Reviewerly/exHarmony huggingface dataset:
The Effect of Genre Approach to Improve University Students’ Critical Thinking Skills

This study aims to determine the impact of using a genre-based approach on students’ critical thinking skills through learning to write essays. This study uses a quasi-experimental design involving 54 students (experimental and control classes) taking writing course one at the Indonesian Language and Literature Education Study Program, Muhammadiyah University of Palembang, South Sumatra, Indonesia. Data were collected using a written essay test which was then assessed using the critical thinking skills assessment rubric. The collected data were analyzed using SPSS 20.00 by performing paired sample t-test, independent sample t-test, and MANOVA. The group of students who used the genre approach was proven to improve critical thinking skills compared to the group that used the conventional method. Thus, it is hoped that teachers can consider this genre approach as an alternative learning method for improving students’ critical thinking skills.
Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs

To maintain and improve an amateur athlete’s fitness throughout training and to achieve peak performance in sports events, good nutrition and physical activity (general and training specifically) must be considered as important factors. In our context, the terminology “amateur athletes” represents those who want to practice sports to protect their health from sickness and diseases and improve their ability to join amateur athlete events (e.g., marathons). Unlike professional athletes with personal trainer support, amateur athletes mostly rely on their experience and feeling. Hence, amateur athletes need another way to be supported in monitoring and recommending more efficient execution of their activities. One of the solutions to (self-)coaching amateur athletes is collecting lifelog data (i.e., daily data captured from different sources around a person) to understand how daily nutrition and physical activities can impact their exercise outcomes. Unfortunately, not all factors of the lifelog data can contribute to understanding the mutual impact of nutrition, physical activities, and exercise frequency on improving endurance, stamina, and weight loss. Hence, there is no guarantee that analyzing all data collected from people can produce good insights towards having a good model to predict what the outcome will be. Besides, analyzing a rich and complicated dataset can consume vast resources (e.g., computational complexity, hardware, bandwidth), and this therefore does not suit deployment on IoT or personal devices. To meet this challenge, we propose a new method to (i) discover the optimal lifelog data that significantly reflect the relation between nutrition and physical activities and training performance and (ii) construct an adaptive model that can predict the performance for both large-scale and individual groups. Our suggested method produces positive results with low MAE and MSE metrics when tested on large-scale and individual datasets and also discovers exciting patterns and correlations among data factors.
Secure reversible data hiding in encrypted images based on adaptive Huffman coding and pixel rearrangement

With the development of cloud computing and the demand for privacy protection, reversible data hiding in encrypted images (RDHEI), which is a technology that embeds secret data in an encrypted image and can ensure error-free extraction of embedded data and lossless recovery of original images, has received widespread attention in recent years. In this paper, an high-capacity and secure RDHEI method is proposed. First, the pixel value of the original image is predicted. Using the properties of the Laplace distribution, the prediction errors are divided into two types of labels. Adaptive Huffman-encoded labeling is performed on these two labels, respectively. Pixel rearrangement is then used to reserve space where additional data can be embedded. Due to the reversibility of Huffman encoding and decoding, the receiver can achieve separable secret information extraction and lossless recovery of the original image. Experimental results show that compared with several existing methods, our method can achieve higher embedding capacity and good security, not only the invisibility of the image content, but also the undetectability of the secret information.
APPLICATION NEURAL NETWORK APPROACH FOR THE ESTIMATION OF HEAVY METAL CONCENTRATIONS IN THE INAOUEN WATERSHED

This paper describes how the multilayer perceptron neural network (MLPNN) trained by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-newton back-propagation approach was used to estimate heavy metal concentrations: Aluminum (Al), Lead (Pb), Copper (Cu), and Iron (Fe), in the province of Taza using sixteen physicochemical factors measured from 100 samples collected from surface water sources by our team, according to the protocol of the national water office (ONE). We chose a network with only one hidden layer to identify the network architecture to employ. The number of neurons in the hidden layer was varied, as were the types of transfer and activation functions, and the BFGS learning method was used. The following statistical metrics were used to evaluate the performance of the neural network’s stochastic models: Examining the adjustment graphs and residue, as well as the Error Sum of Squares (SSE); the mean bias error (MBE) and determination coefficient (R²). The results reveal that the predictive models created using the artificial neural network method (ANN) are quite efficient, thanks to the BFGS algorithm’s efficiency and speed of convergence. An architectural network [16-8-1] (16: number of variables in input layer, 8: number of hidden layer, 1: number of variables in output layer) produced the best results,{R²: Al(0.954), Pb(0.942), Cu(0.921), Fe(0.968)}, {SSE: Al(0.396), Pb(0.0059), Cu(0.252), Fe(4.29)} and {MBE: Al(–0.033), Pb(0.008), Cu(–0.004), Fe(0.091)}, which is developed so that each model is responsible for estimating the concentration of a single heavy metal. This result demonstrates that there is a non-linear relationship between the physical-chemical properties evaluated and the heavy metal content of surface water in the Taza province.
Decoupling Control of an Aviation Remote Sensing Stabilization Platform Based on a Cerebellar Model Articulation Controller

