Reviewerly/exHarmony huggingface dataset:
A Comparison of SONA and MTurk for Cybersecurity Surveys

For almost every online account, people are required to create a password to protect their information online. Since many people have many accounts, they tend to create insecure passwords and re-use passwords. These insecure passwords are often easy to guess, which can lead to compromised data. It is well-known that every person has a different personality type, which can be determined using personality models such as Big Five and True Colors. This research examines if there is a link between personality type and password security among a variety of participants in two groups of participants: SONA and MTurk. Each participant in both surveys answered questions based on password security and their personality type. Our results show that participants in the MTurk survey were more likely to choose a strong password and to exhibit better security behaviors and knowledge than participants in the SONA survey. This is mostly attributed to the age difference. However, the distribution of the results was similar for both MTurk and SONA. Future surveys on cybersecurity should include both types of demographics for a more generalizable result.
Energy and Runtime Performance Optimization of Node.js Web Requests

The Node.js framework uses an event-driven model with a single-threaded event loop and provides asynchronous and non-blocking I/O operations. As with other programs, Node.js web applications take advantage of underlying resources, including CPUs, which can incorporate the dynamic voltage and frequency scaling (DVFS) technique. Using CPU DVFS, the applications can increase their runtime performance, at the expense of the system's energy consumption. Thus, software code that utilizes the CPU DVFS technique efficiently should lead to “green” and high-performing applications with respect to the business logic. To this end, we build a CPU frequency scaling/energy aware system to enable CPU frequency control within Node.js applications and measure the energy consumption of specific tasks. We also build a benchmark suite to analyze the energy consumption and runtime performance of different requests based on the CPU frequency impact and collect information and patterns, as we scale the CPU frequency. The analysis aims to provide data and knowledge on the CPU frequency “suitability” and impact in order to create a model for CPU frequency scaling on Node.js web applications and achieve an efficient and sustainable runtime performance.
Near-Zero-Shot Suggestion Mining with a Little Help from WordNet

In this work we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues and other items of interest. To reduce training costs and annotation efforts needed to build a classifier for a specific label set, we present and evaluate several entailment-based zero-shot approaches to suggestion classification in a label-fully-unseen fashion. In particular, we introduce the strategy of assigning target class labels to sentences with user intentions, which significantly improves prediction quality. The proposed strategies are evaluated with a comprehensive experimental study that validated our results both quantitatively and qualitatively.
Speaker Identification of the Electronically Disguised Voices

Voice is said to be unique for every individual and can be used for the identification. Significant intravariation in Speech samples can be seen with the changes in the physical and mental conditions. Analysisof disguised voice samples is challenging to the Forensic Speaker Identification Expert. Changes inthe voice samples can be done by physical constriction and also with the technologicalinterventions.Speech processing softwares which are freely available and can be used to change voice samples.These softwares add some background variations along with the alterin the frequency distribution inthe voice. This paper focuses on the examination of the voice samples which are processed by a freelyavailable android application which changes the female voice samples to that of male. The examinationwas done on the OT- Expert 6.0. The Examination of the voices was done by the Aural-Acousticsmethod. For the examination of the voice samples on acoustics parameters such as fundamentalfrequency(F0) and Formant Frequencies (F1, F2, F3, F4) for 5 vowels compared with their controlrecordings showed prominent and noteworthy differences. Aural examination parameters such asrelative pauses, background variations, and linguistic features such as delivery of speech, specificpronunciations for words remained similar.Some specific formant frequencies for 5 vowels combinedwith auditory parameters especially the degradation of background of the audio for suspecting a caseof electronically disguised voice and then compared it with a possible list of suspects for speakeridentification. Therefore, based on some specific aural-acoustics features electronically disguisedvoices can be identified amongst the suspected voice samples.
A Lightweight Multi-factor Authentication Scheme based on Digital Watermarking Technique

