Reviewerly/exHarmony huggingface dataset:
The Use of physiological measurement for user experience Assessment: A Bibliometric Analysis

This paper aims to use a quantitative and visual method to evaluate the history, current, and future of publications regarding physiological measurement for user experience (UX). Since UX has attracted the attention of many disciplines, this field has accumulated numerous research outcomes. Therefore, it is important and timely to make a systematic and comprehensive evaluation of the research output of UX over the last decades. Citespace was used to identify and summarize the publications from WoS from 2010 to 2021. The publication time, country, institution, and author distribution of user experience physiological measurement were recorded and analyzed. Bibliometric maps of authorship, citation, co-citation, and network of co-occurrence of keywords are drawn. The research found that: (1) The international physiological measurement of user experience has gradually developed since 2010, and now it has become a topic of focus for scholars. (2) User experience physiological measurement involves a wide range of disciplines, with engineering and computational science as the main disciplines undertaking important tasks. (3) Physiological measurement of user experience will be more used in VR, AR, and adaptive aging in the future. This study provided later researchers with ideas on the physiological measurement of user experience.
Deep Feature Bayesian Classifier for SAR Target Recognition with Small Training Set

In recent years, deep learning algorithms have been popular in recognizing targets in synthetic aperture radar (SAR) images. However, due to the problem of overfitting, the performance of these models tends to worsen when just a small number of training data are available. In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition, in this paper, we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model (RBnet) for SAR image target recognition. In the RBnet, a Bayesian classifier is used to improve the effect of SAR image target recognition and improve the accuracy when the training data is limited. The experimental results on MSTAR dataset show that the RBnet can fully exploit effective information in limited samples and recognize the target of the SAR images more accurately. Compared with other state-of-the-art methods, our method offers significant recognition accuracy improvements under limited training data. Noted that the RBnet is moderately difficult to implement and has the value of popularization and application in engineering application scenarios in the field of small-sample remote sensing target recognition and recognition.
Emoticon Input Method Design Based on Theory of Use and Gratification

A set of input method prototype for emoticons inputting and modifying is designed to improve user experience of emoticons cross platform. Based on use and gratification theory, core requirements and pain points for emoticons usage were summarized. The core needs of using emoticons include communication efficiency, emotional expression, personality interest and identity recognition. The outstanding problems of emoticon usage are expressing ambiguity, misunderstanding and invalid information, also the closeness and exclusivity of circle culture. According to the found key points, a questionnaire mainly composed of Likert Scale was designed to validify the proposed needs through collecting data of college students’ behavior habits and preferences. To explore the influence of various factors and diversities of different groups on experience when using emoticons, the statistics analysis was then carried out by IBM SPSS Statistics, which included descriptive statistics and regression analysis. 7 interviews were carried out based on results of questionnaire statistics analysis to excavate root causes. The results show that (a) a seek sense of integration exits when using emoticons on social media, which means usage tend to vary from usage sceneries and recipients to perform properly and fit into various groups. (b) personalized expression meets the need of self-identity and entertainment but also suffer the risk of misunderstanding and confusion. (c) cross platform emoticon use and emoticon edit are two main factors that damage convenience of emoticon use.Based on the above conclusions, a way of editing and using expression package was designed to improve the user experience and avoid the problems of complex process of emoticon choosing and editing and vague expression of emoticons. The emoticon input method can effectively solve the cumbersome problem of cross platform expression package collection and sending process combined with the functions of expression package editing and crowd classification which further optimizes the use process and improves the use experience.
Machine Learning Algorithms for Document Classification: Comparative Analysis

Automated document classification is the machine learning fundamental that refers to assigning automatic categories among scanned images of the documents.It reached the state-of-art stage but it needs to verify the performance and efficiency of the algorithm by comparing.The objective was to get the most efficient classification algorithms according to the usage of the fundamentals of science.Experimental methods were used by collecting data from a sum of 1080 students and researchers from Ethiopian universities and a meta-data set of Banknotes, Crowdsourced Mapping, and VxHeaven provided by UC Irvine.25% of the respondents felt that KNN is better than the other models.The overall analysis of performance accuracies through various parameters namely accuracy percentage of 99.85%, the precision performance of 0.996, recall ratio of 100%, F-Score 0.997, classification time, and running time of KNN, SVM, Perceptron and Gaussian NB was observed.KNN performed better than the other classification algorithms with a fewer error rate of 0.0002 including the efficiency of the least classification time and running time with ~413 and 3.6978 microseconds consecutively.It is concluded by looking at all the parameters that KNN classifiers have been recognized as the best algorithm.
Introduction

