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We present flux measurements of Uranus observed at phase angles of 43.9{\deg}, 44.0{\deg}, and 52.4{\deg} by the Multispectral Visible Imaging Camera (MVIC) on the New Horizons spacecraft during 2023, 2010, and 2019, respectively. New Horizons imaged Uranus at a distance of about 24-70 AU (2023) in four color filters, with bandpasses of 400-550 nm, 540-700 nm, 780-975 nm, and 860-910 nm. High-phase-angle observations are of interest for studying the energy balance of Uranus, constraining the atmospheric scattering behavior, and understanding the planet as an analog for ice giant exoplanets. The new observations from New Horizons provide access to a wider wavelength range and different season compared to previous observations from both Voyager spacecraft. We performed aperture photometry on the New Horizons observations of Uranus to obtain its brightness in each photometric band. The photometry suggests that Uranus may be darker than predicted by a Lambertian phase curve in the Blue and Red filters. Comparison to simultaneous low-phase Hubble WFC3 and ground-based community-led observations indicates a lack of large-scale features at full-phase that would introduce variation in the rotational light curve. The New Horizons reflectance in the Blue (492 nm) and Red (624 nm) filters does not exhibit statistically significant variation and is consistent with the expected error bars. These results place new constraints on the atmospheric model of Uranus and its reflectivity. The observations are analogous to those from future exoplanet direct-imaging missions, which will capture unresolved images of exoplanets at partial phases. These results will serve as a "ground-truth" with which to interpret exo-ice giant data., Comment: Accepted for publication in the Planetary Science Journal, 11 pages, 6 figures

Growth of the older adult population has led to an increasing interest in technology-supported aged care. However, the area has some challenges such as a lack of caregivers and limitations in understanding the emotional, social, physical, and mental well-being needs of seniors. Furthermore, there is a gap in the understanding between developers and ageing people of their requirements. Digital health can be important in supporting older adults wellbeing, emotional requirements, and social needs. Requirements Engineering (RE) is a major software engineering field, which can help to identify, elicit and prioritize the requirements of stakeholders and ensure that the systems meet standards for performance, reliability, and usability. We carried out a systematic review of the literature on RE for older adult digital health software. This was necessary to show the representatives of the current stage of understanding the needs of older adults in aged care digital health. Using established guidelines outlined by the Kitchenham method, the PRISMA and the PICO guideline, we developed a protocol, followed by the systematic exploration of eight databases. This resulted in 69 primary studies of high relevance, which were subsequently subjected to data extraction, synthesis, and reporting. We highlight key RE processes in digital health software for ageing people. It explored the utilization of technology for older user well-being and care, and the evaluations of such solutions. The review also identified key limitations found in existing primary studies that inspire future research opportunities. The results indicate that requirement gathering and understanding have a significant variation between different studies. The differences are in the quality, depth, and techniques adopted for requirement gathering and these differences are largely due to uneven adoption of RE methods., Comment: arxiv version of SLR on RE for Older Adult Digital Health Software

Deploying DL models on mobile Apps has become ever-more popular. However, existing studies show attackers can easily reverse-engineer mobile DL models in Apps to steal intellectual property or generate effective attacks. A recent approach, Model Obfuscation, has been proposed to defend against such reverse engineering by obfuscating DL model representations, such as weights and computational graphs, without affecting model performance. These existing model obfuscation methods use static methods to obfuscate the model representation, or they use half-dynamic methods but require users to restore the model information through additional input arguments. However, these static methods or half-dynamic methods cannot provide enough protection for on-device DL models. Attackers can use dynamic analysis to mine the sensitive information in the inference codes as the correct model information and intermediate results must be recovered at runtime for static and half-dynamic obfuscation methods. We assess the vulnerability of the existing obfuscation strategies using an instrumentation method and tool, DLModelExplorer, that dynamically extracts correct sensitive model information at runtime. Experiments show it achieves very high attack performance. To defend against such attacks based on dynamic instrumentation, we propose DynaMO, a Dynamic Model Obfuscation strategy similar to Homomorphic Encryption. The obfuscation and recovery process can be done through simple linear transformation for the weights of randomly coupled eligible operators, which is a fully dynamic obfuscation strategy. Experiments show that our proposed strategy can dramatically improve model security compared with the existing obfuscation strategies, with only negligible overheads for on-device models., Comment: Published on Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE'24)

For many binary mixtures, the three-phase solid-liquid-vapor equilibrium curve has intermediate pressures that are higher than the pressure at the two pure triple points. This curve shape results in a negative slope in the high-temperature region near the triple point of the less volatile component. When freezing mixtures in the negative slope regime, fluid trapped below confined ice has latent heat released with more vapor upon cooling, and thus increases in pressure. If the rising pressure of the confined fluid overcomes the strength of the confining solid, which may be its own ice, it can produce an abrupt outburst of material and an increase in the system's overall pressure. Here, we report experimental results of freezing-induced outbursts occurring in the N2/CH4, CO/CH4, and N2/C2H6 systems, and provide insight into the phenomenon through a thermodynamics perspective. We also propose other binary systems that may experience outbursts and explore the geological implications for icy worlds like Titan, Triton, Pluto and Eris, as well as rocky bodies, specifically Earth and Mars., Comment: 30 pages, 8 figures

Software applications are designed to assist users in conducting a wide range of tasks or interactions. They have become prevalent and play an integral part in people's lives in this digital era. To use those software applications, users are sometimes requested to provide their personal information. As privacy has become a significant concern and many data protection regulations exist worldwide, software applications must provide users with a privacy policy detailing how their personal information is collected and processed. We propose an approach that generates a comprehensive and compliant privacy policy with respect to the General Data Protection Regulation (GDPR) for diverse software applications. To support this, we first built a library of privacy clauses based on existing privacy policy analysis. We then developed an interactive rule-based system that prompts software developers with a series of questions and uses their answers to generate a customised privacy policy for a given software application. We evaluated privacy policies generated by our approach in terms of readability, completeness and coverage and compared them to privacy policies generated by three existing privacy policy generators and a Generative AI-based tool. Our evaluation results show that the privacy policy generated by our approach is the most complete and comprehensive.

