Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing and Explaining Black-Box Models for Online Malware Detection

In recent years, a significant amount of research has focused on analyzing the effectiveness of machine learning (ML) models for malware detection. These approaches have ranged from methods such as decision trees and clustering to more complex approaches like support vector machine (SVM) and deep neural networks. In particular, neural networks have proven to be very effective in detecting complex and advanced malware. This, however, comes with a caveat. Neural networks are notoriously complex. Therefore, the decisions that they make are often just accepted without questioning why the model made that specific decision. The black box characteristic of neural networks has challenged researchers to explore methods to explain black-box models such as SVM and neural networks and their decision-making process. Transparency and explainability give the experts and malware analysts assurance and trustworthiness about the ML models’ decisions. In addition, it helps in generating comprehensive reports that can be used to enhance cyber threat intelligence sharing. As such, this much-needed analysis drives our work in this paper to explore the explainability and interpretability of ML models in the field of online malware detection. In this paper, we used the Shapley Additive exPlanations (SHAP) explainability technique to achieve efficient performance in interpreting the outcome of different ML models such as SVM Linear, SVM-RBF (Radial Basis Function), Random Forest (RF), Feed-Forward Neural Net (FFNN), and Convolutional Neural Network (CNN) models trained on an online malware dataset. To explain the output of these models, explainability techniques such as KernalSHAP, TreeSHAP, and DeepSHAP are applied to the obtained results.
On random Schrödinger operators on $\mathbb Z^2$

This paper is concerned with random lattice operators on $\mathbb Z^2$ of the form $H_\omega = \Delta + V_\omega$ where $\Delta$ is the lattice Laplacian and $V_\omega$ a random potential $V_\omega(n) = \omega_nv_n, \{\omega_n\}$ independent Bernoulli or Gaussian variables and $\{v_n\}$ satisfying the condition sup$_n |v_n| |n|^\rho < \infty$ for some $\rho>\frac{1}{2}$. In this setting and restricting the spectrum away from the edges and 0, existence and completeness of the wave operators is shown. This leads to statements on the a.c. spectrum of $H_\omega$. It should be pointed out that, although we do consider here only a specific (and classical) model, the core of our analysis does apply in greater generality.
Sliding mode approach for control and observation of a three phase AC-DC pulse-width modulation rectifier

Introduction. For AC-DC conversion systems, the electrical systems typically use thyristor or diode bridge rectifiers, which have relatively poor performance. Nowadays, three-phase pulse-width modulation rectifiers are widely applied in various applications for their well-known intrinsic benefits, such as adjustable DC link voltage, unity power factor, bidirectional power flow and very low total harmonic distortion. Purpose. The objective of this work is to achieve better stability and dynamic performance using sliding mode strategy for control and observation. Methods. For that purpose, first a sliding mode controller is introduced on the DC-link side to ensure a fast and accurate response of the output load voltage. Then, the sliding mode approach is employed to control the quadrature and direct components of power to maintain the input power factor at unity. Finally, this approach is used to design two observers for grid voltage estimation and online variation of load resistance. To overcome the problem associated with the use of the classical low-pass filter, an adaptive compensation algorithm is used to compensate the attenuation of the amplitude and phase delay of the observed grid voltages. This algorithm is based on the use of the two low-pass filters in cascade and ensures the minimization of chattering. Results. Comparative studies have been carried out between sliding mode control method for controlling the three-phase AC-DC pulse-width modulation rectifier and other conventional techniques. The validation by simulation and the tests carried out gave very satisfactory results and proved the effectiveness and feasibility of the sliding mode for both control and observation of three phase pulse-width modulation rectifier.
Factors associated with changes in healthy lifestyle behaviors among hematological cancer patients during the COVID-19 pandemic

