In the current socio-political climate, computing educators are facing increasing challenges, including threats to academic freedom, reduced funding for higher education, and direct attacks on initiatives supporting diversity, equity, and inclusion. This special session offers an inclusive forum for collective reflection on these pressures. Through facilitated discussion, participants will explore emerging patterns, share practical strategies for navigating institutional and policy constraints, and surface both shared concerns and context specific differences. The session aims to strengthen community connections and support educators in sustaining inclusive, high quality computing education amid ongoing uncertainty.

Students learning to program benefit from access to large numbers of practice problems. Autograders are commonly used to support programming questions by providing quick feedback on submissions. But authoring accurate autograders remains challenging. Autograders are frequently created by enumerating test cases--a tedious process that can produce inaccurate autograders that fail to correctly classify submissions. When authoring accurate autograders is slow, it is difficult to create large banks of practice problems to support beginning programmers. We present solution-generated autograding: a faster, more accurate, and more enjoyable way to create autograders. Our approach leverages a key difference between software testing and autograding: The question author can provide a solution. By starting with a solution, we can eliminate the need to manually enumerate test cases, validate the autograder's accuracy, and evaluate other aspects of submission code quality beyond behavioral correctness. We describe Questioner, an implementation of solution-generated autograding for Java and Kotlin, and share experiences from four years using Questioner to support a large CS1 course: authoring nearly 800 programming questions used by thousands of students to evaluate millions of submissions.

Prior research has established the importance of student instructor preferences and identified various influencing factors. However, the dynamics of how student instructor preferences develop and change are less well understood, due to the limitations of common course structures and reliance on one-time measurements. To bridge this gap, we utilize data from a novel learning platform that provides students with access to instructional content created by multiple instructors. This platform enables the quantification of preference emergence and evolution throughout an entire semester, as students repeatedly select content from different instructors. Examining both initial and final student instructor preferences suggests that preference is a dynamic construct continually shaped by experiences. Furthermore, our analysis of the associations between preferences and student characteristics reveals a nuanced picture: while student attributes did not significantly correlate with initial preferences, substantial differences emerged in final preferences across genders and self-reported prior programming experience. This analysis contributes to the existing body of knowledge by expanding our understanding of student instructor preferences and student-instructor relationships in computer science education. We also provide practical insights that institutions and instructors can draw on when multiple instructors collaborate on a course.

As teaching-focused positions proliferate and university teaching careers become more professionalized, there is growing attention being paid to how teaching faculty are created. However, how teaching faculty are hired also deserves scrutiny.

Office hour utilization in computer science courses can spike near deadlines, producing long wait times, frustrated students, and over-worked staff. To address this problem, a large CS1 course implemented a split deadlines policy. Students were randomly divided into two groups with staggered release and due dates. Each group had the same amount of time to complete assignments, but the number of students with each due date was reduced by half. Our study evaluates the effectiveness of this policy. We measure office hour utilization and staff efficiency near deadlines, examine the policy's impact on student performance, and investigate student perception of the policy's fairness and effectiveness. Overall we found that the split deadline policy increased office hour efficiency, resulted in no significant difference in performance between groups, and was considered fair and effective by most students. Our experience report includes reflections and student feedback indicating how to implement and further improve similar policies.

Heat dissipation and battery life continue to be major challenges for smartphones. Smartphones seldom spend time at their highest performance points due to thermal concerns and frequently undergo thermal-throttling, where performance is limited while the device cools down. While overclocking and computational sprinting can be used to increase system performance, these techniques have not been evaluated on smartphones because they exacerbate both heat dissipation and battery life. In recent years, certain machine-learning workloads such as object detection and speech recognition have been moving away from the cloud and towards the edge. These workloads are short and user-facing making them excellent candidates for sprinting. To successfully overclock any workload however, any applied technique must ensure that it avoids forcing the system to throttle. In this paper, we describe and evaluate a system that can accurately predict the impact of workloads on the thermal state of a smartphone, enabling it to determine whether overclocking a specific workload will result in thermal-throttling. We show that careful application of overclocking using our system can decrease the latency of certain user-facing workloads by up to 18%. In this paper, we describe and evaluate a system that can accurately predict the impact of workloads on the thermal state of a smartphone, enabling it to determine whether overclocking a specific workload will result in thermal-throttling. We show that careful application of overclocking using our system can decrease the latency of certain user-facing workloads by up to 18%.

To defend against collaborative cheating in code writing questions, instructors of courses with online, asynchronous exams can use the strategy of question variants. These question variants are manually written questions to be selected at random during exam time to assess the same learning goal. In order to create these variants, currently the instructors have to rely on intuition to accomplish the competing goals of ensuring that variants are different enough to defend against collaborative cheating, and yet similar enough where students are assessed fairly. In this paper, we propose data-driven investigation into these variants. We apply our data-driven investigation into a dataset of three midterm exams from a large introductory programming course. Our results show that (1) observable inequalities of student performance exist between variants and (2) these differences are not just limited to score. Our results also show that the information gathered from our data-driven investigation can be used to provide recommendations for improving design of future variants.

