Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Skin Cancer using Artificial Intelligence & Machine Learning Concepts

In this paper, the detection of Skin Cancer using Artificial Intelligence & Machine Learning Concepts is presented. Convolutional Neural Networks (CNN) in the area of image recognition are one of the many complex challenges that Deep Learning has gained attention for in recent years. Transfer Learning (TL) approaches are also becoming more and more common as a way to retrain some of the layers of Neural Networks that learnt from a generic dataset, putting it to use in another situation. This is because Neural Networks can occasionally attain more than 50 layers deep. In this work, we try to use CNNs and TL methods on pre-trained Neural Networks to separate seborrheic keratosis and nevus benign tumours from melanoma skin cancer. Skin cancer can be detected early thanks to AI. Deep convolutional neural networks, for instance, can be used to construct a system that assesses skin image data to detect skin cancer. For skin cancer to be effectively treated and to have better outcomes, early detection is crucial. The use of artificial intelligence (AI) technologies, such as shallow and deep machine learning-based techniques that are trained to detect and categorise skin cancer using computer algorithms and deep neural networks, is therefore being made to assist in the diagnosis of skin cancer. The purpose of this study was to categorise and list the many AIbased technologies that are used to find and classify skin cancer. By examining the relationship between the size of the data set, the number of diagnostic classes, and the performance measures employed to assess the models, the study also looked at the validity of the papers that were chosen. The results shows the effectiveness of the method put in this paper
On the Efficiency of LFP Lithium-ion Batteries

Lithium-ion batteries are becoming more and more ubiquitous in many applications and appear as a key element for the success of energy transition. Their energy efficiency needs to be carefully understood and studied. In this work, we study the influence of the state of charge and of the shape of the current on the value of the efficiency of LFP (lithium-ion iron phosphate) lithium-ion cells. This is a preliminary step toward a full efficiency modeling.
Exploring the Habit of using Online Learning Platforms for Higher Order Thinking Skills with Data Mining Techniques

Following the rise of information technology, the development of learning in the twenty-first century is accelerating (IT). To measure higher-order thinking skills (HOTS) using technology, it is necessary to be able to examine and evaluate the learning process However, the pursuit of HOTS can occasionally result in brand-new issues, such as anxiety and despair brought on by thoughts of failure. Using online learning techniques is thought to help people overcome their fear of failing. The purpose of this study is to assess the demand for HOTS in online learning activities in the twenty-first century. About 145 teachers of varying ages were participants in this study. Gender, age range, and years of teaching experience make up the demographic information about teachers. According to the study’s findings, most teachers favor the use of HOTS when implementing online learning activities. Additionally, digital modules, PowerPoint presentations, and other kinds of media are used to create online learning services that are available to assist in the development of HOTS. The application of the study’s findings can be created for evaluation in the deployment of 21st-century-appropriate learning and online learning methodologies.
Fermi surfaces and band structures of the 2H metallic transition-metal dichalcogenides

Non-relativistic calculations on the band structures of the 2H polytype of NbSe2, NbS2, TaSe2 and TaS2 are reported. The slight qualitative differences which occur seem to be determined by the chalcogen rather than the metal. The Fermi surfaces are presented, together with a simple and accurate Fourier fit involving three parameters per sheet. The hole orbits are infinitely extended in the direction normal to the layers. A possible connection of these results to the observed charge-density waves is discussed. Some comments concerning the technique (layer method) are included. Special attention is devoted to the convergence of the method and the use of group theory.
Simulation of the Weighted Product Method in the Tri Pramana-Based Formative-Summative Evaluation Application

The main purpose of this research was to show the percentage level of the simulation results effectiveness of Weighted Product calculation in the Tri Pramana-based Formative-Summative evaluation application for evaluating e-learning. The approach of this research was a development that focused on simulation activities for the calculation of the Weighted Product method. The subjects involved in conducting simulations and effectiveness tests were ten experts, including five education evaluation experts and five informatics experts. The tool used for the effectiveness test by the expert was in the form of a questionnaire consisting of six questions related to the Weighted Productmethod. The technique of analyzing the results of data collection was quantitative descriptive. The research results showed that the effectiveness of the calculation simulation of the Weighted Product method was 93% so it was included in the category of excellence. The impact of this research on the field of education is the existence of new knowledge for evaluators about the importance of calculation simulation of the decision support system method used in evaluation applications in the field of education.
Finite layer analysis of consolidation. I