Security in today&#x2019;s IoT applications becomes extremely important because it is strongly related to social and human life. User authentication is the most important problem and the first step in establishing reliable communication sessions. Biometric authentication solutions including network face recognition have many advantages such as being fast, accurate, and user-friendly. Traditional authentication solutions using only device identification have always to deal with increasingly sophisticated forms of spoofing or sabotage attacks, especially for environments with low-complexity hardware devices. Thus, this paper proposes a novel multi-factor authentication solution exploiting face recognition and user&#x2019;s device characteristics to increase the trustworthiness of the authentication process. Moreover, combined with the watermarking technique, the proposed scheme is lightweight and suitable for resource-constrained computing devices. By using BAN logic, the security of the proposed scheme has been analyzed to prove the reliability and security of the algorithms.
Learning Media for Writing Fantasy Story Text Based on Scientific Plus Using Adobe Flash

The lack of learning media that facilitates students causes students to experience difficulties in learning. In addition, teachers still have difficulty presenting material for writing fantasy story texts and teachers use learning only to provide material, not at the evaluation stage. This has an impact on students' lack of understanding. This study analyses the validity of the learning media based on the alpha test, the practicality of the learning media and the effectiveness of the learning media. This type of research is development research. The model used to develop the media is Alessi and Trolip (2001), consisting of planning, design and development stages. Data was collected through expert review, interviews, and tests. The research subjects were nine experts, which included media experts, material experts, and linguists. The product trial subjects were 32 students. The technique used to analyze the data is descriptive qualitative and quantitative analysis. The alpha test results showed the validity of the learning media of 92.50% with a very appropriate category. Beta test results show the practicality of the product is 85.49%, with an excellent category. It can be concluded that scientific-based learning media plus using adobe flash can be applied in learning to write fantasy story texts. This research implies that teachers in learning can use the developed media.
Developing a Cross-lingual Semantic Word Similarity Corpus for English–Urdu Language Pair

Semantic word similarity is a quantitative measure of how much two words are contextually similar. Evaluation of semantic word similarity models requires a benchmark corpus. However, despite the millions of speakers and the large digital text of the Urdu language on the Internet, there is a lack of benchmark corpus for the Cross-lingual Semantic Word Similarity task for the Urdu language. This article reports our efforts in developing such a corpus. The newly developed corpus is based on the SemEval-2017 task 2 English dataset, and it contains 1,945 cross-lingual English–Urdu word pairs. For each of these pairs of words, semantic similarity scores were assigned by 11 native Urdu speakers. In addition to corpus generation, this article also reports the evaluation results of a baseline approach, namely “Translation Plus Monolingual Analysis” for automated identification of semantic similarity between English–Urdu word pairs. The results showed that the path length similarity measure performs better for the Google and Bing translated words. The newly created corpus and evaluation results are freely available online for further research and development.
Feeling Present in Virtual Environments

Abstract We compare and contrast different current theoretical accounts of telepresence, including presence as a pretence (a simulation of reality), as pretending (making believe the virtual world is real), as a perceptual illusion (“the illusion of non-mediation”), and as embodied attention to the surrounding (or apparently surrounding) environment. These views are well-accepted in the field, and can be seen as contributing to a virtual travel experience, which is a kind of illusion. When we feel highly present, we believe in the perceived world in which we experience ourselves to be—it is in that moment real to us. Creating that effect is a key part of a convincing digital travel experience. To have that experience, we must be attending to the digital world, feeling as if we are physically surrounded by it. Our imaginations are involved in how we perceive our surroundings, and in how we conceptualise being there.
Digital Education Industry and Academic Perception to Improve Business Intelligence

Confronting the rapid transformation due to the spread of COVID-19, higher education institutions have been forced to shift to using digital platforms, e-learning to pursue the education plan. The main question of the current study is, can the digital learning system be applied in higher education. Then, what are the perceptions of academics regarding digital learning? In addition, do the faculties have self-efficacy? This study aims to evaluate the new digital learning experience among academics in Bahrain during the COVID-19 pandemic. We have assessed the perception about the effectiveness of digital learning and their perception about the possibility of integrating digital learning. The descriptive-analytical approach was applied, and statistical methods analyzed the results. Qualitative and quantitative methodology was conducted to answer the study questions. Questionnaires were designed and distributed. Four dimensions have been highlighted to reach the expected results, i.e., the academic perception about using e-learning during the COVID-19 pandemic, self-efficacy, usefulness and challenges and benefits of implementing E-learning in higher education. This study utilizes the model developed by Liaw (2008) to investigate academic self-efficacy and their perception regarding usefulness. This study counts 210 participants from two universities in Bahrain. The findings of this study revealed a high perception of self-efficacy and usefulness; the academics believe in the importance of digital learning. Tremendous feedback revealed from academics regarding the digital learning benefits and challenges are discovered and discussed. Practical recommendations have been summarized in conclusion.
The Two Faces of Facial Recognition Technology

