Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Analysis of Big Data Models in Android Malware Detection

The term "malicious software" (or "Malware") refers to any software designed specifically to cause harm to a computer system. Malware has also progressed in its ability to avoid detection and launch attacks in tandem with the development of computing technology. Attackers are motivated to target Android vulnerabilities due to the growing popularity and reliance on smartphones. There are several types of stealth software, but none are as dangerous as ransomware. Ransomware is a type of malicious software designed to hold users' files and systems hostage in exchange for payment. Numerous ransomware assaults may have been inspired by the meteoric rise in mobile commerce. Desktop PCs and mobile devices are both vulnerable to malware. Microsoft Windows is the most used OS for home desktop computers across the world. As a result, a breach in one system might potentially affect many others. This research study offers a multi-stage system in which the initial stage makes use of features that are simpler to extract with cheaper classifiers. Level 2 is considered for detection if the confidence of the level 1 classifier is below a predetermined threshold. The proposed study is not only concerned on accuracy but also on cost, because integrating many classifiers may not be possible due to the additional memory and computing requirements. In addition, the time to output is crucial for the practicality of a malware detection tool. So as to strike a balance between accuracy and cost, multilevel ensemble system with a modest number of classifiers has been considered.
Ozone and carbon dioxide fluxes in a subalpine spruce-fir forest ecosystem

RMFRES RWU 4452 has made several ozone (0) and carbon dioxide (CO) trace gas flux measurements in the Snowy Range, WY GLEES research area over the past few years. These measurements were made using the micrometeorological eddy correlation technique at two sites: one 6 m above tree canopy height on the Brooklyn tower (ozone only); and the other below canoPY height, 1-2m above a wet alpine meadow surface near the Brooklyn tower. Diel C0 2 vertical flux cycles change dramatically from expected daytime uptake (downward) and nighttime emissions (upward) during the growing season to predominantly upward during winter above the snow surface. Diel 0 3 vertical flux cycles above the tree canopy vary from normal deposition during the summer growing season to upward in the presence of snow cover. Diel 0 3 vertical flux cycles above the wet meadow are downward (deposition) as expected year round, however wintertime deposition measured above 1-2m snow depths are signifi-
Scientific Elite: Nobel Laureates in the United States:

Scientific Elite is about Nobel prize winners and the well-defined stratification system in twentieth-century science. It tracks the careers of all American laureates who won prizes from 1907 until 1972, examining the complex interplay of merit and privilege at each stage of their scientific lives and the creation of the ultra-elite in science. The study draws on biographical and bibliographical data on laureates who did their prize-winning research in the United States, and on detailed interviews with forty-one of the fifty-six laureates living in the United States at the time the study was done. Zuckerman finds laureates being successively advantaged as time passes. These advantages are producing growing disparities between the elite and other scientists both in performance and in rewards, which create and maintain a sharply graded stratification system.
Galvanometer Motor Control Based on Reinforcement Learning

In order to address the problem that the control effect of the galvanometer motor is often dependent on the accuracy of the model parameters, this paper proposes a control method that is based on reinforcement learning. With the galvanometer motor in the steady state as the target, a state feedback control method incorporating an online update control strategy that does not depend on model parameters is proposed on the premise of obtaining the system order, and the method able to identify the state feedback coefficient. As suggested by the computer simulation results, the method proposed in this paper is capable of applying stable control on the galvanometer motor, and the iterative results are found consistent with the design results of the LQR method with the model, which ensures the reliability of the method.
Interpretación y visualización de características en texturas mediante Redes Neuronales Convolucionales

La textura es una característica relevante en muchos tipos de imágenes y su representación e identificación es uno de los problemas esenciales y más desafiantes del procesamiento digital de imágenes. Una región con una textura determinada tiene propiedades estadísticas específicas, repitiendo ciertos grupos de píxeles periódicamente con algún grado de variabilidad en su apariencia y posición relativa. Dentro de las redes basadas en aprendizaje profundo, las Redes Neuronales Convolucionales (CNN) han tomado enorme impulso en procesamiento de imágenes. Su potencial está en que, una vez entrenadas, contienen información que puede ampliar el campo de conocimiento sobre el problema bajo estudio, si se encuentran mecanismos adecuados para su análisis e interpretación. La visualización de características brinda un marco adecuado para esta tarea, generando imágenes que describen qué es lo que cada filtro describe. Con un enfoque adecuado, su análisis permite dar una interpretación de las características y extraer conocimiento. En este trabajo se presenta un enfoque para la interpretación de texturas a partir de visualización de características, utilizando CNN entrenadas con bases de datos de texturas. Los resultados indican que el enfoque propuesto permite identificar patrones/características descriptas por cada filtro, dando interpretación y significado, partiendo de términos asociados con textura, como rugosidad, ondulaciones, transiciones, bordes y formas geométricas.
A Study of Hyperspectral Classification Combined with Minimum Noise Fraction and Variational Mode Decomposition

