Miracle-dz/UniFAR-FTUs huggingface dataset:
Pulmonary Nodules Binary Classification using CNN and LSTM

Pulmonary cancer is amongst the major reasons of death all across the globe. The rate of survival in patients suffering from lung cancer increases only if it is diagnosed at early stages. Nowadays, the employment of Computer-Aided Detection (CAD) systems can lead to the rapid detection of lung cancer and potentially save lives. Deep learning has been arising as an optimistic area of artificial intelligence, and this has attracted rapidly increasing recognition in the medical field. A classification model for lung nodules that utilizes Computed Tomography (CT) scans and relies upon a Convolutional Neural Network (CNN). Images of lung nodules extracted from the CT scan images available in the LIDC-IDRI dataset are utilized by the model. The extraction of nodules has been done utilizing the python library package named pylidc. The graphs have been compiled using tensorBoard. The maximum accuracy attained using the proposed model is 83.86% and the loss was 32.81%.
Ghost imaging with non-negative exponential speckle patterns

The speckle patterns with non-negative exponential statistics are generated through introducing an iterative algorithm: the Gerchberg-Saxton (GS) algorithm. Furthermore, ghost imaging (GI) with the non-negative exponential speckle patterns is investigated, and the effect of the non-negative exponential speckle patterns on the image quality is analyzed.
MCES: Multi-classifier Ensemble System for Malware Detection and Identification

With the development and advances of information and networking technologies in Cyber-physical Power systems (CPPS), the frequency of information exchange between CPPS and the external networks is significantly increasing, leading to an ever-growing risk of cyberspace threats and attacks, e.g., malware injection to the CPPS. The existing detectors against malware are mainly based on static analysis, which deals well with known malware, but cannot cope with unknown or obfuscated malware. To this end, this paper proposed a novel multi-classifier ensemble system (MCES) for malware detection based on behavioral analysis. The developed detection model of MCES is a hierarchical learning model with kernel components consisting of base classifiers and one meta classifier. The proposed solution is assessed through experiments based on in total 14800 malware and 14800 benign samples. The numerical results demonstrated that MCES outperformed existing machine learning-based classifiers in identifying malware through application program interface (API) call sequences. The proposed model achieved the highest accuracy of 97.54%, and the highest recall of 97.85% in comparison with the state-of-the-art deep learning based malware detectors.
Critical phenomena induced by intertwined dynamics on multiplex networks

Network-coupled dynamical processes have emerged as a powerful tool to model and study a wide variety of complex phenomena. Significant research has increased our understanding of many complex systems, specifically enlightening the connection and interplay between dynamics and network structure. However, save for a few notable exceptions, most studies consider a single complex system acting in isolation from other processes. Here we show that new critical phenomena can be induced by intertwining different dynamical processes with distinct network topologies using a multiplex network coupling such that the different processes evolve in parallel. As an example, we study bidirectionally-coupled synchronization and transport dynamics, where the allocation of transport in one layer depends on the degree of synchronization on the other layer, and vice-versa. Multiplexity induces, at the same time, a transition to a heterogeneous distribution of allocations and the emergence of a switch-like bistability in the synchronization dynamics. Our results can find applications in cases where synchronization is subject to the transport of available resources, such as in the brain or in power networks.
Understanding Software Performance Challenges an Empirical Study on Stack Overflow

Performance is a quality aspect describing how the software is performing. Any performance degradation will further affect other quality aspects, such as usability. Software developers continuously conduct testing to ensure that code addition or changes do not damage existing functionalities or negatively affect the quality. Hence, developers set strategies to detect, locate and fix the regression if needed. In this paper, we provide an exploratory study on the challenges developers face in resolving performance regression. The study is based on the questions posted on a technical forum directed to performance regression. We collected 1828 questions discussing the regression of software execution time. All those questions are manually analyzed. The study resulted in a categorization of the challenges. We also discussed the difficulty level of performance regression issues within the developers community. This study provides insights to help developers during the software design and implementation to avoid regression causes.
Novel PM-Assisted Model of the Two-Layer Sub-Harmonic Synchronous Machines

In this paper, a new pm-assisted model has been developed for the already introduced two-layer sub-harmonic synchronous machine. This work aims to increase the torque-producing capability of the brushless wound rotor machines while keeping the use of rare earth magnets to a minimum. A 2D finite element analysis has been performed to validate the proposed model and compare the performance with the reference model. The results in the paper demonstrate that the proposed machine's average torque has increased.
On the Use of Spatial-temporal Feature Graph Convolutional Network for Ultra-Short Term Prediction of Wind Power Generation

