Reviewerly/exHarmony huggingface dataset:
A Comprehensive Security Framework Proposal to Contribute to Sustainability

Well-known international security frameworks try to eliminate or mitigate different kinds of risks on the assets covered by their scopes (e.g., people, goods, information, and reputation). To date, to the best of our knowledge, any of these approaches neither provide a comprehensive perspective on security (considered as the merge of physical and logical security) nor consider sustainability as one of the levers for their design, implementation, and execution. This work presents a comprehensive security proposal through a framework that fits to the organizational security needs and contributes to the achievement of the sustainability objectives of an organization by avoiding duplicities and large security gaps from disjointed approaches. The work is supported by a methodology, which is the result of the wide and long work experience of the co-authors on security over the years in different organizations, businesses, and scopes. As it is shown in the theoretical exemplification included in the paper, the proposed framework combines the complementary and joint action of various actors for the protection of assets capable of achieving efforts and dedication efficiency, by merging the aspects of physical and logical security.
Association between Family Involvement, Play, and Literacy Skills among Preschoolers from Low-income Families in Nigeria: The Mediating Role of Literacy Interest

During the early years, some children experience rapid growth in learning which provides a vital framework for their future success. However, other children especially those with poor literacy skills have to struggle very hard with reading and writing. Hence, understanding potential factors that improve literacy skills especially at an early age is highly desirable. Therefore, this study was designed to determine the direct and indirect relationship between family involvement and play with literacy skills through literacy interest.  This study employed a total of 394 preschoolers from low-income families aged between three and five years old with their parents in Nigeria.  The sample of this study was recruited using the proportionate stratified sampling technique. Path analysis revealed that family environment and play were associated with literacy skills both directly and indirectly via literacy interest. The findings have implications for parents and policymakers in educational sectors especially on how to make effective use of play and family involvement in improving literacy skills especially among preschoolers from a low-income family.
An efficient attribute-based access control system with break-glass capability for cloud-assisted industrial control system

<abstract> In the era of Industry4.0, cloud-assisted industrial control system (ICS) is considered to be the most promising technology for industrial processing automation systems. However, the emerging attack techniques targeted at ICS underlines the importance of data security. To protect the data from the unauthorized accesses, attribute-based encryption is utilized to meet the requirement of confidentiality and access control demand of an open network environment. In ICS scenarios, it is critically important to offer the timely and efficient service, especially in the emergency situations. This paper proposes an efficient access control strategy that enables two access modes: attribute-based access and emergency break-glass access. Normally, users can access the encrypted data as long as their attributes satisfy the access policy specified by the data owner. In emergency cases, emergency situation handlers can override the access control policy of the encrypted data by the break-glass access capability. To eliminate the overhead for data consumers, the scheme outsources the data decryption and policy updating to the semi-trusted fog and cloud. The scheme also implements the CP-ABE scheme in terms of an asymmetric Type-3 pairings instead of the symmetric Type-1 pairings, which are inefficient and have security issues. Finally, the paper analyses the security of the scheme, evaluates its performance, and compares it with related works. </abstract>
Microwave Quantum Radar’s Alphabet Soup: QI, QI-MPA, QCN, QCN-CR

The Internet is agog with stories about quantum radar. Will it really make stealth aircraft vulnerable? Were the microwave experiments reported in 2020 from Europe and Canada really proof-of-principle laboratory demonstrations of quantum radar's advantage over classical radar? Or, does the 25 September 2020 news article in Science-entitled "The short, strange life of quantum radar"-paint the true picture? This paper disentangles microwave quantum radar's alphabet soup: quantum illumination (QI) radar, quantum illumination with a microwave parametric amplifier receiver (QI-MPA) radar, quantum-correlated noise (QCN) radar, and quantum-correlated noise radar with a correlation receiver (QCN-CR). In particular, it evaluates-with no explicit quantum-mechanical notation or calculations-these radars' miss probabilities at fixed false-alarm probability and it compares them to those for classical radar's relevant alphabet soup, viz., coherent-state homodyne (CS-Hom) radar, coherent-state heterodyne (CS-Het) radar, classically-correlated noise (CCN) radar, and classically-correlated noise radar with a correlation receiver (CCN-CR). These comparisons show that, under ideal operating conditions, the QI and QI-MPA radars offer performance advantages over their best classical counterparts. Moreover, QI-MPA's advantage is similar to that for its error-probability exponent when target absence and presence are equally likely and all radars make minimum error-probability decisions based on their respective measurements. Available theory, however, is unable to fully quantify QI's advantage in the operating regime of interest. Ultimately-after accounting for problems that afflict QI and QI-MPA, but not their classical competitors, and factoring in realistic standoff-sensing parameters-it will be concluded that the aforementioned Science article has it correct. QI target detection has little to offer for standoff sensing, i.e., it does not compromise stealth aircraft.
Conversational Chatbots with Memory-based Question and Answer Generation

