Reviewerly/exHarmony huggingface dataset:
Quantitative Evaluation of Navigation Controllers on Mobile Applications

Since users tap several times to reach their targets on mobile applications, the user interface design tool provides a navigation controller to help them to navigate the content on mobile applications. This paper proposed an alternative quantitative metric to evaluate the navigation controller on mobile applications. It is a tapping effort. This metric considers the number of taps and the tapping position on a mobile screen. Experimenting with the Online Lesson Mobile Application, this quantitative evaluation provides valuable information for user interface designers to create proper navigation on mobile applications.
Mobile Micro-Learning in English Vocabulary Learning

With the development of technology in recent years, teachers, in educational settings, are more accessible to utilize various digital media tools in teaching procedures than ever before. Micro-learning has been referred to enhance foreign language learning and performance in the most efficient and effective method possible through short pieces of content. However, few studies investigated the effect of micro-learning on English word learning for those who are ESL learners using a specific mobile application. Therefore, this study aims to find out what factors affect the English wording learning of College students using Superstar, which is a mobile application with micro-learning. Evaluating how micro-learning promotes the capacity, motivation, and satisfaction of English word learning than the traditional learning method. Mixed methods research was used to collect data from 84 freshman in China. The results indicate that using Superstar to learn English vocabulary help to improve the capacity and motivation of college students in English word learning. Superstar as a mobile application for English word learning might be helpful for ESL learners as well.
A Scoping Review of Empirical Studies in Gather.town

There is an increasing interest for people to meet and interact in virtual online environments, including Gather.Town, especially during the COVID-19 pandemic and in the post-pandemic world. We present a scoping review of 11 empirical studies in Gather.town—a 2D Metaverse using a game-like interface with video-chat in which people can create their own virtual spaces and act as avatars to interact with each other for various purposes. To identify knowledge gaps, we summarized the included articles in terms of their application contexts, research issues, research methods, and key findings. We found that most of them were conducted in educational settings with a focus on students’ learning experiences and perceptions. The findings of the reviewed studies generally suggested that the use of Gather.town benefited users’ engagement. However, the available evidence was mostly based on short interventions (e.g., one session) and self-reported measures (e.g., surveys and interviews). This review concluded by presenting several research gaps for future research (e.g., studies with a longer duration and using more objective measurements of learning achievement and work results).
Design and Development of "Virtual AI Teacher" System Based on NLP

As one of the main research directions of artificial intelligence (AI), natural language processing (NLP) contributes to the development of the education industry in the fields of educational application development, teaching text processing, and teaching question and answer. Based on the machine reading comprehension (MRC) technology in NLP, this research has designed and developed a knowledge question-answering system for teaching material content, and combined the "Pagebook" knowledge index function, teaching content voice interaction mode, etc., to form a "virtual AI teacher" to assist students in learning. This study mainly introduces MRC model building and training, AIQA data set building, "Pagebook" and voice interaction interface development. The main application object of this study is primary and secondary school students. It is applied to the in-class and out-of-class scenes of AI popularization education. The system scheme of "virtual AI teacher" also supports the customization and expansion of teaching in other subjects.
For-Each Operations in Collaborative Apps

Conflict-free Replicated Data Types (CRDTs) allow collaborative access to an app's data. We describe a novel CRDT operation, for-each on the list of CRDTs, and demonstrate its use in collaborative apps. Our for-each operation applies a given mutation to each element of a list, including elements inserted concurrently. This often preserves user intention in a way that would otherwise require custom CRDT algorithms. We give example applications of our for-each operation to collaborative rich-text, recipe, and slideshow editors.
Syntactic differences of the probabilistic valency of nouns in Chinese–English Code-switching