Background There is a paucity of research examining the effects of the COVID-19 pandemic on the healthy lifestyle behaviors of hematological cancer patients. We examined changes in healthy lifestyle behaviors since the pandemic and identified factors associated with these changes among members of this high-risk population. Methods Hematological cancer patients (n = 394) completed a self-report online survey from July to August 2020. The survey assessed pandemic-related changes in exercise, alcohol consumption, and consumption of fruit, vegetables, and wholegrains. Information relating to several demographic, clinical, and psychological factors was also collected. Factors associated with changes in healthy lifestyle behaviors were analyzed using logistic regression. Results Just 14% of patients surveyed reported exercising more during the pandemic (39% exercised less). Only a quarter (24%) improved their diet, while nearly half (45%) reported eating less fruit, vegetables, and wholegrains. Just over a quarter (28%) consumed less alcohol (17% consumed more alcohol). Fear of contracting COVID-19 and psychological distress were significantly associated with reduced exercise. Younger age was significantly associated with both increased alcohol consumption and increased exercise. Being a woman was significantly associated with unfavorable changes in diet and being married was significantly associated with decreased alcohol consumption. Conclusion A substantial proportion of hematological cancer patients reported unfavorable changes in healthy lifestyle behaviors during the pandemic. Results highlight the importance of supporting healthy lifestyle practices among this particularly vulnerable group to ensure health is optimized while undergoing treatment and when in remission, particularly during crisis times like the COVID-19 pandemic.
The Vehicle Intention Recognition with Vehicle-Following Scene Based on Probabilistic Neural Networks

In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is proposed, which enables the rear vehicle to obtain the driving intention of the front vehicle without communication between the two vehicles. First, real vehicle data with different intents are collected and time—frequency domain variables are extracted. Secondly, Principal Component Analysis (PCA) is performed on the variables in order to obtain comprehensive features. Meanwhile, two cases are classified according to whether the front vehicle can transmit data to the rear vehicle. Finally, two recognition models are trained separately according to a PNN algorithm, and the two models obtained from the training are verified separately. When the front vehicle can communicate with the rear vehicle, the recognition accuracy of the corresponding PNN model reaches 96.39% (simulation validation) and 95.08% (real vehicle validation). If it cannot, the recognition accuracy of the corresponding PNN model reaches 78.18% (simulation validation) and 73.74% (real vehicle validation).
TILPDeep: A Lightweight Deep Learning Technique for Handwritten Transformed Invariant Pashto Text Recognition

Pashto is the native language of Afghanistan and one of Pakistan’s most essential and regional languages. The Pashto language has a vast number of native speakers who live in various parts of the world. The handwritten Pashto textual trajectories are hard to recognize and detect due to the cursive style and handwriting variation. The transformation behaviour, i.e., scaling, rotation, and shifting of handwritten text, are the prominent but challenging factors. A lightweight deep learning-based model construction for low and medium-resource devices in a less-constrained environment is challenging. This paper provides a practical, light deep learning-based model for predicting handwritten Pashto words. A massive Pashto-transformed invariant inverted handwritten text dataset is prepared with the help of the Pashtun community. A lightweight MobileNetV2 has been highly tuned for Pashto handwritten text classification, extracting images’ features (MoI). We inverted the dataset to make the model more accurate and restrict it to fifteen epochs. Extensive experiments have been conducted to validate the suggested model’s performance. The proposed transformed invariant lightweight Pashto deep learning (TILPDeep) technique achieves a training accuracy of 0.9839 and a validation accuracy of 0.9405 for transformed invariant Pashto handwritten inverted text using recognition matrices.
Influence of Post-Consumer Waste Thermoplastic Elastomers Obtained from Used Car Floor Mats on Concrete Properties

