Miracle-dz/UniFAR-FTUs huggingface dataset:
VPP: Privacy Preserving Machine Learning via Undervolting

Machine Learning (ML) systems are susceptible to membership inference attacks (MIAs), which leak private information from the training data. Specifically, MIAs are able to infer whether a target sample has been used in the training data of a given model. Such privacy breaching concern motivated several defenses against MIAs. However, most of the state-of-theart defenses such as Differential Privacy (DP) come at the cost of lower utility (i.e, classification accuracy). In this work, we propose Privacy Preserving Volt $(V_{PP})$, a new lightweight inference-time approach that leverages undervolting for privacy-preserving ML. Unlike related work, VPP maintains protected models’ utility without requiring re-training. The key insight of our method is to blur the MIA differential analysis outcome by comprehensively garbling the model features using random noise. Unlike DP, which injects noise within the gradient at training time, VPP injects computational randomness in a set of layers’ during inference through carefully designed undervolting Specifically, we propose a bi-objective optimization approach to identify the noise characteristics that yield privacypreserving properties while maintaining the protected model’s utility. Extensive experimental results demonstrate that VPP yields a significantly more interesting utility/privacy tradeoff compared to prior defenses. For example, with comparable privacy protection on CIFAR-10 benchmark, VPP improves the utility by 32.93% over DP-SGD. Besides, while related noisebased defenses are defeated by label-only attacks, VPP shows high resilience to such adaptive MLA. More over, VPP comes with a by-product inference power gain of up to 61%. Finally, for a comprehensive analysis, we propose a new adaptive attacks that operate on the expectation over the stochastic model behavior. We believe that VPP represents a significant step towards practical privacy preserving techniques and considerably improves the state-of-the-art.
The impact of identifiable features in ML Classification algorithms with the HIKARI-2021 Dataset

Network Intrusion Detection Systems keep being the critical part of cybersecurity that researchers are exploring the most. Both datasets and algorithms are being improved, with more computational power, available data, advanced analysis of the data, and better enhancements in the selected algorithms.This paper comes as a need for a new analysis of the HIKARI-2021 dataset where we revisit the features used in previous articles and we discuss the feature importance of the dataset and we notice a significant drop of 20% in the accuracy when removing identifiable features that should be used when training the models.
Comparison of two successive versions 6 and 7 of TMPA satellite precipitation products with rain gauge data over Swat Watershed, Hindukush Mountains, Pakistan

Swat watershed in Hindukush Mountains was selected for the evaluation of Tropical Rainfall Measuring Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) Versions 6 and 7 using rain‐gauge data. Agreement between the satellite and gauge estimates was good at monthly scale but poor at daily and seasonal (monsoon and westerlies, particularly for monsoon) scales. Over and underestimations were observed at foothills and high‐altitude areas, respectively. Although, bias was still present, but overall performance of TMPA‐V7 was improved compared with TMPA‐V6. Bias corrected‐TMPA‐V7 estimates were better than corrected‐TMPA‐V6. Basin average bias‐corrected precipitation estimates of TMPA‐V7 were estimated at daily, monthly, westerlies, monsoon, and annual scales [means (mm) 1.60, 48, 190, 173 and 582, respectively] for the period 1998–2014. Results suggest that regional bias correction of satellite‐precipitation products is critical and can yield to the substantial improvement in capturing the precipitation.
Twitter Sentiment Analysis for Bitcoin Price Prediction

Cryptocurrencies, like Bitcoin, have become increasingly popular over the last decade. The price of Bitcoin has gone through several cycles of highs and lows. As a result, it is a widely discussed topic, especially on platforms like Twitter. Sentiment analysis is a research area of Natural Language Processing. It is used to determine whether the text is positive, negative, or neutral. Twitter tweets are more challenging to analyze when compared to other forms of text, due to the presence of irregular grammar, emoticons, and sarcasm. This study intends to analyze the effect of tweets on the stock price of Bitcoin. In order to study the effect, the sentiment associated with each tweet is calculated using VADER, and also the profession and follower count associated with verified users who tweet about bitcoin is found. Following this, a model is trained and tested using a combined dataset of tweet related data and historical bitcoin price data. It was found that the sentiment of tweets does correlate with the shift in the price of bitcoin.
Embedding Context as Code Dependencies for Neural Program Repair

