Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategic Monitoring for Efficient Detection of Simultaneous APT Attacks with Limited Resources

—Advanced Persistent Threats (APT) are a type of sophisticated multistage cyber attack, and the defense against APT is challenging. Existing studies apply signature-based or behavior-based methods to analyze monitoring data to detect APT, but little research has been dedicated to the important problem of addressing APT detection with limited resources. In order to maintain the primary functionality of a system, the resources allocated for security purposes, for example logging and examining the behavior of a system, are usually constrained. Therefore, when facing multiple simultaneous powerful cyber attacks like APT, the allocation of limited security resources becomes critical. The research in this paper focuses on the threat model where multiple simultaneous APT attacks exist in the defender’s system, but the defender does not have sufficient monitoring resources to check every running process. To capture the footprint of multistage activities including APT attacks and benign activities, this work leverages the provenance graph which is constructed based on dependencies of processes. Furthermore, this work studies the monitoring strategy to efficiently detect APT attacks from incomplete information of paths on the provenance graph, by considering both the “exploitation” effect and the “exploration” effect. The contributions of this work are two-fold. First, it extends the classic UCB algorithm in the domain of the multi-armed bandit problem to solve cyber security problems, and proposes to use the malevolence value of a path, which is generated by a novel LSTM neural network as the exploitation term. Second, the consideration of “exploration” is innovative in the detection of APT attacks with limited monitoring resources. The experimental results show that the use of the LSTM neural network is beneficial to enforce the exploitation effect as it satisfies the same property as the exploitation term in the classic UCB algorithm and that by using the proposed monitoring strategy, multiple simultaneous APT attacks are detected more efficiently than using the random strategy and the greedy strategy, regarding the time needed to detect same number of APT attacks.
Wind Preview-Based Model Predictive Control of Multi-Rotor UAVs Using LiDAR

Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview’s timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts.
Advanced Detections of Norway Lobster (Nephrops Norvegicus) Burrows using Deep Learning Techniques

—Marine experts are facing lot of challenges in habitat monitoring of marine species. One of the biggest challenges is the underwater environment and species movement. The other challenge is the data collection of marine species. People used the camera sensors and satellite data in the past for data collection but in this era the scientists are using underwater Autonomous Underwater Vehicles (AUVs), the Remotely Operated Vehicles (ROVs), and certain sledges with high-definition still and video cameras to record the underwater footages. The ocean is composed of thousands of species which make the environment more challenging to monitor any specific specie. This work will focus on specie named Norway lobster ( Nephrops norvegicus). The Nephrops norvegicus is one of the commercial specie in the Europe and generates millions of dollars yearly. This specie lives under the seabed and leaves behind the burrow structure on the sea ground. The Nephrops spend most of their time under the seabed. The scientists are currently monitoring the habitat of Nephrops norvegicus by underwater television (UWTV) surveys that is collected yearly on many European grounds. The collected data is reviewed manually by the experts who count the burrows on the sheet. This work focuses on the automatic detection of Nephrops burrows from underwater videos using the deep learning techniques. This work trained the Faster R-CNN models Inceptionv2, MobileNetv2, ResNet50, and ResNet101. Instead of training the models from scratch we used the transfer learning technique to fine tune these networks. The data is obtained from the Gulf of Cadiz (FU30) station. Twenty-eight different set of experiments are performed. The models are evaluated quantitatively using the mean Average Precision (mAP), precision and recall curves. Also, the models are qualitatively analyzed by visually presenting the output. The results prove that deep learning techniques are very helpful for marine scientists to assess the Nephrops norvegicus abundance.
Health Behaviors of Colombian First-Semester University Students in Association with Behaviors of Close Social Ties, Living Arrangement, and Time Spent with Peers