Abstract The rise of civilization is synonymous with the creation of tools that extend the intellectual and physical reach of human beings [133]. The pinnacle of such endeavours is to replicate the flexible reasoning capacity of human intelligence within a machine, making it capable of performing useful work on command, despite the complexity and adversity of the real world. In order to achieve such Artificial Intelligence (AI), a new approach is required: traditional symbolic AI has long been known to be too rigid to model complex and noisy phenomena and the sample-driven approach of Deep Learning cannot scale to the long-tailed distributions of the real world. In this book, we describe a new approach for building a situated system that reflects upon its own reasoning and is capable of making decisions in light of its limited knowledge and resources. This reflective reasoning process addresses the vital safety issues that inevitably accompany open-ended reasoning: the system must perform its mission within a specifiable operational envelope.
Individual Risk Classification of Crime Groups using Ensemble Classifier Method

The most significant challenge for humanity worldwide to crime, especially terrorist attacks, should be considered.Determining the priority scale for anticipating individual terrorist groups is not easy and will significantly affect work activities and subsequent decision-making measures.Priority scale determination decisions should be made carefully so team members cannot choose the desired priority target.Determining the exact priority scale for a target can be influenced by several factors, such as desire factors and ability factors, using Dataset Intelligence.This research aims to find out the ability of each target and pattern to be carried out.Based on this problem, the study used the K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), and Ensemble Bagging methods.Each of these algorithms has its characteristics; This classification technique can group priority targets according to their similarities, abilities, and desires.The value of each method used can be used as a reference to determine the correct group information for officers to determine the next steps.The study obtained a maximum accuracy of 70.25% using the Ensemble Bagging-Backward Elimination-K-Nearest Neighbor (KNN) classification method using 20 features.The results showed tests conducted and final analysis and conclusions based on accuracy and recall performance.The precision performance revealed that the Ensemble Bagged KNN, more precisely than KNN, Naïve Bayes, Decision Tree, and Bagging Naïve Bayes and Bagging Decision Tree.The KNN Bagging ensemble model can add accuracy, map individuals, and detect who should be intensely monitored based on predictive results.
Modeling Stock Price of Transnational companies using Brownian motion

This article is concerned with creating a model for stock price using the Brownian motion. At first, we consider the notion of a discrete time stochastic process, simple random walk, and then move on to its continuous analogue, the Brownian motion. Next we identify the problem with using regular Calculus for stochastic differential equations and derive Ito’s Lemma. After that we derive a model for stock prices and use lognormal distribution to determine its expected value and variance. Finally, we use sample volatility to make predictions for Apple and Gazprom stock prices.
Tagging Without Rewriting: A Probabilistic Model for Unpaired Sentiment and Style Transfer

Style transfer is the task of paraphrasing text into a target-style domain while retaining the content. Unsupervised approaches mainly focus on training a generator to rewrite input sentences. In this work, we assume that text styles are determined by only a small proportion of words; therefore, rewriting sentences via generative models may be unnecessary. As an alternative, we consider style transfer as a sequence tagging task. Specifically, we use edit operations (i.e., deletion, insertion and substitution) to tag words in an input sentence. We train a classifier and a language model to score tagged sequences and build a conditional random field. Finally, the optimal path in the conditional random field is used as the output. The results of experiments comparing models indicate that our proposed model exceeds end-to-end baselines in terms of accuracy on both sentiment and style transfer tasks with comparable or better content preservation.
Optimal binary linear codes from posets of the disjoint union of two chains

<p style='text-indent:20px;'>Recently, some infinite families of optimal binary linear codes are constructed from simplicial complexes. Afterwards, the construction method was extended to using arbitrary posets. In this paper, based on a generic construction of linear codes, we obtain four classes of optimal binary linear codes by using the posets of two chains. Two of them induce Griesmer codes which are not equivalent to the linear codes constructed by Belov. Those codes are exploited to construct secret sharing schemes in cryptography as well.
Project-based Learning Method Applied in College English Teaching: Case Study of an Instructional Design