Supervised classification is one of the widespread applications in hyperspectral data analysis. Due to the large number of hyperspectral data bands and the redundancy of information between the bands, it brings great challenges to hyperspectral classification. The effect of hyperspectral data feature extraction determines the performance of classification accuracy. In order to improve the classification accuracy, this paper proposes a joint feature extraction method based on minimum noise fraction (MNF) and variational mode decomposition (VMD). The hyperspectral data is firstly minimum noise fraction transformed, and then the first few MNF sequences containing the main information of the hyperspectral are subjected to VMD. Then, identify and classify each mode component obtained by decomposing. Finally, through the support vector machine (SVM) classification and comparative analysis, the method has a good accuracy.
Composites Based on Thermoplastic Polymeric Matrix and Carbon Nanoparticles with Special Functional Properties

Polymer composite materials based on polypropylene matrix and carbon nanoparticles of various shapes: spherical particles - carbon black and anisotropic nanoparticles - carbon nanofibers, were obtained by the melt technology. A character of the dependences of the electrical conductivities on the concentration of carbon nanofillers has been revealed. A simulation of the electrical conductivity of the polymer composite in the region of the concentration of the filler at the threshold of the flow of electric current and below it was carried out.
Surfactant and Colloid Self-Assembly Simulations

Surfactants and colloids are ubiquitous in biology, the environment, home and personal care products, and industrial applications. The self-assembly of surfactants into micellar aggregates is crucial to their use in many industrial applications, including: detergency, cosmetics, oil-spill remediation and nanoparticle dispersion. Because of their ubiquity, surfactant and colloid self-assembly are canonical subjects of soft matter study, yet there are concentration effects, for example the free surfactant concentration, that remain difficult to measure and understand. We present our findings on the driving forces behind aspects of self-assembly and methodologies for characterizing the behavior of three self-assembling systems: colloids with shortrange attraction long-range repulsion, nonionic surfactants and ionic surfactants. To study molecular details of these systems, we use Monte Carlo and molecular dynamics simulations with implicit-solvent models. We directly compare different methods for calculating the critical micelle concentration in all self-assembling systems. We establish that excluded volume and counterion condensation are the main driving forces for the decrease of free surfactant concentration in nonionic and ionic surfactants, respectively. We compare counterion condensation and mean ionic activity measurements directly to experiments, and address experimental disagreements. For short-range attraction long-range repulsion colloids, conditions are found where large preferred aggregates have no net effect on the pressure, which is strikingly different behavior from surfactant self-assembly. The results in this thesis offer a deeper understanding of the phenomena of low-concentration self-assembly, leading to improved methods for estimating micellar properties over wider concentration ranges. These methods will benefit the development of more accurate molecular models and speed up development of new industrial formulations.
Optimization of Artificial Neural Network Architecture Using Neuroplasticity

Artificial neural networks, which are inspired by the behavior of central nervous system have the capability of finding good generalized solutions for many real world problems due to their characteristics such as massively parallel, ability to learn and adapt to the environment by altering the synaptic weights. However, despite of all the advantages of artificial neural networks, determining the most appropriate architecture for the given problem still remains as an unsolved problem. This paper presents a pruning method based on the backpropagation algorithm to solve this problem. The pruning method is inspired by the concepts of neuroplasticity and experimental results show that the proposed method approaches the minimal architecture faster than the other existing methods.
Simulation of a Boeing 737-500 Aircraft Electrical System

The aircraft electrical system must provide electrical power from the ground and throughout all flight phases. Its role and functional characteristics play a critical aspect in the design, certification and operation of military and commercial aircraft. This article details the implementation of equipment, control panels and enabling them through the SimVimCockpit / HCSCI configurator integrating the hardware with X-Plane flight simulator to perform the operational check of the Electrical System of the Boeing 737-500 aircraft. The results have been verified through the procedure of the Maintenance Manual of an applicable aircraft according to ATA 24-21-00.
Impact of Sampling Rate and Eye-Condition on Resting Quantitative EEG