Objective: in Colombia, many first-year university students consume unhealthy food, are physically inactive, and drink regularly, which can be associated with the behavior of social ties, living with social ties, and time with peers. The present cross-sectional study assessed the association between health behaviors of first-semester students and these factors. Method: N = 189 (Mage = 18.79; SD = 1.07; female = 68.8%) first-semester students in Colombia completed an online questionnaire investigating current and expected health behaviors as well as influencing factors. ANCOVAs, bivariate correlations, moderation analyses, and hierarchical regressions were used to analyze the data. Results: expected food consumption (stronger among participants who live with parents) as well as current and expected heavy drinking and binge drinking (stronger among participants who do not live with parents) were significantly correlated to the respective parental behavior. Current and expected drinking was significantly correlated to partners’ drinking. Expected physical activity was correlated with peers’ physical activity. Partners’ attempts to encourage drinking moderated the association between participants’ current and expected drinking with partners’ drinking. Time spent with peers was related to heavy drinking and engaging in more physical activity. Conclusion: in Colombia, parents appear to play a significant role in their offspring’s health behaviors during their first semester at university, particularly regarding food consumption and alcohol use. Partners’ drinking and time spent with peers are strongly related to heavy drinking.
Psychosocial Factors Affecting Drug Relapse among Youth in Punjab, Pakistan

The present study was intended to examine the effect of psychosocial factors on the stigma of mental illness among people addicted to drugs who have relapsed in Punjab, Pakistan. A sample composed of 116 people addicted to drugs and who have relapsed was collected through the purposive sampling technique. Certain self-report measures were used to assess the pertinent study variables. The results elucidated that self-efficacy, social support, and conscientiousness were found to be negative predictors and neuroticism a positive predictor of stigma. The results also confirmed social support as being a significant moderator in the relationship between self-efficacy and stigma, and self-efficacy as being a significant moderator in the relationship between social support and stigma. It was safe to conclude that psychosocial factors such as self-efficacy, social support, personality traits and stigma have a significant role in causing addiction relapse. The conclusions made have been discussed thoroughly.
Gaskinetic Solutions for High Knudsen Number Planar Jet Impingement Flows

This paper presents a gaskinetic study and analytical results on high speed rarefied gas flows from a planar exit. The beginning of this paper reviews the results for planar free jet expanding into a vacuum, followed by an investigation of jet impingement on normally set plates with either a diffuse or a specular surface. Presented results include exact solutions for flowfield and surface properties. Numerical simulations with the direct simulation Monte Carlo method were performed to validate these analytical results, and good agreement with this is obtained for flows at high Knudsen numbers. These highly rarefied jet and jet impingement results can provide references for real jet and jet impingement flows. PACS: 98.58. Fd, 47.45. Ab, 52.25. Fi, 52.55. Fa, 52.35. Py
The application of wavelet transform in the image enhancement processing

According to the time-frequency characteristic and multiresolution wavelet characteristic,this paper proposed an image enhancement algorithm based on wavelet transform.The image was transformated by wavelet to get different resolution characterization of the low profile and high frequency wavelet coefficients of the detail,wavelet coefficient was single reconstructed,low-frequency single refactoring information was piecewise linear enhancement and linear superposition,image low-frequency outline was enhanced;high frequency single refactoring information was piecewise linearly enhanced and linearly superposied,image edges was enhanced.The experimental results showed that the algorithm had high flexibility and that it could not only realize the image contour enhancement,but also realize the image edge enhancement.
An approximation algorithm for a special case of the asymmetric travelling salesman problem

We consider the following optimisation problem that we encountered during the consolidation process of trains in a container transhipment terminal as well as in the intermediate storage of containers in sea ports in order to accelerate the loading and unloading of the vessels. There are n ordered pairs of points in the m-dimensional metric space: . The problem is to find a permutation of numbers minimising the function where is the metric of the space. The problem can be considered as a special case of the asymmetric travelling salesman problem. As for Euclidean, Manhattan and Chebyshev metric the problem is NP-hard (as a generalisation of the well-known TSP problem) we propose the simple approximation algorithm with the approximation guarantee equal to 3. The approximation guarantee is tight as will be shown by a sequence of instances for which the approximation ratio converges to 3.
Ecoenzymatic stoichiometry reveals soil P limitation under biochar addition in a reclaimed mine area in Shanxi Province, China

