Reviewerly/exHarmony huggingface dataset:
A Study on the Effect of Educational Service Quality on Career Decision-Making Self-efficacy

The ‘New Normal’ era in South Korea with low growth and employment and the fourth industrial revolution in the world that brought about the environmental change leading to ‘jobless growth’ are giving new roles and obligations to universities, and to respond to the foregoing, qualitative changes in education and changes in educational services are becoming essential elements of competitiveness. According to the results of the study, among the effects of the faculty, educational environments, educational programs, and administrative services, which are elements of educational service quality, on learning flow and educational satisfaction, the effect of educational environments on learning flow, and the effect of administrative services on educational satisfaction were not significant and all other effects were significant. The effects of learning flow and educational satisfaction on academic achievement and career decision-making self-efficacy were adopted as significant effects but the effect of academic achievement of university students on career decision-making self-efficacy was not shown to be significant. Through the foregoing, the effect of educational service quality on career decision-making was empirically verified thereby seeking qualitative improvement of educational services for cultivation of men of talents, which is the ultimate goal of universities and expected effects in the strategic aspect were presented.
Design and Research of Coagulant Dosing Control System in Water Plant Based on Light GBM Integrated Learning Algorithm

According to the characteristics of long lag, nonlinear, multi-input human factors, uncertainty, time-varying, fuzziness and so on, an automatic control system for coagulant dosing in waterworks is studied and developed by using the artificial neural network algorithm to adapt and self-study the surrounding environment. The system takes a water plant of Zongguan, the largest water plant in Wuhan, as an example to study the influence of light GBM integrated algorithm on the coagulant dosing effect of the control system. Based on the open data access standard of OLE-DB, it realizes the pretreatment of sample data reading and storage of WinCC industrial control system. The system mainly includes the function modules of dosing process data query, curve generation, dosing query, alarm log, alarm statistics, drug consumption statistics, fluctuation evaluation alarm setting, etc., and the successful operation of the system in Zongguan water plant realizes the online monitoring and full automatic operation of the production and operation parameters of the coagulation dosing process. It provides guarantee for the safety production of the water plant, achieves the purpose of saving drug consumption, reducing labor and reducing the labor intensity of the operators.
Shortest Bounded Curvature Trajectory Via A Moving Circle: Theory and Applications

This paper presents a characterization of the time-optimal Dubins trajectory from an initial configuration (heading and orientation) to a final configuration via a moving circle. The circle can be moving on any arbitrary trajectory as long as its trajectory satisfies some regularity constraints. These paths are essential for applications in surveillance, collision avoidance, routing problem for delivery systems, \textit{etc}. By Pontryagin's maximum principle, the fundamental segments of the optimal trajectory are established. Thereafter, from the necessary conditions for the state inequality constraints, the analytical relations and geometric properties dictating the concatenation of the segments are proposed. Utilizing the proposed properties, a finite set of candidate optimal trajectory types is established. Numerical results are presented to validate the properties and highlight the application of these paths.
RePEL: Blockchain-Empowered Conditional Privacy-Preserving Encrypted Learning

Business organization performs its business activities within a headquarter-branch relationship, especially for multinationals, by establishing work places in accordance with business requirements. The data-driven decision making stimulates the importance of data analysis. Machine learning (ML), as a method of data analysis, automates analytical model building based on the idea that systems can train and learn from data to make decisions with minimal human intervention. For high accuracy, traditional ML algorithms make design tradeoffs, conceding privacy and reliability, and are thereby unable to satisfy strong security demands. To resolve design tensions, in this article, we propose RePEL, which harmonizes functional encryption (FE) and blockchain on top of encrypted learning. RePEL allows the headquarter to only share partial information about business data collected from branches while manages data transfer with the consensus mechanism in the blockchain, which works in a coordinated way for preserving conditional privacy and high reliability. We instantiate a RePEL design with the three-layer framework and formally reduce its security to a provably secure FE scheme. We further deploy Feel_BC to implement a RePEL prototype system, so as to realize the performance evaluation. Experimental results show that RePEL, with the basic premise of privacy and reliability, can achieve high accuracy and reasonable throughputs.
StackBC: Deep learning and transfer learning techniques based stacking approach for accurate Invasive Ductal Carcinoma classification using histology images

