Reviewerly/exHarmony huggingface dataset:
The Impact of Economic Policy on Stable SCM of Daegu Automobile Tier 2 Company: Focusing on the Delivery Unit Price Indexation System

This study analyzes the factors affecting the stable SCM management using the AHP analysis method for Tier 2 auto parts companies in Daegu. In particular, how the recently enacted “52-Hour Work Week Act” and “Serious Accident Punishment Act” are impacting on Tier2 companies. The study also analyzes how the application of the “Supply Unit Price Indexation System”, which is being legalized, is affecting Tier 2 companies. Most Tier 2 companies have a large impact on SCM from government policies, and they hope that the government will prepare a more delicate implementation plan and remove negative factors that may affect Tier 2 in advance in order to maintain customer relationships and high-level competitiveness. As a result, the government’s economic policies and the rise in labor costs have the greatest impact on the stable SCM management of Tier 2 companies.
A Comparative Study on Employee Engagement Activities in Private Sector Banks

A Comparative Study of employee engagement activities in private sector banks “In this Research study we have taken various private sector banks such as Kotak Mahindra bank, Axis bank, ICICI bank, HDFC bank, and discussed about the level of employee engagement activities followed in private sector banks and then researcher have seen the satisfaction of employees, based on their engagement. The questionnaire method of data collection was developed. Descriptive statistics and cross tabs were used in this study. It was conducted on 50-60 employees as sample size was selected based on the non - probability convenience sampling.
Generating keyphrases for readers: A controllable keyphrase generation framework

Abstract With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users’ comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
A Traffic Sign Recognition Method Based on YOLOv5 Deep Learning Algorithm

Aiming at the problems of low accuracy and slow recognition efficiency of the traditional traffic sign recognition algorithm in complex environment, a deep learning traffic sign recognition method based on YOLOv5 is proposed. Firstly, the Chinese traffic sign data set TT100K is randomly divided into training set and test set. Convolutional neural network YOLOv4 and convolutional neural network YOLOv5 are used to train respectively on the training set, so as to build the prediction model of traffic signs. Then the trained model is validated on the test set. Through the evaluation of the experimental, it is found that compared with YOLOv4 model, YOLOv5 model has higher recognition accuracy and faster recognition speed.
Post Processing Selection of Automatic Item Generation in Testing to Ensure Human-Like Quality with Machine Learning

Automatic Item Generation (AIG) is increasingly used to process large amounts of information and scale the demand for computerized testing. Recent work in Artificial Intelligence for AIG (aka Natural Question Generation-NQG), states that even newer AIG techniques are short in syntactic, semantic, and contextual relevance when evaluated qualitatively on small datasets. We confirm this deficiency quantitatively over large datasets. Additionally, we find that human evaluation by Subject Matter Experts (SMEs) conservatively rejects at least ∼9% portion of AI test questions in our experiment over large diverse dataset topics. Here we present an analytical study of these differences, and this motivates our two-phased post-processing AI daisy chain machine learning (ML) architecture for selection and editing of AI generated questions using current techniques. Finally, we identify and propose the first selection step in the daisy chain using ML with 97+% accuracy, and provide analytical guidance for development of the second editing step with a measured lower bound on a BLEU score improvement of 2.4+%.
Federated Learning based Object Detection using Dampened Harmonic Optimization

Object Detection is the task of detecting and localizing objects of importance in visual media. The rapid increase in the number of powerful end devices such as mobiles and surveillance cameras have to lead to increasing in both resources and media generation. Object Detection has now become possible on end devices, but certain challenges need to be tackled to fully utilize the resources. Federated Learning is one such framework that leverages the end device resources to build machine learning models while preserving data privacy. We model the federated learning framework for object detection on real-world heterogeneous datasets using a novel dampened harmonic optimizer to enhance local learning on the end device and hence reducing the communication cost during the learning process. We provide comparison of the commonly used FedAvg with our FedHarm optimization on multiple object detection models and datasets to demonstrate the merits of our proposition. Our method FedHarm with its dampened updates allows for greater local computation which reduces the overall communication rounds between edge devices and cloud and better handles heterogeneity in real-world datasets. FedHarm leads to faster convergence by 41% on average over FedAvg which is supported by extensive experiments.
CascadeTransformer: Multi-label Classification with Transformer in Chronic Disease Prediction