Extracellular enzymatic activity plays an important role in carbon (C), nitrogen (N), and phosphorous (P) cycling, and consequently, serves as an indicator for soil function. However, microbial activities are inhibited in degraded lands due to nutrition limitations, which pose a challenge to ecological management. We investigated the effects of maize biochar addition (1, 3, and 5%) on soil and enzyme stoichiometries in a post‐mining area in Shanxi province. The results showed that soil organic C and total N increased significantly with increasing biochar addition, leading to significant increases in soil C:P (by 157.9%) and N:P (by 76.1%) under 5% of addition. Biochar addition stimulated enzyme activities significantly, and the maximum of enzyme activities occurred in treatment of 5% addition. Soil enzyme C:N increased significantly following biochar addition, while enzyme C:P and N:P decreased at 5% biochar addition. Vector analysis indicated that microbial activity tended to be limited by soil N before addition, whereas it was limited by P availability at high biochar addition. This was mainly because that biochar addition could promote sorption of P and lower P bioavailability in soil. Our research suggested that biochar application could substantially improve soil nutrition status and microbial activity in infertile land, however, it would likely result in imbalances between substrate stoichiometry and microbial demand. The result might provide a reference for the management of biochar application during soil restoration.
A Property of a Triangle Inscribed in a Convex Curve

The purpose of this paper is to prove the following theorem: Theorem. Given a convex curve C, of perimeter length I and three points M, N, and P which divide the perimeter of C into three parts of equal length, the perimeter length of the triangle MNP is never less than ½l. Equality holds only in the case where C is an equilateral triangle and M, N, and P are the mid-points of the three sides.
Physics-Based Analytical Formulation of the Soft Error Rate in CMOS Circuits

The exponential dependence of the soft error rate (SER) with critical charge in CMOS circuits, empirically proposed by Hazucha and Svensson, is derived in the framework of the diffusion–collection approach. A full analytical formulation is established, linking the SER with physical and technological parameters, notably the circuit supply voltage, carrier diffusion coefficient, and ion characteristics.
Particleboard experimental production with bamboo, pine and mate for one product of new applications

Particleboard can be produced from a mixture of different lignocellulosic materials, which can be chosen depending on the density required for the panel and its applications. The aim of this study was to evaluate the mechanical properties of particleboard with bamboo, pine and mate for a new product of high density for the special applications currently served by HDF. Particles of bamboo ( Phyllostachys aurea ) finely chopped sticks of mate ( Ilex paraguariensis ) and commercial particles of southern pine ( Pinus taeda ) wood were used. These particles used 100 % by weight in the panel or in mixtures of 50 % each (three mixtures) or in a triple mixture of one third each, were glued in a drum-type rotary mixer with melamine-urea-formaldehyde (MUF) resin,
An FPGA-CAPH Stereo Matching Processor Based on the Sum of Hamming Distances

Stereo matching is a useful algorithm to infer depth informa-tion from two or more of images and has uses in mobile robotics, three-dimensional building mapping and three-dimensional reconstruction of objects. In area-based algorithms, the similarity between one pixel of an image (key frame) and one pixel of another image is measured using a correlation index computed on neighbors of these pixels (correlation win-dows). In order to preserve edges, the use of small correlation windows is necessary while for homogeneous areas, large windows are required. In addition, to improve the execution time, stereo matching algorithms often are implemented in dedicated hardware such as FPGA or GPU devices. In this article, we present an FPGA stereo matching processor based on the Sum of Hamming Distances (SHD). We propose a grayscale-based similarity criterion, which allows separating the objects and background from the correlation window. By using the similarity criterion, it is possible to improve the performance of any grayscale-based correlation coeﬃcient and reach high performance for homogeneous areas and edges. The developed FPGA architecture reaches high performance compared to other real-time stereo matching algorithms, up to 10% more accuracy and enables to increase the processing speed near to 20 megapixels per second.
EPO Optimized FLC Controller for PQ Improvement in Wind Integrated Power Distribution System

Abstract Nowadays, Power Quality (PQ) problems are economically affecting customers and utilities. In our work, the PQ problems are mitigated using the Unified Power Quality Conditioner (UPQC) and the proposed controller. Fuzzy Logic Controller (FLC) and Emperor Penguin Optimization algorithm (EPO) are utilized to design the proposed controller. The proposed control scheme analyses the PQ issues of harmonics, oscillatory transients, swell, and voltage sag. The optimized FLC controller-based UPQC compensates the PQ problems at a common coupling point. Using the decomposition technique, the Total Harmonic Distortion (THD) and harmonics are moderated, and the voltages sag and swell problem is minimized to improve the compensation capability. The MATLAB/SIMULINK platform is utilized to evaluate the performances of PQ issues in UPQC compensating devices. The generated, injected, and load voltages results are compared with existing controller techniques.
Prediction of Covid-19 New Cases and New Deaths in Nigeria Using Polynomial Regression Model