Artificial intelligence (AI) based computer-aided diagnostic (CAD) systems can effectively diagnose critical disease. AI-based detection of breast cancer (BC) through images data is more efficient and accurate than professional radiologists. However, the existing AI-based BC diagnosis methods have complexity in low prediction accuracy and high computation time. Due to these reasons, medical professionals are not employing the current proposed techniques in E-Healthcare to effectively diagnose the BC. To diagnose the breast cancer effectively need to incorporate advanced AI techniques based methods in diagnosis process. In this work, we proposed a deep learning based diagnosis method (StackBC) to detect breast cancer in the early stage for effective treatment and recovery. In particular, we have incorporated deep learning models including Convolutional neural network (CNN), Long short term memory (LSTM), and Gated recurrent unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, data augmentation and transfer learning techniques have been incorporated for data set balancing and for effective training the model. To further improve the predictive performance of model we used stacking technique. Among the three base classifiers (CNN, LSTM, GRU) the predictive performance of GRU are better as compared to individual model. The GRU is selected as a meta classifier to distinguish between Non-IDC and IDC breast images. The method Hold-Out has been incorporated and the data set is split into 90% and 10% for training and testing of the model, respectively. Model evaluation metrics have been computed for model performance evaluation. To analyze the efficacy of the model, we have used breast histology images data set. Our experimental results demonstrated that the proposed StackBC method achieved improved performance by gaining 99.02% accuracy and 100% area under the receiver operating characteristics curve (AUC-ROC) compared to state-of-the-art methods. Due to the high performance of the proposed method, we recommend it for early recognition of breast cancer in E-Healthcare.
Educational quality, social media and public accountability: a global perspective

This inquiry relates to the empirical linkages between educational quality, Facebook penetration and accountability dynamics. The empirical investigation is based on the Ordinary Least Squares (OLS) technique and Quantile regression for the conditional linkages which articulate low, middle, and high initial levels of public accountability. It explores a cross-section of 168 countries. The main finding is that there is an overwhelming positive connection between Facebook penetration and accountability dynamics. The established positive nexus is apparent in all quantiles of public accountability. In addition, tertiary and secondary school enrollment positively influence public accountability. By utilizing a novel dataset in analyzing the established nexuses, this study adds to the existing literature on social media and governance (i.e., educational quality, Facebook penetration and accountability dynamics). Similarly, the posture addresses contemporary policy concerns regarding a lack of documentation on the impacts of social media.
Fake Tweet Data Analysis using Machine Learning Methods

As social networking websites have grown in popularity, so has the amount of data being generated online. M any people set up social media profiles to share their thoughts, images, and videos with the world. There are various ways for people to express themselves succinctly on Twitter, for instance. Twitter is a service that allows users to collaborate with one other. Tweets like this one might launch a worldwide movement for change or a global debate. It was necessary to classify them, and just the tweet itself could be posted. Kaggle's KFC challenge, dubbed “the M cDonald's of AI,” provided the data for this piece. Approximately 40000 tweets were analysed in this study. Cleaning the data using Inverse Term Frequency (TF-IDF). Clean data may be analysed using SVM, RF, and Decision Tree classification algorithms (DT). With an accuracy rate of 88.51%, the Decision Tree algorithm is the most reliable. The accuracy, recall, precision, and F1 of this result are evaluated.
A Deep Learning Approach for QR Code Based Printed Source Identification

With the widespread popularity and its ease of use, QR codes are relatively easy to be reproduced or forged illegally through printed documents. One solution to tackle this problem is to identify the source printer which produced the QR codes. In this paper, we study the CNN models for QR code based printed source identification through a series of experiments which involved with grayscale QR codes. Our experimental results show that the pretrained CNN models such as AlexNet, GoogleNet and ResNet could identify printer source with high accuracy even though the models are trained with limited input dataset.
Improved A* Algorithm based on Node Expansion and Adaptive Fuzzy Controller

This paper proposes an improved A* algorithm by introducing course angle information while considering the robot’s minimum turning radius and optimized node expansion into a six-way extension based on course angle. At the same time, the course fluctuation cost and reversing cost are added into the evaluation function, and a fuzzy controller is established to make the planning strategy according to the complexity of the environment. Besides, a time elastic band (TEB) method is applied as the local planner to track the global path. The simulation and experimental results show that the improved algorithm can not only avoid dead point problem, but also has certain adaptability to the changing environment.
Ushering a Trust-based Benefit Delivery Ecosystem in Rural India Powered by Blockchain

