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Mobile health applications (mHealth apps), particularly in the health and fitness category, have experienced an increase in popularity due to their convenience and availability. However, this widespread adoption raises concerns regarding the security of the user's data. In this study, we investigate the security vulnerabilities of ten top-ranked Android health and fitness apps, a set that accounts for 237 million downloads. We performed several static and dynamic security analyses using tools such as the Mobile Security Framework (MobSF) and Android emulators. We also checked the server's security levels with Qualys SSL, which allowed us to gain insights into the security posture of the servers communicating with the mHealth fitness apps. Our findings revealed many vulnerabilities, such as insecure coding, hardcoded sensitive information, over-privileged permissions, misconfiguration, and excessive communication with third-party domains. For instance, some apps store their database API key directly in the code while also exposing their database URL. We found insecure encryption methods in six apps, such as using AES with ECB mode. Two apps communicated with an alarming number of approximately 230 domains each, and a third app with over 100 domains, exacerbating privacy linkability threats. The study underscores the importance of continuous security assessments of top-ranked mHealth fitness apps to better understand the threat landscape and inform app developers., Comment: 19 pages, 2 figures, 8 tables, NordSec 2024

Relational databases often suffer from uninformative descriptors of table contents, such as ambiguous columns and hard-to-interpret values, impacting both human users and text-to-SQL models. In this paper, we explore the use of large language models (LLMs) to automatically generate detailed natural language descriptions for SQL database columns, aiming to improve text-to-SQL performance and automate metadata creation. We create a dataset of gold column descriptions based on the BIRD-Bench benchmark, manually refining its column descriptions and creating a taxonomy for categorizing column difficulty. Through evaluating several LLMs, we find that incorporating these column descriptions consistently enhances text-to-SQL model performance, particularly for larger models like GPT-4o, Qwen2 72B and Mixtral 22Bx8. However, models struggle with columns that exhibit inherent ambiguity, highlighting the need for manual expert input. Notably, Qwen2-generated descriptions, containing by annotators deemed superfluous information, outperform manually curated gold descriptions, suggesting that models benefit from more detailed metadata than humans expect. Future work will investigate the specific features of these high-performing descriptions and explore other types of metadata, such as numerical reasoning and synonyms, to further improve text-to-SQL systems. The dataset, annotations and code will all be made available.

This paper develops an approach to classify instances of product failure in a complex textiles manufacturing dataset using explainable techniques. The dataset used in this study was obtained from a New Zealand manufacturer of woollen carpets and rugs. In investigating the trade-off between accuracy and explainability, three different tree-based classification algorithms were evaluated: a Decision Tree and two ensemble methods, Random Forest and XGBoost. Additionally, three feature selection methods were also evaluated: the SelectKBest method, using chi-squared as the scoring function, the Pearson Correlation Coefficient, and the Boruta algorithm. Not surprisingly, the ensemble methods typically produced better results than the Decision Tree model. The Random Forest model yielded the best results overall when combined with the Boruta feature selection technique. Finally, a tree ensemble explaining technique was used to extract rule lists to capture necessary and sufficient conditions for classification by a trained model that could be easily interpreted by a human. Notably, several features that were in the extracted rule lists were statistical features and calculated features that were added to the original dataset. This demonstrates the influence that bringing in additional information during the data preprocessing stages can have on the ultimate model performance., Comment: Accepted at the 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE 2024), awaiting publication Contains seven pages and five figures

In this study, state-of-the-art unsupervised detection models were evaluated for the purpose of automated anomaly inspection of wool carpets. A custom dataset of four unique types of carpet textures was created to thoroughly test the models and their robustness in detecting subtle anomalies in complex textures. Due to the requirements of an inline inspection system in a manufacturing use case, the metrics of importance in this study were accuracy in detecting anomalous areas, the number of false detections, and the inference times of each model for real-time performance. Of the evaluated models, the student-teacher network based methods were found on average to yield the highest detection accuracy and lowest false detection rates. When trained on a multi-class dataset the models were found to yield comparable if not better results than single-class training. Finally, in terms of detection speed, with exception to the generative model, all other evaluated models were found to have comparable inference times on a GPU, with an average of 0.16s per image. On a CPU, most of these models typically produced results between 1.5 to 2 times the respective GPU inference times., Comment: Accepted at the 2023 Australasian Conference on Robotics and Automation (ACRA 2023) Publication url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184380272&partnerID=40&md5=74fde263f4a24a1bff75d6560b423994 ISSN: 14482053 Contains 10 pages and three figures