The Coronavirus disease 2019 (COVID-2019) is a global pandemic and Nigeria is not left out in being affected. This study is focused on the analysis prediction of the spread of Covid-19 in Nigeria, applying statistical model (Polynomial Regression) and available data from the NCDC. The study is an insight into the spread of Covid-19 in Nigeria in order to establish a suitable prediction model, which can be applied as a decision-supportive tool for assigning health interventions and mitigating the spread of the Covid-19 infection. The polynomial regression models are prominent tools in predicting the impact of certain factors on COVID19 outbreak and taking the necessary measures to respond to this crisis. The objective of this study is to implement a predictive model in forecasting the near future number of new death and positive COVID-19 cases expected in Nigeria following the present trend. A polynomial regression model prediction on the epidemiological data obtained from Nigerian Centre for Disease Control to predict the epidemiological trend of the prevalence and incidence of COVID-2019. In this study, we examined the inadequacies involved in the manual method of predicting new death and COVID-19 case to enable government to make decision in reducing the death and spread of COVID-19 in Nigeria. The methodology we adopted in this study is the Object-Oriented Design Methodology (OODM) which combines data and processes (methods) into single entities called objects while Microsoft Visual Basic is our programming language of implementation. The new system will be used in generation of reports and results to create, process, and validate a model that can be used to forecast future outcomes of new death and COVID-19 case. The system was implemented using VB.net programming language and SQLite because it is not a server side application, which needs a standard database management system. Keywords: Prediction, COVID-19, Coronavirus, Pandemic, Morbidity, Polynomial regression, Prominent, Predictors, Nigerian Centre for Disease Control (NCDC).
sp-band tight-binding model for the Bychkov-Rashba effect in a two-dimensional electron system including nearest-neighbor contributions from an electric field

We present a tight-binding calculation for a two-dimensional electron gas (2DEG) including the spin-orbit interaction as well as an electric field perpendicular to the system in order to model the Bychkov-Rashba spin splitting. The associated potential gradient introduces two contributions to the tight-binding matrix: an on-site contribution coupling orbitals of the same atom and a nearest-neighbor contribution. At the point the first-order Rashba constant αR only depends on this nearest-neighbor contribution regardless of the lattices considered (square, hexagonal, honeycomb). Applying the model to graphene reveals that this nearest-neighbor contribution induces a significant increase in the zeroth-order Rashba constant λR and introduces a spin-splitting component, which varies linearly in momentum.
Large-scale computing for complementarity and variational inequalities

Complementarity problems and variational inequalities are tools for expressing the equilibrium conditions in diverse applications, along with the optimality conditions for linear and nonlinear programs. Representative economics models utilizing Nash equilibria and dynamics can be formulated and solved in this framework. Enhancements to a mixed complementarity solver (PATH) for large-scale computing efficiency can be achieved by improving the underlying linear system routines (factor, solve and update), since a key component of the PATH algorithm is the solution of a series of linear complementarity subproblems via a pivotal scheme requiring such functionalities. Two new options besides the default LUSOL package are incorporated in PATH for these linear system routines. One of the options employs the UMFPACK package for factor and solve operations and a block-LU updating scheme. The other option exploits the COIN-OR utilities that are enhanced by adapting the linear refinement and scaling schemes used in the COIN-LP routines. Together, these options lead to a significantly more effective version of PATH for solving many large-scale sparse systems. These options are incorporated in the new GAMS/PATH distribution. An affine variational inequality solver (PathAVI) is developed that exploits the special underlying polyhedral set structure. It is able to process a class of models (formulated as AVIs) whose equivalent complementarity reformulations cannot be processed by existing complementarity solvers. This algorithm is guaranteed to process affine variational inequalities involving matrices that are copositive-plus with respect to the recession cone of the polyhedral set. The central component of the algorithm is to employ a pivotal strategy to construct a piecewise linear path to a solution. It critically relies on linear system packages for its performance and can use the sparse linear system packages and updating schemes developed above. Finally, a scheme for solving parametric monotone linear complementarity problems is implemented. It is based on Lemke's method and consecutively solves a sequence of linear complementarity problems as the parameter value varies over a feasible range determined by the solution of two linear programs. Application of this algorithm to portfolio selection and other problems is also described.