In developing countries across the globe, the resources are scarce with governments, but the demand from citizens are enormous, resources are often so thinly spread by the government that they rarely manage to have the socio-economic impact they are intended to bring. To elucidate this point, we took the case of India, where multiple beneficiary-oriented programmes are implemented in rural areas of the country targeting about a billion people. Billions of dollars are spent by both Federal Governments and State Governments in undertaking similar exercises for outreach, identification, and enrolment of beneficiaries under various welfare programmes that provide cash transfers, subsidies and other social services to the disadvantaged youth, women and aged in society. Some countries have also undertaken household surveys to identify gaps in demand and the supply of government services. However, limited success has been achieved in adopting such static database as the single source of truth for the identification of deprived households across all government programmes, due to its inherent potential for creating errors of inclusion and exclusion. Another major deterrent in the adoption of such a singular identity associated with socio-economic attribute is the concentration of information and associated risks. Through this paper, an attempt is being made to bridge this ‘trust deficit’ in governance through the application of distributed ledger technology for social inclusion. We further discuss how government is better placed than the private sector to tap the full potential of Blockchain technology as several public organizations working in the same sector are often controlled by the same parent government department. A mandate from government can give the necessary thrust to reap the benefits of this ecosystem-centric technology. At the last, feasibility, approaches and challenges are analyzed. The results from the adoption of this approach to governance can transform the entire service delivery ecosystem.
Developing GCN: Graph Convolutional Network with Evolving Parameters for Dynamic Graphs

Dynamic graph representation learning has caused much attention in many practical applications. There is an interesting method that uses RNNS (e.g., LSTM, GRU) to update the GCN’s weights dynamically with weights from the previous time step. However, it only uses one time-step weights, which eventually leads to the lack of sufficient historical information. In this work, we focus on this method for the developing parameters and propose a developing GCN model, which adapts an attention mechanism to get richer historical information so that the RNNs will decode better fused historical representations to capture the temporal correlation of weights in the GCN, which not only can learn those dynamic graphs with fewer features, but also can extract richer historical information to learn. We evaluate our method on the task for link prediction and the result shows a better performance in most data sets we test.
Threat and Risk Assessment Using Continuous Logic

Threat and Risk Assessment is an important area in cybersecurity. It covers multiple systems and organizations where cybersecurity is significant, such as banking, industry, SCADA, Energy Management System, among many others. The chapter presents a method to help assessing threats and risks associated with computer and networks systems. It integrates the Framework for Improving Critical Infrastructure Cybersecurity—developed by the National Institute of Standards and Technology—with a quantitative method based on the use of a Continuous Logic, the Logic Scoring of Preference (LSP) method. LSP is a method suitable for decision making that provides the guidelines to produce a model to assist the expert in the process of assessing how much a product or system satisfy a number of requirements, in this case associated to the identification, protection, detection, response and recovery of threat and risks in an organization.
<i>Retracted:</i> Dynamics of professional motivation of technical university students

Abstract Modern knowledge‐intensive industrial production technologies increase the demand for engineering personnel in the labour market which actualises the problem of training competent, highly qualified specialists. This process is strongly determined by the motives that guide future engineers in their professional learning activities. The study analyses the dynamics of professional motivation of technical students of I–III courses. This study presents the results of research conducted by assessing the professional motivations of students at the Norilsk State Industrial Institute. The survey was conducted by the authors in September–October 2020. Fourteen motives were assessed and grouped into seven groups: social, moral, collectivist, cognitive, material, prestige motivation, and those associated with the peculiarities of professional activity. The study of professional motivation helped to identify the dominant motives of future engineers, trace their dynamics among students of different courses and analyse the peculiarity of professional motivation of students of different technical training areas. Based on the results of the study and analysis of the data obtained in the structure of future engineers’ professional motivation, the dominance of social, material and moral motivations, growth of collectivist and cognitive motives, insufficient degree of cognitive and work‐related motivation, low level of assessment of the prestige of the chosen profession are stated. The comparative data presented in the study with the 2009–2010 academic year's research on professional motivation identifies the need for further systematic vocational guidance work with future engineers.
Product Clustering Analysis Based on the Retail Product Knowledge Graph

Product clustering analysis is essential in designing retail marketing strategies. It is a common practice that retailers use to effectively manage their product inventory, marketing promotions, etc. The most intuitive way of clustering products is by their explicit attributes, such as brand, size, and flavor. However, these approaches do not integrate the customer-product interactions, thus ignore the implicit product attributes. In this work, we construct a retail product knowledge graph based on Amazon product metadata. Leveraging a state-of-the-art network embedding method, RotatE, our main objective is to unveil hidden interactions of products by including implicit product attributes. These hidden interactions bring insights to downstream operations such as demand forecasting, production planning, assortment optimization, etc.
Study on Phonologycal Process: A Case on Indonesian EFL Students' Pronunciations