The Mid-infrared ELT Imager and Spectrograph (METIS) is one of the first-generation scientific instruments for the ELT, built under the supervision of ESO by a consortium of research institutes across and beyond Europe. Designed to cover the 3 to 13 $\mu$m wavelength range, METIS had its final design reviewed in Fall 2022, and has then entered in earnest its manufacture, assembly, integration, and test (MAIT) phase. Here, we present the final design of the METIS high-contrast imaging (HCI) modes. We detail the implementation of the two main coronagraphic solutions selected for METIS, namely the vortex coronagraph and the apodizing phase plate, including their combination with the high-resolution integral field spectrograph of METIS, and briefly describe their respective backup plans (Lyot coronagraph and shaped pupil plate). We then describe the status of the MAIT phase for HCI modes, including a review of the final design of individual components such as the vortex phase masks, the grayscale ring apodizer, and the apodizing phase plates, as well as a description of their on-going performance tests and of our plans for system-level integration and tests. Using end-to-end simulations, we predict the performance that will be reached on sky by the METIS HCI modes in presence of various environmental and instrumental disturbances, including non-common path aberrations and water vapor seeing, and discuss our strategy to mitigate these various effects. We finally illustrate with mock observations and data processing that METIS should be capable of directly imaging temperate rocky planets around the nearest stars., Comment: 16 pages, paper presented at SPIE Astronomical Telescopes + Instrumentation 2024

We report metallicities for three $\sim$Gyr-old stars in the Milky Way nuclear star cluster (NSC) using high-resolution near-infrared spectroscopy. We derive effective temperatures from a calibration with Sc line strength, which yields results in good agreement with other methods, and metallicities from spectral fits to Fe I lines. Our derived metallicities range from -1.2 < [Fe/H] < +0.5, a span of 1.7 dex. In addition we use isochrone projection to obtain masses of 1.6 to 4.3 M$_\odot$, and ages assuming single-star evolution. The oldest of these stars is 1.5 Gyr while the youngest and most metal-rich is only 100 Myr. The wide range in metallicity poses interesting questions concerning the chemical evolution and enrichment of the NSC and adds to the evidence for the presence of a young, metal-rich population in the NSC. We suggest that the candidate intermediate-age, metal-poor ([Fe/H] = -1.2) star may be best explained as a blue straggler from an underlying old population., Comment: 6 pages, 4 figures

One of the main challenges in multi-agent reinforcement learning is scalability as the number of agents increases. This issue is further exacerbated if the problem considered is temporally dependent. State-of-the-art solutions today mainly follow centralized training with decentralized execution paradigm in order to handle the scalability concerns. In this paper, we propose time-dependent multi-agent transformers which can solve the temporally dependent multi-agent problem efficiently with a centralized approach via the use of transformers that proficiently handle the large input. We highlight the efficacy of this method on two problems and use tools from statistics to verify the probability that the trajectories generated under the policy satisfy the task. The experiments show that our approach has superior performance against the literature baseline algorithms in both cases., Comment: Submitted to L4DC 2024 conference

In order to investigate the chemical history of the entire MilkyWay, it is imperative to also study the dust-obscured regions, where most of the mass lies. The Galactic Center is an example of such a region of interest, where due to the intervening dust along the line-of-sight, near-infrared spectroscopic investigations are necessary. We demonstrate that M giants observed at high spectral resolution in the H and K bands (1.5-2.4 {\mu}m) can yield useful abundance-ratio trends versus metallicity for 21 elements. These elements can therefore be studied also for heavily dust-obscured regions of the Galaxy, such as the Galactic Center, and will be important for the further investigation of the Galactic chemical evolution in these regions. We have observed near-infrared spectra of 50 M giants in the solar neighbourhood at high SNR and at a high spectral resolution (R = 45, 000) with the IGRINS spectrometer on the GEMINI South telescope. We adopted the fundamental stellar parameters for these stars from Nandakumar et al. (2023a), with Teff ranging from 3400 to 3800 K. With a manual spectral synthesis method, we have derived stellar abundances for 21 elements, namely F, Mg, Si, S, Ca, Na, Al, K, Sc, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, Y, Ce, Nd, and Yb. We demonstrate what elements can be analysed from H- and K-band high-resolution spectra, and we show which spectral lines can be used for abundance analysis, showing them line by line. We discuss the 21 abundance-ratio trends and compared them with those determined from APOGEE and from the optical GILD sample. Especially, we determine the trends of the heavy elements Cu, Zn, Y, Ce, Nd, and Yb. This opens up these nucleosynthetic channels, including both the s- and the r-process, in dust-obscured populations. The [Mn/Fe] versus [Fe/H] trend is shown to be more or less flat at low metallicities, implying that existing NLTE correction are relevant., Comment: 21 pages, 27 figures, Accepted for publication in A&A

