Reviewerly/exHarmony huggingface dataset:
Integration of Local Interoperable Cyber Environments of Virtual Organizations into a Single Cyber-Environment of a Post-Industrial Society

An approach to the formation of a unified cyber environment of a post-industrial society from local interoperable cyber environments of virtual organizations is proposed. The concept and basic principles of building a cyber-environment for virtual organizations are presented. Models of integration of separate cyber environments into a single cyber environment of post-industrial society are presented. It is shown that the creation of interoperable cyber environments will provide info-communication interaction between participants, regardless of the cyber environment in which they are registered, which will create a single global cyberspace.
Learning Japanese through VR Technology. The Case of <i>Altspace VR</i>

Through this paper, I intend to analyse the state of Japanese learning through virtual reality technologies, avoiding as much as possible the analysis of prototypes and laboratory experiments, focusing instead on a case study that implements software and hardware already present on the consumer market. After analysing the VR technology currently available to end-users and its hardware, which has lately increased in popularity due to the COVID-19 pandemic, and the specificities of virtual reality compared to other communication technologies, I will proceed to the analysis of the case study Altspace VR , a social VR software used also by an active Japanese learning community, to try to identify the characteristics that could make this technology particularly suitable for language learning.
Graph Attention Network for Word Embeddings

The word embeddings approaches have attracted extensive attention and widely used in many natural language processing (NLP) tasks. Relatedness between words can be reflected in vector space by word embeddings. However, the current word embeddings approaches commonly do not explore the context-specific information of word deeply in the overall corpus. In this paper, we propose to use graph attention network for word embeddings. We build a large single word graph for a corpus based on word order, then learn a word embeddings graph attention network (WEGAT) for the corpus. Our WEGAT is initialized with one-hot representation for word. We propose to use masked language model (MLM) as supervised task. In addition, through the text classification experiment, it is showed that accuracy of the word embeddings represented by WEGAT is higher than the current method for the same classification method.
Employment quality evaluation model of Finance graduates based on Data Envelopment Analysis

Aiming at the problem of low evaluation accuracy in the traditional employment quality evaluation model of finance majors, a data envelopment analysis-based employment quality evaluation model of finance majors is proposed and designed. Based on the principles of systemicity, subjective and objective combination, conciseness and independence, the C2R model in data envelopment analysis is used to construct the system of the employment quality evaluation model for finance graduates. The judgment matrix of each index is constructed by the pairwise comparison matrix judgment method to determine the actual weight coefficient of the employment quality evaluation system for financial graduates. After obtaining the corresponding weights of all indicators in the employment quality evaluation model system of finance majors, the employment quality evaluation model of finance majors can be constructed. The comparative experiment verifies that the evaluation accuracy of the model is higher and it has strong applicability.
Understanding Failed Software Projects through Forensic Analysis

Software project failure has cost trillions of dollars over the past fifty years. The many reasons for failure are widely discussed in the project management and software development literatures. This paper assesses methods for software project forensic analysis to develop principles and practices that would provide a comprehensive and more complete understanding of the why, when, where, how, and what causes software project failure. This paper discusses the approaches followed to determine the causes of failures, examines the issues and challenges of assessing failures, and proposes a structured approach (forensic analysis) for determining the causes of software project failures.
Comparative Analysis of Regression and Neural Network Models on Students' Grades

Machine Learning(ML) is an important scientific area and applied science reality. It is being used by numerous industries, its use is vast and now it seems endless. In this paper we use ML methods to analyze students' grades for similar subjects, and trying to find the best model for prediction of targeted subject grade. The success of the model, or lack of it, can pinpoint to specific conclusions regarding other aspect of students performance and organization of the subject.
A Strategic Design Analysis to Facilitate the Transition for All to the New Patient-Centered Healthcare Model in China

The China healthcare system is strongly relying on large hospitals, managing many thousands of patients every day. The limited number of doctors, the limited data interoperability even in the same city or province, and the patients' limited access to their own medical data challenge this model, reducing efficiency (unnecessary duplication of procedures) and patient satisfaction. Although standardization of the “one size fits all” model is an essential part of providing safe and cost-effective care, especially in a vast country like China with a relatively low patient-doctor ratio, effective healthcare requires participation from both healthcare providers, patients and caregivers, establishing new relationships to empower and involve them. Through co-creation-driven design for All in healthcare, it is possible to identify patient and operators Abilities, Necessities and Aspirations and design systems and services comfortable for diverse people. A co-created app may facilitate the transition to a patient-centred system without requiring a structural change of the current organizational model. In this way, it is also possible to test user and experience-centred technological solutions in pilot form, respecting the established hierarchy and facilitating policymaking.
Halftone Image Steganography Based on Reassigned Distortion Measurement