Deep learning-based program repair has received significant attention from the research community lately. Most existing techniques treat source code as a sequence of tokens or abstract syntax trees. Consequently, they cannot incorporate semantic contextual information pertaining to a buggy line of code and its fix. In this work, we propose a program repair technique called GLANCE that combines static program analysis with graph-to-sequence learning for capturing contextual information. To represent contextual information, we introduce a graph representation that can encode information about the buggy code and its repair ingredients by embedding control and data flow information. We employ a fine-grained graphical code representation, which explicitly describes code change context and embeds semantic relationships between code elements. GLANCE leverages a graph neural network and a sequence-based decoder to learn from this semantic code representation. We evaluated our work against six state-of-the-art techniques, and our results show that GLANCE fixes 52% more bugs than the best performing technique.
Model Generation For Java Frameworks

Modern applications often rely on rich frameworks to provide functionality. Android, for instance, handles many aspects of building a mobile app. But these frameworks also have costs. Given the importance of application security and tools to ensure it, one major cost is that framework complicate tools based on static analysis: (1) They hurt analysis quality by including large amounts of complex, dynamic, and native library code. (2) Frameworks like Android become the main program, making whole program analysis of the app problematic.Mechanisms such as Averroes have been developed to handle unknown library code for Java, and have proven effective for some analyses. However, they have two main limitations in the context of our complications: (1) They do not provide the precision required for security analysis. (2) They assume a main program, which is not the case for frameworks. To address this, we present GenCG, which extends Averroes to support taint analysis for Android and Spring. Evaluation with real-world Android applications shows that call graphs using the models generated by GenCG cover significantly more code of the app, improves recall of a client security analysis, and, at the same time, does not introduce more false positives.
Charge Trapping Layer Enabled Normally-Off <italic></italic>-Ga<sub>2</sub>O<sub>3</sub> MOSFET

In this work, a normally-OFF β-Ga2O3 MOSFET enabled by charge trapping layer (CTL) is demonstrated for the first time. The CTL consists of Al:HfOx 1:5 and the tunneling barrier (TB) is Al2O3/HfOx/Al2O3 stack. The developed Ga2O3 MOSFET exhibits a wide Vth tuning range to normally-OFF operation and a long-lasting retention characteristic of −0.3 V (ten years). The device obtains a breakdown voltage (BV) of 1815 V, showing high reverse blocking capability based on the proposed CTL technique. I–V and C–V analyses are carried out under various temperatures to study the mechanism and explore the charge trapping and de-trapping processes. The results indicate a promising method to achieve E-mode operation for Ga2O3 MOSFET with low charge loss and stable Vth.
Singing training predicts increased insula connectivity with speech and respi‐ ratory sensorimotor areas at rest

The insula contributes to the detection of salient events during goal-directed behavior and participates in the coordination of motor, multisensory, and cognitive systems. Recent task-fMRI studies with trained singers suggest that singing experience can enhance the access to these resources. However, the long-term effects of vocal training on insula-based networks are still unknown. In this study, we employed resting-state fMRI to assess experience-dependent differences in insula co-activation patterns between conservatory-trained singers and non-singers. Results indicate enhanced bilateral anterior insula connectivity in singers relative to non-singers with constituents of the speech sensorimotor network. Specifically, with the cerebellum (lobule V-VI) and the superior parietal lobes. The reversed comparison showed no effects. The amount of accumulated singing training predicted enhanced bilateral insula co-activation with primary sensorimotor areas representing the diaphragm and the larynx/phonation area– crucial regions for cortico-motor control of complex vocalizations—as well as the bilateral thalamus and the left putamen. Together, these findings highlight the neuroplastic effect of expert singing training on insula-based networks, as evidenced by the association between enhanced insula co-activation profiles in singers and the brain's speech motor system components.
Strategic Information Suppression in Borrowing and Pre-Lending Cognition: Theory and Evidence