In the current high-performance and embedded computing era, full-stack energy-centric design is paramount. Use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Extreme heterogeneity and parallelism address these issues but greatly complicate online power consumption assessment, which is essential for dynamic hardware and software stack adaptations. We introduce a novel architecture-agnostic power modeling methodology with state-of-the-art accuracy, low overhead, and high responsiveness. Our methodology identifies the best Performance Monitoring Counters (PMCs) to model the power consumption of each hardware sub-system at each Dynamic Voltage and Frequency Scaling (DVFS) state. The individual linear models are combined into a complete model that effectively describes the power consumption of the whole system, achieving high accuracy and low overhead. Our evaluation reports an average estimation error of 7.5 % for power consumption and 1.3 % for energy. Furthermore, we propose Runmeter, an open-source, PMC-based monitoring framework integrated into the Linux kernel. Runmeter manages PMC samples collection and manipulation, efficiently evaluating our power models at runtime. With a time overhead of only 0.7 % in the worst case, Runmeter provides responsive and accurate power measurements directly in the kernel, which can be employed for actuation policies such as Dynamic Power Management (DPM) and power-aware task scheduling., Comment: 13 pages, 5 figures, submitted to the IEEE for possible publication

Enterprise Application Integration deals with the problem of connecting heterogeneous applications, and is the centerpiece of current on-premise, cloud and device integration scenarios. For integration scenarios, structurally correct composition of patterns into processes and improvements of integration processes are crucial. In order to achieve this, we formalize compositions of integration patterns based on their characteristics, and describe optimization strategies that help to reduce the model complexity, and improve the process execution efficiency using design time techniques. Using the formalism of timed DB-nets - a refinement of Petri nets - we model integration logic features such as control- and data flow, transactional data storage, compensation and exception handling, and time aspects that are present in reoccurring solutions as separate integration patterns. We then propose a realization of optimization strategies using graph rewriting, and prove that the optimizations we consider preserve both structural and functional correctness. We evaluate the improvements on a real-world catalog of pattern compositions, containing over 900 integration processes, and illustrate the correctness properties in case studies based on two of these processes., Comment: 37 pages

Computing systems have shifted towards highly parallel and heterogeneous architectures to tackle the challenges imposed by limited power budgets. These architectures must be supported by novel power management paradigms addressing the increasing design size, parallelism, and heterogeneity while ensuring high accuracy and low overhead. In this work, we propose a systematic, automated, and architecture-agnostic approach to accurate and lightweight DVFS-aware statistical power modeling of the CPU and GPU sub-systems of a heterogeneous platform, driven by the sub-systems' local performance monitoring counters (PMCs). Counter selection is guided by a generally applicable statistical method that identifies the minimal subsets of counters robustly correlating to power dissipation. Based on the selected counters, we train a set of lightweight, linear models characterizing each sub-system over a range of frequencies. Such models compose a lookup-table-based system-level model that efficiently captures the non-linearity of power consumption, showing desirable responsiveness and decomposability. We validate the system-level model on real hardware by measuring the total energy consumption of an NVIDIA Jetson AGX Xavier platform over a set of benchmarks. The resulting average estimation error is 1.3%, with a maximum of 3.1%. Furthermore, the model shows a maximum evaluation runtime of 500 ns, thus implying a negligible impact on system utilization and applicability to online dynamic power management (DPM).