Chronic diseases are serious threats to human safety and major public health problems worldwide. Many chronic diseases tend to have co-morbidities. Most machine learning techniques nowadays tend to focus on predicting a single disease while ignoring the study of co-morbidities. It is urgent to develop an artificial intelligence-based multi-label classification model based on patients' physical data, which is useful for the early detection and treatment of patients' diseases. In this study, we proposed a layer-by-layer processing structure, termed CascadeTransformer, that applies the Transformer architecture as weak classifiers, to solve the multi-label prediction problem of chronic diseases. We built a chronic diseases dataset using real-world data from West China Hospital, which consists of 1174 anonymous instances and 131 features. Systematic experiments show that our method shows better experimental performance compared to other methods on our chronic disease dataset.
Deep Learning-Enabled Prediction of Daily Solar Irradiance from Simulated Climate Data

Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. This energy is important for renewable energy generation and is intrinsically fluctuating. Forecasting solar irradiance is crucial for efficient solar energy generation and management. Work in the literature focused on the short-term prediction of solar irradiance, using meteorological data to forecast the irradiance for the next hours, days, or weeks. Facing climate change and the continuous increase of greenhouse gas emissions, particularly from the use of fossil fuels, the reliance on renewable energy sources, such as solar energy, is expanding. Consequently, governments and practitioners are calling for efficient long-term energy generation plans, which could enable 100% renewable-based electricity systems to match energy demand. In this paper, we aim to perform the long-term prediction of solar irradiance, by leveraging the downscaled climate simulations of Global Circulation Models (GCMs). We propose a novel Bayesian deep learning framework, named DeepSI (denoting Deep Solar Irradiance), that employs bidirectional long short-term memory autoencoders, prefixed to a transformer, with an uncertainty quantification component based on the Monte-Carlo dropout sampling technique. We use DeepSI to predict daily solar irradiance for three different locations within the United States. These locations include the Solar Star power station in California, Medford in New Jersey, and Farmers Branch in Texas. Experimental results showcase the suitability of DeepSI for predicting daily solar irradiance from the simulated climate data. We further use DeepSI with future climate simulations to produce long-term projections of daily solar irradiance, up to year 2099.
Spatio-Temporal Deep Fusion Graph Convolutional Networks for Crime Prediction

Effective crime prediction plays a key role in sustaining the stability of society. In recent years, researchers have proposed a number of prediction methods that extract spatial and temporal features separately and fuse afterward. However, the strict distinction between spatial feature extraction and temporal feature extraction can result in the loss of useful information. To this end, we propose a spatio-temporal deep fusion graph convolution network (STDGCN), which embodies the intra-region spatio-temporal features and the inter-region spatio-temporal associations on a single graph. STDGCN performs the convolution without distinguishing between space and time to simultaneously extract spatio-temporal features. Our evaluations of two real-world datasets demonstrate the effectiveness of STDGCN.
Proposal to Improve The Classification of School Violence

Nowadays, violence in movies and in society is on the rise, which has a significant impact on children, particularly adolescents. The prevalence of school violence is increasing and it is becoming a concern for schools, families, and society as a whole. However, because the school violence detection system has not yet been developed, our lab created VSiSGU data based on the collection of camera data from within the school as well as data from social networks. There are also many techniques for processing continuous image sequence data from cameras in order to detect school violence. As a result, we propose a method for improving performance by selecting frames at the l, l+k, l+2k,..., l+nk positions in the videos to train. After that, we use the VGGNet algorithm combined with RNN to develop a training model on the above data. The evaluation results show that our proposed method is more efficient in terms of time and still ensures higher or equivalent accuracy than the traditional sampling method.
ERRORS IN THE PREDICTION OF EXTRAPOLATORS BASED ON CHEBYSHEV POLYNOMIALS