A method for the analysis of the consolidation of a horizontally layered soil under plane conditions is developed. The method depends upon the transformation of the governing equations by a Fourier transform. This transformation has the effect of reducing the partial differential equations of consolidation to ordinary differential equations. The ordinary differential equations are then solved using a finite layer or finite difference approach. Once the solution in the transformed plane has been found the actual solution is synthesized by Fourier inversion. The method leads to a considerable reduction in the amount of core storage necessary for solution and enables the solution of quite significant problems to be obtained on a mini-computer. (a) for part 2 of the article see TRIS 367147. (TRRL)
Mathematical discovery and affect: the effect of AHA! experiences on undergraduate mathematics students

The AHA! experience – the moment of illumination on the heels of lengthy, and seemingly fruitless, intentional effort – has long been the basis for lore in mathematics. Unfortunately, such lore is often restricted to the discussion of these phenomena in the context of great mathematicians and great mathematical advancement. But are such experiences reserved only for the upper echelons of mathematical practice? This study focuses on the impact of these AHA! experiences on undergraduate mathematics students’ affective domain. In particular, the role of the positive emotion that accompanies such moments of illumination in changing the attitudes and beliefs of ‘resistant’ students is examined. That is, pre-service elementary school teachers who deem themselves to be incapable and/or phobic of mathematics and the learning of mathematics but are forced to take an undergraduate mathematics course as qualification for entry into a teacher education programme. The results indicate that an AHA! experience has a transformative effect on ‘resistant’ students’ affective domains, creating positive beliefs and attitudes about mathematics as well as their abilities to do mathematics.
Bidirectional Equalization Receiver for Underwater Acoustic OFDM Systems Using Frequency-Domain Oversampling

Underwater acoustic orthogonal frequency division multiplexing (OFDM) systems suffer from frequency-selective fading and inter-carrier interference (ICI) caused by the underwater acoustic channels. As a commonly adopted solution, channel equalization is performed after two-dimensional channel estimation of the doubly selective channels. To achieve better performance, a bidirectional equalization receiver based on frequency-domain oversampling (FDO) for underwater acoustic OFDM systems is proposed. We use a pilot design that facilitates FDO based sparse channel estimation and bidirectional equalization. On one hand, FDO contributes more observations to both channel estimation and equalization. On the other hand, the bidirectional equalization structure is employed to mitigate the error propagation caused by linear minimum mean-squared error (MMSE) equalizer while maintaining low equalization complexity. Simulation and experimental results demonstrate the advantage of the proposed FDO based bidirectional equalization receiver.
A denoising method of seismic data based on self-supervised learning

Random noise adversely affects seismic signal resolution, subsequent interpretation, and reservoir prediction accuracy. In actual seismic exploration, it is often difficult and costly to obtain clean labels to train a denoising network. Compared with supervised learning, self-supervised learning does not need clean labels, but constructs supervised information according to the data itself. This paper presents a denoising network of seismic data based on self-supervised learning, which mainly includes four parts: data processing module, encoder, decoder, and residual noise separation module. The data processing module performs Bernoulli sampling on the input single 2D seismic signal to construct the supervision information. The encoder consists of four parts: partial convolution, dilated convolution, residual learning block, and down sampling. Dilated convolution can increase receptive fields and make the encoder better capture the features of useful signals. The decoder consists of up-sampling and standard convolution with a dropout strategy. The encoder and decoder use skip connections between the layers of the same height to realize the feature fusion of deep and shallow layers. The residual noise separation module obtains the predicted noise by calculating the residual between actual seismic data and predicted useful data, then uses the noise prior information as the regularization constraint to avoid the phenomenon of overfitting during training. The experimental results of synthetic and real seismic data indicate that our network not only suppresses random noise with effect, but also does have high fidelity.
Difference Quotients and Elliptic Mixed Boundary Problems of Second Order

This article concerns regularity of solutions u of second order elliptic mixed boundary value problems in the upper halfspace R n + . Conditions on the boundary data which ensure that u be in the smoothness class B s 2,∞ (R n + ), s ≤ 2 3, are established. Linear operators with variable coefficients and certain nonlinear operators are studied. The novel feature of the techniques developed involves results on the L 2 smoothness of distributions.
RISC-V vector processor for acceleration of machine learning algorithms