The large-scale access of wind power generation units brings hidden dangers to the safe and stable operation of power systems, and accurate wind power prediction can somewhat abate the adverse effects of new energy volatility. With the development of wind farm clusters, the power of each wind farm forms a high-dimensional spatial-temporal graph with complex coupling due to natural factors such as location, distance and weather. In recent years, the graph neural network has received increasing attention due to its excellent performance on graph structure data, so this paper studies wind power prediction methods based on graph convolutional networks, and single-step prediction models and multi-step prediction models are established using graph convolutional networks to achieve collaborative prediction of multiple wind farms. Compared with the existing solutions, the proposed method obtains better ultra-short-term wind power prediction performance in terms of accuracy.
Analysis of the Optimized KNN Algorithm for the Data Security of DR Service

The data of large-scale distributed demand-side iot devices are gradually migrated to the cloud. This cloud deployment mode makes it convenient for IoT devices to participate in the interaction between supply and demand, and at the same time exposes various vulnerabilities of IoT devices to the Internet, which can be easily accessed and manipulated by hackers to launch large-scale DDoS attacks. As an easy-to-understand supervised learning classification algorithm, KNN can obtain more accurate classification results without too many adjustment parameters, and has achieved many research achievements in the field of DDoS detection. However, in the face of high-dimensional data, this method has high operation cost, high cost and not practical. Aiming at this disadvantage, this chapter explores the potential of classical KNN algorithm in data storage structure, K-nearest neighbor search and hyperparameter optimization, and proposes an improved KNN algorithm for DDoS attack detection of demand-side IoT devices.
Research on the Influencing Factors of the Dual System of Agricultural Product E-commerce and Cold Chain Logistics under Low-carbon Economy

Focusing on the development of agricultural products e-commerce and cold chain logistics under the low-carbon economy, this paper analyzes the influencing factors of cold chain logistics and agricultural products e-commerce, uses principal component analysis method to extract the principal components of agricultural products e-commerce and cold chain logistics, and carries out correlation analysis on the dual system. It is found that the dual system has a strong correlation with the infrastructure construction and development potential, and the carbon emission has a strong correlation with the infrastructure construction of the dual system. According to the analysis results, puts forward to promote agricultural electricity under low carbon economy and the coordinated development of cold chain logistics ideas and Suggestions, to obtain rapid development of cold chain logistics and produce electricity matching degrees, to speed up the pace of reduction, decreasing the consumption and low-carbon transformation, promote agricultural electricity and cold chain logistics structure adjustment, realize the healthy and sustainable development.
Deep Learning Approaches for Pneumonia Classification in Healthcare

In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known ImageNet Computer Vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of Image classification was later extended to Image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed.
Turbulence Statistics of Wave-Current Flow over a Submerged Cube

AbstractThis paper describes an experimental study carried out in a laboratory flume to investigate the interaction of a surface wave with a unidirectional current over a submerged cubic obstacle. The three-dimensional velocity field was measured using an acoustic Doppler velocimeter (ADV). The results highlight the changes induced in the mean velocity profile, turbulent intensity, and Reynolds shear stress in a plane of symmetry from the superposition of surface waves of different frequencies. Modifications in the mean velocities, turbulence intensities, and Reynolds shear stresses with respect to the flat surface case, in the vicinity of the cube, are explored. This study also investigates the dominant turbulent bursting event that contributes to the Reynolds shear stress in the near-bed flow influenced by the cube. The results show that near the boundary, the contributions to the total shear stress from ejection and sweep are dominant. However, away from the boundary, the outward and inward interaction.
Image Classification Method by Combining Multi-features and Sparse Coding

Using a single image feature to describe the image content is one-sided because of the insufficient information. Besides,the single coding method usually loses the spatial information. To solve these problems,an approach of integrating multi-features and sparse coding methods is proposed. Images are firstly divided into sub regions according to the spatial pyramid,and then the complementary advantages of scale invariant feature transform and the histogram of oriented gradients features are combined to produce various feature sets. Then,different clustering methods are used on different feature sets to acquire different codebooks. Next,two sparse coding methods,locality constrained linear coding and sparse coding based on each codebook are further employed respectively to get various image description sets. Finally,linear support vector machines are applied to image classification,and a voting method is used to determine the final classification. Experimental results show that the proposed method has good accuracy and robustness compared with some state-of-the-art methods.
Finite Element Method: An Overview

