In 2021, the Computer Science and Engineering department at UC Riverside added a second pathway to the major with a new CS1 and CS2 course sequence. Our goal was to address disparities in course outcomes between different populations, particularly between those with and without prior coding experience and majors versus non-majors. The hope was that students new to computing would have a viable path to discover whether they had interest and aptitude in computing without the added stress of being in a classroom with students who have prior coding experience. In this paper, we report the data analysis that led to our decision to add a second pathway, design choices, and challenges (particularly in university politics) in launching and adding a second pathway. We present the results over the last three years, which illustrate that the new series has leveled the playing field. Most importantly, the course outcome data and changes to computing major enrollment illustrate that we achieved our goal of attracting students new to computing and thus are broadening participation in computing.

With the rapid surge of generative AI, many tools have been introduced, such as Google's Gemini and OpenAI's GPT-4, with the well-intentioned goal of supporting programmers [5,8]. These tools can be used by professional programmers to help write code efficiently as well as support debugging and testing; however, we recently began to notice an increase in the number of novice programmers who have become highly dependent on Large Language Models (LLMs) to code for them rather than using LLMs as a learning tool [2,10]. In our CS1 course, approximately 10-15% of the students (out of ~350) were cited for academic misconduct due to direct plagiarism from LLMs, many of which performed poorly due to an over-reliance on generative AI.

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.

Student cheating on at-home programming assignments is a well- known problem. A key contributor is externally-obtained solutions from websites, contractors, and recently generative AI. In our experience, such externally-obtained solutions often use coding styles that depart from a class' style, which we call "style anomalies," such as using untaught or advanced constructs like pointers or ternary operators, or having different indenting or brace usage from the class style. We developed a tool to auto-count style anomalies. For six labs across four terms in 2021-2022, and 50 sampled students per lab, we found 18% of submissions on average had unusually-high style anomaly counts. Importantly, 8% of submissions on average had a high style anomaly count but were not flagged by a similarity checker, meaning 8% of submissions are suspicious but might have been missed if using similarity checking alone. We repeated a similar analysis for Spring 2023 when generative AI (ChatGPT) was gaining popularity, and the numbers rose to 26% and 18%, respectively. Detailed investigations by instructors led to a majority (but not all) high style anomaly submissions being deemed cheating. Even for high-similarity submissions, counting style anomalies can help instructors focus investigations on the most-likely cheating cases, and can strengthen cases sent to student conduct offices. With the rise of externally-obtained solutions from websites, contractors, and generative AI, counting style anomalies may become an increasingly important complement to similarity checking; in fact, it is now the primary cheat-detection tool in our CS1 at a large state university, with similarity secondary.

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.

Riders of public transportation in recent years tend to expect the intelligence of transit systems to progress in tandem with modern technology, with a key factor of this being an improved on-time prediction and scheduling of bus systems. This work explores various regression algorithms, including Support Vector Regression and Linear Regression, along with evaluation of loss functions and regulation terms to address the bus arrival time problem. The experiment is a case study of Route 280 from the Foothill Transit Agency, and resulted in Huber Regression with ElasticNet penalty giving the most accurate prediction.

This paper describes a newly developed Data Science major that is jointly offered between two departments (Computer Science and Statistics) and across two colleges (College of Engineering and College of Natural and Architectural Sciences). The paper explores the inspiration and motivation for creating the undergraduate Data Science program, the program curriculum, and the governing body of the program. We aim to recruit undergraduates, specifically women, into the new major which are underrepresented in Computer Science at our University. We expect that this Major in Data Science will serve as a general framework for establishing similar interdisciplinary programs.

Just about everyone in the U.S., from the National Science Foundation down to local districts, has been pushing to introduce computer science concepts into K-12. Nevertheless, many students complete high school never having the chance to learn CS. We have created a summer coding camp for high-school students (including 8th graders entering 9th grade) and designed a multi-year study to assess its effectiveness as an informal learning environment, based on theories of human motivation such as Self-Determination Theory. The camp is a 1-week immersion experience, 9am to 5pm with food and activities, that introduces basic programming via MIT APP Inventor. Lecture material and in-class exercises draw upon meaningful applications, ones appealing to "social good." One unique aspect is the inclusion of professional and career development activities that engage students and broaden perspectives on CS and its applications. For example, the camp includes a college information session, alumni Skype and in-person talks, off-site visits to nearby companies, and research talks and demos by faculty. Using a pre-and-post survey design, the current study examines the effects of the camp on student self-efficacy and interest in computing, as well as general school engagement and motivation. Results confirm that participation in the summer camp increased students' self-efficacy and interest in computing, enhanced engagement in school on topics in general, and strengthened intrinsic motivation for completing schoolwork. The effects were similar for boys and girls.