The aim of the study is to contribute to research in the field of maintaining long-term engagingness in chatbots, which is done through rapport building with the help of user and agent specific mem ...
Continuous quantum walk in a 1-dimensional plasmonic lattice structure based on metal strip waveguides

We experimentally studied a continuous time evolution of a “plasmonic” walker in a 1-dimensional lattice structure based on long-range surface plasmon polariton waveguides. The plasmonic walker exhibited a typical time evolution of a 1-dimensional quantum walk, which indicates that the plasmonic system is a potential platform to construct quantum walk simulators. By comparing experimental results to numerical simulations, the fidelity of the plasmonic quantum walk simulator is estimated to be &gt; 0.96, which demonstrates that the plasmonic system can be a feasible platform for large-scale and high dimensional quantum walk simulators.
Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine &amp;amp; power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detection
Overcoming issues in frequency-based extraction and lexicographic lnclusion of Korean neologisms

This paper discusses issues regarding frequency as a criterion for Korean neologism extraction from the perspective of corpus linguistics and lexicography. Most studies agree that frequency plays a central role in the inclusion of neologisms in the dictionary; however, frequency entails a number of complex factors such as the time span of a word’s use as well as the variety of registers. The use of web data to extract neologisms – instead of a balanced corpus – has brought about a new range of issues that call for new ways to address them. Section 2 reviews previous research trends related to neologism frequency from the point of view of linguistics and neologism studies. Section 3 examines and discusses issues in the detection of phrasal and semantic neologisms, and in the use of Web corpora. Section 4 suggests the use of triangulation in order to cope with such shortcomings, combining use-based methodology and used-based approach.
An Approach for Optimal Feature Selection in Machine Learning using Global Sensitivity Analysis

The classification application is an important procedure for selecting the feature.The classification is mainly based on the features extracted from the object.You can select the best feature using the following three methods: wrapper selection, filter and embedded procedure.All three practices have been implemented by single or combined two approaches.As a result, there is no important feature in the classification process.This problem is solved by the proposed integrated global analysis of sensitivity.Each feature is selected in a classification based on the sensitivity of the feature and the correlation from the target vector in this integrated sensitivity and correlation approach.Likewise, the GSA approach uses a variety of filtering techniques for ranking attributes and optimization using particle swarm technique.Then, the optimum attributes are trained and tested using the Random Forest Classifier grid search via MATLAB software.In comparison to the existing method, wrapper-based selection, the performance of our integrated model is measured using sensitivity, specificity and accuracy.The experimental results of our proposed approach outweigh the sensitivities by 93.72%, 94.74% and the accuracy of 89.921% and 90% where, wrapper selection approach as sensitivity by 89.83% and the accuracy of 93%.
Reducing Textural Bias Improves Robustness of Deep Segmentation Models

Despite advances in deep learning, robustness under domain shift remains a major bottleneck in medical imaging settings. Findings on natural images suggest that deep neural models can show a strong textural bias when carrying out image classification tasks. In this thorough empirical study, we draw inspiration from findings on natural images and investigate ways in which addressing the textural bias phenomenon could bring up the robustness of deep segmentation models when applied to three-dimensional (3D) medical data. To achieve this, publicly available MRI scans from the Developing Human Connectome Project are used to study ways in which simulating textural noise can help train robust models in a complex semantic segmentation task. We contribute an extensive empirical investigation consisting of 176 experiments and illustrate how applying specific types of simulated textural noise prior to training can lead to texture invariant models, resulting in improved robustness when segmenting scans corrupted by previously unseen noise types and levels.
Leap Motion Controller Enabled Simulations of Personal Computer Assembly Programs

