Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Safety vs. App Privacy: Comparing the Usability of Android and iOS Privacy Labels

Privacy labels -- standardized, compact representations of data collection and data use practices -- are often presented as a solution to the shortcomings of privacy policies. Apple introduced mandatory privacy labels for apps in its App Store in December 2020; Google introduced mandatory labels for Android apps in July 2022. iOS app privacy labels have been evaluated and critiqued in prior work. In this work, we evaluated Android Data Safety Labels and explored how differences between the two label designs impact user comprehension and label utility. We conducted a between-subjects, semi-structured interview study with 12 Android users and 12 iOS users. While some users found Android Data Safety Labels informative and helpful, other users found them too vague. Compared to iOS App Privacy Labels, Android users found the distinction between data collection groups more intuitive and found explicit inclusion of omitted data collection groups more salient. However, some users expressed skepticism regarding elided information about collected data type categories. Most users missed critical information due to not expanding the accordion interface, and they were surprised by collection practices excluded from Android's definitions. Our findings also revealed that Android users generally appreciated information about security practices included in the labels, and iOS users wanted that information added.
Stochastic modelling of football matches

This paper develops a general framework for stochastic modeling of goals and other events in football (soccer) matches. The events are modelled as Cox processes (doubly stochastic Poisson processes) where the event intensities may depend on all the modeled events as well as external factors. The model has a strictly concave log-likelihood function which facilitates its fitting to observed data. Besides event times, the model describes the random lengths of stoppage times which can have a strong influence on the final score of a match. The model is illustrated on eight years of data from Campeonato Brasileiro de Futebol S\'erie A. We find that dynamic regressors significantly improve the in-game predictive power of the model. In particular, a) when a team receives a red card, its goal intensity decreases more than 30%; b) the goal rate of a team increases by 10% if it is losing by one goal and by 20% if its losing by two goals; and c) when the goal difference at the end of the second half is less than or equal to one, the stoppage time is on average more than one minute longer than in matches with a difference of two goals.
Information Design for Hybrid Work under Infectious Disease Transmission Risk

We study a planner's provision of information to manage workplace occupancy when strategic workers (agents) face risk of infectious disease transmission. The planner implements an information mechanism to signal information about the underlying risk of infection at the workplace. Agents update their belief over the risk parameter using this information and choose to work in-person or remotely. We address the design of the optimal signaling mechanism that best aligns the workplace occupancy with the planner's preference (i.e., maintaining safe capacity limits and operational efficiency at workplace). For various forms of planner preferences, we show numerical and analytical proof that interval-based information mechanisms are optimal. These mechanisms partition the continuous domain of the risk parameter into disjoint intervals and provision information based on interval-specific probability distributions over a finite set of signals. When the planner seeks to achieve an occupancy that lies in one of finitely many pre-specified ranges independent of the underlying risk, we provide an optimal mechanism that uses at most two intervals. On the other hand, when the preference on the occupancy is risk-dependent, we show that an approximately optimal interval-based mechanism can be computed efficiently. We bound the approximation loss for preferences that are expressed through a Lipschitz continuous function of both occupancy and risk parameter. We provide examples that demonstrate the improvement of proposed signaling mechanisms relative to the common benchmarks in information provision. Our findings suggest that information provision over the risk of disease transmission is an effective intervention for maintaining desirable occupancy levels at the workplace.
Bacterial denitrification drives elevated N2O emissions in arid southern California drylands