This paper theoretically studies the interaction between an informed borrower and an uninformed lender facing possible default of a loan application. The lender is motivated to invest cognitive resources before making a lending decision. If the regulatory fine is weak, it is impossible for a bad-debt borrower to fully disclose his situation in the application. In this case, when the likelihood of a bad debt is low, the borrower always claims that nothing in the application is wrong. Otherwise, the borrower randomizes between full disclosure and information suppression. The transaction cost of the lender’s pre-lending cognition increases with the default probability, as the default probability is small and decreases thereafter. Evidence from a peer-to-peer lending platform with 816,274 observations between 2012 and 2015 in the United States is largely consistent with our model implications.
Evaluation of Different Modes for Yarding Windthrown Timber with a Double-Hitch Carriage

Yarding whole trees is the most efficient way of extracting timber in steep terrain and allows reaping the combined benefits of mechanization and biomass recovery. In downhill yarding, however, whole-tree extraction is associated with a greater risk of loosening rocks or debris by the incoming loads as they bounce around along the extraction corridor. That may also cause damage to the cables and anchors by corresponding shock loads, ultimately endangering the yarder and its crew. To avoid these risks, »double-hitch carriages« can be employed. They combine a conventional motorized dropline carriage with a secondary carriage (»trailer«), equipped with a further, independent dropline winch. Thus, loads can be attached at two points and transported fully suspended above the ground in a horizontal position.Operation of these carriages may not be limited to the »horizontal« mode: the main carriage could also be operated without trailer (»single« mode), or separate loads may be attached to the two droplines (»double« mode), but their impact on the efficiency and economy of yarding operations is yet unknown. Therefore, the present study investigated how these modes affect the productivity and cost of downhill whole tree yarding. To this end, a classic time and motion study was conducted during a salvage logging operation in Northern Italy under a strictly controlled experimental design.Average productivity (18.2±7.2 to 24.5±15.4 m³ PSH0-1 merchantable volume per productive system hour excluding delays) and extraction cost (18 to 20 Euro m-³) did not differ significantly between treatments, while load composition and time consumption by task did. More (2.2±0.5) pieces per load were yarded under the »double«, than under the »single« (1.4±0.5) and »horizontal« (1.1±0.3) treatments. Inhaul speed (3.1±0.6 m s-1) was significantly higher under the »horizontal« treatment, which compensated for increased loading time derived from attaching the load at least at one point outside the corridor. Unloading took significantly longer under the »double« treatment, as loads had to be dropped successively due to the confined conditions on the landing. Though slowest (2.5±0.9 m s-1) during inhaul, the »single« treatment exhibited none of the other treatments disadvantages and larger loads could be accumulated due to partial suspension. From an economic viewpoint, the »horizontal« mode may only be warranted over yarding distances substantially beyond average. On shorter ones, it must be justified by other reasons, such as minimizing product contamination, soil disturbance or excessive strain to the skyline when the terrain profile impedes sufficient ground clearance.
Stochastic Optimal Design for Unknown Linear Discrete‐Time System Zero‐Sum Games in Input‐Output form Under Communication Constraints

In this paper, stochastic optimal strategy for unknown linear discrete‐time system quadratic zero‐sum games in input‐output form with communication imperfections such as network‐induced delays and packet losses, otherwise referred to as networked control system (NCS) zero‐sum games, relating to the H∞ optimal control problem is solved in a forward‐in‐time manner. First, the linear discrete‐time zero sum state space representation is transformed into a linear NCS in the state space form after incorporating random delays and packet losses and then into the input‐output form. Subsequently, the stochastic optimal approach, referred to as adaptive dynamic programming (ADP), is introduced which estimates the cost or value function to solve the infinite horizon optimal regulation of unknown linear NCS quadratic zero‐sum games in the presence of communication imperfections. The optimal control and worst case disturbance inputs are derived based on the estimated value function in the absence of state measurements. An update law for tuning the unknown parameters of the value function estimator is derived and Lyapunov theory is used to show that all signals are asymptotically stable (AS) and that the estimated control and disturbance signals converge to optimal control and worst case disturbances, respectively. Simulation results are included to verify the theoretical claims.
Magnetic Resonance Elastography of the Brain

