While there has been exciting recent progress in developing curricula for data education, more work is needed to establish connection points between data science, computer science, and other disciplines. This position paper argues for a broader, more all-encompassing perspective on data education to ensure opportunities are not missed. Our primary contribution is a comprehensive framework to visualize the data education landscape with the goal of improving understanding of how the various data education disciplines, work roles, core competencies, and skills fit together. Students and educators could benefit from such a framework, and all constituents of data education might better communicate requirements and more effectively make use of data and the data workforce.

Existing approaches to computing-based cyber undergraduate majors typically take one of two forms: a broad exploration of both technical and human aspects, or a deep technical exploration of a single discipline relevant to cybersecurity. This paper describes the creation of a third approach--a multifarious major, consistent with Cybersecurity Curricula 2017, the ABET Cybersecurity Program Criteria, and the National Security Agency Center for Academic Excellence--Cyber Operations criteria. Our novel curriculum relies on a 10-course common foundation extended by one of five possible concentrations, each of which is delivered through a disciplinary lens and specialized into a highly relevant computing interest area serving society's diverse cyber needs. The journey began years ago when we infused cybersecurity education throughout our programs, seeking to keep offerings and extracurricular activities relevant in society's increasingly complex relationship with cyberspace. This paper details the overarching design principles, decision-making process, benchmarking, and feedback elicitation activities. A surprising key step was merging several curricula proposals into a single hybrid option. The new major attracted a strong initial cohort, meeting our enrollment goals and exceeding our diversity goals. We provide several recommendations for any institution embarking on a process of designing a new cyber-named major.

Prompted by the skyrocketing demand for data scientists, progress made by the ACM Data Science Task Force on defining data science competencies, and inquiries about data science accreditation, ABET is in the process of developing accreditation criteria for undergraduate data science programs. The effort is led by members of a joint data science criteria subcommittee appointed by ABET's Computing Accreditation Commission (CAC) and CSAB (the lead society for computing accreditation). Establishing data science accreditation criteria is a notable milestone in the maturing data science discipline, indicating the presence of an accepted body of knowledge, standards of practice, and ethical codes for practitioners. This position paper motivates the effort and discusses prior work towards defining data science education requirements. It describes the ongoing process for creating and obtaining approval of the accreditation criteria, and how feedback was and will be solicited from the computing and statistical communities. The current draft data science criteria, which was approved in July 2020 by the relevant ABET bodies for a year of public review and comment, is presented. These criteria emphasize the three pillars of data science: computing foundations, mathematical/statistical foundations, and experience in at least one data application domain. This report thus serves both to inform and to stimulate the academic discussion needed to finalize appropriate data science accreditation by ABET.

Because edge modification problems are computationally difficult for most target graph classes, considerable attention has been devoted to inclusion-minimal edge modifications, which are usually polynomial-time computable and which can serve as an approximation of minimum cardinality edge modifications, albeit with no guarantee on the cardinality of the resulting modification set. Over the past fifteen years, the primary design approach used for inclusion-minimal edge modification algorithms is based on a specific incremental scheme. Unfortunately, nothing guarantees that the set E of edge modifications of a graph G that can be obtained in this specific way spans all the inclusion-minimal edge modifications of G. Here, we focus on edge modification problems into the class of chordal graphs and we show that for this the set E may not even contain any solution of minimum size and may not even contain a solution close to the minimum; in fact, we show that it may not contain a solution better than within an Ω(n) factor of the minimum. These results show strong limitations on the use of the current favored algorithmic approach to inclusion-minimal edge modification in heuristics for computing a minimum cardinality edge modification. They suggest that further developments might be better using other approaches.

In recent years, all computing disciplinary communities and curricular guidelines have increased their expectations of and requirements for incorporating cybersecurity into their discipline. For computer science, this has been a daunting task for a number of reasons, including the fast-paced evolution and expansion of the discipline, the perceived challenge of finding space in the curriculum, and the difficulty of selecting the best content. This paper takes the position that infusing security concepts pervasively into an undergraduate Computer Science program is a crucial and attainable best practice. A five-step methodology is presented to incorporate cybersecurity into a traditional computer science curriculum in a way that maintains disciplinary integrity without adding significant new curricular content. This methodology is consistent with the philosophy and recommendations of the latest computer science and cybersecurity curricular guidelines. The paper also illustrates the application of these techniques to a typical Computer Science program.