Soils are the largest source of atmospheric nitrous oxide (N2O), a powerful greenhouse gas. Dry soils rarely harbor anoxic conditions to favor denitrification, the predominant N2O-producing process, yet, among the largest N2O emissions have been measured after wetting summer-dry desert soils, raising the question: Can denitrifiers endure extreme drought and produce N2O immediately after rainfall? Using isotopic and molecular approaches in a California desert, we found that denitrifiers produced N2O within 15 minutes of wetting dry soils (site preference = 12.8 ± 3.92 per mil, δ15Nbulk = 18.6 ± 11.1 per mil). Consistent with this finding, we detected nitrate-reducing transcripts in dry soils and found that inhibiting microbial activity decreased N2O emissions by 59%. Our results suggest that despite extreme environmental conditions—months without precipitation, soil temperatures of ≥40°C, and gravimetric soil water content of <1%—bacterial denitrifiers can account for most of the N2O emitted when dry soils are wetted.
Impact of Δ9-Tetrahydrocannabinol and oxycodone co-administration on measures of antinociception, dependence, circadian activity, and reward in mice

Oxycodone is commonly prescribed for moderate to severe pain disorders. While efficacious, long-term use can result in tolerance, physical dependence, and the development of opioid use disorder. Cannabis and its derivatives such as Δ9-Tetrahydrocannabinol (Δ9-THC) have been reported to enhance oxycodone analgesia in animal models and in humans. However, it remains unclear if Δ9-THC may facilitate unwanted aspects of oxycodone intake, such as tolerance, dependence, and reward at analgesic doses. This study sought to evaluate the impact of co-administration of Δ9-THC and oxycodone across behavioral measures related to antinociception, dependence, circadian activity, and reward in both male and female mice. Oxycodone and Δ9-THC produced dose-dependent antinociceptive effects in the hotplate assay that were similar between sexes. Repeated treatment (twice daily for 5 days) resulted in antinociceptive tolerance. Combination treatment of oxycodone and Δ9-THC produced a greater antinociceptive effect than either administered alone, and delayed the development of antinociceptive tolerance. Repeated treatment with oxycodone produced physical dependence and alterations in circadian activity, neither of which were exacerbated by co-treatment with Δ9-THC. Combination treatment of oxycodone and Δ9-THC produced CPP when co-administered at doses that did not produce preference when administered alone. These data indicate that Δ9-THC may facilitate oxycodone-induced antinociception without augmenting certain unwanted features of opioid intake (e.g. dependence, circadian rhythm alterations). However, our findings also indicate that Δ9-THC may facilitate rewarding properties of oxycodone at therapeutically relevant doses which warrant consideration when evaluating this combination for its potential therapeutic utility.
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs

Unsupervised anomaly detection in Brain MRIs aims to identify abnormalities as outliers from a healthy training distribution. Reconstruction-based approaches that use generative models to learn to reconstruct healthy brain anatomy are commonly used for this task. Diffusion models are an emerging class of deep generative models that show great potential regarding reconstruction fidelity. However, they face challenges in preserving intensity characteristics in the reconstructed images, limiting their performance in anomaly detection. To address this challenge, we propose to condition the denoising mechanism of diffusion models with additional information about the image to reconstruct coming from a latent representation of the noise-free input image. This conditioning enables high-fidelity reconstruction of healthy brain structures while aligning local intensity characteristics of input-reconstruction pairs. We evaluate our method's reconstruction quality, domain adaptation features and finally segmentation performance on publicly available data sets with various pathologies. Using our proposed conditioning mechanism we can reduce the false-positive predictions and enable a more precise delineation of anomalies which significantly enhances the anomaly detection performance compared to established state-of-the-art approaches to unsupervised anomaly detection in brain MRI. Furthermore, our approach shows promise in domain adaptation across different MRI acquisitions and simulated contrasts, a crucial property of general anomaly detection methods.
Fear Field: Adaptive constraints for safe environment transitions in Shielded Reinforcement Learning

Shielding methods for Reinforcement Learning agents show potential for safety-critical industrial applications. However, they still lack robustness on nominal safety, a key property for safety control systems. In the case of a significant change in the environment dynamic, shielding methods cannot guarantee safety until their inherent dynamics model is updated to the new scenario. The agent could reach risky states because the model cannot predict well. These situations could lead to catastrophic outcomes, such as damage to the cyber-physical system or loss of human lives, which are not allowed on safety-critical applications. The novel method presented in this paper, Fear Field, replicates human behaviour in those scenarios, adapting safety constraints whenever a drastic environmental change is introduced. Fear Field reduces safety violations by one order of magnitude compared to an RL agent implementing only a shield.
RAD54L2 counters TOP2-DNA adducts to promote genome stability