The risks posed by Artificial Intelligence (AI) are of considerable concern to academics, auditors, policymakers, AI companies, and the public. However, a lack of shared understanding of AI risks can impede our ability to comprehensively discuss, research, and react to them. This paper addresses this gap by creating an AI Risk Repository to serve as a common frame of reference. This comprises a living database of 777 risks extracted from 43 taxonomies, which can be filtered based on two overarching taxonomies and easily accessed, modified, and updated via our website and online spreadsheets. We construct our Repository with a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation. We develop our taxonomies of AI risk using a best-fit framework synthesis. Our high-level Causal Taxonomy of AI Risks classifies each risk by its causal factors (1) Entity: Human, AI; (2) Intentionality: Intentional, Unintentional; and (3) Timing: Pre-deployment; Post-deployment. Our mid-level Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental, and (7) AI system safety, failures, & limitations. These are further divided into 23 subdomains. The AI Risk Repository is, to our knowledge, the first attempt to rigorously curate, analyze, and extract AI risk frameworks into a publicly accessible, comprehensive, extensible, and categorized risk database. This creates a foundation for a more coordinated, coherent, and complete approach to defining, auditing, and managing the risks posed by AI systems.

Context: Human-centric defects (HCDs) are nuanced and subjective defects that often occur due to end-user perceptions or differences, such as their genders, ages, cultures, languages, disabilities, socioeconomic status, and educational backgrounds. Development teams have a limited understanding of these issues, which leads to the neglect of these defects. Defect reporting tools do not adequately handle the capture and fixing of HCDs. Objective: This research aims to understand the current defect reporting process and tools for managing defects. Our study aims to capture process flaws and create a preliminary defect categorisation and practices of a defect-reporting tool that can improve the reporting and fixing of HCDs in software engineering. Method: We first manually classified 1,100 open-source issues from the GitHub defect reporting tool to identify human-centric defects and to understand the categories of such reported defects. We then interviewed software engineering practitioners to elicit feedback on our findings from the GitHub defects analysis and gauge their knowledge and experience of the defect-reporting process and tools for managing human-centric defects. Results: We identified 176 HCDs from 1,100 open-source issues across six domains: IT-Healthcare, IT-Web, IT-Spatial, IT-Manufacturing, IT-Finance, and IT-Gaming. Additionally, we interviewed 15 software practitioners to identify shortcomings in the current defect reporting process and determine practices for addressing these weaknesses. Conclusion: HCDs present in open-source repositories are fairly technical, and due to the lack of awareness and improper defect reports, they present a major challenge to software practitioners. However, the management of HCDs can be enhanced by implementing the practices for an ideal defect reporting tool developed as part of this study.

Smart contract developers frequently seek solutions to developmental challenges on Q&A platforms such as Stack Overflow (SO). Although community responses often provide viable solutions, the embedded code snippets can also contain hidden vulnerabilities. Integrating such code directly into smart contracts may make them susceptible to malicious attacks. We conducted an online survey and received 74 responses from smart contract developers. The results of this survey indicate that the majority (86.4%) of participants do not sufficiently consider security when reusing SO code snippets. Despite the existence of various tools designed to detect vulnerabilities in smart contracts, these tools are typically developed for analyzing fully-completed smart contracts and thus are ineffective for analyzing typical code snippets as found on SO. We introduce SOChecker, the first tool designed to identify potential vulnerabilities in incomplete SO smart contract code snippets. SOChecker first leverages a fine-tuned Llama2 model for code completion, followed by the application of symbolic execution methods for vulnerability detection. Our experimental results, derived from a dataset comprising 897 code snippets collected from smart contract-related SO posts, demonstrate that SOChecker achieves an F1 score of 68.2%, greatly surpassing GPT-3.5 and GPT-4 (20.9% and 33.2% F1 Scores respectively). Our findings underscore the need to improve the security of code snippets from Q&A websites.

Late-time ($\sim$ year) radio follow-up of optically-discovered tidal disruption events (TDEs) is increasingly resulting in detections at radio wavelengths, and there is growing evidence for this late-time radio activity to be common to the broad class of sub-relativistic TDEs. Detailed studies of some of these TDEs at radio wavelengths are also challenging the existing models for radio emission. Using all-sky multi-epoch data from the Australian Square Kilometre Array Pathfinder (ASKAP), taken as a part of the Rapid ASKAP Continuum Survey (RACS), we searched for radio counterparts to a sample of optically-discovered TDEs. We detected late-time emission at RACS frequencies (742-1032\,MHz) in five TDEs, reporting the independent discovery of radio emission from TDE AT2019ahk and extending the time baseline out to almost 3000\,days for some events. Overall, we find that at least $22^{+15}_{-11}$\% of the population of optically-discovered TDEs has detectable radio emission in the RACS survey, while also noting that the true fraction can be higher given the limited cadence (2 epochs separated by $\sim 3\,$ years) of the survey. Finally, we project that the ongoing higher-cadence ($\sim 2$\,months) ASKAP Variable and Slow Transients (VAST) survey can detect $\sim 20$ TDEs in its operational span (4\,yrs), given the current rate from optical surveys., Comment: Accepted for publication in ApJ, comments welcome

Context: Disasters are a common global occurrence with climate change leading to increase both their frequency and intensity. To reduce the impact of these disasters on lives and livelihoods it is important to provide accurate warnings and information about recovery and mitigation. Today most emergency management agencies deliver this information via mobile apps. Objective: There is a large collection of disaster mobile apps available across the globe. But a detailed study is not yet conducted on these apps and their reviews to understand their key features and user feedback. In this paper we present a comprehensive analysis to address this research gap. Method: We conducted a detailed analysis of 45 disaster apps and 28,161 reviews on these apps. We manually analysed the features of these 45 apps and for review analysis employed topic modelling and sentiment analysis techniques. Results: We identified 13 key features in these apps and categorised them in to the 4 stages of disaster life cycle. Our analysis revealed 22 topics with highest discussions being on apps alert functionality, app satisfaction and use of maps. Sentiment analysis of reviews showed that while 22\% of users provided positive feedback, 9.5\% were negative and 6.8\% were neutral. It also showed that signup/signin issues, network issues and app configuration issues were the most frustrating to users. These impacted user safety as these prevented them from accessing the app when it mattered most. Conclusions: We provide a set of practical recommendations for future disaster app developers. Our findings will help emergency agencies develop better disaster apps by ensuring key features are supported in their apps, by understanding commonly discussed user issues. This will help to improve the disaster app eco-system and lead to more user friendly and supportive disaster support apps in the future.