In this paper, the influence of post-consumer thermoplastic elastomer (TPE) additive derived from used car floor mats on the physical and mechanical properties of concrete is presented. Waste elastomer (fractions 0–2 and 2–8 mm) was used as a substitute for sand or fine aggregate in the amount of 2.5, 5.0, 7.5, and 10.0% by weight of cement. For all series, the physical and mechanical properties of concrete (for example, compressive strength, flexural tensile strength, water absorption, density, and frost resistance), as well as its microstructure, were tested. It has been shown that post-consumer elastomer waste from used car floor mats in the amount of 2.5% of cement weight can replace sand and gravel aggregate in concrete without reducing their mechanical strength and without changing their microstructure. The compressive strength (after 28 days) of concretes in which the waste was introduced as a substitute for sand and aggregate was 57.0 and 57.2 MPa, respectively (the strength of the control sample was 57.0 MPa). The use of post-consumer waste in concrete allows for a reduction in the consumption of natural aggregate (the addition of 2.5% of waste material saves natural aggregate approximately 20 kg/m3), which reduces the cost of concrete production and also has a positive impact on the environment (i.e., it saves cost and space in landfills, where currently used car floor mat are deposited).
Quasi-invariants of hyperplane arrangements

The ring of quasi-invariants $Q_m$ can be associated with the root system $R$ and multiplicity function $m$. It first appeared in the work of Chalykh and Veselov in the context of quantum Calogero-Moser systems. One can define an analogue $Q_{\mathcal{A}}$ of this ring for a collection $\mathcal{A}$ of vectors with multiplicities. We study the algebraic properties of these rings. For the class of arrangements on the plane with at most one multiplicity greater than one we show that the Gorenstein property for $Q_{\mathcal{A}}$ is equivalent to the existence of the Baker-Akhiezer function, thus suggesting a new perspective on systems of Calogero-Moser type. The rings of quasi-invariants $Q_m$ have a well known interpretation as modules for the spherical subalgebra of the rational Cherednik algebra with integer valued multiplicity function. We explicitly construct the anti-invariant quasi-invariant polynomials corresponding to the root system $A_n$ as certain representations of the spherical subalgebra of the Cherednik algebra $H_{1/m}(S_{mn})$. We also study the relation of the algebra $\Lambda_{n,1,k}$ introduced by Sergeev and Veselov to the ring of quasi-invariants for the deformed root system $\mathcal{A}_n(k)$. We find the Poincar\'e series for a `symmetric part' of $Q_{\mathcal{A}_n(k)}$ for positive integer values of $k$.
Adaptive Lightweight License Plate Image Recovery Using Deep Learning Based on Generative Adversarial Network

Many Convolutional Neural Networks (CNNs) methods have already surpassed traditional approaches to image restoration tasks. Those CNNs models were usually designed to enhance single tasks such as an image resolution (super-resolution) or image denoising, but we came up with unconventional goals, that is, multiple recovery tasks from a single network design. Although the Transformer design has recently gained attention in image recovery tasks, they are too slow. In order to work with license plate images from a traffic camera stream, the system has to be responsive. So, we proposed a fast and lightweight deep learning-based data recovery system using a Generative Adversarial Network (GAN) principle named License Plate Recovery GAN (LPRGAN). The design has a proposed encoder-decoder style inspired by an autoencoder aided by dual classification networks. This style suits problem-characteristic learning because strong contextual information is retrieved from the down-scaled representations. This proposed system has three main features such as identifying a problem, data recovery, and fail-safe mechanism. The core of system is a data recovery unit (LPRGAN), is used to recover license plate images from multiple degraded input images. Most existing image restoration systems do not have self-awareness, leading to an inefficiency problem. Unlike existing works, this system has anomaly detection and will only process on a degraded input, reducing workload overhead, improving efficiency and a fail-safe feature that prevents an unexpected bad output. Hence, it is light enough to deploy on a low-power machine such as edge computing devices, opening up new possibilities in on-device computing. Our proposed research can recover several degraded problems up to 720p resolution at 15 frames per second on a single graphic card, $256\times 128$ resolution at 17 frames per second on a CPU-only workstation machine, or 7 frames per second on an ultra-low-power tablet PC.
Spiny Tails and Clades: A Fully Sampled Phylogeny of Hoplocercine Lizards (Iguanidae/ Hoplocercinae) and its Taxonomic and Nomenclatural Implications