To obtain high-resolution and high-precision images, the aviation remote sensing stabilization platform (ARSSP) is used, which enables the isolation of unpredictable aerial camera movements during aerial photography. However, because of the interaxle coupling interference and other nonlinear interferences in the ARSSP system, the imaging quality of the aerial camera is adversely affected. Therefore, we derived the dynamic model of moment coupling between shafts to illustrate the problem. On the basis of the former proportion integration differentiation (PID) controller based on cerebellar neural network, a nonlinear cross feedback decoupling scheme is adopted to reduce the adverse effects of these interferences. The cerebellar model articulation controller (CMAC) based on synovial membrane control (SMC) is used to reduce the nonlinear interferences caused by the new cross-decoupling module. To verify the effectiveness of the scheme, simulation, indoor and outdoor experiments were conducted. The results showed that the SMC-CMAC significantly reduced the interaxle coupling effect and effectively suppressed the nonlinear interference, resulting in a good tracking performance of the ARSSP system.
Authentication of WSN for Secured Medical Data Transmission Using Diffie Hellman Algorithm

The applications of wireless sensor network (WSN) exhibits a significant rise in recent days since it is enveloped with various advantageous benefits.In the medical field, the emergence of WSN has created marvelous changes in monitoring the health conditions of the patients and so it is attracted by doctors and physicians.WSN assists in providing health care services without any delay and so it plays predominant role in saving the life of human.The data of different persons, time, places and networks have been linked with certain devices, which are collectively known as Internet of Things (IOT); it is regarded as the essential requirement of people in recent days.In the health care monitoring system, IOT plays a magnificent role, which has produced the real time monitoring of patient's condition.However the medical data transmission is accomplished quickly with high security by the routing and key management.When the data from the digital record system (cloud) is accessed by the patients or doctors, the medical data is transferred quickly through WSN by performing routing.The Probabilistic Neural Network (PNN) is utilized, which authenticates the shortest path to reach the destination and its performance is identified by comparing it with the Dynamic Source Routing (DSR) protocol and Energy aware and Stable Routing (ESR) protocol.While performing routing, the secured transmission is achieved by key management, for which the Diffie Hellman key exchange is utilized, which performs encryption and decryption to secure the medical data.This enables the quick and secured transmission of data from source to destination with improved throughput and delivery ratio.
Software Defect Prediction Based Ensemble Approach

Software systems have grown significantly and in complexity.As a result of these qualities, preventing software faults is extremely difficult.Software defect prediction (SDP) can assist developers in finding potential bugs and reducing maintenance costs.When it comes to lowering software costs and assuring software quality, SDP plays a critical role in software development.As a result, automatically forecasting the number of errors in software modules is important, and it may assist developers in allocating limited resources more efficiently.Several methods for detecting and addressing such flaws at a low cost have been offered.These approaches, on the other hand, need to be significantly improved in terms of performance.Therefore in this paper, two deep learning (DL) models Multilayer preceptor (MLP) and deep neural network (DNN) are proposed.The proposed approaches combine the newly established Whale optimization algorithm (WOA) with the complementary Firefly algorithm (FA) to establish the emphasized metaheuristic search EMWS algorithm, which selects fewer but closely related representative features.To find the best-implemented classifier in terms of prediction achievement measurement factor, classifiers were applied to five PROMISE repository datasets.When compared to existing methods, the proposed technique for SDP outperforms, with 0.91% for the JM1 dataset, 0.98% accuracy for the KC2 dataset, 0.91% accuracy for the PC1 dataset, 0.93% accuracy for the MC2 dataset, and 0.92% accuracy for KC3.
3D Echocardiogram Reconstruction Employing a Flip Directional Texture Pyramid