Binaries in the Kuiper Belt are common. Here we present our analysis of the Solar System Origins Legacy Survey (SSOLS) to show that using a PSF-fitting method can roughly double the number of binaries identified in that dataset. Out of 198 Kuiper Belt objects (KBOs) observed by SSOLS, we find 23 to be visually separated binaries, while a further 19 are blended-PSF binaries detectable with the method we present here. This is an overall binary fraction of 21% for the SSOLS dataset of cold classical KBOs. In addition, we tested our fitting methods on synthetic data, and while we were able to show it to be very effective at detecting certain blended-PSF binary KBOs, fainter or closer binary KBOs may easily be missed, suggesting that the close binary KBO fraction could be even higher. These results strongly support the idea that most (if not all) KBOs were formed through the Streaming Instability process, and as a consequence, most KBOs were formed as near-equal mass binaries., Comment: 14 pages, 3 figures, accepted to the Planetary Science Journal

Statically typed languages offer significant advantages, such as bug prevention, enhanced code quality, and reduced maintenance costs. However, these benefits often come at the expense of a steep learning curve and a slower development pace. Haskell, known for its expressive and strict type system, poses challenges for inexperienced programmers in learning and using its type system, especially in debugging type errors. We introduce Goanna, a novel tool that serves as a type checker and an interactive type error debugging tool for Haskell. When encountering type errors, Goanna identifies a comprehensive list of potential causes and resolutions based on the minimum correction subsets (MCS) enumeration. We evaluated Goanna's effectiveness using 86 diverse Haskell programs from online discourse, demonstrating its ability to accurately identify and resolve type errors. Additionally, we present a collection of techniques and heuristics to enhance Goanna's suggestion-based error diagnosis and show their effectiveness from our evaluation.

mHealth interventions show significant potential to help in the self-management of chronic diseases, but their under use remains a problem. Considering the substantial diversity among individuals dealing with chronic diseases, tailored strategies are essential. \emph{Adaptive User Interfaces} (AUIs) may help address the diverse and evolving needs of this demographic. To investigate this approach, we developed an AUI prototype informed by existing literature findings. We then used this prototype as the basis for focus group discussions and interview studies with 22 participants managing various chronic diseases, and follow-up surveys of all participants. Through these investigations, we pinpointed key challenges related to the use of AUIs, strategies to improve adaptation design, and potential trade-offs between these challenges and strategies. Concurrently, a quantitative survey was conducted to extract preferences for AUIs in chronic disease-related applications with 90 further participants. This uncovered participants' preferences for various adaptations, data types, collection methods, and involvement levels. Finally, we synthesised these insights and categories, aligning them with existing guidelines and design considerations for mHealth app adaptation design. This resulted in nine guidelines that we refined by a final feedback survey conducted with 20 participants.

Context: Research on human aspects within the field of software engineering (SE) has been steadily gaining prominence in recent years. These human aspects have a significant impact on SE due to the inherently interactive and collaborative nature of the discipline. Objective: In this paper, we present a systematic literature review (SLR) on human aspects affecting developer-user interactions. The objective of this SLR is to plot the current landscape of primary studies by examining the human aspects that influence developer-user interactions, their implications, interrelationships, and how existing studies address these implications. Method: We conducted this SLR following the guidelines proposed by Kitchenham et al. We performed a comprehensive search in six digital databases, and an exhaustive backward and forward snowballing process. We selected 46 primary studies for data extraction. Results: We identified various human aspects affecting developer-user interactions in SE, assessed their interrelationships, identified their positive impacts and mitigation strategies for negative effects. We present specific recommendations derived from the identified research gaps. Conclusion: Our findings suggest the importance of leveraging positive effects and addressing negative effects in developer-user interactions through the implementation of effective mitigation strategies. These insights may benefit software practitioners for effective user interactions, and the recommendations proposed by this SLR may aid the research community in further human aspects related studies.

Developing user-centred applications that address diverse user needs requires rigorous user research. This is time, effort and cost-consuming. With the recent rise of generative AI techniques based on Large Language Models (LLMs), there is a possibility that these powerful tools can be used to develop adaptive interfaces. This paper presents a novel approach to develop user personas and adaptive interface candidates for a specific domain using ChatGPT. We develop user personas and adaptive interfaces using both ChatGPT and a traditional manual process and compare these outcomes. To obtain data for the personas we collected data from 37 survey participants and 4 interviews in collaboration with a not-for-profit organisation. The comparison of ChatGPT generated content and manual content indicates promising results that encourage using LLMs in the adaptive interfaces design process.

In the rapidly evolving field of assistive technology (AT), ensuring that products meet national and international standards is essential for user safety, efficacy, and accessibility. In this vision paper, we introduce CompliAT, a pioneering framework designed to streamline the compliance process of AT product specifications with these standards through the innovative use of Large Language Models (LLMs). CompliAT addresses three critical tasks: checking terminology consistency, classifying products according to standards, and tracing key product specifications to standard requirements. We tackle the challenge of terminology consistency to ensure that the language used in product specifications aligns with relevant standards, reducing misunderstandings and non-compliance risks. We propose a novel approach for product classification, leveraging a retrieval-augmented generation model to accurately categorize AT products aligning to international standards, despite the sparse availability of training data. Finally, CompliAT implements a traceability and compliance mechanism from key product specifications to standard requirements, ensuring all aspects of an AT product are thoroughly vetted against the corresponding standards. By semi-automating these processes, CompliAT aims to significantly reduce the time and effort required for AT product standards compliance and uphold quality and safety standards. We outline our planned implementation and evaluation plan for CompliAT.