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The face is</b> the most public part of human bodies. We usually recognize or identify someone by their face. Our brains are wired to quickly spot faces—even in arrangements of inanimate objects—and to instantly identify the emotions they convey.
The Fourth Industrial Revolution: Technology and Education

The convergence of data, computation, and globalization in education has far-reaching consequences for educational stakeholders, institutions, and learners. Terms such as the Fourth Industrial Revolution, artificial intelligence (AI), exponential technology, deep learning, personalized competency-based learning, and distributed ledger technology networks (DLTNs) are indicators of the changing dialogue between education stakeholders, businesses, and government aspirations on a global scale. Location-independent virtual environments promise an exponential expansion that goes beyond brick-and-mortar schools, colleges, and universities. AI and intelligent systems are poised to become global change agents in education, ushering in profound changes in administrative functions, strategic planning, data aggregation, student acquisition and retention, and alternative currencies, as well as curriculum design, assessment, personal learning networks, and global competitiveness generally of both institutions and their graduates. The quality of the education a nation's schools, educational institutions, and teachers provide, along with investments in science, technology engineering, and mathematics education, directly impact economic prosperity and global competitiveness. This paper explores some of the interdependencies that arise from supercharged technological advances such as AI augmented reality digital technologies (ARDTs) and DLTNs and their possible impact on education, educators, learners, and society. In addition, it unbundles the meaning and use cases of AI, ARDTs, and DLTNs in education.
A Generic Fuzzy-Based Recommendation Approach (GFBRA)

Recommender Systems aim to automatically provide users with personalized information in an overloaded search space. To dual with vagueness and imprecision problems in RS, several researches have been proposed fuzzy based approaches. Even though, these works have incorporated experimental evaluation; they were used in different recommendation scenarios which makes it difficult to have a fair comparison between them. Also, some of them performed an items and/or users clustering before generating recommendations. For this reason they need additional information such as item attributes or trust between users which are not always available. In this paper, we propose to use fuzzy set techniques to predict the rating of a target user for each unrated item. It uses the target user's history in addition with rating of similar users which allows to the target user to contribute in the recommendation process. Experimental results on several datasets seem to be promising in term of MAE (Mean Average Error), RMSE (Root Mean Square Error), accuracy, precision, recall and F-measure.
Towards expert-guided elucidation of cyber attacks through interactive inductive logic programming

This paper proposes a logic-based machine learning approach called Acuity which is designed to facilitate user-guided elucidation of novel phenomena from evidence sparsely distributed across large volumes of linked relational data. The work builds on systems from the field of Inductive Logic Programming (ILP) by introducing a suite of new techniques for interacting with domain experts and data sources in a way that allows complex logical reasoning to be strategically exploited on large real-world databases through intuitive hypothesis-shaping and data-caching functionality. We propose two methods for rebutting or shaping candidate hypotheses and two methods for querying or importing relevant data from multiple sources. The benefits of Acuity are illustrated in a proof-of-principle case study involving a retrospective analysis of the CryptoWall ransomware attack using data from a cyber security testbed comprising a small business network and an infected laptop.
INTEGRATION OF ICT IN TEACHER EDUCATION: PROBLEMS AND SUGGESTIONS

In today’s techno-intensive world, technology is an integral part of education. Over the last few decades, technology has been playing an increasingly vital role in the field of education, which has led to a profound improvement in the teaching and learning processes. The present learning environment demands that the teacher be skilled in use of technology to educate his pupils in an effective and efficient manner. Additionally, the school curriculum has to be in sync with the modern technology. In others words the curriculum has to so designed that it is easy to adapt and integrate it with the modern technologies of teaching and learning processes. This scenario requires that the teachers are trained and adept at handling modern techno-intensive teaching aids so that both teaching and learning become a joy for the teacher as well as the pupils.
Towards Specific of Definition for Concept of a “Digital Competencies”