Hoplocercine lizards (Enyalioides, Morunasaurus, and Hoplocercus) form a clade of ca. 20 currently recognized species. The phylogenetic relationships among hoplocercine lizards, whose members exhibit striking differences in morphology (e.g., spiny vs. non-spiny tails), have not been clearly resolved by previous molecular phylogenetic studies. We generated a considerably larger dataset including 130 new DNA sequences from one mitochondrial and four nuclear loci for all named and two unnamed species of Hoplocercinae. We analyzed the data under concatenated maximum likelihood (RAxML) and Bayesian (MrBayes) as well as summary coalescent (ASTRAL) approaches. While our phylogenetic hypotheses strongly supported the monophyly of Hoplocercinae, neither Enyalioides nor Morunasaurus was supported as monophyletic. Instead, M. groi was inferred with strong support to form a clade with E. heterolepis and E. laticeps. This clade was in turn the sister taxon to other species of Morunasaurus (M. annularis, M. peruvianus). The remaining species of Enyalioides formed a separate clade with a basal split between western (3 species) and eastern (13 species) subclades. Tree topology tests rejected the monophyly of Enyalioides but failed to reject monophyly of Morunasaurus, suggesting that further study is needed to resolve its taxonomic status. Based on our results, we establish the converted clade names Hoplocercinae, Hoplocercus, Enyalioides, and Morunasaurus, as well as the new clade name Zimiamviasaurus.
Temporal stability and spatial dependency of pixel-specific red-NIR soil lines from multi-temporal satellite remote sensing imagery

ABSTRACT The temporal stability and spatial dependence of pixel-specific red-NIR soil line coefficients were studied using analyses of geostatistics and covariance. We used time series of MODIS 8-day composite reflectance data from 2000 to 2019 of bare soil surfaces near Kabul, Afghanistan. The goal was to explore the feasibility of reducing the soil background signal in red-NIR vegetation indices (VIs). The results were that 1) Red-NIR pixel-specific soil line coefficients can be obtained using multi-temporal remote sensing imagery; 2) pixel-specific Red-NIR soil line coefficients derived from multi-temporal remote sensing imagery exhibit clear spatial dependency related to the soil line coefficients of soils sampled in the field, and 3) Red-NIR pixel-specific soil line coefficients were stable for years, but showed instability for periods longer than 16 years. These results imply that A) some changes in soil surface characteristics can be deduced using long-term observations, B) pixel-specific Red-NIR soil lines at any location can be estimated by kriging interpolation. Consequently, soil line coefficients derived from multi-temporal satellite observations can be used to derive information about vegetation, such as the germination stages pixel-by-pixel or above-ground biomass in sparsely vegetated lands. The derived values should be valid over wide areas, minimizing soil background effects.
Neuronal Cultures: Exploring Biophysics, Complex Systems, and Medicine in a Dish

Neuronal cultures are one of the most important experimental models in modern interdisciplinary neuroscience, allowing to investigate in a control environment the emergence of complex behavior from an ensemble of interconnected neurons. Here, I review the research that we have conducted at the neurophysics laboratory at the University of Barcelona over the last 15 years, describing first the neuronal cultures that we prepare and the associated tools to acquire and analyze data, to next delve into the different research projects in which we actively participated to progress in the understanding of open questions, extend neuroscience research on new paradigms, and advance the treatment of neurological disorders. I finish the review by discussing the drawbacks and limitations of neuronal cultures, particularly in the context of brain-like models and biomedicine.
Anisotropy in Electron Mobility and Microstructure of GaN Grown by Metalorganic Vapor Phase Epitaxy

The mobility of the electrons in GaN films grown by metalorganic vapor phase epitaxy has been investigated. It is observed that the mobility is anisotropic with a maximum in 〈101-0〉 direction and a minimum in 〈112-0〉 direction. Microstructure analyses illustrate that the density and distribution of defects in these two directions are different. These results suggest that the anisotropy in electronic mobility is caused by a hexagonally symmetric distribution of defects (grain boundaries and dislocations).
Deep silicon cell fabrication for chip-scale atomic sensors