The present article seeks to discuss the phonological process concerning the alterations in English sounds produced by Indonesian EFL students. This study drew on generative phonology and utilized Schane’s (1973) categories of the phonological process to highlight the changes in English sounds. This study applied a qualitative method with a descriptive research design. All data of this study were gathered through digitally recording eight EFL students’ oral presentations in the research proposal seminars. According to the results of this study, the transformations in the English sounds arose in both the consonant and vowel sounds of the English, where these changes attributed to the four categories of phonological process, including assimilation, dissimilation, deletion, and insertion. The present study also found the occurrences of a double and triple different phonological process in single word utterance produced by participants of this study. This study signified that such changes in English sounds occurred owing to the mother tongue influence. The results of this study offer a significant contribution to the insights into both linguistics and EFL instruction.
Performance Profile of Transformer Fine-Tuning in Multi-GPU Cloud Environments

The study presented here focuses on performance characteristics and trade-offs associated with running machine-learning tasks in multi-GPU environments on both on-site cloud computing resources and commercial cloud services (Azure). Specifically, this study examines these tradeoffs by examining the performance of training and fine-tuning of transformer-based deep-learning (DL) networks on clinical notes and data, a task of critical importance in the medical domain. To this end, we perform DL-related experiments on the widely deployed NVIDIA V100 GPUs and on the newer A100 GPUs connected via NVLink or PCIe. This study analyzes the execution time of major operations to train DL models and investigate popular options to optimize each of them. We examine and present the findings on the impacts that various operations (e.g. data loading into GPUs, training, fine-tuning), optimizations, and system configurations (single vs. multi-GPU, NVLink vs. PCIe) have on the overall training performance.
Learning Coarsened Dynamic Graph Representations for Deformable Object Manipulation

Manipulation of deformable objects has long been a challenging task in robotics. Their high-dimensional configuration space and complex dynamics make them difficult to consider for tasks such as robotic manipulation. In this paper, we address the problem of learning efficient representations of deformable objects which lend themselves better suitable for downstream robotics tasks. In particular, we consider graph-based representations of deformable objects which arise naturally from their point-cloud representation. Through manipulation, we learn to coarsen this graph into a simpler representation which still captures the necessary dynamics of the object. Our model consists of (a) a Cluster Assignment Model which takes the initial graph and coarsens it, (b) a Coarsened Dynamics Model that approximates the dynamics of the coarsened graph and (c) a Forward Prediction Model which predicts the next state of the ground truth graph. After end-to-end training, the Cluster Assignment Model learns to build coarse representations which better capture the dynamics compared to conventional clustering methods such as K-means. We evaluate our method on three sets of experiments: rigid objects, rigid objects with pair-wise interactions and a simulated dataset of a shirt.
VORDi*

Cloud computing services mature both economically and technologically and play a more and more extensive role in the domain of software and information systems engineering. SaaS offers an advantage for both service providers and consumers. SaaS is faced with the question of appropriate techniques applied at an early phase of requirements engineering of the producing system. The paper highlights two traditional methods, namely i* and VORD, belonging respectively to goal-oriented requirements engineering and viewpoints approaches. The approach proposed dealing with the requirements elicitation in the context of software-as-a-service (SaaS). So, the approach benefits from strengths of both VORD and i* models and proposes a combination of them in a new approach, namely VORDi*.
A Region Descriptive Pre-training Approach with Self-attention Towards Visual Question Answering

Concatenation of text (question-answer) and image has been the bedrock of most visual language systems. Existing models concatenate the text (question-answer) and image inputs in a forced manner. In this paper, we introduce a region descriptive pre-training approach with self-attention towards VQA. The model is a new learning method that uses the image region descriptions combined with object labels to create a proper alignment between the text(question-answer) and the image inputs. We study the text associated with each image and discover that extracting the region descriptions from the image and using it during training greatly improves the model’s performance. In this research work, we use the region description extracted from the images as a bridge to map the text and image inputs. The addition of region description makes our model perform better against some recent state-of-the-art models. Experiments demonstrated in this paper show that our model significantly outperforms most of these models.