Magnetic resonance elastography (MRE) has emerged as an important technique to extract information about the mechanics of the brain. In MRE, magnetic resonance images (MRI) of waves propagating in soft tissue are acquired non-invasively and analyzed to estimate the tissue’s mechanical properties [1]. Because the brain it is well hidden and delicate, its mechanical behavior in vivo remains incompletely characterized. MRE measurement of mechanical properties and their variations can illuminate the processes of injury, development and disease in the human brain. Traumatic brain injury (TBI) is a leading cause of death and disability in children and young adults [2, 3], and TBI due to explosive blast is a common and debilitating injury among military personnel in combat [4]. In most TBI, the direct cause of injury is the large, fast deformation of brain tissue caused by acceleration of the skull. Brain deformation depends on tissue stiffness (shear modulus) as well as on the brain’s anatomical structure and connections to the skull. The magnitudes, locations, and directions of deformation remain largely unknown however, due to the difficulty of measuring or modeling the brain’s mechanical response. Reliable computer simulations of TBI would be valuable, but such models require (i) accurate estimates of material parameters, (ii) knowledge of mechanical interactions between the brain and skull, and perhaps most importantly, (iii) data with which to compare model predictions. MRE can address all three requirements in vivo. Changes in the brain’s material properties are thought to accompany normal aging [5, 6]. These changes may be amplified in degenerative disorders like Alzheimer’s disease [7, 8]. Brain tumors, like tumors in superficial tissue, also exhibit differences in stiffness relative to healthy brain [9, 10]. These changes in mechanical properties might be used for clinical purposes, for example as techniques for diagnosis or evaluation of therapy. Macroscopic mechanical changes, because they are related to cellular and microstructural properties, might also elucidate the underlying biology of disease and degeneration. Here, I review the experimental methods used in these studies and the techniques to estimate mechanical properties from MRE data. We discuss the assumptions, technical choices, and limitations specific to MRE of the mammalian brain. Particular attention is given to safe and effective actuation, the relationships between frequency, sampling, and length scale, and the choice of an appropriate material model of brain tissue, which in general is nonlinear, dissipative, anisotropic, and heterogeneous.
Starspots and photometric noise on observed minus calculated (O–C) diagrams

The eects of starspots on the O{C diagrams of eclipsing binaries are investigated, with a special em- phasis on contact binaries. It is shown that starspots modulate the O{C dierences in a fundamentally dierent way than real orbital period variations. Migrating starspots can only introduce high frequency, low amplitude disturbances (generally less than 0:01 d), whose characteristics are numerically specied for typical contact bina- ries. It becomes evident that the observed systematic short timescale variations of the O{C diagrams of active contact binaries (even those like VW Cep) cannot be attributed to starspots. Furthermore, the basic property of non-linear modulation of O{C dierences is emphasised. This non-linearity has serious consequences for the widespread traditional method of O{C diagram analysis. This traditional method is found inappropriate even on a fundamental level, and its mathematical and physical inconsistencies are reviewed.
Narrative Review: Update on the Molecular Diagnosis of Fragile X Syndrome

