Abstract
Mineral oil has long been the standard insulating fluid in power transformers due to its excellent dielectric and thermal properties. However, growing environmental and safety concerns have sparked interest in alternative, eco-friendly insulating fluids. Esters have emerged as promising candidates due to their high biodegradability, flame retardance, and lower ecological impact. This paper compares two such insulating fluids-a natural ester (Envirotemp FR3) and a synthetic ester (Midel 7131)-under the influence of lightning impulse voltages, representing a critical stress condition for transformer insulation. High voltage tests, including dielectric loss factor (delta tangent) measurements, were performed before and after applying standardized impulse sequences. Results indicate that both esters maintained dielectric performance within acceptable limits, with the synthetic ester demonstrating superior stability under impulse stress. The findings confirm the technical feasibility of ester-based insulating oils as viable and sustainable alternatives to mineral oil in power transformers, supporting broader environmental and operational safety goals in modern power systems. © 2025 IEEE.

Abstract
Electronic Control Units are embedded devices that control various critical features of an automobile. Consequently, it is crucial to develop tools that enable penetration testers to identify security vulnerabilities within these ECUs as efficiently as possible. Fuzzing, a widely-used technique, can help uncover vulnerabilities in various types of applications. Fuzzing can then be applied to test ECUs through their communication protocols, the most common being the Controller Area Network (CAN). We present oCANada, a generation-based fuzzer which can be utilized in order to craft CAN messages for fuzzing. Many existing CAN fuzzers rely on simple mutation-based fuzzing, which involves randomly changing bits in the CAN payload. This paper introduces a novel generation-based fuzzing approach that leverages CAN database files (DBCs) in order to craft syntactically correct messages. oCANada also incorporates State-of-the-Art CAN reverse engineering techniques in order to enable syntax-aware fuzzing even when DBCs are not available. Additionally, this paper discusses test oracle techniques employed for fuzzing ECUs over CAN in both greybox and blackbox environments. Finally, we present our results while running the tool which we used two CANoe simulations, a Gateway ECU, and a modified version of the instrument cluster simulator ICSim. In these results, we also compare our fuzzer to the well-known CaringCaribou fuzzer.

Abstract
Airborne Wind Energy (AWE) systems represent an innovative method for capturing wind energy at high altitudes, where wind conditions are typically stronger and more consistent. These systems utilize flying devices tethered to a ground station to harness wind energy. An AWE system comprises a tether connecting the flying device to a base station, a control system for maneuvering the device, and a mechanism for converting kinetic energy into electricity. Researchers are exploring various materials, designs, and control methods to enhance the efficiency and reliability of AWE systems. Over the past decade, interest in AWE has surged, leading to a substantial increase in scholarly publications on the topic. This research conducts an in-depth bibliometric analysis. This analysis highlights emerging topics, allowing researchers to identify new trends and areas of interest within a field. By emphasizing these emerging topics, researchers and stakeholders can better align their efforts with the latest developments and opportunities in their area of study. Findings reveal that research on control techniques in AWE has grown at an average annual rate of 16% since 2013. Additionally, the study identifies the most influential aspects of the literature, including key topics, articles, authors, and keywords. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Platforms to support novices learning to program are often accompanied by automated next-step hints that guide them towards correct solutions. Many of those approaches are data-driven, building on historical data to generate higher quality hints. Formal specifications are increasingly relevant in software engineering activities, but very little support exists to help novices while learning. Alloy is a formal specification language often used in courses on formal software development methods, and a platform-Alloy4Fun-has been proposed to support autonomous learning. While non-data-driven specification repair techniques have been proposed for Alloy that could be leveraged to generate next-step hints, no data-driven hint generation approach has been proposed so far. This paper presents the first data-driven hint generation technique for Alloy and its implementation as an extension to Alloy4Fun, being based on the data collected by that platform. This historical data is processed into graphs that capture past students' progress while solving specification challenges. Hint generation can be customized with policies that take into consideration diverse factors, such as the popularity of paths in those graphs successfully traversed by previous students. Our evaluation shows that the performance of this new technique is competitive with non-data-driven repair techniques. To assess the quality of the hints, and help select the most appropriate hint generation policy, we conducted a survey with experienced Alloy instructors.

Abstract
Accurately estimating workload runtime is a longstanding goal in computer systems, and plays a key role in efficient resource provisioning, latency minimization, and various other system management tasks. Runtime prediction is particularly important for managing increasingly complex distributed systems in which more sophisticated processing is pushed to the edge in search of better latency. Previous approaches for runtime prediction in edge systems suffer from poor data efficiency or require intensive instrumentation; these challenges are compounded in heterogeneous edge computing environments, where historical runtime data may be sparsely available and instrumentation is often challenging. Moreover, edge computing environments often feature multi-tenancy due to limited resources at the network edge, potentially leading to interference between workloads and further complicating the runtime prediction problem. Drawing from insights across machine learning and computer systems, we design a matrix factorization-inspired method that generates accurate interference-aware predictions with tight provably-guaranteed uncertainty bounds. We validate our method on a novel WebAssembly runtime dataset collected from 24 unique devices, achieving a prediction error of 5.2% -- 2x better than a naive application of existing methods.

Abstract
We conducted a workshop on ''Addressing Challenges in Recruiting Participants for Human-Centric Computing Research Studies'' at the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)'24 Conference. In the workshop, we conducted a brainstorming session on ''roadmap development of making participant recruitment easier for human-centric computing studies in both industry and academia''. This article presents 7 stages of participant recruitment and key strategies identified by the authors (workshop participants) during the brainstorming session.