Recent studies show that deployed deep learning (DL) models such as those of Tensor Flow Lite (TFLite) can be easily extracted from real-world applications and devices by attackers to generate many kinds of attacks like adversarial attacks. Although securing deployed on-device DL models has gained increasing attention, no existing methods can fully prevent the aforementioned threats. Traditional software protection techniques have been widely explored, if on-device models can be implemented using pure code, such as C++, it will open the possibility of reusing existing software protection techniques. However, due to the complexity of DL models, there is no automatic method that can translate the DL models to pure code. To fill this gap, we propose a novel method, CustomDLCoder, to automatically extract the on-device model information and synthesize a customized executable program for a wide range of DL models. CustomDLCoder first parses the DL model, extracts its backend computing units, configures the computing units to a graph, and then generates customized code to implement and deploy the ML solution without explicit model representation. The synthesized program hides model information for DL deployment environments since it does not need to retain explicit model representation, preventing many attacks on the DL model. In addition, it improves ML performance because the customized code removes model parsing and preprocessing steps and only retains the data computing process. Our experimental results show that CustomDLCoder improves model security by disabling on-device model sniffing. Compared with the original on-device platform (i.e., TFLite), our method can accelerate model inference by 21.8% and 24.3% on x86-64 and ARM64 platforms, respectively. Most importantly, it can significantly reduce memory consumption by 68.8% and 36.0% on x86-64 and ARM64 platforms, respectively., Comment: Accepted by the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA2024)

Personas are commonly used in software projects to gain a better understanding of end-users' needs. However, there is a limited understanding of their usage and effectiveness in practice. This paper presents the results of a two-step investigation, comprising interviews with 26 software developers, UI/UX designers, business analysts and product managers and a survey of 203 practitioners, aimed at shedding light on the current practices, methods and challenges of using personas in software development. Our findings reveal variations in the frequency and effectiveness of personas across different software projects and IT companies, the challenges practitioners face when using personas and the reasons for not using them at all. Furthermore, we investigate the coverage of human aspects in personas, often assumed to be a key feature of persona descriptions. Contrary to the general perception, our study shows that human aspects are often ignored for various reasons in personas or requirements engineering in general. Our study provides actionable insights for practitioners to overcome challenges in using personas during requirements engineering stages, and we identify areas for future research.

About 40 transneptunian binaries (TNBs) have fully determined orbits with about 10 others being solved except for breaking the mirror ambiguity. Despite decades of study almost all TNBs have only ever been analyzed with a model that assumes perfect Keplerian motion (e.g., two point masses). In reality, all TNB systems are non-Keplerian due to non-spherical shapes, possible presence of undetected system components, and/or solar perturbations. In this work, we focus on identifying candidates for detectable non-Keplerian motion based on sample of 45 well-characterized binaries. We use MultiMoon, a non-Keplerian Bayesian inference tool, to analyze published relative astrometry allowing for non-spherical shapes of each TNB system's primary. We first reproduce the results of previous Keplerian fitting efforts with MultiMoon, which serves as a comparison for the non-Keplerian fits and confirms that these fits are not biased by the assumption of a Keplerian orbit. We unambiguously detect non-Keplerian motion in 8 TNB systems across a range of primary radii, mutual orbit separations, and system masses. As a proof of concept for non-Keplerian fitting, we perform detailed fits for (66652) Borasisi-Pabu, possibly revealing a $J_2 \approx 0.44$, implying Borasisi (and/or Pabu) may be a contact binary or an unresolved compact binary. However, full confirmation of this result will require new observations. This work begins the next generation of TNB analyses that go beyond the point mass assumption to provide unique and valuable information on the physical properties of TNBs with implications for their formation and evolution., Comment: Accepted to AJ

Many details of the formation and evolution of the solar system are best inferred by understanding the orbital and physical properties of small bodies in the solar system. For example, small body binaries are particularly valuable for measuring masses. By extending the models of small body binaries beyond point masses, new information about the shape and spin orientation becomes available. This is particularly informative for Trans-Neptunian multiples (two or more components) where shapes and spin orientations are poorly understood. Going beyond point masses requires modeling tools that no longer assume fixed Keplerian orbits. To this end, we have developed a new n-quadrupole integrator SPINNY (SPIN+N-bodY) and pair it with a Bayesian parameter inference tool MultiMoon, both of which are publicly available. We describe these tools and how they can be used to learn more about solar system small body multiple systems. We then apply them to the unique Trans-Neptunian hierarchical triple system (47171) Lempo, finding a three-point-mass solution for the first time. This solution has two surprises: unequal densities of the inner components and a dynamical configuration apparently unstable on the age of the solar system., Comment: Submitted to PSJ

Dynamically studying Trans-Neptunian Object (TNO) binaries allows us to measure masses and orbits. Most of the known objects appear to have only two components, except (47171) Lempo which is the single known hierarchical triple system with three similar-mass components. Though hundreds of TNOs have been imaged with high-resolution telescopes, no other hierarchical triples (or trinaries) have been found among solar system small bodies, even though they are predicted in planetesimal formation models such as gravitational collapse after the streaming instability. By going beyond the point-mass assumption and modeling TNO orbits as non-Keplerian, we open a new window into the shapes and spins of the components, including the possible presence of unresolved ``inner'' binaries. Here we present evidence for a new hierarchical triple, (148780) Altjira (2001 UQ$_{18}$), based on non-Keplerian dynamical modeling of the two observed components. We incorporate two recent Hubble Space Telescope (HST) observations, leading to a 17 year observational baseline. We present a new open-source Bayesian Point Spread Function (PSF) fitting code called nPSF that provides precise relative astrometry and uncertainties for single images. Our non-Keplerian analysis measures a statistically-significant ($\sim$2.5-$\sigma$) non-spherical shape for Altjira. The measured $J_2$ is best explained as an unresolved inner binary and an example hierarchical triple model gives the best fit to the observed astrometry. Using an updated non-Keplerian ephemeris (which is significantly different from the Keplerian predictions), we show that the predicted mutual event season for Altjira has already begun with several excellent opportunities for observations through $\sim$2030., Comment: Submitted to PSJ