Quantitative EEG has been explored to evaluate brain conditions; this is an increasing trend, as non-invasive EEG equipment becomes more accessible and powerful. In this paper, we discuss the possible impacts of sampling rate and eye condition choices in three commonly used qEEG features: absolute band power, Higuchi's fractal dimension, and frontal alpha asymmetry; these features are extracted from a high-quality database of healthy subjects. The setup impact is measured using the traditional frequentist statistical approach and Bayesian analysis; the later allowing for conclusions to be made either for the alternative or the null hypothesis. We noticed that sampling rate impact is more uniform, and task impact depends on the channel; moreover, the definition of bandpass and the gamma band may be critical for some qEEG features, and standardization is advised.
5G Communication Technology Application in Active Distribution Network Protection

With the high proportion access of distributed power supply, the traditional three-stage current protection has been difficult to adapt to the increasingly complex fault patterns of distribution network. Limited by the communication level of distribution network, multi-terminal protection is difficult to be popularized and applied in distribution network. The characteristics of large bandwidth, low delay and high reliability of 5G communication provide a new communication mode for protection service of distribution network. In this paper, the characteristics of low delay and high reliability of 5G communication technology are considered to achieve accurate fault location and isolation, improve the protection level of distribution network and power supply reliability. The research results of this paper can provide a useful reference for the application of 5G communication in the field of relay protection of distribution network.
An Improved CCM Rate Adaptation Method for Fading Channels

As a new type of rate adaptation method, Compressive Coded Modulation (CCM) can realize a wide range and smooth adaptation of the wireless communication rate in the Additive White Gaussian Noise (AWGN) channel. However, under the fading channel condition faced by high dynamic communication, the CCM rate adaptation method cannot be carried out, resulting in communication interruption. The Error Detection and Deletion CCM (EDD-CCM) method proposed in this paper can not only perform rate adaptation in fading channels, but also obtain a lower communication outage probability. And the analytical expressions of rate for EDD-CCM is given in this paper. The simulation results show that in the fading channel, when the Signal-to-Noise Ratio (SNR) of the traditional Hybrid Automatic Repeat Request (HARQ) method is lower than 10dB, the communication is completely interrupted, while the EDD-CCM method can guarantee normal communication when the SNR is higher than 5dB, and the required SNR is 5dB lower than that of the HARQ method under the condition of ensuring the same outage probability.
A Study on Robustness for the Single Server Location in Distributed Interactive Applications

In distributed interactive application ecosystems, service provision and availability are key factors for the end user's perceived quality. Therefore, the location of any server facility is important from the provider's standpoint for efficient capital expenditure. Given the dynamicity of such environments (e.g., due to traffic load variance over time), modeling this challenge as a median problem may require the continuous calculation of the optimal location. To address this robustness issue, a new robustness metric is introduced here for the case of the single server in edge computing, which depends on the overall delay experienced by the data traffic. Moreover, the smallest eigenvalue of the median matrix is considered as an alternative means for measuring this robustness. Using trace-driven simulation results, it is demonstrated that there is indeed a relationship between the proposed robustness metric and the smallest eigenvalue for the under consideration environment.
Pick-by-vision of Augmented Reality in Warehouse Picking Process Optimization – A Review

Augmented Reality (AR) is one of the most notable technologies in the Fourth Industrial Revolution (IR4.0), which uses the capabilities of computer-generated display, sound, text, and effects to improve the user's real-world experience through wearable equipment. Order picking operations in warehouse management systems (WMS) have a significant impact on overall operating efficiency. The conventional picking process is laborious to handle, which may result in deviations from predefined picking performance. Pick-by-vision, a new technology solution for order picking, is becoming increasingly popular and has been recognized as an essential technology supporting WMS nowadays. This article is a short review of AR pick-by-vision utilization to investigate the potential benefits and opportunities in optimizing warehouse picking operations. Besides presenting the basic concept of AR pick-by-vision, this study also produces a taxonomy layout of literature reviews for AR technology in WMS, to demonstrate the focus of the main area of the study. By reviewing 23 documents of AR pick-by-vision technology application, the analysis has produced important key findings, which are significant to the potential benefits of AR pick-by-vision implementation in optimizing the warehouse operation. The accumulation of knowledge and actionable insights in this study will benefit both academics and practitioners interested in this emerging smart technology for future research.
Fast power line detection based on semantic flow