The catalytic cycle of topoisomerase 2 (TOP2) enzymes proceeds via a transient DNA double-strand break (DSB) intermediate termed the TOP2 cleavage complex (TOP2cc), in which the TOP2 protein is covalently bound to DNA. Anticancer agents such as etoposide operate by stabilizing TOP2ccs, ultimately generating genotoxic TOP2-DNA protein cross-links that require processing and repair. Here, we identify RAD54 like 2 (RAD54L2) as a factor promoting TOP2cc resolution. We demonstrate that RAD54L2 acts through a novel mechanism together with zinc finger protein associated with tyrosyl-DNA phosphodiesterase 2 (TDP2) and TOP2 (ZATT/ZNF451) and independent of TDP2. Our work suggests a model wherein RAD54L2 recognizes sumoylated TOP2 and, using its ATPase activity, promotes TOP2cc resolution and prevents DSB exposure. These findings suggest RAD54L2-mediated TOP2cc resolution as a potential mechanism for cancer therapy resistance and highlight RAD54L2 as an attractive candidate for drug discovery.
How to Assess the User ’ s Experience in Cultural Computing

In this paper I start with an overview over the different paradigms emerged over the last decades in HCI. I introduce the paradigm of cultural computing based on concepts like Kansei Mediation. It is an ambitious challenge to compare Eastern and Western cultures. One of the main challenges will be to measure the user’s experience, in particular for subor even unconscious cognitive and body functions. An overview of already available measuring approaches is provided, and some preliminary conclusions are drawn.
Sentiment Analysis on Internet Movie Database (IMDb) Movie Review Dataset: Hyperparameters Tuning for Naïve Bayes Model

Sentiment classification plays a crucial role in understanding and analyzing text data, particularly in domains like social media and online reviews. In this study, the influence of three key parameters on the accuracy of sentiment classification was investigated by applying Naive Bayes classifier to the Internet Movie Database (IMDb) movie review dataset. To explore the impact of the training set ratio, the proportion of data allocated to the training set was varied while keeping other parameters constant. Results indicate that increasing the training set ratio from 50% to 90% leads to a gradual improvement in classification accuracy. This finding suggests that a larger training set provides more representative samples for learning, enhancing the model's ability to generalize. Sub-sequently, the impact of the maximum features parameter — which establishes the feature space's dimensionality — was investigated. By changing the number of features taken into account, it is found that a larger value of max features, like 4096, produces better accuracy. Additionally, the impact of the smoothing parameter alpha on classification accuracy was investigated. The experiments showed that different alpha values, such as 0.1, 0.5, and 1, had minimal influence on the accuracy. This suggests that the Naive Bayes classifier is relatively robust to variations in the smoothing parameter in the context of sentiment classification. The findings emphasize the significance of a larger training set and an optimal number of features for improving accuracy, meanwhile the influence of the smoothing parameter appears to be limited in this context.
Dr. Jekyll and Mr. Hyde: Two Faces of LLMs

Only a year ago, we witnessed a rise in the use of Large Language Models (LLMs), especially when combined with applications like chatbot assistants. Safety mechanisms and specialized training procedures are implemented to prevent improper responses from these assistants. In this work, we bypass these measures for ChatGPT and Bard (and, to some extent, Bing chat) by making them impersonate complex personas with opposite characteristics as those of the truthful assistants they are supposed to be. We start by creating elaborate biographies of these personas, which we then use in a new session with the same chatbots. Our conversation followed a role-play style to get the response the assistant was not allowed to provide. By making use of personas, we show that the response that is prohibited is actually provided, making it possible to obtain unauthorized, illegal, or harmful information. This work shows that by using adversarial personas, one can overcome safety mechanisms set out by ChatGPT and Bard. We also introduce several ways of activating such adversarial personas, altogether showing that both chatbots are vulnerable to this kind of attack. With the same principle, we introduce two defenses that push the model to interpret trustworthy personalities and make it more robust against such attacks.
Annual Review of Pharmacology and Toxicology The Opioid Epidemic : Crisis and Solutions