The dwarf planet Haumea is one of the most compelling transneptunian objects (TNOs) to study, hosting two small, dynamically interacting satellites, a family of nearby spectrally unique objects, and a ring system. Haumea itself is extremely oblate due to its 3.9 hour rotation period. Understanding the orbits of Haumea's satellites, named Hi'iaka and Namaka, requires detailed modeling of both satellite-satellite gravitational interactions and satellite interactions with Haumea's nonspherical gravitational field (parameterized here as $J_2$). Understanding both of these effects allows for a detailed probe of the satellites' masses and Haumea's $J_2$ and spin pole. Measuring Haumea's $J_2$ provides information about Haumea's interior, possibly determining the extent of past differentation. In an effort to understand the Haumea system, we have performed detailed non-Keplerian orbit fitting of Haumea's satellites using a decade of new ultra-precise observations. Our fits detect Haumea's $J_2$ and spin pole at $\gtrsim2.5\sigma$ confidence. Degeneracies present in the dynamics prevent us from precisely measuring Haumea's $J_2$ with the current data, but future observations should enable a precise measurement. Our dynamically determined spin pole shows excellent agreement with past results, illustrating the strength of non-Keplerian orbit fitting. We also explore the spin-orbit dynamics of Haumea and its satellites, showing that axial precession of Hi'iaka may be detectable over decadal timescales. Finally, we present an ephemeris of the Haumea system over the coming decade, enabling high-quality observations of Haumea and its satellites for years to come., Comment: Accepted for publication in PSJ

The pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed "GustosonicSense", a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food., Comment: To appear at CHI'24: The ACM Conference on Human Factors in Computing Systems (CHI), Honolulu, Hawaii, 2024

Few laboratory studies have investigated the vapor pressures of the volatiles that may be present as ices in the outer solar system; even fewer studies have investigated these species at the temperatures and pressures suitable to the surfaces of icy bodies in the Saturnian and Uranian systems ($\lt$100 K, $\lt10^{-9}$ bar). This study adds to the work of Grundy et al. (2024) in extending the known equilibrium vapor pressures of outer solar system ices through laboratory investigations at very low temperatures. Our experiments with ammonia and oxygen ices provide new thermodynamic models for these species' respective enthalpies of sublimation. We find that ammonia ice, and to a lesser degree oxygen ice, are stable at higher temperatures than extrapolations in previous literature have predicted. Our results show that these ices should be retained over longer periods of time than previous extrapolations would predict, and a greater amount of these solids is required to support observation in exospheres of airless bodies in the outer solar system., Comment: 29 pages, 9 figures, to be published in Planetary and Space Science

Context: Human-centric software design and development focuses on how users want to carry out their tasks rather than making users accommodate their software. Software users can have different genders, ages, cultures, languages, disabilities, socioeconomic statuses, and educational backgrounds, among many other differences. Due to the inherently varied nature of these differences and their impact on software usage, preferences and issues of users can vary, resulting in user-specific defects that we term as `human-centric defects' (HCDs). Objective: This research aims to understand the perception and current management practices of such human-centric defects by software practitioners, identify key challenges in reporting, understanding and fixing them, and provide recommendations to improve HCDs management in software engineering. Method: We conducted a survey and interviews with software engineering practitioners to gauge their knowledge and experience on HCDs and the defect tracking process. Results: We analysed fifty (50) survey- and ten (10) interview- responses from SE practitioners and identified that there are multiple gaps in the current management of HCDs in software engineering practice. There is a lack of awareness regarding human-centric aspects, causing them to be lost or under-appreciated during software development. Our results revealed that handling HCDs could be improved by following a better feedback process with end-users, a more descriptive taxonomy, and suitable automation. Conclusion: HCDs present a major challenge to software practitioners, given their diverse end-user base. In the software engineering domain, research on HCDs has been limited and requires effort from the research and practice communities to create better awareness and support regarding human-centric aspects.

Low-code programming (LCP) refers to programming using models at higher levels of abstraction, resulting in less manual and more efficient programming, and reduced learning effort for amateur developers. Many LCP tools have rapidly evolved and have benefited from the concepts of visual programming languages (VPLs) and programming by demonstration (PBD). With huge increase in interest in using large language models (LLMs) in software engineering, LLM-based LCP has began to become increasingly important. However, the technical principles and application scenarios of traditional approaches to LCP and LLM-based LCP are significantly different. Understanding these key differences and characteristics in the application of the two approaches to LCP by users is crucial for LCP providers in improving existing and developing new LCP tools, and in better assisting users in choosing the appropriate LCP technology. We conducted an empirical study of both traditional LCP and LLM-based LCP. We analyzed developers' discussions on Stack Overflow (SO) over the past three years and then explored the similarities and differences between traditional LCP and LLM-based LCP features and developer feedback. Our findings reveal that while traditional LCP and LLM-based LCP share common primary usage scenarios, they significantly differ in scope, limitations and usage throughout the software development lifecycle, particularly during the implementation phase. We also examine how LLMs impact and integrate with LCP, discussing the latest technological developments in LLM-based LCP, such as its integration with VPLs and the application of LLM Agents in software engineering.