In this paper we present an RTL implementation of a 32-bit parametrizable vector processor for acceleration of algorithms working in fixed-point arithmetic. The processor uses the latest RISC-V vector extension ISA specification and is deployed and tested on a Zynq Soc using Avnet Zedboard. Our microarchitecture exploits the inherent parallelism in algorithms by splitting execution across multiple vector lanes and enabling chaining of vector instructions. To provide the required number of read/write ports for instruction chaining, the vector register bank uses the double-pumping technique in combination with an XOR-based approach. First, the microarchitecture of the system is explained in detail, and the results of the implementation on the Zedboard are presented for some different processor configurations. We then compared the performance of the implemented design with some different modern processor cores.
A metrological and application-related comparison of six consumer grade stereo depth cameras for the use in robotics

Consumer grade depth cameras are important devices to enable robots and unmanned vehicles to perceive their environments. Three major technologies are relevant for those devices: Time-of-Flight, structured light and stereo vision. Stereo cameras in particular have conquered the market in recent years, with Intel's D-series systems leading the way. Besides stereo cameras from Intel, there are many models from other manufacturers, but it has not yet been investigated how these cameras perform in comparison. This paper proposes and applies methods to measure the performance of depth cameras in a metrological way as well as with a focus on the use in robotics. Six stereo depth cameras are tested and compared. In addition, a real world benchmark test and a qualitative evaluation of the accompanying software and documentation is done. A clear recommendation is given in the end.
Immersed Boundary-Finite Difference Lattice Boltzmann Method for Liquid-Solid Two-Phase Flows

An immersed boundary method based on a finite difference lattice Boltzmann method (IB-FDLBM) is presented. The FDLBM solves the discrete Boltzmann equation including an additional collision term by using finite difference schemes. The additional term works as a negative viscosity in the macroscopic level and allows us to alter the fluid viscosity while keeping the other relevant parameters of the simulation fixed. The immersed boundary method employs a direct-forcing method, which utilizes external forces at Lagrangian points embedded on immersed boundaries to impose the no-slip boundary condition. Several benchmark simulations are carried out to validate the developed method, i.e., flows past a circular cylinder, a falling particle, and interaction between two falling particles. Couette flows between a stationary and a rotating cylinder are also simulated at various values of the relaxation time for collision. The main conclusions obtained are as follows: (1) steady flows past a stationary circular cylinder are well predicted, (2) the motions of particles falling through liquids predicted using IB-FDLBM quantitatively agree well with those obtained using immersed boundary methods based on the lattice Boltzmann equation (IB-LBM), (3) the developed method well predicts the interaction between two particles falling through a liquid, e.g., the drafting-kissing-tumbling motion, and (4) distortion of velocity fields in circular Couette flows at high relaxation times is removed by the additional collision term.
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW)

Abstract An ensemble-based four-dimensional variational data assimilation (En4DVAR) algorithm and its performance in a low-dimension space with a one-dimensional shallow-water model have been presented in Part I. This algorithm adopts the standard incremental approach and preconditioning in the variational algorithm but avoids the need for a tangent linear model and its adjoint so that it can be easily incorporated into variational assimilation systems. The current study explores techniques for En4DVAR application in real-dimension data assimilation. The EOF decomposed correlation function operator and analysis time tuning are formulated to reduce the impact of sampling errors in En4DVAR upon its analysis. With the Advanced Research Weather Research and Forecasting (ARW-WRF) model, Observing System Simulation Experiments (OSSEs) are designed and their performance in real-dimension data assimilation is examined. It is found that the designed En4DVAR localization techniques can effectively alleviate the imp.
The Effect of Hybrid Learning Strategy and Achievement Motivation on Student Learning Outcomes

Online learning has become a central element in the discussion in universities. Many institutes and universities are starting to offer online classes as an alternative to face-to-face learning. Hybrid learning (HL) is a pedagogical approach that combines face-to-face meetings (F2F) with technology or computer-assisted learning. The purpose of this study was to find out the effect of implementing a hybrid learning strategy on the learning outcomes of students who have high and low achievement motivation. Data analysis used the 2x2 factorial ANOVA (Analysis of Variance) statistical method for learning outcomes. The results of ANOVA on testing the influence of learning strategy factors on learning outcomes resulted in the value of F = 2.703 with sig = 0.104.
SEMS: Scalable Embedding Memory System for Accelerating Embedding-Based DNNs