In this paper, a Semantic-flow-based fully convolutional network model (SFCN) is proposed to solve the problems of low recall rate in the extraction of thin and long transmission lines in UAV images and are easily affected by complex background and illumination. The backbone of the model network adopts a smaller number of channels to reduce the number of parameters and speed up the learning speed. The inverted residual module is used to enhance the feature learning ability of the network under low number of channels and to prevent model degradation. The semantic flow module replaces the skip connection to complete the accurate fusion of high-dimensional features and low-dimensional features, and finally outputs the pixel-by-pixel recognition results. The method in this paper can realize quickly power line detection. Compared with the regular semantic segmentation models ENet, UNet, NestedUnet, DeepLabv3_plus, GCN, SegFormer, FCHarDNet, BiSeNetv2, and DDRNet, the method in this paper performs the best, with an F1 value of 83.693% and a recall rate of 80.64%.
Detrital zircon record of Cambrian (meta‐)sedimentary strata in the western part of the Baoshan Block: Constraints on its eastern boundary and Early Palaeozoic palaeoposition

The premise to discuss the evolution of an orogenic belt is to understand the origins and tectonic affinities of various blocks that constitute the orogen. The Baoshan Block in the SE Tibetan Plateau is an ultimate window to observe the orogenic evolution, but its origin and eastern boundary remains debated. In this contribution, we report new detrital zircon U–Pb ages and Hf isotopic data from the Cambrian Baoshan Formation in the middle–western part of the Baoshan Block. Detrital zircon ages range from the Archean to Early Palaeozoic, with age peaks at ~2.5, ~0.95, and ~0.55 Ga. Zircon εHf(t) values exhibit a wide range from negative to positive for each of the three major age groups, indicating diverse magma sources. The eastern boundary for the Baoshan Block should be extended from the Kejie‐Nandinghe Fault to the Changning‐Menglian suture belt, based on not only comparisons of Early Palaeozoic magmatism, sedimentary facies, and provenances for the Palaeozoic strata that developed on both sides of the Kejie‐Nandinghe Fault but also on the regional tectonic framework correlation. The Baoshan Block should be along the Indian margin as the Qiangtang, Tengchong, and Simao‐Indochina blocks in Early Palaeozoic according to the provenance analyses.
Featured Collection Introduction: The Emerging Science of Aquatic System Connectivity II

The science of aquatic systems connectivity has developed rapidly over the past 20 years (Jones, Nelson, et al. 2019). Research spans the different forms and functions of connectivity (hydrologic, biogeochemical, and biological) at vastly different spatial scales and hydrologic settings (Larsen et al. 2012; Bracken et al. 2013; Harvey and Gooseff 2015; U.S. Environmental Protection Agency 2015; Cohen et al. 2016; Covino 2017; Fritz et al. 2018; Wohl et al. 2019). This collection, The Emerging Science of Aquatic Systems Connectivity II, includes eight papers focused on aquatic system connectivity and follows the featured collection The Emerging Science of Aquatic Systems Connectivity I (Jones, Nelson, et al. 2019). The papers in both collections are products of the 2017 American Water Resources (AWRA) Specialty Conference, Connecting the Dots: The Emerging Science of Aquatic System Connectivity, which took place in May 2017 in Snowbird, Utah. The conference consisted of 33 technical sessions with over 140 presenters and with this most recent collection produced a total of 19 papers. As with Collection I, these papers increase our understanding of the functions of aquatic systems and include novel modeling approaches to characterizing connectivity and contribute to management and restoration of these functions. Three papers in this issue used modeling approaches to examine the hydrologic role of wetlands at the landscape scale. Ameli and Creed (2019) modeled effects of wetland location relative to stream networks on flows during floods and droughts in the Nose Creek watershed of the Prairie Pothole Region. Their model combined historical, existing, and drained wetlands in the watershed to estimate the hydrological functions of wetlands located at different distances from the main stream network. They found wetlands close to the main stream network play a disproportionately important role in attenuating peakflow, but wetland location is less important for regulating baseflow. These findings can help managers prioritize wetland restoration efforts for flood or drought risk mitigation. Green et al. (2019) modeled the runoff storage potential of drained upland depressions on the Des Moines Lobe of Iowa using hydrologically enforced Light Detection and Ranging (LIDAR)-derived Digital Elevation Models and a unique geoprocessing algorithm to determine storage capacities. In contrast to Ameli and Creed (2019), Green et al. (2019) determined the drained upland depressions in this region have insufficient storage capacity to significantly alter regional and local flood events. Jones, Ameli, et al. (2019) review the current capabilities of hydrologic models and their ability to simulate hydrologic connectivity of non-floodplain wetlands. They present four distinct case studies that employ process-based models which vary in complexity, spatial representation of hydrologic processes, and fidelity (i.e., the models ability to faithfully