Public interest in ranked choice voting mechanisms such as Single Transferable Vote (STV) has increased in recent years, as a potentially more equitable approach, leading to its adoption for political elections in various cities, states, and countries. A recently-studied measure of interest is metric distortion, which captures how much worse an elected candidate is from the socially optimal choice. Building upon the known lower bound of 3 on the metric distortion of any deterministic voting rule shown by Anshelevich et al. [3], and the known upper bound of 15 on the metric distortion of STV on the line by Anagnostides et al. [2], we improve the gap between these bounds by providing an upper bound of 11 on the metric distortion of STV on the line. In addition, we consider the impact of voter turnout on elections, giving a lower bound on the metric distortion of STV on the line that is a function of the voter turnout percentage; in particular, 50

We study a variant of the offline Dial-a-Ride problem, where each request has a source and destination and the goal is to maximize the number of requests served within a specified time limit. We investigate this problem for the uniform metric space and show that the problem is NP-hard. We then present a 2/3 approximation algorithm called twochain , which simply looks for pairs of requests that are “chained” together and serves those before serving requests that are not connected to any others. We also show that a natural generalization of this algorithm, k-chain, has an approximation ratio at most 7/9. We also analyze the longest-chain-first algorithm for the problem, characterizing graphs on which it is optimal, and showing that it has an approximation ratio no better than 5/6. Our experiments on all of these algorithms show that twochain is a promising algorithm, performing nearly as well as more computationally intensive variants. We dedicate this article to the memory of Gerhard Woeginger, whose life and work greatly influenced our professional lives, as expanded upon in the Acknowledgments. Woeginger’s prolific research in scheduling, matching, bin-packing, TSP, and online algorithms in general, all served as important parts of the foundation on which our own scholarly pursuits were shaped and formed over the years. Woeginger also studied Dial-a-Ride (DARP) Problems, as DARP is a generalization both of scheduling problems and of TSP, which were two of his most active areas of research.

Course scheduling is a necessary but often challenging task, requiring consideration of university requirements, practical limitations, departmental needs, and faculty preferences. Furthermore, it can be difficult to determine if a proposed schedule satisfies all of the requirements, especially as changes may be made within the process. Clearly articulated schedule requirements can be translated into an integer linear program by operations researchers, and those with domain-specific knowledge can then use linear programming solvers like PyGLPK. However, few faculty members in a university are likely to have that experience, so we developed a web-based tool, including the underlying Python code, that allows for entry of information about courses and the faculty teaching them and creates a schedule consistent with the requirements. More than simply a scheduler, a user can propose changes such as swapping two courses or placing a course at a specific time; the website will either do so, or report that such a change is infeasible. In addition, a user can share the website and file with colleagues, allowing them to better appreciate what is and is not possible for the overall schedule. Recognizing that the precise set of scheduling requirements is often unique to a given university, the source code is made available in a public GitHub repository, providing opportunities for other universities to create a version customized to their needs and structure.

We consider a variant of the offline Dial-a-Ride problem on weighted graphs with a single server where each request has a source and destination. The server's goal is to serve requests so as to maximize the total number of requests served within a given time limit. We first prove that no polynomial-time algorithm will always serve the optimal number of requests, even when the algorithm's time limit is augmented by any factor c >= 1, unless P = NP. We also show that the approximation ratio is unbounded for a reasonable class of algorithms for this problem. We then present k-Sequence, an algorithm that repeatedly serves the fastest set of k remaining requests. We show that k-Sequence has approximation ratio at most 2 + [lambda]/k and at least 1 + lambda/k, where lambda denotes the aspect ratio of the graph, and that the ratio 1 + lambda/k is tight when 1 + lambda/k >= k. We also show that even as k grows beyond the size of lambda, the ratio never improves below 9/7.

We revisit the Segmented Best Path (sbp) algorithm for online DARP in an offline setting with revenues and a time limit. The goal is to find a subset of the inputted ride requests that can be served within the time limit while maximizing the total revenue earned. sbp divides the time into segments and greedily chooses the highest-revenue path of requests to serve within each time segment. We show that sbp's performance has an upper bound of 5. Further, while sbp is a tight 4-approximation in the uniform-revenue case, we find that with non-uniform revenues, the approximation ratio of sbp has a lower bound strictly greater than 4; in particular, we provide a lower bound of (root e + 1)/(root e - 1) approximate to 4.08299, which we show can be generalized to instances with ratio greater than 4.278.

A typical US college student who is asked to name people who have made significant contributions to computing will likely produce a short, predictable, fairly homogeneous list. To help increase awareness of the diversity of people involved in computing, we introduced a biography project into an introductory non-majors course at a small liberal arts college. With each student exploring and presenting on the life and contributions of a less well-known contributor to computing, the class is exposed to a number of people that students likely haven't heard of previously. Moreover, with fairly open-ended requirements for the assignment, we can learn what students find interesting and noteworthy about these individuals, perhaps finding ways to more broadly increase awareness.