Process variation can cause the performance and energy consumption of smartphones of the same model to vary significantly. While process variation has been studied in detail, the effects on smartphone performance have not been quantified and evaluated. In this work we study the performance and energy differences of 5 recent SoC generations caused by underlying process variation. We make two important contributions. First, we present a methodology to construct a temperature-stabilized environment to perform repeatable power and performance measurements. Studying power-performance characteristics of smartphones is difficult. Running a benchmark back-to-back often produces significantly different results due to heat. Temperature, both device and ambient, play a significant role in determining performance and energy. Our methodology allows us to control for various factors and isolate the effects of the underlying process variation. We then apply our methodology to investigate performance and energy characteristics of several recent generations of smart-phone CPUs that result from process variation. Our results show that devices of the same model may exhibit differences of 10% and 12% difference in performance and energy over a fixed-duration workload.

Enrollment in introductory programming (CS1) courses continues to surge and hundreds of CS1 students can produce thousands of submissions for a single problem, all requiring timely and accurate grading. One way that instructors can efficiently grade is to construct a custom instructor test suite that compares a student submission to a reference solution over randomly generated or hand-crafted inputs. However, such test suite is often insufficient, causing incorrect submissions to be marked as correct. To address this issue, we propose the Grasa (GRAding-based test Suite Augmentation) approach consisting of two techniques. Grasa first detects and clusters incorrect submissions by approximating their behavioral equivalence to each other. To augment the existing instructor test suite, Grasa generates a minimal set of additional tests that help detect the incorrect submissions. We evaluate our Grasa approach on a dataset of CS1 student submissions for three programming problems. Our preliminary results show that Grasa can effectively identify incorrect student submissions and minimally augment the instructor test suite.

Dynamic voltage and frequency scaling (DVFS) of both the core and DRAM provides opportunities to trade-off performance in order to save energy. Previous approaches to core and DRAM power management using DVFS used performance, specifically acceptable performance loss, as a constraint. We present energy management algorithms that coordinate core and DRAM frequency scaling under a specified energy budget. Approaches that work under performance constraints, as we will show, are not directly applicable to systems operating under energy constraints, as it is difficult to calculate the correct performance bounds in real-time to stay under an energy budget. Setting arbitrary energy budgets for a diverse set of applications can be harmful to application performance. We use the previously introduced concept of Inefficiency—the additional amount of energy above the minimum required energy that can be used to improve performance—to provide a dynamic energy constraint to our system. We introduce new power management algorithms that search the power and performance space to find the best performing point under this constraint. We demonstrate the efficacy of our algorithms using CPU DVFS and DRAM frequency scaling. We show that our algorithms have 24% lower tuning cost and save up to 5% energy with a little performance loss compared to a state-of-the-art performance constrained system.

During the one minute it takes to read this abstract, two billion smartphones worldwide will perform billions of Wifi channel scans recording the signal strength of nearby Wifi Access Points (APs). Yet despite this ongoing planetary-scale wireless network measurement, few systematic efforts are made today to recover this potentially valuable data. In this paper we ask the question: “Are the smartphone channel scans useful in monitoring enterprise Wifi networks?” More specifically, can these client-side measurements provide new insights compared to the AP-side measurements that enterprise Wifi networks already perform? Beginning with two Wifi scan datasets collected on two large scale smartphone testbeds, we conduct case studies that show how smartphone channel scans can be used to (1) improve AP spectrum management, and (2) predict the impact of AP failure or overload. In each case, a walk on the client side yields valuable insights for network operators that are otherwise impossible to gain from AP-side measurements, and together our results demonstrate the value of smartphone channel scans.

Indoor location information plays a fundamental role in supporting various interesting location- aware indoor applications. Widely deployed WiFi networks make it feasible to perform indoor localization by first establishing a received signal strength (RSS) map covering the whole area based on a signal propagation model, then determining a location from an online RSS measurement given the RSS map. However, challenges remain in practical deployments, due to inaccurately estimated RSS values in the RSS map and insufficient number of access points (APs) in large indoor areas. To address these challenges, we develop a robust, cost-effective and scalable localization system (REAL). Our approach takes the error from the indoor radio signal propagation model into consideration. It also exploits information of unobserved APs at a given location and an optimal clustering method in the location searching phase. Our real-world experimental results demonstrate that REAL achieves considerable localization accuracy at a very low training cost even for a large indoor area. In addition, the results show that our scheme can also be effectively applied to Bluetooth networks with sparse signal coverage.