Most state-of-the-art halftone image data hiding methods aim to preserve good visual quality when embedding messages, while ignoring the statistical security. This paper proposed a halftone steganographic scheme that improves the visual quality and the statistical security of the anti-steganalysis. First, a general distortion measurement for halftone images based on human visual system (HVS) model is proposed. Utilizing the Least-Mean-Square (LMS) method, halftone images can be converted to grayscale images and the objective image quality assessment is applied to evaluate the distortion caused by flipping pixels. Different distortion measurements can be derived from different image quality assessments. Then, to further measure the embedding distortions, we combine these distortion measurements to construct a reassigned distortion measurement based on the controversial pixels prior (CPP) rule. Finally, syndrome-trellis code (STC) is employed to minimize the number of flipping pixels. Experimental results have shown that the proposed steganographic scheme achieves strong statistical security with high capacity and visual quality.
Towards a Semantic Classification of Possible Human-to-Environment Interactions in IoT

When looking at the way in which humans choose to participate in and interact with cyber physical-embedded environments such as Internet of Things (IoT), one could assume that such environments are permeated with 'ad hoc', 'heterogeneous' and 'dynamic' interactions in them. Existing literature on human to computer interactions, their types and definitions fail to provide a concrete understanding of these dimensions in cyber physical-embedded environments. Therefore, this paper presents the results of investigating existing categories of Human Computer Interaction (HCI) to make sense of these interactions and the inherent heterogeneity they carry. An integrative literature review using the PRISMA model to locate, select, and include 120 relevant articles has been carried out. The main finding of this review is a semantic classification of possible Human-to-Environment Interactions (HEI). The classification plays an important role as a starting point when looking at the current and future offerings of the HEI in the IoT. The classification also serves input as formal knowledge representations, such as Ontology Web Language (OWL) ontologies, which could assist in creating explicit representations of interaction.
Scholarly Editing and AI: Machine Predicted Text and Herculaneum Papyri

In 2016 the Digital Restoration Initiative (DRI) at the University of Kentucky, under the direction of Professor Brent Seales, virtually unrolled a carbonized parchment scroll from Ein Gedi, revealing a copy of Leviticus written in iron gall ink. In 2019 the DRI applied a new machine learning method to reveal a Greek character written in carbon ink from an actual Herculaneum papyrus fragment. Virtual unwrapping of cultural heritage objects is a reality. The application of machine and deep learning methods to enhance difficult-to-detect ink signals in tomography will continue to evolve. This raises an important question. How will the process of editing texts that are ‘true-born virtual’ (the object can never be opened to verify the results) change to reflect the presence and dependency on AI? This paper produces a theoretical model for how a critical edition of a virtually unwrapped papyrus text must document the role of the machine. It also engages the possible requirements, in terms of Data Science, that this new type of text compels in order to ensure transparency at the level of its ‘birth’. Put simply, a new virtual edition model that is a fusion of humanities and science is needed.
Resolution of the Frobenius Problem with an Adiabatic Quantum Computer

The (Diophantine) Frobenius problem is a well-known NP-hard problem (also called the stamp problem or the chicken nugget problem) whose origins lie in the realm of combinatorial number theory. In this paper we present an algorithm which solves it, via a translation into a QUBO problem of the so-called Apéry set of a numerical semigroup. This algorithm was specifically designed to run in an adiabatic quantum computer (a D-Wave 2X machine), and the performance problems for this precise setting are also discussed.
Bayesian Selective Transfer Learning for Patient-Specific Inference in Thyroid Radiotherapy

This paper outlines a selective transfer approach for Bayesian estimation of patient-specific levels of radioiodine activity in the thyroid during the treatment of differentiated thyroid carcinoma. The work addresses some limitations of previous approaches which involved generic, non-selective transfer of archival data. It is proposed that improvements in patient-specific inferences may be achieved via transferring external population knowledge selectively. This involves matching the patient to a similar sub-population based on available metadata and formally transferring a feature-space-conditioned, probabilistic data predictor from the sub-population to the specific patient. In addition, the transfer times are chosen to complement the patient's own data. Currently the proposed method yields positive transfer, with stable performance improvements up to 34%. Although this is found to be 9% below the performance of the current state-of-the-art, the proposed method is significant in that it can be applied to other transfer learning applications where inhomogeneous parameter knowledge is available in the source feature space.
Multi-guide Particle Swarm Optimisation Control Parameter Importance in High Dimensional Spaces