Dial-a-Ride problems (DARP) require determining a schedule to efficiently serve transportation requests in various scenarios. We consider a variant of offline DARP in a uniform metric space where requests have release times and deadlines, and are all of equal duration and value. The goal is for a single unit-speed, unit-capacity server to serve as many requests as possible by an overall time limit, and this problem is NP-hard. We show that a natural greedy algorithm, Earliest Deadline First, is a 2-approximation, and this is tight.

As automated tools become commonplace for coordinating meeting times and other forms of decentralized cooperative decision-making, it is important to understand the behavior of people using those tools. Even when a tool or online platform is simply a form of approval voting, the specifics of the voting scenario need to be considered. Approval voting often assumes that voters are sincere, never voting yes to an option that is less desirable than one for which they have voted no. A small study suggested that the assumption of sincerity among users in cooperative polls should not be taken for granted. This work expands the study to a larger sample of college students at multiple institutions, showing that people responding to polls may aim to be sincere, but are influenced by various factors, including the nature of the meeting.

We use "unplugged" activities to introduce parallel concepts in a first-year seminar for Computer Science majors. Student teams explore parallel approaches to computational tasks. Pre- and post-activity surveys, and a reflection paper, measure the impact of these activities on students' views about parallel programming. Our goal is to encourage parallel thinking about programming tasks before sequential approaches become ingrained. Computer Science curricula have traditionally focused on sequential approaches to programming, which were well matched to earlier computer systems. However, current systems almost all use multiprocessor CPUs, and are frequently used in clusters or networks of multiple computers. Recent curricular guidelines from organizations such as ACM and ABET recommend exposure to parallel computing concepts.

Students can benefit greatly from working with real databases in their first Database course. A database for a university is a common textbook example, in part due to its familiarity, but privacy and other considerations typically preclude course access to this and many other large, meaningful databases. This paper reports on two semesters' experience using the University of California Santa Cruz Genome Browser [6] in a Database course, allowing mid-level computer science undergraduates to gain hands-on experience with a large real-world database. Anonymous survey feedback from students in both semesters was positive for both engagement and increased knowledge. The activity described within can easily be adopted by others, requires no software installation, and can be adapted to the desired length and difficulty level.

Online tools like Doodle polls are frequently used for meeting coordination and other decentralized cooperative decision-making. Since Doodle polls are a form of approval voting, theoretical results from voting theory often underpin work in this area. Sincerity, where a voter never says yes to a less-preferred option without saying yes to all more preferable choices, is a common assumption in approval voting. However, that does not take into account cooperative behavior sometimes exhibited by users when others’ responses are known. We conduct a user study investigating the extent to which college-student participants in Doodle-style polls were sincere, reporting on responses from one institution.

We consider a variant of the offline Dial-a-Ride problem with a single server where each request has a source, destination, and a prize earned for serving it. The goal for the server is to serve requests within a given time limit so as to maximize the total prize money. We consider the variant where prize amounts are uniform which is equivalent to maximizing the number of requests served. This setting is applicable when all rides may have equal importance such as paratransit services. We first prove that no polynomial-time algorithm can be guaranteed to serve the optimal number of requests, even when the time limit for the algorithm is augmented by any constant factor c ≥ 1 . We also show that if λ = t_max/t_min , where t_max and t_min denote the largest and smallest edge weights in the graph, the approximation ratio for a reasonable class of algorithms for this problem is unbounded, unless λ is bounded. We then show that the segmented best path ( sbp ) algorithm from [8] is a 4-approximation. We then present our main result, an algorithm, k-Sequence, that repeatedly serves the fastest set of k remaining requests, and provide upper and lower bounds on its performance. We show k-Sequence has approximation ratio at most 2+⌈λ⌉ /k and at least 1 + λ /k and that 1 + λ /k is tight when 1 + λ /k ≥ k . Thus, for the case of k=1 , i.e., when the algorithm repeatedly serves the quickest request, it has approximation ratio 1+λ , which is tight for all λ . We also show that even as k grows beyond the size of λ , the ratio never improves below 9/7.

We propose to strengthen the computer science (CS) curriculum by embedding parallel concepts in a required first-semester seminar taken by all incoming declared CS majors. We introduce students to parallel computing concepts through a series of unplugged activities so that students see parallel approaches as a natural form of solution to a task. We describe a pilot offering of the class and activities, with measurements and analysis of what students self-report and their performance on assessments.

Zagreb indices for undirected graphs were introduced nearly 50 years ago. Their original development was related to uses in chemistry, but over time mathematicians have also found them to be an interesting topic of study. We define and introduce Zagreb indices for directed graphs, give results that parallel many of the conjectures and theorems that exist for the original Zagreb indices, and produce results specific to the directed graph case.