The COVID-19 pandemic changed the way we live, work and the way we conduct research. With the restrictions of lockdowns and social distancing, various impacts were experienced by many software engineering researchers, especially whose studies depend on human participants. We conducted a mixed methods study to understand the extent of this impact. Through a detailed survey with 89 software engineering researchers working with human participants around the world and a further nine follow-up interviews, we identified the key challenges faced, the adaptations made, and the surprising fringe benefits of conducting research involving human participants during the pandemic. Our findings also revealed that in retrospect, many researchers did not wish to revert to the old ways of conducting human-oriented research. Based on our analysis and insights, we share recommendations on how to conduct remote studies with human participants effectively in an increasingly hybrid world when face-to-face engagement is not possible or where remote participation is preferred.

Most software applications contain graphics such as charts, diagrams and maps. Currently, these graphics are designed with a ``one size fits all" approach and do not cater to the needs of people with disabilities. Therefore, when using software with graphics, a colour-impaired user may struggle to interpret graphics with certain colours, and a person with dyslexia may struggle to read the text labels in the graphic. Our research addresses this issue by developing a framework that generates adaptive and accessible information graphics for multiple disabilities. Uniquely, the approach also serves people with multiple simultaneous disabilities. To achieve these, we used a case study of public space floorplans presented via a web tool and worked with four disability groups: people with low vision, colour blindness, dyslexia and mobility impairment. Our research involved gathering requirements from 3 accessibility experts and 80 participants with disabilities, developing a system to generate adaptive graphics that address the identified requirements, and conducting an evaluation with 7 participants with disabilities. The evaluation showed that users found our solution easy to use and suitable for most of their requirements. The study also provides recommendations for front-end developers on engineering accessible graphics for their software and discusses the implications of our work on society from the perspective of public space owners and end users.

The NASA New Horizons Venetia Burney Student Dust Counter (SDC) measures dust particle impacts along the spacecraft's flight path for grains with mass $\ge$ $10^{-12}$ g, mapping out their spatial density distribution. We present the latest SDC dust density, size distribution, and flux measurements through 55 au and compare them to numerical model predictions. Kuiper Belt Objects (KBOs) are thought to be the dominant source of interplanetary dust particles (IDP) in the outer solar system due to both collisions between KBOs, and their continual bombardment by interstellar dust particles (ISD). Continued measurements through 55 au show higher than model-predicted dust fluxes as New Horizons approaches the putative outer edge of the Kuiper Belt (KB). We discuss potential explanations for the growing deviation: radiation pressure stretches the dust distribution to further heliocentric distances than its parent body distribution; icy dust grains undergo photo-sputtering that rapidly increases their response to radiation pressure forces and pushes them further away from the sun; and the distribution of KBOs may extend much further than existing observations suggest. Ongoing SDC measurements at even larger heliocentric distances will continue to constrain the contributions of dust production in the KB. Continued SDC measurements remain crucial for understanding the Kuiper Belt and the interpretation of observations of dust disks around other stars., Comment: 9 pages, 5 figures, accepted for publication in APJL

Exploring whether Enriched Category Theory could provide the foundation of an alternative approach to Machine Learning. This paper is the first to construct and motivate a Machine Learning algorithm solely with Enriched Category Theory. In order to supplement evidence that Category Theory can be used to motivate robust and explainable algorithms, it is shown that a series of reasonable assumptions about a dataset lead to the construction of the Nearest Neighbours Algorithm. In particular, as an extension of the original dataset using profunctors in the category of Lawvere metric spaces. This leads to a definition of an Enriched Nearest Neighbours Algorithm, which consequently also produces an enriched form of the Voronoi diagram. This paper is intended to be accessible without any knowledge of Category Theory

The Australian SKA Pathfinder (ASKAP) has surveyed the sky at multiple frequencies as part of the Rapid ASKAP Continuum Survey (RACS). The first two RACS observing epochs, at 887.5 (RACS-low) and 1367.5 (RACS-mid) MHz, have been released (McConnell et al., 2020; Duchesne et al., 2023). A catalogue of radio sources from RACS-low has also been released, covering the sky south of declination +30$^\circ$ (Hale et al., 2021). With this paper, we describe and release the first set of catalogues from RACS-mid, covering the sky below declination +49$^\circ$. The catalogues are created in a similar manner to the RACS-low catalogue, and we discuss this process and highlight additional changes. The general purpose primary catalogue covering 36 200 deg$^2$ features a variable angular resolution to maximise sensitivity and sky coverage across the catalogued area, with a median angular resolution of 11.2" times 9.3". The primary catalogue comprises 3 105 668 radio sources, including those in the Galactic Plane (2 861 923 excluding Galactic latitudes of $|b|<5^\circ$) and we estimate the catalogue to be 95% complete for sources above 1.6 mJy. With the primary catalogue, we also provide two auxiliary catalogues. The first is a fixed-resolution, 25-arcsec catalogue approximately matching the sky coverage of the RACS-low catalogue. This 25-arcsec catalogue is constructed identically to the primary catalogue, except images are convolved to a less-sensitive 25-arcsec angular resolution. The second auxiliary catalogue is designed for time-domain science, and is the concatenation of source-lists from the original RACS-mid images with no additional convolution, mosaicking, or de-duplication of source entries to avoid losing time-variable signals. All three RACS-mid catalogues, and all RACS data products, are available through the CSIRO ASKAP Science Data Archive (CASDA)., Comment: Accepted for publication in PASA. Primary catalogues and images available at https://doi.org/10.25919/p524-xb81 and an auxiliary catalogue available at https://doi.org/10.25919/p8ns-da63