Three dimensional (3D) echocardiogram enables cardiologists to visualize suspicious cardiac structures in detail.In recent years, this three-dimensional echocardiogram carries important clinical value in virtual surgical simulation.However, this 3D echocardiogram involves a trade-off difficulty between accuracy and efficient computation in clinical diagnosis.This paper presents a novel Flip Directional 3D Volume Reconstruction (FD-3DVR) method for the reconstruction of echocardiogram images.The proposed method consists of two main steps: multiplanar volumetric imaging and 3D volume reconstruction.In the creation of multiplanar volumetric imaging, two-dimensional (2D) image pixels are mapped into voxels of the volumetric grid.As the obtained slices are discontinuous, there are some missing voxels in the volume data.To restore the structural and textural information of 3D ultrasound volume, the proposed method creates a volume pyramid in parallel with the flip directional texture pyramid.Initially, the nearest neighbors of missing voxels in the multiplanar volumetric imaging are identified by 3D ANN (Approximate Nearest Neighbor) patch matching method.Furthermore, a flip directional texture pyramid is proposed and aggregated with distance in patch matching to find out the most similar neighbors.In the reconstruction step, structural and textural information obtained from different flip angle directions can reconstruct 3D volume well with the desired accuracy.Compared with existing 3D reconstruction methods, the proposed Flip Directional 3D Volume Reconstruction (FD-3DVR) method provides superior performance for the mean peak signal-to-noise ratio (40.538 for the proposed method I and 39.626 for the proposed method II).Experimental results performed on the cardiac datasets demonstrate the efficiency of the proposed method for the reconstruction of echocardiogram images.
The Red Hen Anonymizer and the Red Hen Protocol for De-Identifying Audiovisual Recordings

Scientists of multimodal communication have no established policy or default tool for sharingde-identified audiovisual recordings. Recently, new technology has been developed that enablesresearchers to de-identify voice and appearance. These software tools can produce output inJSON format that specifies bodypose and face and hand keypoints in numerical form, suitablefor computer search, machine learning, and sharing. The Red Hen Anonymizer is a new tool forde-identification. This article presents the Red Hen Anonymizer and discusses guidelines for itsuse.
High-Accuracy Gaussian Function Generator for Neural Networks

A new improved accuracy CMOS Gaussian function generator will be presented. The original sixth-order approximation function that represents the basis for designing the proposed Gaussian circuit allows a large increase in the circuit accuracy and also of the input variable maximal range. The original proposed computational structure has a large dynamic output range of 27 dB, for a variation smaller than 1 dB as compared with the ideal Gaussian function. The circuit is simulated for 0.18 μm CMOS technology and has a low supply voltage (VDD = 0.7 V). Its power consumption is smaller than 0.22 μW, for VDD = 0.7 V, while the chip area is about 7 μm2. The new proposed architecture is re-configurable, the convenient modification of the coefficients allowing to obtain many mathematical functions using the same computational structure.
Interpol Review Paper of Marks and Impression Evidence 2019-2022

This section contains an overview of publications relevant to advances in scientific methods and general discussions concerning shoe and toolmark examiners, which were published between January 2019 and May of 2022 and is the sequel to the review for the 19th Interpol International Forensic Science Managers Symposium in 2019 by Martin Baiker-Sorensen. A literature search was conducted covering relevant articles published in the main forensic journals.
A Moving Window Double Locally Weighted Extreme Learning Machine on an Improved Sparrow Searching Algorithm and Its Case Study on a Hematite Grinding Process

In this paper, a double locally weighted extreme learning machine model based on a moving window is developed to realize process modeling. To improve model performances, an improved sparrow-searching algorithm is proposed to optimize the parameters of the proposed model. The effectiveness of the proposed model and algorithm are verified by data from a hematite grinding process. The experimental results show that the proposed algorithm can significantly improve the global search ability and convergence speed in the parameter optimization of the proposed model. The proposed model can correctly estimate the grinding particle size which is expected to be applied to other complex industries.
The concept of organizing the work of research universities: prerequisites, tasks, recommendations for Ukraine

Long before the emergence of modern research universities, in society, it became necessary to create educational institutions of an absolutely new type: with the latest approaches and methods to the principles of teaching; with mechanisms for mastering knowledge, modern by that time; with the structure of transferring acquired experience into practical activities. Thus, based on the conclusions of many specialists, scientists, teachers, it were developed and presented general and specific recommendations for the opening and development of such educational institutions, which we now call institutions of higher education of an innovative type, or research universities. The purpose of this article was to study the specifics of the functioning of research institutions by analyzing the features of the organization of their work, the main tasks, key problems and expected results of activities. The main scientific method used in this work was a system-analytical method, using which the main prerequisites for the creation of a research university were determined, as well as a holistic structure of the activities of such an institution of higher education in order to present practical recommendations for opening innovative institutions in Ukraine. The main results obtained in the course of this study can be identified as follows: determination of the characteristics of the “triple helix” model in the work of research universities; characteristics of the basic tasks, goals and problems in the work of such institutions; providing relevant proposals for the development of the concept of creating a research university in Ukraine. The results of this scientific work and the conclusions obtained in the course of the presented study can be used for further practical scientific research on this topic, as well as for: teachers and students of higher education in the specialty history, pedagogy, educational technology.
Meetings: PhotonicsViews 1/2023