The hundreds of millions of deployed smartphones provide an unprecedented opportunity to collect data to monitor, debug, and continuously adapt wireless networks to improve performance. In contrast with previous mobile devices, such as laptops, smartphones are always on but mostly idle, making them available to perform measurements that help other nearby active devices make better use of available network resources. We present the design of PocketSniffer, a system delivering wireless measurements from smartphones both to network administrators for monitoring and debugging purposes and to algorithms performing realtime network adaptation. By collecting data from smartphones, PocketSniffer supports novel adaptation algorithms designed around common deployment scenarios involving both cooperative and self-interested clients and networks. We present preliminary results from a prototype and discuss challenges to realizing this vision.

Searching for parking spots generates frustration and pollution. To address these parking problems, we present PocketParker, a crowdsourcing system using smartphones to predict parking lot availability. PocketParker is an example of a subset of crowdsourcing we call pocketsourcing. Pocketsourcing applications require no explicit user input or additional infrastructure, running effectively without the phone leaving the user's pocket. PocketParker detects arrivals and departures by leveraging existing activity recognition algorithms. Detected events are used to maintain per-lot availability models and respond to queries. By estimating the number of drivers not using PocketParker, a small fraction of drivers can generate accurate predictions. Our evaluation shows that PocketParker quickly and correctly detects parking events and is robust to the presence of hidden drivers. Camera monitoring of several parking lots as 105 PocketParker users generated 10;827 events over 45 days shows that PocketParker was able to correctly predict lot availability 94% of the time.

Smartphones represent the most serious threat to user privacy of any widely-deployed computing technology. Unfortunately, existing permission models provide smartphone users with limited protection, in part due to the difficulty users have distinguishing between legitimate and illegitimate use of their data. A mapping app may upload the same location information it uses to download maps (legitimate) to a marketing agency interested in delivering location-based ads (illegitimate). However, armed with the right technology users can turn apps' interest in personal data against them by intentionally manipulating the data that they expose. We refer to the intentional substitution of real data with artificial data intended to alter an apps perception of a user as mocking to differentiate this approach from other privacy-motivated techniques that focus on concealing data. In this paper, we explore the desirability and implications of this approach, present results from a survey suggesting that many users are interested in mocking apps, and discuss ethical and practical issues related to widespread app mocking.

This paper presents Pixie, a new sensor node operating system designed to support the needs of data-intensive applications. These applications, which include high-resolution monitoring of acoustic, seismic, acceleration, and other signals, involve high data rates and extensive in-network processing. Given the fundamentally resource-limited nature of sensor networks, a pressing concern for such applications is their ability to receive feedback on, and adapt their behavior to, fluctuations in both resource availability and load.

An emerging class of sensor networks focuses on reliable collection of high-resolution signals from across the network. In these applications, the system is capable of acquiring more data than can be delivered to the base station, due to severe limits on radio bandwidth and energy. Moreover, these systems are unable to take advantage of conventional approaches to in-network data aggregation, given the high data rates and need for raw signals. These systems face an important challenge: how to maximize the overall value of the collected data, subject to resource constraints.

Synchronicity is a useful abstraction in many sensor network applications. Communication scheduling, coordinated duty cycling, and time synchronization can make use of a synchronicity primitive that achieves a tight alignment of individual nodes' firing phases. In this paper we present the Reachback Firefly Algorithm (RFA), a decentralized synchronicity algorithm implemented on TinyOS-based motes. Our algorithm is based on a mathematical model that describes how fireflies and neurons spontaneously synchronize. Previous work has assumed idealized nodes and not considered realistic effects of sensor network communication, such as message delays and loss. Our algorithm accounts for these effects by allowing nodes to use delayed information from the past to adjust the future firing phase. We present an evaluation of RFA that proceeds on three fronts. First, we prove the convergence of our algorithm in simple cases and predict the effect of parameter choices. Second, we leverage the TinyOS simulator to investigate the effects of varying parameter choice and network topology. Finally, we present results obtained on an indoor sensor network testbed demonstrating that our algorithm can synchronize sensor network devices to within 100 μsec on a real multi-hop topology with links of varying quality.

We developed and deployed a wireless sensor network for monitoring seismoacoustic activity at Volcán Reventador, Ecuador. Wireless sensor networks are a new technology and our group is among the first to apply them to monitoring volcanoes. The small size, low power, and wireless communication capabilities can greatly simplify deployments of large sensor arrays and are very attractive for this application domain. This project is a follow-on to our previous infrasonic sensor network deployed at Volcán Tungurahua, also in Ecuador, in July 2004 [1].

Developing sensor network applications demands a new set of tools to aid programmers. A number of simulation environments have been developed that provide varying degrees of scalability, realism, and detail for understanding the behavior of sensor networks. To date, however, none of these tools have addressed one of the most important aspects of sensor application design: that of power consumption. While simple approximations of overall power usage can be derived from estimates of node duty cycle and communication rates, these techniques often fail to capture the detailed, low-level energy requirements of the CPU, radio, sensors, and other peripherals.