Extrapolators of random processes constructed on the basis of Chebyshev polynomials orthogonal on a set of equidistant points are described. The coefficients of the predictive polynomial are obtained using the least squares method. The analysis of estimates of forecast errors for various stationary processes defined by their autocorrelation function is carried out. The relationship of estimates of forecast errors with the correlation coefficient between the values of random variables at the beginning and end of the time interval used to calculate the forecast is shown. The dependences of the prediction errors of stationary random processes on the parameters of autocorrelation functions, the number of points at which the values of the random process are known in the time interval preceding the calculation of the forecast, and the time sampling step are obtained. Based on the obtained dependencies, a method for selecting the parameters of an extrapolator operating in real time is given.
TECHNICAL DEPT OR FIVE REASONS TO REFACTORING

Many of us face the problem of legacy code, which can compare with a "black box" that has only inputs and outputs. A small change in one place in the code leads to a cascading change in the entire program. You need to constantly «clean up» while developing software to prevent transformation of the modern one to legacy code. The author proves the truth of this statement on the example of working with legacy code.
Research on Cursive Font Recognition Based on Improved Hash Algorithm

This paper mainly explores the method of Chinese cursive character recognition, establishes the Standard Cursive database in the process of research, and puts forward the similarity distance to measure the similarity between the cursive font to be recognized and the character set, and improves the calculation method of the similarity distance. Through experimental comparison, pH algorithm performs best in cursive character recognition.
HACKATHONS AS AN EFFECTIVE WAY TO INCREASE IT AND CYBER SECURITY SKILLS

In the article, the authors analyze intellectual games, consider examples of hackathons and competitions held in the field of cybersecurity. To study the impact of hackathons on skills and learning, the authors conducted a survey and reflected the results in the article. It is shown that the main reason for student participation is learning and skill development, while the professional skills of hackathon participants improve. The use of such intellectual games as a hackathon in the educational process of computer security specialists will help to bridge the gap between theoretical training and practical skills of students.
INDIVIDUALIZATION OF THE APPROACH TO TEACHING A FOREIGN LANGUAGE TO SPECIALISTS IN THE TRANSPORT INDUSTRY

The article considers the relevance of individual educational approach to the discipline of the general cycle – "Foreign language" in the context of training specialists in the transport industry; examines such concepts as "individual educational trajectory", "individual educational route"; outlines the stages of compilation, work and evaluation of the results of the learning process of higher school students using an individual approach.
ABOUT PROFESSION - ORIENTED TEACHING OF A FOREIGN LANGUAGE IN A NON-LINGUISTIC UNIVERSITY

The article considers professionally-oriented teaching of a foreign language in a non-linguistic university as an important condition for training a young specialist, a graduate of a university. The article discusses the features of teaching a foreign language to students of technical specialties, in particular, studying at the Russian University of Transport. Based on the teaching experience, the authors point out a certain peculiarity in the perception of students of engineering and other specialties of a foreign language, analyze the motivation of students and ways to improve it. Special attention is paid to the use of advanced technologies in teaching a foreign language, including ICT.
Neural Network Optimization Objective Vector Representation based on Genetic Algorithm and Its Multi-objective Optimization Method

Deep learning algorithms mostly have network parameters that can affect their training results, and the combination of neural network architectures also has a significant impact on the algorithm performance. The performance of deep learning algorithms is usually proportional to the overall number of network parameters, leading to excessive resource consumption for exploring neural network architectures with a large number of hyper-parameters. To solve this problem, a vector representation is proposed which for neural network architectures, and a multi-objective optimization model is established based on genetic algorithms in this paper, and it is short for “NNOO Vector Representation based on GA and Its Optimization Method”. The multi-objective optimization model can automatically optimize the neural network architecture and hyper-parameters in the network, improve the network accuracy, and reduce the overall number of network parameters. It is shown in the test results with the MNIST data set, and the accuracy is 95.61% for the traditional empirical setting network, and the average accuracy is 86.2% for the network optimized by TensorFlow’s optimization algorithm. While the network accuracy is improved to 96.86% with the proposed optimization method in this paper and the network parameters are reduced by 32.6% compared with the traditional empirical network, and the network parameters are reduced by13.2% compared with the network by TensorFlow’s optimization algorithm. Therefore, the method is presented which has obvious practical application value in neural network optimization problems and provides a new way of thinking for large and deep network optimization problems.
A Mask Detection Algorithm Based on RetinaFace