PhotonicsViewsVolume 20, Issue 1 p. 75-76 MeetingsFree Access Meetings: PhotonicsViews 1/2023 First published: 03 January 2023 https://doi.org/10.1002/phvs.202370112AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume20, Issue1February/March 2023Pages 75-76 RelatedInformation
Graph Mining and Machine Learning for Shader Codes Analysis to Accelerate GPU Tuning

The graphics processing unit (GPU) has become one of the most important computing technologies. Disassembly shader codes, which are machine-level codes, are important for GPU designers (e.g., AMD, Intel, NVIDIA) to tune the hardware, including customization of clock speeds and voltages. Due to many use-cases of modern GPUs, engineers generally find it difficult to manually inspect a large number of shader codes emerging from these applications. To this end, we develop a framework that converts shader codes into graphs, and employs sophisticated graph mining and machine learning techniques over a number of applications to simplify shader graphs analysis in an effective and explainable manner, aiming at accelerating the whole debugging process and improving the overall hardware performance. We study shader codes’ evolution via temporal graph analysis and structure mining with frequent subgraphs. Using them as the underlying tools, we conduct a frame’s scene detection and representative frames selection. We group the scenes (applications) to identify the representative scenes, and predict a new application’s inefficient shaders. We empirically demonstrate the effectiveness of our solution and discuss future directions.
Realization of Astatic Control Systems with Respect to External Disturbances

The paper is devoted to the problem of construction of systems of automatic motion control, which is one of the main problems considered in the scientific publications. The most important requirement for such systems is astaticism on regulated coordinates, i.e. the ability of the system to provide zero static error under the constant external disturbances. The known analytical and numerical methods of construction of automatic motion control systems generally aim to improve local dynamic characteristics of control processes, while the practical application of these systems requires a multi-purpose orientation of used approaches. The aim of the paper is the development of methods to provide the astaticism using the multi-purpose structure control laws. An example of modeling control system for the marine vessel is performed. Particularly, in the paper much prominence is given to the complex of questions, associated with computer synthesis and modeling of control laws, that provide the astaticism of the closed-loop system on yaw.
OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning

Deep learning technology has been widely applied in emitter identification. With the deepening research, the problem of emitter identification under the few-shots condition has become a frontier research direction. As a special communication signal, OFDM (Orthogonal Frequency Division Multiplexing) signal is of high complexity so emitter identification technology under OFDM is extremely challenging. In this paper, an emitter identification method based on contrastive learning and residual network is proposed. First, according to the particularity of OFDM, we adjust the structure of ResNet and propose a targeted data preprocessing method. Then, some data augmentation strategies are designed to construct positive samples. We conduct self-supervised pretraining to distinguish features of positive and negative samples in hidden space. Based on the pretrained feature extractor, the classifier is no longer trained from scratch. Extensive experiments have validated the effectiveness of our proposed methods.
A Framework for the Design, Implementation and Evaluation of a Multi-Variant Augmented Reality Application

Augmented Reality (AR) is one of the key technologies of the fourth Industrial Revolution (4IR) and plays an increasingly important role in many companies. However, while the demand for new AR applications is rapidly increasing, fundamental best practices and frameworks for the industrial AR sector are still scarce or in their infancy stage. This paper addresses this gap by proposing a framework for the design and efficient implementation of AR applications with multiple models and variants. The proposed framework is built around: i) a development process that describes the different steps for the design of a model-based AR application and its implementation with Unity and Vuforia model targets; and ii) a multilayer orchestration model that describes the different interactions between a user and a server layer. The proposed framework is successfully implemented, and its performance analyzed using both quantitative and qualitative evaluation based on the Brooke’s System Usability Scale.
New Differentially 4-Uniform Piecewise Permutations over F22k from the Inverse Function

Permutations with low differential uniformity, high nonlinearity and high algebraic degree over F22k are preferred substitution boxes in modern block ciphers. In this paper, we study the bijectivity and the difference uniformity of piecewise function with the help of permutation group theory. Based on our results, We found many at least differentially 6-uniform and differentially 4-uniform permutations over F22k, which can be chosen as the substitution boxes.