This article presents an investigation into the effects of the search space dimension on the control parameter importance of the multi-guide particle swarm optimization (MGPSO) algorithm over time. The MGPSO algorithm is a multi-objective optimization algorithm that uses multiple swarms, each swarm focusing on an individual objective. This relative control parameter importance of the MGPSO is identified using functional analysis of variance (fANOVA). The fANOVA process quantifies the control parameter importance through analysing variance in the objective function values associated with a change in control parameter values. The results indicate that the inertia component value is the most influential control parameter to tune when optimizing the MGPSO throughout the run time. The relative importance of the inertia weight remains dominant with an increase in the search space dimensions.
A Novel Three-Way Merge Algorithm for HTML/XML Documents Using a Hidden Markov Model

Ever since the advent of modern World Wide Web (WWW), collaborative editing of Web Pages at a programming level (via HTML/XML) has been identified as a major programming challenge. Yet surprisingly, relatively little effort has been put into the direction of developing sound algorithms and methodologies for meeting this challenge by automated means. In this paper a novel algorithmic approach to merging HTML/XML code documents is presented that is based on the “Three-way Merge” approach using Hidden Markov Models, the “line-of-code-per-line-of-code” comparison between the documents involved and the “Nested Parenthesis” principle. The algorithm can be easily extended to any level higher than the “Three-way Merge”, with, of course, its computational complexity increasing accordingly.
SmartRPL: Secure RPL Routing Protocol for Internet of Things Using Smart Contracts

Numerous nodes are connected to Internet of Things (IoT), nodes need to communicate and synchronize with each other, and these nodes are resource constrained with limited processing power, memory, energy with unstable links, and low bandwidth. There is a standard routing protocol for IoT-LLN (low-power and Lossy networks) known as RPL (routing protocol for low-power and Lossy networks); this protocol is used for communication between low power devices and exchange data. For secure communication among IoT nodes, the routing protocols require central authority which results in various security attacks and can severely degrades the network performance. In the proposed system, the blockchain-based framework with smart contracts is used to offer secure route establishment between IoT nodes in the network and protects the control packets information from being modified by an external attacker. Thus, it can prevent the formation of variety of attacks and to ensure secure route formation between nodes in the network.
Movie Review Sentimental Analysis Based on Human Frame of Reference

It explored an innovative technology of web development, “React js”. It is one of the versatile, optimized and latest technologies developed by Facebook, which consist of some of its distinctive features like reusability of components and hot reloading. It uses a JavaScript library for building prodigious interfaces. React is very efficient for building any kind of a single or a mobile-based application, and there is also some predefined library built-in it which provides some additional features such as routing and statement management. Redux and React router are some of the examples of such kind of libraries. There are notable built-in features of react js which are able to “explored” in our web applications. Reusing any components, books, virtual DOM are some of the useful concepts implemented in our web applications. React makes our programming very efficient and facile, because of its inbuilt features that are specifically developed for react js by Facebook. So, react js use hot reloading, and it is kind of features which you can see in daily life such as when you post a comment on Facebook or YouTube or the life someone post on the Instagram the whole page of the web application does not get reloaded only the components get reloaded, which is very efficient and makes the web application fast in nature.
Deep Neural Networks as Interpretable Cognitive Models for the Quine’s Uncertainty Thesis

Although artificial intelligence has advanced greatly so surpassed human performance in many tasks, interpretability for their processing and results still be a weakness because artificial intelligence is not able to learn Gestalt cognition, a unique phenomenon in humans. The Gestalt cognition also is an enhance version for well-known “Qunie’s uncertainty thesis”, and both implicit that human understanding of images and words is not well defined. Whether Gestalt cognition as unique cognitive patterns can be expressed by the deep neural networks or not is focus of this paper, and some potential networks models are discussed and further direction will be given.
Theoretical fractional formulation of a three-dimensional radio frequency ion trap (Paul-trap) for optimum mass separation