The article describes the specifics of the concept of digital competencies in the context of the development of an innovative economy. A brief comparison of the concepts of digital literacy, media literacy and digital competencies is carried out, their general and specific features are indicated. The main applications of digital competencies, both economic and socio-cultural, are indicated. An example of a comprehensive program for the development of digital competencies within the European Union is presented, as well as the place androle of the concept of “digital competencies” in programs for long-term socio-economic and educational development of Russia.
Logical Intelligent Detection Algorithm of Chinese Language Articles Based on Text Mining

With the advent of the big data era and the rapid development of the Internet industry, the information processing technology of text mining has become an indispensable role in natural language processing. In our daily life, many things cannot be separated from natural language processing technology, such as machine translation, intelligent response, and semantic search. At the same time, with the development of artificial intelligence, text mining technology has gradually developed into a research hotspot. There are many ways to realize text mining. This paper mainly describes the realization of web text mining and the realization of text structure algorithm based on HTML through a variety of methods to compare the specific clustering time of web text mining. Through this comparison, we can also get which web mining is the most efficient. The use of WebKB datasets for many times in experimental comparison also reflects that Web text mining for the Chinese language logic intelligent detection algorithm provides a basis.
Rapid Segmentation of Thoracic Organs using U-net Architecture

Medical imaging provides a non-invasive method to diagnose, monitor, and plan the treatment of disease inside the human body. The increasing prevalence of radiological scanners and prescription of their use has presented a significant challenge for radiologists in accurately diagnosing disease whilst dealing with a growing number of scans to review. Recent advances in Artificial Intelligence (AI), especially in machine learning, are enabling researchers to improve the patient experience, enhance the planning of medical treatments and increase the rate of examination of scans. In this study,a 2-dimensional (2D) U-net based deep learning model was used to automatically segment five organs of interest from Computed Tomography (CT) scans of the thoracic region. Comparable results were achieved in comparison to the top seven models from a prior thoracic organ segmentation challenge. The framework can perform the segmentation tasks within 20 seconds, reducing workload for radiologists and increasing throughput. This study shows that a simple U-net based framework can be sufficient for the task at hand rather than pursuing much more complicated architectures, depending upon the complexity of the problem. Furthermore, we investigated the effect of 3D interpolation on dice scores in anticipation of further research applications in mapping segments to a 3D volume render. We find performance degradation with respect to the dice score after mapping the masks to original dimensions.
Data Association with Amplitude Information Based on the Copula Theory

In radar data association, amplitudes of radar target returns are widely used in tracking algorithms, but the correlation between signal amplitudes, depending on the correlation of returns from the same target, is not well addressed, especially when jamming signals with higher powers are present. After the target detection operation, the amplitudes are typically positive real numbers, which makes the correlation even more complicated. In this paper, we analyze the correlation of squared amplitudes of signals based on a statistical correlation model of target returns. The Copula theory is used to approximate the joint distribution of squared amplitudes and the probability density function (PDF) of the signal amplitude conditioned on former amplitudes are formulated in theory and through numerical results. This method can be used for data association in the target-detection-during-tracking problem with intensive jamming or clutter returns.
Nonparametric Functional Graphical Modeling Through Functional Additive Regression Operator

In this article, we develop a nonparametric graphical model for multivariate random functions. Most existing graphical models are restricted by the assumptions of multivariate Gaussian or copula Gaussian distributions, which also imply linear relations among the random variables or functions on different nodes. We relax those assumptions by building our graphical model based on a new statistical object—the functional additive regression operator. By carrying out regression and neighborhood selection at the operator level, our method can capture nonlinear relations without requiring any distributional assumptions. Moreover, the method is built up using only one-dimensional kernel, thus, avoids the curse of dimensionality from which a fully nonparametric approach often suffers, and enables us to work with large-scale networks. We derive error bounds for the estimated regression operator and establish graph estimation consistency, while allowing the number of functions to diverge at the exponential rate of the sample size. We demonstrate the efficacy of our method by both simulations and analysis of an electroencephalography dataset. Supplementary materials for this article are available online.