Embedding layers, which are widely used in various deep learning (DL) applications, are very large in size and are increasing. We propose scalable embedding memory system (SEMS) to deal with the inference of DL applications with a large embedding layer. SEMS is built using scalable embedding memory (SEM) modules, which include FPGA for acceleration. In SEMS, PCIe bus, which is scalable and versatile, is used to expand the system memory and processing in SEMs reduces the amount of data transferred from SEMs to host, improving the effective bandwidth of PCIe. In order to achieve better performance, we apply various optimization techniques at different levels. We develop SEMlib, a Python library to provide convenience in using SEMS. We implement a proof-of-concept prototype of SEMS and using SEMS yields DLRM execution time that is <inline-formula><tex-math notation="LaTeX">$32.85\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>32</mml:mn><mml:mo>. </mml:mo><mml:mn>85</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="kim-ieq1-3227560.gif"/></alternatives></inline-formula> faster than that of a CPU-based system when there is a lack of DRAM to hold the entire embedding layer.
FusionNet: a New Perspective of CNN for Image Classification

The civil livelihood plan is related to corn, or maize, as one of the most significant grain products that suffers from various kinds of diseases, resulting in a decline in production. Traditional manual examination for disease in terms of prevention and control is time-consuming with low efficiency. Therefore, this paper proposes a new architecture called FusionNet fully based on deep convolution network to improve the speed and accuracy of the classification of various maize diseases. Using focus splitting, identity shortcut, and multi-feature channel fusion, this model presents a new consideration of larger kernels utilized in concatenated for features that offer more effective receptive fields to facilitate the reduction of reused redundancy, especially for middle-layer abstract information, which could compensate for the shortage of feature selectivity of CNN. Moreover, the total number of parameters is limited by depth-wise(DW) convolutions. The validity of the model is demonstrated on modified PlantVillage and PlantDoc datasets, achieving an accuracy of 94.9% on average.
Electromagnetic Compatibility Evaluation of Wireless Charging Systems for Public Spaces

Almost worldwide, various agreements and regulations are enacted and vigorously enforced to address long-lasting climate change. Public transport with conventional combustion engines is affected by driving bans in restricted areas in cities. Nevertheless, electric vehicles with conductive charging are not suitable for all applications, e.g. taxis. That is due to the high daily route distances and the irregular waiting periods in queues (taxi on demand). As a solution to this problem, wireless charging for taxis in a public space is developed and realized in the project TALAKO. In this context, a guaranteed safeguarding of the electromagnetic environmental compatibility (EMCE) is essential so that the developed system is not a hazard to the health of humans and animals. This paper addresses the investigation of the resulting magnetic fields in the context of wireless power transfer for taxis in public areas using numerical simulation methods. Furthermore, the analyses are compared by field measurements.
A bernoulli excursion and its various applications

This paper is concerned with a random walk process in which and for i = 1, 2, ···, 2n . This process is called a Bernoulli excursion. The main object is to find the distribution, the moments, and the asymptotic distribution of the random variable ω n defined by . The results derived have various applications in the theory of probability, including random graphs, tournaments and order statistics.
An anisotropic damage–plasticity model for saturated quasi‐brittle materials

This article is devoted to numerical modeling of anisotropic damage and plasticity in saturated quasi‐brittle materials such as rocks and concrete. The damaged materials are represented by an isotropic poroelastic matrix containing a number of families of microcracks. Based on previous works, a discrete thermodynamic approach is proposed. Each family of microcracks exhibits frictional sliding along crack surfaces as well as crack propagation. The frictional sliding is described by a Coulomb–Mohr‐type plastic criterion by taking into account the effect of fluid pressure through a generalized effective stress concept. The damage evolution is entirely controlled by and coupled with the frictional sliding. The effective elastic properties as well as Biot's coefficients of cracked porous materials are determined as functions of induced damage. The inelastic deformation due to frictional sliding is also taken into account. The procedure for the identification of the model's parameters is presented. The proposed model is finally applied to study both mechanical and poromechanical responses of a typical porous brittle rock in drained and undrained compression tests as well as in interstitial pressure controlled tests. The main features of material behaviors are well reproduced by the model. Copyright © 2012 John Wiley & Sons, Ltd.