The finite element method (FEM) is a numerical analysis technique for obtaining approximate solutions to a wide variety of engineering problems. A finite element model of a problem gives a piecewise approximation to the governing equations. The basic premise of the FEM is that a solution region can be analytically modeled or approximated by replacing it with an assemblage of discrete elements (discretization). Since these elements can be put together in a variety of ways, they can be used to represent exceedingly complex shapes.
Obstacles and Satisfaction to Balance Between Family Life and Medical Career Among Saudi Women Doctors

Background: Besides their medical career obligations, female physicians traditionally have assumed responsibility for raising families and maintaining the household. Finding an acceptable balance between their career and family life is challenging. Objective: The study aimed to discover the obstacles and the relationship between the barriers/factors with the satisfaction in balancing career and family life. Methodology: A cross-sectional study that analyzed data from Saudi female physicians. The study included 165 female physicians from the six Ministry of Health hospitals in Al-Madinah Al-Munawarah; 65 were specialists and consultants, and 100 were general practitioners and residents. The data were collected from October until the end of November 2022 through a semi-structured, self-administered questionnaire gathered by convenience sampling. The data were collected and analyzed with SAS software. Results: The study's main findings include a satisfaction rate in balancing career and family life among the studied female physicians, which was low at 15.7%. In comparison, female physicians unsatisfied with such balancing were 38.2%. The effect of family responsibilities on career choice was nearly equal, where 50.3% of the studied female physicians affected them. There was a statistically significant difference regarding the satisfaction in balancing career with family life by their specialty; female surgeons and gyn/obs physicians found a higher percentage of unsatisfaction, whereas, among family medicine physicians, the least rate of unsatisfaction was found (P-value < 0.01). At 80% of the studied physicians suggested providing childcare centers as the main solution to their difficulties and obstacles; also, 46.5% suggested taking more days off of maternity leave. Transportation difficulties, however, represented the lowest type of difficulty, 12.7%. Conclusion: The present study has revealed several obstacles facing female physicians that negatively impact relationships with their families.
The Impact of an Elementary Whole-School Nurturing Approach: A Controlled Multiple-Perspective Follow-Up Study

Introduction. Children’s social and emotional wellbeing is associated with subsequent academic achievement and behavioural outcomes, as well as functioning in later life. Nurturing approaches are one way of developing such wellbeing. Whole-school approaches to nurturing have been discussed in the literature, but no studies of any quality have evaluated effects. The aim of this study was to evaluate whether whole-school nurturing had any effect on children’s wellbeing from the perceptions of pupils, parents and teachers. Method. The present study in primary (elementary) schools is controlled, has follow-up, and multiple perspectives from teachers, parents and pupils. Pupils (n=322) from Years/Grades 1, 3 and 4 (aged 6, 8 and 9) (Years limited by school staff time availability) in three intervention and three control schools and their parents and teachers participated over two years. On a pre-post basis, the Strengths and Difficulties Questionnaire (SDQ) was used with teachers and parents and the Stirling Children’s Wellbeing Scale (SCWS) with pupils. Results. On the SDQ for Teachers, the pre-post Total Difficulties scores and the Prosocial scores were significantly better for the intervention than the control group. On the SDQ for Parents, both intervention and control groups improved, and there was no difference. On the SCWS for Children, again both intervention and control groups improved, and there was no difference. Discussion and Conclusion. The nurturing intervention group was significantly better than controls according to the perceptions of Teachers, but for parents and children both intervention and control groups improved. A number of recommendations for future research were made, and implications for practitioners and policy-makers outlined.
Simulation and modeling of silicon pore optics for the ATHENA x-ray telescope

The ATHENA X-ray observatory is a large-class ESA approved mission, with launch scheduled in 2028. The technology of silicon pore optics (SPO) was selected as baseline to assemble ATHENA's optic with more than 1000 mirror modules, obtained by stacking wedged and ribbed silicon wafer plates onto silicon mandrels to form the Wolter-I configuration. Even if the current baseline design fulfills the required effective area of 2 m2 at 1 keV on-axis, alternative design solutions, e.g., privileging the field of view or the off-axis angular resolution, are also possible. Moreover, the stringent requirement of a 5 arcsec HEW angular resolution at 1 keV entails very small profile errors and excellent surface smoothness, as well as a precise alignment of the 1000 mirror modules to avoid imaging degradation and effective area loss. Finally, the stray light issue has to be kept under control. In this paper we show the preliminary results of simulations of optical systems based on SPO for the ATHENA X-ray telescope, from pore to telescope level, carried out at INAF/OAB and DTU Space under ESA contract. We show ray-tracing results, including assessment of the misalignments of mirror modules and the impact of stray light. We also deal with a detailed description of diffractive effects expected in an SPO module from UV light, where the aperture diffraction prevails, to X-rays where the surface diffraction plays a major role. Finally, we analyze the results of X-ray tests performed at the BESSY synchrotron, we compare them with surface finishing measurements, and we estimate the expected HEW degradation caused by the X-ray scattering.
An Improved Color-Based Particle Filter Algorithm for Target Tracking