The diagnosis and management of fragile X syndrome (FXS) have significantly improved in the last three decades, although the current diagnostic techniques are not yet able to precisely identify the number of repeats, methylation status, level of mosaicism, and/or the presence of AGG interruptions. A high number of repeats (>200) in the fragile X messenger ribonucleoprotein 1 gene (FMR1) results in hypermethylation of promoter and gene silencing. The actual molecular diagnosis is performed using a Southern blot, TP-PCR (Triplet-Repeat PCR), MS-PCR (Methylation-Specific PCR), and MS-MLPA (Methylation-Specific MLPA) with some limitations, with multiple assays being necessary to completely characterise a patient with FXS. The actual gold standard diagnosis uses Southern blot; however, it cannot accurately characterise all cases. Optical genome mapping is a new technology that has also been developed to approach the diagnosis of fragile X syndrome. Long-range sequencing represented by PacBio and Oxford Nanopore has the potential to replace the actual diagnosis and offers a complete characterization of molecular profiles in a single test. The new technologies have improved the diagnosis of fragile X syndrome and revealed unknown aberrations, but they are a long way from being used routinely in clinical practice.
Epidemiology and outcome of an acute kidney injuries in the polytrauma victims admitted at the apex trauma center in Dubai

Background Polytrauma from road accidents is a common cause of hospital admissions and deaths, frequently leading to acute kidney injury (AKI) and impacting patient outcomes. Methods This retrospective, single-center study included polytrauma victims with an Injury Severity Score (ISS) >25 at a tertiary healthcare center in Dubai. Results The incidence of AKI in polytrauma victims is 30.5%, associated with higher Carlson comorbidity index (P=0.021) and ISS (P=0.001). Logistic regression shows a significant relationship between ISS and AKI (odds ratio [OR], 1.191; 95% confidence interval [CI], 1.150–1.233; P<0.05). The main causes of trauma-induced AKI are hemorrhagic shock (P=0.001), need for massive transfusion (P<0.001), rhabdomyolysis (P=0.001), and abdominal compartment syndrome (ACS; P<0.001). On multivariate logistic regression AKI can be predicated by higher ISS (OR, 1.08; 95% CI, 1.00–1.17; P=0.05) and low mixed venous oxygen saturation (OR, 1.13; 95% CI, 1.05–1.22; P<0.001). The development of AKI after polytrauma increases length of stay (LOS)-hospital (P=0.006), LOS-intensive care unit (ICU; P=0.003), need for mechanical ventilation (MV) (P<0.001), ventilator days (P=0.001), and mortality (P<0.001). Conclusions After polytrauma, the occurrence of AKI leads to prolonged hospital and ICU stays, increased need for mechanical ventilation, more ventilator days, and a higher mortality rate. AKI could significantly impact their prognosis.
Histological evaluation of ProRoot mineral trioxide aggregate and Cold ceramic as root-end filling materials in animal models

ABSTRACT Background: This study aimed to evaluate the reaction of the periapical tissue to Cold ceramic and mineral trioxide aggregate (MTA) following periapical endodontic surgery. Materials and Methods: In this experimental study, a total of 12 mandibular first, second, and third premolars of two male dogs were selected. All procedures were performed under general anesthesia. The access cavities were prepared, and the length of canals was determined. Root canal treatment was performed. A week later, periradicular surgery was performed. After osteotomy, 3 mm of the root end was cut. Then, a 3-mm cavity was created by an ultrasonic. The teeth were randomly divided into two groups (n = 12). The root-end cavities were filled with MTA in the first group and with Cold ceramic in the second group. After 4 months, the animals were scarified. Histological evaluation of the periapical tissues was performed. Data were analyzed using SPSS 22 and Chi-square test and P = 0.05. Results: The findings showed 87.5% and 58.3% cementum formation in MTA and Cold ceramic groups, respectively, indicating a significant difference (P < 0.001). In addition, the results showed 91.7% and 83.3% bone formation in MTA and Cold ceramic groups, respectively, but the difference was not statistically significant (P = 0.6). Furthermore, the findings revealed 87.5% and 58.3% periodontal ligament (PDL) formation in MTA and Cold ceramic groups, respectively (P = 0.05). Conclusion: Cold ceramic was able to induce the regeneration of cementum, bone, and PDL; hence, it can be considered as a biocompatible root-end filling material in endodontic surgery.