Using a simple and cost-effective water-jet process, we have overcome silicon etch depth limitations to realize a 6 mm deep atomic vapor cell. We have successfully used this approach to demonstrate a two-chamber geometry by including a 25 mm meandering channel between the alkali pill chamber and main interrogation chamber. Additionally, we have recently used this approach in the fabrication of deep cut silicon cells for cold atom systems. The results will be highlighted, as well as providing an overview of our advancements on mass producible components for cold-atom systems and the amalgamation of this technology towards a fully integrated system.
Processes and mechanisms for the model SST biases in the North Atlantic and North Pacific: A link with the Atlantic meridional overturning circulation

Almost all of CMIP5 climate models show cold SST biases in the extratropical North Atlantic (ENA) and tropical North Atlantic (TNA) as well as in the North Pacific which are commonly linked with the weak simulated Atlantic meridional overturning circulation (AMOC). A weak AMOC and its associated reduced northward oceanic heat transport are associated with a cooling of the ENA Ocean, whereas the TNA cooling is attributable to both weak AMOC and surface heat flux. The cold biases in the ENA and TNA have remote impacts on the SST bias in the North Pacific. Here we use coupled ocean‐atmosphere model experiments to show the mechanisms and pathways by which the ENA and TNA affect the North Pacific. The model simulations demonstrate that the cooling SST bias in the North Pacific is largely due to the remote effect of the cooling SST bias in the ENA, while the remote impact of the TNA cooling SST bias is of secondary importance. The ENA cooling bias triggers the circumglobal teleconnection via the Northern Hemisphere annular mode, producing a strengthening of the Aleutian low, an enhancement of the southward Ekman and Oyashio cold advection, and thus a cooling SST in the North Pacific. In contrast, the TNA cooling produces a surface high extending to the eastern tropical North Pacific, inducing the northeasterly wind anomalies north, northerly cross‐equatorial wind anomalies, and northwesterly wind anomalies south of the equator. This C‐shape wind anomaly pattern generates an SST warming in the tropical southeastern Pacific, which eventually leads to an SST warming in the tropical central and western Pacific by the wind‐evaporation‐SST feedback. The tropical Pacific warming in turn leads to an SST cooling in the North Pacific by the Pacific North American teleconnection pattern.
THE GALACTIC CENTER ENVIRONMENT

▪ Abstract The central half kiloparsec region of our Galaxy harbors a variety of phenomena unique to the central environment. This review discusses the observed structure and activity of the interstellar medium in this region in terms of its inevitable inflow toward the center of the Galactic gravitational potential well. A number of dissipative processes lead to a strong concentration of gas into a “Central Molecular Zone” of about 200-pc radius, in which the molecular medium is characterized by large densities, large velocity dispersions, high temperatures, and apparently strong magnetic fields. The physical state of the gas and the resultant star formation processes occurring in this environment are therefore quite unlike those occurring in the large-scale disk. Gas not consumed by star formation either enters a hot X ray–emitting halo and is lost as a thermally driven galactic wind or continues moving inward, probably discontinuously, through the domain of the few parsec-sized circumnuclear disks and .
Semi-supervised Hierarchical Clustering

Hierarchisches Clusterverfahren findet im Bereich Data Mining wegen seiner Komplexitat geringere Anwendung. In dieser Arbeit werden Constraints eingefuhrt, um die Effizienz des traditionellen hierarchischen Clusterverfahrens zu verbessern. Jedoch ist das Clustering-Problem unter manchen Constraintkombinationen NP-vollstandig. Solche Kobminationen sollen daher vermieden werden. Am Ende der Arbeit wird ein verbesserter Algorithmus fur das hierarchische Clusterverfahren vorgestellt.