Many proposed self-stabilizing algorithms require an exponential number of moves before stabilizing on a global solution, including some rooting algorithms for tree networks [1, 2, 3]. These results are vastly improved upon in [6] with tree rooting algorithms that require only O(n/sup 3/ + n/sup 2//spl middot/c/sub h/) moves, where n is the number of nodes in the network and c/sub h/ is the highest initial value of a variable. In the current paper, we describe a new set of tree rooting algorithms that brings the complexity down to O(n/sup 2/) moves. This not only reduces the first term by an order of magnitude, but also reduces the second term by an unbounded factor We further show a generic mapping that can be used to instantiate an efficient self-stabilizing tree algorithm from any traditional sequential tree algorithm that makes a single bottom-up pass through a rooted tree. The new generic mapping improves on the complexity of the technique presented in [8].

Because students have different learning styles, it's important to incorporate multiple teaching techniques into the classroom experience. One such technique is the use of puzzles and games in the classroom to reinforce the learning objectives. Many topics in Computer Science are well suited for coverage in such a game. Several in-class puzzles and games have been used in the Computer Science program at this institution in recent years. In basic and advanced courses, simple crossword puzzles reinforce terminology and Jeopardy!®-style games help students master material with short answers. In the most recent iteration of the Operating Systems course, a BattleThreads game and a Process State Transition game helped students appreciate different approaches to process and thread management. The latter two games have been assessed for their effectiveness, providing several insights into what makes a good in-class game for teaching operating systems concepts, and how the existing games can be improved.

For an arbitrary filled graph G+ of a given original graph G, we consider the problem of removing fill edges from G+ in order to obtain a graph M that is both a minimal filled graph of G and a subgraph of G+. For G+ with f fill edges and e original edges, we give a simple O(f(e+f)) algorithm which solves the problem and computes a corresponding minimal elimination ordering of G. We report on experiments with an implementation of our algorithm, where we test graphs G corresponding to some real sparse matrix applications and apply well-known and widely used ordering heuristics to find G+. Our findings show the amount of fill that is commonly removed by a minimalization for each of these heuristics, and also indicate that the runtime of our algorithm on these practical graphs is better than the presented worst-case bound.

Games with imperfect information are an interesting and important class of games. They include most card games (e.g., bridge and poker) as well as many economic and political models. Here we investigate algorithms for findi ng the simplest form of a solution (a pure‐strategy equilibrium point) to imperfect information games expressed in their extensive (game tree) form. We introduce to the artificial intelligence community a classic algorithm, due to Wilson, that solves one‐player games with perfect recall. Wilson's algorithm, which we call iMP‐minimax, runs in time linear in the size of the game‐tree searched. In contrast to Wilson's result, Koller and Meggido have shown that finding a pure‐strategy equilibrium point in one‐player games without perfect recall is NP‐hard. Here, we provide another contrast to Wilson's result–we show that in games with perfect recall but more than one player, finding a pure‐strategy equilibrium point, given that such an equilibrium point exists, is NP‐hard. Our second contribution is to present a pruning technique for Wilson's IMP‐minimax algorithm to make the latter more tractable. We call this new algorithm IMP‐alpha‐beta. We provide a theoretical framework (model) and analyze IMP‐alpha‐beta in that model. IMP‐alpha‐beta is of direct value for one‐player, perfect‐recall games. It also has strong potential for other imperfect information games, as it is a natural (but as yet untested) heuristic in those cases.

Much of the recent work on the automated design of VLSI chips has concentrated on routing problems associated with such designs. One major class of routing problems focuses on single-row routing. Recently, the traditional single-row routing model has been generalized to allow external wires. Under this generalized model, it is possible to route many more single-row routing instances than in the traditional model. There is, however, a clear disadvantage in the use of external wires, since they force a lengthening of the channels surrounding the single row of terminals. Thus, it is desirable for these generalized single-row routings to use a minimum number of external wires. A linear-time algorithm for determining the minimum number of external wires needed to route a given instance of single-row routing is provided here.>