The merging of both physical and virtual worlds into one seamless reality is an idea that has been tackled by many disciplines such as computer science, psychology, sociology and even literary genres like science fiction. One such amalgamation is Mixed Reality, which allows for physical and digital objects to co-exist and interact in real time, and it has been used in different areas such as entertainment to military training. This research explores the possibilities for creating collaborative mixed reality in an educational context. In this paper, we developed two (2) simulated realities. The one uses Conventional Screen typically found or see in games and where there are perspective cameras which is First Person Perspective and Third Person Perspective. While the other one is inside Mixed Reality paradigm, where reality and virtual objects co-exist, and used the Leap Motion Controller to support mid-air gestures (controls). We performed comparison between the two simulations and also measure Mixed Reality application. Also, we present how mixed reality applications can be used as an alternative method in exhibiting topics.
Some Problems in Engineering Education with Computer Science Profile During COVID-19

The COVID-19 pandemic introduced many limitations and changes in our lives and in particular in education. This article presents problems in distance learning in the field of computer networks and information security between March and June 2020 in the training of engineers with computer science profile.
Immersive Tourism - State of the Art of Immersive Tourism Realities through XR Technology.

Whitepaper about Immersive Technologies in Tourism and state of the art application cases and best practices within the field of museums, art, destination marketing, virtual events and much more.
IMPLEMENTASI KEBIJAKAN PELAYANAN KARTU TANDA PENDUDUK ELEKTRONIK (E-KTP) DI KANTOR CAMAT RABA KOTA BIMA

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; The purpose of this study was to determine the friendliness of employees in the implementation of identity card service (E-KTP) in the office of the Camat Raba Kota Bima, and the ability of employees to answer questions and information about the service of identity cards (E-KTP) in the office of the Camat Raba Kota Bima. The population of this study were all 1,100 respondents from 11 Kelurahan, Kecamatan Raba Kota Bima. Sampling using the Quota sampling technique by determining the number of respondents who were set as 100 (one hundred) people accidentally by giving a questionnaire to the respondent. With the type of quantitative research then data collection techniques are observation, questionnaires, and literature study. The collected data were then analyzed with data analysis techniques using SPSs 16.0 for windows and a Likert scale. Most of the service policies for the making of electronic identity cards (e-KTP) stated that they were “good” (70%), which stated “not good” as many (14.5%) of respondents and as many (15.5%) of respondents rated “ not good". The weakness of this dimension lies in the willingness to provide assistance to the community without being asked.&#x0D; &#x0D; &#x0D; &#x0D;
Multifactor authentication technique for a secure electronic voting system

Security of information is an important factor in an electoral process in a country. Recent pervasiveness of digitalisation in every aspect of human endeavour has evolved a drastic change in the voting process of democratic decision making. The shift from the existing conventional voting systems to electronic voting system will enable faster, convenient and more reliable means of franchising citizen's voting rights even in remote voting centres. But the problem of voters' authentication and protection from malicious attacks remains dominant. In this paper, we have used a multi-factor authentication technique using fingerprint biometrics and enhanced cryptographically secured smart card to provide a more secure e-voting system's authentication. The technique involves the combination of an enhanced Feistel block cipher and first moment feature extraction technique. The results obtained from experimental simulation shows the viability of the developed technique to avert common problems encountered in voters' authentication during the electioneering process in a digitally divided democratic environment.
Identification of Abusive Behavior Towards Religious Beliefs and Practices on Social Media Platforms

The ubiquitous use of social media has enabled many people, including religious scholars and priests, to share their religious views.Unfortunately, exploiting people's religious beliefs and practices, some extremist groups intentionally or unintentionally spread religious hatred among different communities and thus hamper social stability.This paper aims to propose an abusive behavior detection approach to identify hatred, violence, harassment, and extremist expressions against people of any religious belief on social media.For this, first religious posts from social media users' activities are captured and then the abusive behaviors are identified through a number of sequential processing steps.In the experiment, Twitter has been chosen as an example of social media for collecting dataset of six major religions in English Twittersphere.In order to show the performance of the proposed approach, five classic classifiers on n-gram TF-IDF model have been used.Besides, Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) classifiers on trained embedding and pre-trained GloVe word embedding models have been used.The experimental result showed 85% accuracy in terms of precision.However, to the best of our knowledge, this is the first work that will be able to distinguish between hateful and non-hateful contents in other application domains on social media in addition to religious context.
Impact of information and communication technologies in Higher Education Institutions in times of COVID-19 : A look from collaborative work and study modality