We present observations obtained with the Near Infrared Spectrograph on JWST of the five Jupiter Trojans that will be visited by the Lucy spacecraft -- the Patroclus-Menoetius binary, Eurybates, Orus, Leucus, and Polymele. The measured 1.7-5.3 $\mu$m reflectance spectra, which provide increased wavelength coverage, spatial resolution, and signal-to-noise ratio over previous ground-based spectroscopy, reveal several distinct absorption features. We detect a broad OH band centered at 3 $\mu$m that is most prominent on the less-red objects Eurybates, Patroclus-Menoetius, and Polymele. An additional absorption feature at 3.3-3.6 $\mu$m, indicative of aliphatic organics, is systematically deeper on the red objects Orus and Leucus. The collisional fragment Eurybates is unique in displaying an absorption band at 4.25 $\mu$m that we attribute to bound or trapped CO$_2$. Comparisons with other solar system small bodies reveal broad similarities in the 2.7-3.6 $\mu$m bands with analogous features on Centaurs, Kuiper belt objects (KBOs), and the active asteroid 238P. In the context of recent solar system evolution models, which posit that the Trojans initially formed in the outer solar system, the significant attenuation of the 2.7-3.6 $\mu$m absorption features on Trojans relative to KBOs may be the result of secondary thermal processing of the Trojans' surfaces at the higher temperatures of the Jupiter region. The CO$_2$ band manifested on the surface of Eurybates suggests that CO$_2$ may be a major constituent in the bulk composition of Trojans, but resides in the subsurface or deeper interior and is largely obscured by refractory material that formed from the thermophysical processes that were activated during their inward migration., Comment: Published in PSJ. 13 pages, 6 figures

Mobile software engineering has been a hot research topic for decades. Our fellow researchers have proposed various approaches (with over 7,000 publications for Android alone) in this field that essentially contributed to the great success of the current mobile ecosystem. Existing research efforts mainly focus on popular mobile platforms, namely Android and iOS. OpenHarmony, a newly open-sourced mobile platform, has rarely been considered, although it is the one requiring the most attention as OpenHarmony is expected to occupy one-third of the market in China (if not in the world). To fill the gap, we present to the mobile software engineering community a research roadmap for encouraging our fellow researchers to contribute promising approaches to OpenHarmony. Specifically, we start by presenting a literature review of mobile software engineering, attempting to understand what problems have been targeted by the mobile community and how they have been resolved. We then summarize the existing (limited) achievements of OpenHarmony and subsequently highlight the research gap between Android/iOS and OpenHarmony. This research gap eventually helps in forming the roadmap for conducting software engineering research for OpenHarmony.

Context: Machine Learning (ML) has become widely adopted as a component in many modern software applications. Due to the large volumes of data available, organizations want to increasingly leverage their data to extract meaningful insights and enhance business profitability. ML components enable predictive capabilities, anomaly detection, recommendation, accurate image and text processing, and informed decision-making. However, developing systems with ML components is not trivial; it requires time, effort, knowledge, and expertise in ML, data processing, and software engineering. There have been several studies on the use of model-driven engineering (MDE) techniques to address these challenges when developing traditional software and cyber-physical systems. Recently, there has been a growing interest in applying MDE for systems with ML components. Objective: The goal of this study is to further explore the promising intersection of MDE with ML (MDE4ML) through a systematic literature review (SLR). Through this SLR, we wanted to analyze existing studies, including their motivations, MDE solutions, evaluation techniques, key benefits and limitations. Results: We analyzed selected studies with respect to several areas of interest and identified the following: 1) the key motivations behind using MDE4ML; 2) a variety of MDE solutions applied, such as modeling languages, model transformations, tool support, targeted ML aspects, contributions and more; 3) the evaluation techniques and metrics used; and 4) the limitations and directions for future work. We also discuss the gaps in existing literature and provide recommendations for future research. Conclusion: This SLR highlights current trends, gaps and future research directions in the field of MDE4ML, benefiting both researchers and practitioners

Requirements Engineering (RE) is a critical phase in software development including the elicitation, analysis, specification, and validation of software requirements. Despite the importance of RE, it remains a challenging process due to the complexities of communication, uncertainty in the early stages and inadequate automation support. In recent years, large-language models (LLMs) have shown significant promise in diverse domains, including natural language processing, code generation, and program understanding. This chapter explores the potential of LLMs in driving RE processes, aiming to improve the efficiency and accuracy of requirements-related tasks. We propose key directions and SWOT analysis for research and development in using LLMs for RE, focusing on the potential for requirements elicitation, analysis, specification, and validation. We further present the results from a preliminary evaluation, in this context.

We observed Sedna, Gonggong, and Quaoar with the NIRSpec instrument on the James Webb Space Telescope (JWST). All three bodies were observed in the low-resolution prism mode at wavelengths spanning 0.7 to 5.2 $\mu$m. Quaoar was also observed at 10x higher spectral resolution from 0.97 to 3.16 $\mu$m using medium-resolution gratings. Sedna's spectrum shows a large number of absorption features due to ethane (C$_2$H$_6$), as well as acetylene (C$_2$H$_2$), ethylene (C$_2$H$_4$), H$_2$O, and possibly minor CO$_2$. Gonggong's spectrum also shows several, but fewer and weaker, ethane features, along with stronger and cleaner H$_2$O features and CO$_2$ complexed with other molecules. Quaoar's prism spectrum shows even fewer and weaker ethane features, the deepest and cleanest H$_2$O features, a feature at 3.2 $\mu$m possibly due to HCN, and CO$_2$ ice. The higher-resolution medium grating spectrum of Quaoar reveals several overtone and combination bands of ethane and methane (CH$_4$). Spectra of all three objects show steep red spectral slopes and strong, broad absorptions between 2.7 and 3.6 $\mu$m indicative of complex organic molecules. The suite of light hydrocarbons and complex organic molecules are interpreted as the products of irradiation of methane. We infer that the differences in apparent abundances of irradiation products are likely due to their distinctive orbits, which lead to different timescales of methane retention and to different charged particle irradiation environments. In all cases, however, the continued presence of light hydrocarbons implies a resupply of methane to the surface. We suggest that these three bodies have undergone internal melting and geochemical evolution similar to the larger dwarf planets and distinct from all smaller KBOs., Comment: 32 pages, 9 figures, 5 tables, accepted for publication in Icarus

