine learning systems are vulnerable to new attacks including adversarial attacks crafted to fool a classifier to the attacker's advantage, membership inference attacks attempting to compromise the privacy of the training data, and model extraction attacks seeking to recover the hyperparameters of a (secret) model. Architecture can be a target of these attacks when supporting ML (or is supported by ML), but also provides an opportunity to develop defenses against them, which we will illustrate with three examples from our recent work. First, we show how ML based hardware malware detectors can be attacked with adversarial perturbations to the Malware and how we can develop detectors that resist these attacks. Second, we show an example of microarchitectural side channel attacks that can be used to extract the secret parameters of a neural network and potential defenses against it. Finally, we discuss how hardware and systems can be used to make ML more robust against adversarial and other attacks.

Spectre attacks and their many subsequent variants are a new vulnerability class affecting modern CPUs. The attacks rely on the ability to misguide speculative execution, generally by exploiting the branch prediction structures, to execute a vulnerable code sequence speculatively. In this paper, we propose to use Control-Flow Integrity (CFI), a security technique used to stop control-flow hijacking attacks, on the committed path, to prevent speculative control-flow from being hijacked to launch the most dangerous variants of the Spectre attacks (Spectre-BTB and Spectre-RSB). Specifically, CFI attempts to constrain the possible targets of an indirect branch to a set of legal targets defined by a pre-calculated control-flow graph (CFG). As CFI is being adopted by commodity software (e.g., Windows and Android) and commodity hardware (e.g., Intel's CET and ARM's BTI), the CFI information becomes readily available through the hardware CFI extensions. With the CFI information, we apply CFI principles to also constrain illegal control-flow during speculative execution. Specifically, our proposed defense, SpecCFI, ensures that control flow instructions target legal destinations to constrain dangerous speculation on forward control-flow paths (indirect calls and branches). We augment this protection with a precise speculation-aware hardware stack to constrain speculation on backward control-flow edges (returns). We combine this solution with existing solutions against branch target predictor attacks (Spectre-PHT) to close all known non-vendor-specific Spectre vulnerabilities. We show that SpecCFI results in small overheads both in terms of performance and additional hardware complexity.