We consider a variant of the offline Dial-a-Ride problem with a single server where each request has a source, destination, and a revenue earned for serving it. The goal for the server is to serve requests within a given time limit so as to maximize the total revenue. We consider the uniform-revenue variant (equivalent to maximizing the number of requests served), whose applications include paratransit services. We first show that no polynomial-time algorithm can be guaranteed to earn the optimal revenue, even when the time limit for the algorithm is augmented by any constant factor c ≥ 1. We present an algorithm, k-Sequence, that repeatedly serves the fastest set of k remaining requests, and provide upper and lower bounds on its performance. We show k-Sequence has approximation ratio at most (2 + dλe/k) and at least 1 + λ/k, where λ denotes the ratio between the maximum and minimum distances in the graph, and that 1 + λ/k is tight when 1 + λ/k ≥ k. Thus, for the case of k = 1, i.e., when the algorithm repeatedly serves the quickest request, it has approximation ratio 1+λ, which is tight for all λ. We also show that even as k grows beyond the size of λ, the ratio never improves below 9/7.

Doodle polls allow people to schedule meetings or events based on time preferences of participants. Each participant indicates on a web-based poll form which time slots they find acceptable and a time slot with the most votes is chosen. This is a social choice mechanism known as approval voting, in which a standard assumption is that all voters vote sincerely—no one votes “no” on a time slot they prefer to a time slot they have voted “yes” on. We take a game-theoretic approach to understanding what happens in approval voting assuming participants vote sincerely. While our instances are framed in the context of the Doodle poll application, the results apply more broadly to approval voting. First we characterize Doodle poll instances where sincere pure Nash Equilibria (NE) exist, under lexicographic tie-breaking, random candidate, and random voter tie-breaking. We then study the quality of such NE voting profiles in Doodle polls, showing the price of anarchy and price of stability are both unbounded, even when a time slot that many participants vote yes for is selected. Finally, we find some reasonable conditions under which the quality of the NE (and strong NE) is good.

The undergraduate computer science curriculum is ever-changing but has seen particular turmoil recently. Topics such as machine learning, data science, and concurrency and parallelism have grown in importance over the last few years. As the content of our curriculum changes, so too does the mathematical foundations on which it rests. Do our current theoretical courses adequately support these foundations or must we consider new pedagogy that is more relevant to our students' needs? In this BoF, we will discuss what a modern mathematics curriculum for computer scientists should cover and how we should go about accomplishing this in our classrooms.

Doodle polls allow people to schedule meetings or events based on the time preferences of participants. Each participant indicates on a web-based poll form which time slots they find acceptable and a time slot with the most votes is chosen. This is a social choice mechanism known as approval voting, in which a standard assumption is that all voters vote sincerely ---no one votes no on a time slot they prefer to a time slot they have voted yes on. We take a game theoretical approach to understanding what happens in Doodle polls assuming participants vote sincerely. First we characterize Doodle poll instances where sincere pure Nash Equilibria (NE) exist, both under lexicographic tie-breaking and randomized tie-breaking. We then study the quality of such NE voting profiles in Doodle polls, showing that the price of anarchy and price of stability are both unbounded, even when a time slot that many participants vote yes for is selected. Finally, we give some conditions under which the quality of the NE (and strong NE) is good.

Doodle polls allow people to schedule meetings or events based on time preferences of participants. Each participant indicates on a web-based poll form which time slots they find acceptable and a time slot with the most votes is chosen. This is a social choice mechanism known as approval voting, in which a standard assumption is that all voters vote sincerely—no one votes "no" on a time slot they prefer to a time slot they have voted "yes" on. We take a game-theoretic approach to understanding what happens in Doodle polls assuming participants vote sincerely. First we characterize Doodle poll instances where sincere pure Nash Equilibria (NE) exist, both under lexicographic tie-breaking and randomized tie-breaking. We then study the quality of such NE voting profiles in Doodle polls, showing the price of anarchy and price of stability are both unbounded, even when a time slot that many participants vote yes for is selected. Finally, we find some reasonable conditions under which the quality of the NE (and strong NE) is good.

The Mythical Man-Month: Essays on Software Engineering [4] by Dr. Fred Brooks is often required reading in many software engineering classes and recommended by practitioners throughout industry as a text with which computer scientists should be familiar. Yet, while many of the conceptual ideas that were first presented decades ago are still applicable today, aspects of the presentation of the material are lacking in inclusivity, posing additional challenges for faculty who are trying to increase diversity within the discipline. Thus, we propose a student-developed portrayal of some of the concepts in a modernized fashion, presented as a menu and incorporating ideas from the food-service industry, a model that more college students are directly familiar with than the surgical team.