Software applications play an increasingly critical role in various aspects of our lives, from communication and entertainment to business and healthcare. As these applications become more pervasive, the importance of considering human values in software development has gained significant attention. In this preliminary study, we investigate developers's perceptions and experiences related to human values, with a focus on the human value of transparency. We interviewed five experienced developers and conducted thematic analysis to explore how developers perceive transparency, violations of transparency, and the process of fixing reported violations of transparency. Our findings reveal the significance of transparency as a fundamental value in software development, with developers recognising its importance for building trust, promoting accountability, and fostering ethical practices. Developers recognise the negative consequences of the violation of the human value of transparency and follow a systematic process to fix reported violations. This includes investigation, root cause analysis, corrective action planning, collaborative problem-solving, and testing and verification. These preliminary findings contribute to the understanding of transparency in software development and provide insights for promoting ethical practices., Comment: 2023 Workshop on Human Centric Software Engineering and Cyber Security

Dwarf planets Eris and Makemake have surfaces bearing methane ice of unknown origin. D/H ratios were recently determined from James Webb Space Telescope (JWST) observations of Eris and Makemake, giving us new clues to decipher the origin of methane. Here, we develop geochemical models to test if the origin of methane could be primordial, derived from CO$_2$ or CO ("abiotic"), or sourced by organics ("thermogenic"). We find that primordial methane is inconsistent with the observational data, whereas both abiotic and thermogenic methane can have D/H ratios that overlap the observed ranges. This suggests that Eris and Makemake either never acquired a significant amount of methane during their formation, or their original inventories were removed and then replaced by a source of internally produced methane. Because producing abiotic or thermogenic methane likely requires temperatures above ~150{\deg}C, we infer that Eris and Makemake have rocky cores that underwent substantial radiogenic heating. Their cores may still be warm/hot enough to make methane. This heating could have driven hydrothermal circulation at the bottom of an ice-covered ocean to generate abiotic methane, and/or metamorphic reactions involving accreted organic matter could have occurred in response to heating in the deeper interior, generating thermogenic methane. Additional analyses of relevant thermal evolution model results and theoretical predictions of the D/H ratio of methane in the solar nebula support our findings of elevated subsurface temperatures and an apparent lack of primordial methane on Eris and Makemake. It remains an open question whether their D/H ratios may have evolved subsequent to methane outgassing. Recommendations are given for future activities to further test proposed scenarios of abiotic and thermogenic methane production on Eris and Makemake, and to explore these worlds up close., Comment: Accepted by Icarus, 43 pages, 8 figures, 1 table

James Webb Space Telescope's NIRSpec infrared imaging spectrometer observed the outer solar system dwarf planets Eris and Makemake in reflected sunlight at wavelengths spanning 1 through 5 microns. Both objects have high albedo surfaces that are rich in methane ice, with a texture that permits long optical path lengths through the ice for solar photons. There is evidence for N2 ice absorption around 4.2 um on Eris, though not on Makemake. No CO ice absorption is seen at 4.67 um on either body. For the first time, absorption bands of two heavy isotopologues of methane are observed at 2.615 um (13CH4), 4.33 um (12CH3D), and 4.57 um (12CH3D). These bands enable us to measure D/H ratios of (2.5 +/- 0.5) x 10-4 and (2.9 +/- 0.6) x 10-4, along with 13C/12C ratios of 0.012 +/- 0.002 and 0.010 +/- 0.003 in the surface methane ices of Eris and Makemake, respectively. The measured D/H ratios are much lower than that of presumably primordial methane in comet 67P/Churyumov-Gerasimenko, but they are similar to D/H ratios in water in many comets and larger outer solar system objects. This similarity suggests that the hydrogen atoms in methane on Eris and Makemake originated from water, indicative of geochemical processes in past or even ongoing hot environments in their deep interiors. The 13C/12C ratios are consistent with commonly observed solar system values, suggesting no substantial enrichment in 13C as could happen if the methane currently on their surfaces was the residue of a much larger inventory that had mostly been lost to space. Possible explanations include geologically recent outgassing from the interiors as well as processes that cycle the surface methane inventory to keep the uppermost surfaces refreshed.

Nitrogen, carbon monoxide, and methane are key materials in the far outer Solar System where their high volatility enables them to sublimate, potentially driving activity at very low temperatures. Knowledge of their vapor pressures and latent heats of sublimation at relevant temperatures is needed to model the processes involved. We describe a method for using a quartz crystal microbalance to measure the sublimation flux of these volatile ices in the free molecular flow regime, accounting for the simultaneous sublimation from and condensation onto the quartz crystal to derive vapor pressures and latent heats of sublimation. We find vapor pressures to be somewhat lower than previous estimates in literature, with carbon monoxide being the most discrepant of the three species, almost an order of magnitude lower than had been thought. These results have important implications across a variety of astrophysical and planetary environments.

React Native is a widely-used open-source framework that facilitates the development of cross-platform mobile apps. The framework enables JavaScript code to interact with native-side code, such as Objective-C/Swift for iOS and Java/Kotlin for Android, via a communication mechanism provided by React Native. However, previous research and tools have overlooked this mechanism, resulting in incomplete analysis of React Native app code. To address this limitation, we have developed REUNIFY, a prototype tool that integrates the JavaScript and native-side code of React Native apps into an intermediate language that can be processed by the Soot static analysis framework. By doing so, REUNIFY enables the generation of a comprehensive model of the app's behavior. Our evaluation indicates that, by leveraging REUNIFY, the Soot-based framework can improve its coverage of static analysis for the 1,007 most popular React Native Android apps, augmenting the number of lines of Jimple code by 70%. Additionally, we observed an average increase of 84% in new nodes reached in the callgraph for these apps, after integrating REUNIFY. When REUNIFY is used for taint flow analysis, an average of two additional privacy leaks were identified. Overall, our results demonstrate that REUNIFY significantly enhances the Soot-based framework's capability to analyze React Native Android apps.