To overcome the disadvantages that the traditional particle filters based on color histogram is susceptible to environmental interference and illumination variations,an improved particle filter algorithm was proposed.This article starts from improving the description ability of the target feature model.First,the histogram weighted function was optimized.Second,for the shortcoming of the color feature,a new color local entropy target observation model was constructed by mapping the target from color feature space to local entropy space.In addition,in order to make the model better adjust to environmental interference and target deformation,an adaptive updating strategy of the target model was designed and the number of particle was adjusted dynamically.Experimental results demonstrate that the proposed algorithm is effective.
A Review on Image Inpainting to Restore Image

With the advent of lots of multimedia instruments in today's world peoples are clicking lots of Picture of theirs and also trying to preserve their past pictures. As the time goes on those pictures got damaged. Image inpainting is technique which tries to recover such images which are damaged. Inpainting is an art of modifying the digital image in such a way that the modifications/alterations are undetectable to an observer who has no idea about the original image. Image Inpainting is used to filling the region which are damaged and want to recover from unwanted object by collecting the information from the more promising neighbouring pixels which will add details to the image such that image have close resemblance to the original image and we are not able to differentiate between original image and the inpainted image. In this paper we are trying to study various technique of image inpainting, where the authors used the multiresoultion technique, wavelet transform, counterlet transform, fragment based image completition etc. to complete the image. We also discuss about the advantages and disadvantages of that technique with respect to efficiency and time limit.
APPROXIMATE WEAK AMENABILITY OF

In this paper we deal with three generalized notions of amenabil- ity, approximate, approximate weak and approximate n-weak amenability. The first two were introduced and studied by Ghahramani and Loy in (5). We introduce the third one. Then we investigate some properties of Banach alge- bras in each of these classes. Also we consider approximate weak amenability and approximate n-weak amenability of second duals.
Contour extraction of regular targets in images

In this paper,a method about contour extraction of regular targets in images is proposed,which is based on an improved rectangle fitting method and a polygon fitting method.Being applied to simulative binary images and real scene images,it is demonstrated that this method is effective and robust.At the same time,this method can be applied to military,mapping annotation,city planning and subject mapping,etc.
Law of Large Graphs

Estimating the mean of a population of graphs based on a sample is a core problem in network science. Often, this problem is especially difficult because the sample or cohort size is relatively small as compared to the number of parameters to estimate. While using the element-wise sample mean of the adjacency matrices is a common approach, this method does not exploit any underlying graph structure. We propose using a low-rank method together with tools for dimension selection and diagonal augmentation to improve performance over the naive methodology for small sample sizes. Theoretical results for the stochastic blockmodel show that this method will offer major improvements when there are many vertices. Similarly, in analyzing human connectome data, we demonstrate that the low-rank methods outperform the standard sample mean for many settings. These results indicate that low-rank methods should be a key part of the tool box for researchers studying populations of graphs.
Loss probability in priority limited processing queueing system

In this paper, a queueing system with a preemptive priority limited processor sharing system with some versions of the discipline is considered. The problem of lost probability is an important aspect for developing methods to improve efficiency and productivity of in modern communications system and computer networks. In the paper, the following conditions are considered. The total service rate depends on the number of jobs serviced simultaneously, and this number is limited. The service rate of simultaneously serviced jobs is the same. There are jobs of different priorities. If the maximum quantity of jobs are serviced simultaneously, and a new job arrives, then depending on the priorities of the arriving and serviced jobs, the arriving job can be lost or preempt a serviced job. Four versions of the service discipline are considered. Depending on the discipline version, an arriving job can preempt or not preempt a job of the same priority, and preempted job is lost or its service is resumed later. This paper develops an approach to compute the loss probability of a prescribed priority job for each version of the discipline.