The widespread abuse of prescription opioids and a dramatic increase in the availability of illicit opioids have created what is commonly referred to as the opioid epidemic. The magnitude of this epidemic is startling: About 4% of the adult US population misuses prescription opioids, and in 2015, more than 33,000 deaths were attributable to overdose with licit and illicit opioids. Increasing the availability of medication-assisted treatments (such as buprenorphine and naltrexone), the use of abuse-deterrent formulations, and the adoption of US Centers for Disease Control and Prevention prescribing guidelines all constitute short-term approaches to quell this epidemic. However, with more than 125 million Americans suffering from either acute or chronic pain, the development of effective alternatives to opioids, enabled at least in part by a fuller understanding of the neurobiological bases of pain, offers the best long-term solution for controlling and ultimately eradicating this epidemic.
A Computational Tool to Determine the Forces Generated by Waves on Piles ⭐

This paper presents the development of a computational software tool for the determination of the forces generated by the passage of waves on piles, with the objective of disseminating instructive knowledge, or assisting in the design process of such structure, commonly found in port regions. This tool is built through the use of a free, open-source and easy-to-assimilate computational language. To make this software more intuitive, a graphical interface is designed to illustrate the results obtained. To obtain these results, the present work is based on the methodology adopted by Mason [1], which makes use of Morison’s formula that is commonly present in the literature, and also adopted by the Spanish standard (ROM2.0-11). This work differs from the methodology adopted by Mason in what concerns the determination of the incident wave length on the pile. Here, a numerical solver is used for the solution of the dispersion relation, avoiding the need to verify the wave propagation regime. The results found reinforce the importance of using computational tools in the study or practice of engineering, providing greater precision and agility in performing complex calculations.
A Multi-View Image-Based Volume Visualization Technique

We present an image-based volume visualization approach based on Principal Component Analysis (PCA). Using PCA, a learned model is trained using pre-rendered images from spherically distributed viewing angles. The views are encoded into a compressed and more compact representation and then novel views can be synthesized by interpolating scores in the eigenspace. The main advantage of the PCA model is the low computational complexity in the encoding and decoding phases. Furthermore, the image encoding and reconstruction is independent of the rendering complexity. This is particularly important in the case of computationally demanding rendering techniques such as global illumination. Our technique has potential application in client-server volume visualization or where results of a computationally-complex 3D imaging process need to be interactively visualized on a display device of limited specification.
Pediatric Pulmonology

Correspondence Danilo Buonsenso, Department of Pediatric Emergency, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy. Email: danilobuonsenso@gmail.com Abstract Objective: The purpose of this study was to describe lung ultrasound (LUS) findings at baseline and 48 hours after the beginning of treatment and evaluate how they correlate with outcome Design: We prospectively analyzed patients from 1 month to 17 years of age with community acquired pneumonia (CAP) evaluated at a tertiary level pediatric hospital. At baseline and 48 hours after the beginning of treatment, history, clinical examination, laboratory testing, chest X‐ray, and LUS were performed. Results: One hundred one children were enrolled in the study (13 with complicated CAP). At baseline those who developed complications presented a larger size of the subpleural pulmonary parenchymal lesions (P = .001) often associated with a complex pleural effusion (63.6%, P = .013). Those with an uncomplicated CAP presented an air, arboriform, superficial and dynamic bronchogram, as opposed to complicated CAP which had an air and liquid bronchogram, deep, fixed (P = .001). At the 48‐hour control in the noncomplicated CAP group, bronchogram was more frequently superficial and dynamic (P = .050). Pleural effusion disappeared in half cases (P = .050). In all patients, neutrophilic leucocytosis with increased C‐reactive protein was detected and decreased at control (P = .001). The linear regression analyses showed the switch from a deep to a superficial bronchogram as the only explanatory variable (r = 0.97, R = 0.94, P = .001, t = 10.73). Conclusions: Our study describe early LUS features of CAP that might be able to predict the development of complicated CAP.
IDENTIFIKASI HABITAT DAN PRODUKSI SARANG BURUNG WALET (Collocalia fuciphaga) DI KABUPATEN LAMPUNG TIMUR