The development of information technology pushes forward the change of the teaching model. In general, the instructional design is becoming student-centered. In China, in college English class, more emphases are put on fostering students' language output ability. Therefore, PBL (Project-based Learning) is becoming increasingly popular, and BOPPPS (Bridge-in, Objective/Outcome, Pre-assessment, Participatory Learning, Post-assessment, Summary) mode is adopted by a growing number of English teachers. This paper tries to illustrate the application of PBL into college English class by studying an instructional design.
REGIONAL FORECASTING OF URANIUM DEPOSITS IN THE REPUBLIC OF NIGER

The paper deals with the methodology and results of forecasting sandstone-type uranium deposits on the territory of the Republic of Niger, based on the use of a set of regional geological and geophysical materials and a new technology for spatial analysis of data by the method of group accounting of arguments. The necessary information on the genesis and localization of sandstone-type uranium deposits in the study area is briefly outlined. Examples of identification of formal geological, tectonic and geophysical features that characterize the location of known large objects of uranium mineralization are given. On the example of reference ore and ore-less objects, an identification analysis of the features is carried out, focused on determining their aggregate information content. The results of the identification analysis made it possible to assess the weights of the features used and perform a comprehensive regional forecasting of sandstone-type uranium deposits in the territory of the Republic of Niger. Based on the results of the forecast, the three of the most promising sites were identified, where detailed work on the detection of industrial uranium mineralization is recommended.
Steganalysis of Content-Adaptive JPEG Steganography Based on Ensemble of Convolutional Neural Networks with Image Preprocessing Using Gabor Filters

As we know, compared with the feature-based steganalysis method, deep convolutional neural networks (DCNNs) have firmly established themselves as the superior steganography detectors. In this paper, considering the advantages of Gabor image representation and classifier ensemble, a steganalysis method is proposed based on ensemble of convolutional neural networks with image preprocessing using Gabor filters. First, multiple DCNNs are trained which used different 2D Gabor filter banks in the image preprocessing layers. Then, for a testing image, the steganography detection is performed by combing the total number of votes and the total confidence values which are from the outputs of classification modules of all the trained DNNs. The experiment results show that the proposed steganalysis method can improve the detection performance for content-adaptive JPEG steganography.
The Effect of a Suggested Project-Based Blended Learning Program on Developing University Students' EFL Writing Skills and Reducing their Writing Apprehension

This study aimed at exploring the impact of a suggested projectbased blended learning program on improving EFL Mass Communication students' writing skills and decreasing their writing apprehension.The participants of this study were (50) third-year Mass Communication students enrolled in the journalism section at Ahram Canadian University.The study employed the quasitreatment design using two equal groups randomly assigned to the treatment and control groups.To collect data, three instruments were developed and validated; an EFL writing skills test, an EFL writing skills scoring rubric, and a writing apprehension test.They were administered as a pre-post-test for both groups before and after the eight weeks training program.The treatment group received the suggested project-based blended learning program treatment, while the control group followed their regular method of teaching writing.Results revealed that the project-based blended learning program effectively developed the treatment group's EFL writing skills and reduced their writing apprehension.Therefore, this research recommended that project-based blended learning should be incorporated into language pedagogy programs at different educational stages.
Problem-оriented approach to training specialists in Russia and abroad

The article considers the current situation in Russian vocational education and its focus on training specialists for the digital economy. It presents the results of the study of various approaches to the training of teachers and specialists for the field of education in Russian and foreign universities. The article reveals the contradiction between the selection of the content of the professional training of future specialists and the understanding of the image of the profession by teachers and students, within which this professional training is carried out. The article substantiates the need for understanding the pedagogical meaning of the profession in the process of professional training of future specialists, which allows improving the selection of the content of professional training and ensuring that the graduate becomes mobile, competitive, ready to solve professional problems in conditions of dynamic changes in various areas of professional work. The article presents the experience of organizing training based on a problem-based approach in Russia, France and Canada. It substantiates the effectiveness of this approach in training of specialists, subject to the use of information and communication technologies.
Principles of programming in R

R is a programming language used for developing applications in statistical computing and graphics. It has several features that make it one of the most popular programming languages among computational biologists, including bioinformaticians. Powerful graphical visualization, several easy-to-use packages, and the user-friendly graphical user interface make it suitable for beginners to advanced programmers. In this chapter, we briefly introduce this programming environment and overview the basics of data manipulation in R.
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation

A common way to combat exposure bias is by applying scores from evaluation metrics as rewards in reinforcement learning (RL).Metrics leveraging contextualized embeddings appear more flexible than those that match n-grams and thus ideal as training rewards.Yet metrics such as BERTSCORE greedily align candidate and reference tokens, which can give system outputs excess credit relative to a reference.Past systems using such semantic similarity rewards further suffer from repetitive outputs and overfitting.To address these issues, we propose metrics that replace the greedy alignments in BERTSCORE with optimized ones.Our model optimizing discrete alignment metrics consistently outperforms cross-entropy and BLEU reward baselines on AMR-to-text generation.Additionally, we find that this model enjoys stable training relative to a non-RL setting.
An improved ViBe-based approach for moving object detection

Moving object detection is a challenging task in the automatic monitoring field, which plays a crucial role in most video-based applications. The visual background extractor (ViBe) algorithm has been widely used to deal with this problem due to its high detection rate and low computational complexity. However, there are some shortcomings in the general ViBe algorithm, such as the ghost area problem and the dynamic background problem. To deal with these problems, an improved ViBe approach is presented in this paper. In the proposed approach, a mode background modeling method is used to accelerate the process of the ghost elimination. For the detection of moving object in dynamic background, a local adaptive threshold and update rate is proposed for the ViBe approach to detect foreground and update background. Furthermore, an improved shadow removal method is presented, which is based on the HSV color space combined with the edge detection method. Finally, some experiments were conducted, and the results show the efficiency and effectiveness of the proposed approach.
Target-adaptive, compressive and ultra-high-density wide-field diffuse optical tomography

We show the iterative derivation of target-specific patterns by principle component analyses of ultra-high-density measurements. By tailoring patterns to the domain, we reconstruct high quality images with enhanced contrast while minimizing artifacts.
Path Model Analysis on Academic Writing Motivation

The study’s main objective is to determine the best fit model in academic writing motivation. Additionally, the research aims to assure the significance of the relationship between the exogenous variables: metacognitive awareness of reading strategies, language exposure, language skills development, and the endogenous variable on academic writing motivation. The study used descriptive -correlational design and Path Model Analysis to determine the most appropriate model on academic writing motivation. Random sampling was used to determine the number of students on each campus. The data was gathered from four hundred (400) students under the education program from five (5) branches of the University of Mindanao: UM Tagum, UM Panabo, UM Peñaplata, UM Bansalan, and UM Digos. Survey questionnaires were used to gather data. The results revealed that the level of metacognitive awareness of reading strategies, language exposure, development of language skills, and academic writing motivation was at a high level. Based on the results, it was clearly explained that the exogenous variable on metacognitive awareness of reading strategies, language exposure, language skills development, and the endogenous variable on academic writing motivation that can be seen in the fourth model was the most appropriate model for the research.
Vision-Based Human Presence Detection by Means of Transfer Learning Approach

Human–robot interaction (HRI) and human–robot collaboration (HRC) has become more important as the industries are stepping towards the phase of digitalization and Industry 4.0. Indeed, the emphasis is often placed on the safety of the physical well-being of the human workers. To safeguard the human operators from being hurt by the robots or collaborative robots (cobots), a traditional method is to isolate the robots from the human workers by means of fences and sensors. However, the deployment of deep learning models is unknown and shown to be non-trivial in downstream tasks such as image classification and object detection. The present study aimed to exploit the effectiveness of object detection models, particularly EfficientDet models via a transfer learning approach—fine-tuning. A total of 1463 images were obtained from the surveillance cameras from TT Vision Holdings Berhad and split into training, validation, and test by a ratio of 70:20:10. The training images were further augmented using horizontal flip and scale jittering techniques to increase the total training images up to 3072 images. As an outcome, the result revealed that the EfficientDet-D2 fine-tuned model achieved a test AP of 81.70% with an inference speed of 97.06 ms on Tesla T4 while the EfficientDet-D0 fine-tuned model attained a test AP of 69.30% with an inference speed of 30.24 ms on Tesla T4. In comparison between the EfficientDet-D0 fine-tuned model and EfficientDet-D2 fine-tuned model, the performance improved in terms of AP with the inference speed as the trade-off. The research has shown that it is feasible to detect the presence of human workers and can possibly serve as the visual perception of the robot with regards to human presence detection. Last but not least, the present work proved the applicability of transfer learning methods in human presence detection, specifically fine-tuned object detection models.