Higher education institutions, like other organizations worldwide, have had to adapt their educational model to the pandemic situation, as a human and at the same time forced response, to maintain their educational offer and their enrollment income. Under this context, the research objective of this article focuses in determining the perception of university students in relation to collaborative work through the use of information and communication technologies (ICT). The methodology used is descriptive with the application of statistical tools that allow, on the one hand, to determine the validity of the dimensions evaluated and, on the other, to obtain a description of the university feeling. The main findings focus on confirming the validity of the ACOES questionnaire and the relevance of its structure, as well as ratifying the role of collaborative work as an active methodology in Higher Education that invites university professors to be more flexible and innovative constantly, since regardless of the study modality, personal interaction or ICT-mediated, constitutes the cornerstone of a relevant teaching-learning process.
Financial Structure and Financial Performance of Listed Multinational Oil Companies

This study investigated the impact of financial structure on firm performance in multinational oil firms listed on the Nigerian stock exchange from 2009 to 2017. The traditional theory of capital structure known as pecking order theory was adopted in this study as the theoretical framework. Correlation and regression analysis were used to achieve the objectives of the study. Using correlational analysis, a relationship was established between financial performance (proxied by return on equity) and financial structure of the oil firms over a period of eight years. The results provide strong evidence in support of the pecking order theory, which asserts that debt is a significant determinant of firms’ performance. A significant negative relationship was established between financial structure and financial performance. From the study findings, it is clear that total debt and debt to equity ratio had strong impact on financial performance. This study is in line with previous studies reviewed which suggests that multinational oil firms listed on the Nigerian stock exchange should use more equity than debt in financing their intensive capital operations
Knowledge Sharing among Academic Staff in the Higher Education Institution

Organizational success relies heavily on knowledge sharing. Having a knowledge-sharing philosophy within an organization has a significant impact on its success because it stimulates staff's desire to address the organization's challenges and concerns. This study aims to examine the effect of attitude, Subjective norm, and perceived behavioral control on knowledge sharing among academic staff—the study conducted among academic staff at three private universities located in Erbil. The data were collected by questionnaires method, 163 valid questionnaires analyzed by Structural Equation Modelling SEM, Analysis of Moment Structure (AMOS) after ensuring validity and reliability. The results showed a positive and significant impact of attitude, Subjective norm, and perceived behavioral control on knowledge sharing among academic staff. As well, perceived behavioral control had a major impact on knowledge sharing followed by Subjective norm. The university should support academics and establish innovative climates and norms to develop positive attitudes in the organization to enable university staff to share their knowledge.
Modified Blowfish algorithm analysis using derivation cases

This study analyzed and enhanced the modified Blowfish algorithm (MBA) encryption. The modification retained the original structure, process and the use of two S-boxes in the MBA but presented two derivation processes in the f-function which was originally placed to prevent symmetry. The derivation case’s performance was analyzed using avalanche effect and time efficiency. After comparing the first and second derivation process presented in the MBA, the second derivation further improved the avalanche effect by 5.47%, thus improving security. The performance also showed that the second modification is faster by 39.48% in encryption time, and 38.34% faster in decryption time. The first derivation case in the modified Blowfish was slower in time because of the difference in the placement of the shift rotation. The key generation time was found to be independent of the input size while the encryption and decryption time was found to be directly proportional to file size. With this, the second modification is considered to be better.
Does End-to-End Trained Deep Model Always Perform Better than Non-End-to-End Counterpart?

It has been rigorously demonstrated that an end-to-end (E2E) differentiable formulation of a deep neural network can turn a complex recognition problem into a unified optimization task that can be solved by some gradient descent method. Although E2E network optimization yields a powerful fitting ability, the joint optimization of layers is known to potentially bring situations where layers co-adapt one to the other in a complex way that harms generalization ability. This work aims to numerically evaluate the generalization ability of a particular non-E2E network optimization approach known as FOCA (Feature-extractor Optimization through Classifier Anonymization) that helps to avoid complex co-adaptation, with careful hyperparameter tuning. In this report, we present intriguing empirical results where the non-E2E trained models consistently outperform the corresponding E2E trained models on three image-classification datasets. We further show that E2E network fine-tuning, applied after the feature-extractor optimization by FOCA and the following classifier optimization with the fixed feature extractor, indeed gives no improvement on the test accuracy. The source code is available at https://github.com/DensoITLab/FOCA-v1.