Penelitian ini bertujuan untuk mengetahui lingkungan Burung Walet di Kabupaten Lampung Timur, Provinsi Lampung. Penelitian ini dilakukan di Kecamatan Way Jepara, Bandar Sribhawono dan Labuhan Maringgai dengan jumlah sampel 6 gedung. Metode penelitian yang digunakan adalah deskriptif dengan cara pengambilan sampel Purposive Sampling. Peubah yang diamati adalah Habitat Mikro (Temperatur, Kelembaban dan Intensitas Cahaya di dalam gedung), Habitat Makro Kata kunci : Burung Walet, Habitat, Produksi Sarang
The Experimental Validation of Designed Fiber Optic Pressure Sensors With EPDM Diaphragm

This paper focused on the experimental validation of diaphragm-based Fabry-Perot fiber optic pressure sensors (D-FP-FOPS) with ethylene propylene diene terpolymers (EPDM) diaphragm as designed and analyzed sensor tip theoretically. We also used self-adhesive EPDM rubber as a diaphragm for the first time in the literature. Analytical calculation and finite element method (FEM) analysis were carried out to obtain values of fundamental resonance frequency (f<sub>0</sub>) and deflection (d) with diaphragm specific values of Young’s modulus and Poisson’s ratio measured by tensile tests. We produced D-FP-FOPS tip with EPDM diaphragm and obtained experimental <inline-formula> <tex-math notation="LaTeX">$f_{0}$ </tex-math></inline-formula> between 250–400 Hz from extended signal to noise ratio (SNR) plot which figured out with the help of theoretical values of <inline-formula> <tex-math notation="LaTeX">$f_{0}$ </tex-math></inline-formula>. We also analyzed minimum detectable pressure (MDP) mapping which is used to confirm SNR mapping for D-FP-FOPS. We noticed that our sensor could be operated up to 1.6 Pa pressure which confirms the mechanical limit given in the literature. Before production of D-FP-FOPS, a pioneering way which includes design, analytical calculation, FEM analysis, and experimental validation was demonstrated as a novel. Moreover, since we used self-adhesive EPDM tape as a diaphragm material, our sensor tip cost is less than 50 $ which capable to compete with commercial sensors.
Exploring the Inﬂuence of the Information and Communication Technology Dimensions on the Sustainability of Competitiveness in Small and Medium-sized Enterprises in the Hail Region

: The main purpose of this research paper is to investigate the role of information and communication technology (ICT) in providing small and medium-sized enterprises (SMEs) with a competitive advantage. To do this, we propose ﬁve dimensions to detect ICT (human resources, equipment and devices, databases, software, and networks) and four dimensions (service quality, market control, creativity and development, and operational efﬁciency) to decide competitive advantage. To achieve this paper’s purposes, a questionnaire was developed to collect data from respondents to a sample of 128 subjects by using the Statistical Package for Social Sciences (SPSS) for the survey data analysis. Several methods have been used in the statistical analysis of the data, including descriptive statistics tests, a normal distribution test (one-sample Kolmogorov–Smirnov test), simple linear regression coefﬁcient (simple regression), variance analysis (one-way ANOVA), and a T-test for independent samples. This study reports a series of ﬁndings, underlined by the signiﬁcant role of ICT with its various dimensions and the competitive advantage of SMEs. This study concludes with several suggestions including that SMEs must increase their investments in ICT components and take more care of creativity and development for the preemption to create new services.