We investigate the dynamics of an ion confined in a Paul–trap supplied by a fractional periodic impulsional potential. The Cantor–type cylindrical coordinate method is a powerful tool to convert differential equations on Cantor sets from cantorian–coordinate systems to Cantor–type cylindrical coordinate systems. By applying this method to the classical Laplace equation, a fractional Laplace equation in the Cantor–type cylindrical coordinate is obtained. The fractional Laplace equation is solved in the Cantor–type cylindrical coordinate, then the ions is modelled and studied for confined ions inside a Paul–trap characterized by a fractional potential. In addition, the effect of the fractional parameter on the stability regions, ion trajectories, phase space, maximum trapping voltage, spacing between two signals and fractional resolution is investigated and discussed.
Rows Permutation Based Non-uniformity Correction Algorithm for Video Sequences

An algorithm for a fixed pattern noise correction for video sequences with motion objects and/or entire scene based on rows permutation in previous frames and their recurrent accumulation is considered. It is shown that fixed pattern noise is successfully compensated if number of previous frames in sequence more than 300 frames.
Accelerating Analytics Using Improved Binary Particle Swarm Optimization for Discrete Feature Selection

Abstract Feature selection, a combinatorial optimization problem, remains broadly applied in the area of Computational Learning with the aim to construct a model with reduced features so as to improve the performance of the model. Feature selection algorithm aims to identify admissible subgroup of features without sacrificing the accuracy of the model. This research works uses Improved Binary Particle Swarm Optimization (IBPSO) to optimally identify subset of features. The problem of stagnation, trapping in local optima and premature convergence of Binary Particle Swarm Optimization (BPSO) for solving discrete feature selection dispute has been tackled using IBPSO. IBPSO prevents the model from overfitting and also takes less computational time for constructing the model because of reduced feature subset. The sine function, cosine function, position of the random particle and linear decrement of inertial weight are integrated in IBPSO, which balances between exploration and exploitation to identify optimal subset of features. The linear decrement of inertial weight tends to do good level of exploration at the starting phase, whereas at the end it tends to exploit solution space to find the optimal subset of features that are more informative and thereby discarding redundant and irrelevant features. Experimentation is carried out on seven benchmarking datasets obtained from University of California, Irvine repository, which includes various real-world datasets for processing with machine learning algorithms. The proposed IBPSO is compared with conventional metaheuristic algorithms such as BPSO, Simulated Annealing, Ant Colony Optimization, Genetic Algorithm and other hybrid metaheuristic feature selection algorithms. The result proves that IBPSO maximizes the accuracy of the classifier together with maximum dimensionality reduction ratio. Also, statistical tests such as T-test, Wilcoxon signed-pair test are also carried out to demonstrate IBPSO is better than other algorithms taken for experimentation with confidence level of 0.05.
The Face of Bad Advertising: Assessing the Effects of Human Face Images in Advertisement Design Using Eye-Tracking

Visual messaging has been widely researched in psychology and communications specifically within the area of advertising [2, 3]. Such messaging research seeks to identify the characteristics and variables within an advertisement that contribute most to its effectiveness [4]. The present study aimed to investigate the relationship between the presence of a human face image in print advertising and viewer affinity for ad content. Thirty-three participants were instructed to preview 42 different personal injury law firm advertisements. Mean eye gaze fixation durations were recorded within both text and image areas of interest (AOIs). Additionally, self-reported advertisement ratings were used to stratify ads into low and high-affinity categories. Findings from the study indicate that there was less time spent engaging with text content in poorly rated advertisements when an image of a human face was present. Interestingly, this effect was not present in advertisements with favorable ratings, where longer fixation durations were dedicated to text AOIs as opposed to image AOIs regardless of the presence of a face image. These results suggest that negatively perceived human faces may impact the perception of an advertisement’s message and demonstrates that combined eye-tracking and self-reported measures can provide a comprehensive neuroergonomic assessment of advertisement preference and engagement in real-world environments.
Object Detection Using Artificial Intelligence: Predicting Traffic Congestion to Improve Emergency Response to Mass Casualty Incidents

In the last decade, over two billion people have become victims of mass casualty incidents (MCIs). The success of the emergency response to such events depends heavily on efficient emergency vehicle transportation. During a MCI, the status of transportation pathways is constantly fluctuating, making it difficult to evaluate real-time traffic delays. Standard traffic delay software is not reliable in routing emergency resources to incident sites. Thus, the question of how can image recognition and deep-learning be used in real-time to aid emergency vehicles in MCI efforts emerges. The “You-Only-Look-Once” method is applied to provide accurate vehicle detection; a convex hull is implemented to conduct road detection; and simple supporting methods are used to describe traffic states. This combination yields a classification of traffic congestion based on defined parameter thresholds. The resulting output will ultimately guide a decision-maker or supplemental model to optimize emergency vehicle deployment.