This poster presents a one-quarter seminar required of first-time and re-turning teaching assistants in the Computer Science and Engineering department at UC Riverside. The seminar covers departmental expectations, introduces TAs to methods for engaging large and diverse classrooms, and covers pedagogical practices in Computer Science. In addition to coverage of general issues surrounding teaching, the seminar includes many exercises that relate specifically to the teaching of computer science. Returning TAs serve as mentors for first-time TAs and participate in peer-observation and other activities. Initial results show a significant improvement in TA evaluations over the past year since the revamping of this seminar course.Moreover, TAs self-report an increased confidence in their teaching ability, communicating with students as well as motivating students.

Estimating object count from images is a difficult problem that has a wide range of applications. In this work, we examine the object counting problem for images from the Amazon Bin Images Dataset. This task is riddled with many challenges, including occasional low image quality, object occlusions, and diversity in objects. This work explores a deep-learning approach using a CNN architecture for this object counting problem. Our solution combines end-to-end training on ResNet with test time augmentation, achieving promising results for this difficult task.

Publish-subscribe systems present the state of the art in information dissemination to multiple users. Such systems have evolved from simple topic-based to the current XML-based systems. XML-based pub-sub systems provide users with more flexibility by allowing the formulation of complex queries on the content as well as the structure of the streaming messages. Messages that match a given user query are forwarded to the user. This article examines how to exploit the parallelism found in XPath filtering. Using an incoming XML stream, parsing and matching thousands of user profiles are performed simultaneously by matching engines. We show the benefits and trade-offs of mapping the proposed filtering approach onto FPGAs, processing streams of XML at wire speed, and GPUs, providing the flexibility of software. This is in contrast to conventional approaches bound by the sequential aspect of software computing, associated with a large memory footprint. By converting XPath expressions into custom stacks, our solution is the first to provide support for complex XPath structural constructs, such as parent-child and ancestor descendant relations, whilst allowing wildcarding and recursion. The measured speedups resulting from the GPU and FPGA accelerations versus single-core CPUs are up to 6.6X and 2.5 orders of magnitude, respectively. The FPGA approaches are up to 31X faster than software running on 12 CPU cores.

Data integration systems offer a uniform interface for querying a large number of autonomous and heterogeneous data sources. Ideally, answers are returned as sources are queried and the answer list is updated as more answers arrive. Choosing a good ordering in which the sources are queried is critical for increasing the rate at which answers are returned. However, this problem is challenging since we often do not have complete or precise statistics of the sources, such as their coverage and overlap. It is further exacerbated in the Big Data era, which is witnessing two trends in Deep-Web data: first, obtaining a full coverage of data in a particular domain often requires extracting data from thousands of sources; second, there is often a big variation in overlap between different data sources. In this paper we present OASIS , an O nline query A nswering S ystem for overlapp I ng Sources. OASIS has three key components for source ordering. First, the Overlap Estimation component estimates overlaps between sources according to available statistics under the Maximum Entropy principle. Second, the Source Ordering component orders the sources according to the new contribution they are expected to provide, and adjusts the ordering based on statistics collected during query answering. Third, the Statistics Enrichment component selects critical missing statistics to enrich at runtime. Experimental results on both real and synthetic data show high efficiency and scalability of our algorithm.

In recent years, XML-based Publish-Subscribe Systems have become popular due to the increased demand of timely event-notification. Users (or subscribers) pose complex profiles on the structure and content of the published messages. If a profile matches the message, the message is forwarded to the interested subscriber. As the amount of published content continues to grow, current software-based systems will not scale. We thus propose a novel architecture to exploit parallelism of twig matching on FPGAs. This approach yields up to three orders of magnitude higher throughput when compared to conventional approaches bound by the sequential aspect of software computing. This paper, presents a novel method for performing unordered holistic twig matching on FPGAs without any false positives, and whose throughput is independent of the complexity of the user queries or the characteristics of the input XML stream. Furthermore, we present experimental comparison of different granularities of twig matching, namely path-based (root-to-leaf) and pair-based (parent-child or ancestor-descendant).We provide comprehensive experiments that compare the throughput, area utilization and the accuracy of matching (percent of false positives) of our holistic, path-based and pair-based FPGA approaches.