In the context of the normalization of the COVID-19, wearing a mask is an effective way to prevent the spread of the COVID-19. It is an important but highly challenging task to detect people not wearing masks in crowded places in time. Automatic mask wearing detection based on monitored images has also become a current research hotspot. This paper proposes a model to detect the masked face by utilizing RetinaFace, which uses Res2Net as the backbone network, and enhances feature extraction by introducing a weighted bidirectional feature pyramid and CBAM (Convolutional Block Attention Module). Comparative experiments are done based on the actual scene, The experimental results demonstrate that the Retinaface_Mask has achieved better detection results than the Retinaface. The mean average precision of Retinaface_Mask reaches 86.92%, compared with the Retinaface, it is improved by 1.43 percentage points.
Investigating the Navigational Behavior of Wheelchair Users in Urban Environments Using Eye Movement Data

People with mobility disabilities (PWMD) often struggle with challenges in getting around independently for their daily activities. Mobility is one of the most important life habits which might be constrained by diverse environmental and social obstacles, limiting the social participation of PWMD. Upgrading the social integration of these people is a major challenge in Canada and internationally. Even though the advent of assistive navigation technologies improves the interaction of PWMD with their environments during their mobility, these tools mostly ignore the capabilities, capacities, and specific needs of this population. It is required to better understand PWMD’s navigational behavior in the environment to make these navigation tools adapted to their profile and specific needs. Hence, this research aims at using state-of-the-art technology (i.e., eye-tracking glasses) to explore the navigational behavior of PWMD. To do so, we designed and carried out an experiment in which a wheelchair user wearing eye-tracking glasses navigated a route following the instructions given by Google Maps. Several eye-tracking metrics for the collected eye movement data were computed and analyzed to explore the participant’s visual and mental activities while performing the navigation task. Artificial intelligence was used to automatically assign eye movement data to specific features in the environment during navigation. The preliminary findings of this research show that the highest level of fixation was assigned to the cell phone for receiving the route instructions, distracting thus the participant from his surroundings. In this sense, we have noticed that these route instructions were not sufficient and clear for wheelchair users in some situations. In addition, fixations on sidewalks and crosswalks were the second-highest amount because of the low accessibility level of several parts of the route. Some buildings as landmarks were also eye-catching for the wheelchair user during exploring the environment, and searching for the route, particularly when the route was accessible. In this way, it is required to help the wheelchair user to become aware of information on the accessibility of routes and salient environmental objects in advance to draw more attention to the environment, better orient in the environment, and make sure of following the correct route, therefore, upgrading wheelchair users’ spatial learning and being autonomous.
Supporting the Prediction of AKI Evolution Through Interval-Based Approximate Predictive Functional Dependencies

In this paper, we focus on the early prediction of patterns related to the severity stage of Acute Kidney Injury (AKI) in an ICU setting. Such problem is challenging from several points of view: (i) AKI in ICU is a high-risk complication for ICU patients and needs to be suitably prevented, and (ii) the detection of AKI pathological states is done with some delay, due to the required data collection. To support the early prediction of AKI diagnosis, we extend a recently-proposed temporal framework to deal with the prediction of multivalued interval-based patterns, representing the evolution of pathological states of patients. We evaluated our approach on the MIMIC-IV dataset.
Explainable Artificial Intelligence for Cytological Image Analysis

Emerging new technologies are entering the medical market. Among them, the use of Machine Learning (ML) is becoming more common. This work explores the associated Explainable Artificial Intelligence (XAI) approaches, which should help to provide insight into the often opaque methods and thus gain trust of users and patients as well as facilitate interdisciplinary work. Using the differentiation of white blood cells with the aid of a high throughput quantitative phase microscope as an example, we developed a web-based XAI dashboard to assess the effect of different XAI methods on the perception and the judgment of our users. Therefore, we conducted a study with two user groups of data scientists and biomedical researchers and evaluated their interaction with our XAI modules, with respect to the aspects of behavioral understanding of the algorithm, its ability to detect biases and its trustworthiness. The results of the user tests show considerable improvement achieved through the XAI dashboard on the measured set of aspects. A deep dive analysis aggregated on the different user groups compares the five implemented modules. Furthermore, the results reveal that using a combination of modules achieves higher appreciation than the individual modules. Finally, one observes a user's tendency of overestimating the trustworthiness of the algorithm compared to their perceived abilities to understand the behavior of the algorithm and to detect biases.