Based on a Chinese–English code-switching (CS) treebank, this study analyzes the probabilistic valency of nouns for the holistic examination of the syntactic features and functions of Chinese and English nouns in CS. It shows that: (1) Chinese or English nouns mainly head four types of monolingual or mixed syntactic relationsrespectively, among which attributives make up the biggest share. (2) Chinese or English nouns as dependents are mainly involved in six types of monolingual or mixed syntactic relationsrespectively. With Chinese noun dependents, objects and subjects are the top dependencies in monolingual and mixed dependencies, respectively. With English noun dependents, attributives and objects are the top dependencies in monolingual and mixed dependencies respectively. (3) Syntactic differences are found in dependency distance. Specifically, all mixed dependencies present longer mean dependence distances than the corresponding monolingual dependencies, except for dependencies headed by Chinese nouns. (4) The major causes for syntactic differences are syntactic transference, syntactic convergence, differences in placements of adverbials or attributives between Chinese and English, and switched chunks. These findings indicate that syntactic differences between Chinese and English nouns in CS are a consequence of language contact. However, their "specific" syntactic functions are still constrained by the grammar involved in CS.
Appling the Roulette Wheel Selection Approach to Address the Issues of Premature Convergence and Stagnation in the Discrete Differential Evolution Algorithm

The discrete differential evolution (DDE) algorithm is an evolutionary algorithm (EA) that has effectively solved challenging optimization problems. However, like many other EAs, it still faces problems such as premature convergence and stagnation during the iterative process. To address these concerns in the DDE algorithm, this work aims to achieve the following objectives: (i) investigate the causes of premature convergence and stagnation in the DDE algorithm; (ii) propose techniques to prevent premature convergence and stagnation in DDE, including a quantitative measurement of premature convergence based on the level of mismatching between the population solutions and then divide the population into individual groups based on the level of mismatching between the population solutions and the best solution; and applying the roulette wheel selection (RWS) approach to determine whether a higher degree of nonmatching is more suitable for choosing a population of separate groups to be able to produce a new solution with more options to prevent the occurrence of premature convergence; (iii) evaluate the effectiveness of the proposed techniques through employing the DDE algorithm to solve the quadratic assignment problem (QAP) as a standard to evaluate our results and their effect on avoiding premature convergence and stagnation issues, which led to the enhancement of the algorithm’s accuracy. Our comparative study based on the statistical analysis shows that the DDE algorithm that uses the proposed techniques is more efficient than the traditional DDE algorithm and the state-of-the-art methods.
Determining psycholinguistic features of deception in phishing messages

Purpose Distinguishing phishing emails from legitimate emails continues to be a difficult task for most individuals. This study aims to investigate the psycholinguistic factors associated with deception in phishing email text and their effect on end-user ability to discriminate phishing emails from legitimate emails. Design/methodology/approach Email messages and end-user decisions collected from a laboratory phishing study were validated and analyzed using natural language processing methods (Linguistic Inquiry Word Count) and penalized regression models (LASSO and Elastic Net) to determine the linguistic dimensions that attackers may use in phishing emails to deceive end-users and measure the impact of such choices on end-user susceptibility to phishing. Findings We found that most participants, who played the role of a phisher in the study, chose to deceive their end-user targets by pretending to be a familiar individual and presenting time pressure or deadlines. Results show that use of words conveying certainty (e.g. always, never) and work-related features in the phishing messages predicted higher end-user vulnerability. On the contrary, use of words that convey achievement (e.g. earn, win) or reward (cash, money) in the phishing messages predicted lower end-user vulnerability because such features are usually observed in scam-like messages. Practical implications Insights from this research show that analyzing emails for psycholinguistic features associated with computer-mediated deception could be used to fine-tune and improve spam and phishing detection technologies. This research also informs the kinds of phishing attacks that must be prioritized in antiphishing training programs. Originality/value Applying natural language processing and statistical modeling methods to analyze results from a laboratory phishing experiment to understand deception from both attacker and end-user is novel. Furthermore, results from this work advance our understanding of the linguistic factors associated with deception in phishing email text and its impact on end-user susceptibility.
Watermarking approach based on Hermite transform and a sliding window algorithm

Nowadays, the distribution of huge amounts of medical images through open networks in telemedicine applications has become increasingly faster and easier. Therefore, a number of considerations are introduced related to the risks of the illegal use of these images, as total diagnosis depends on them. Indeed, the patient's data management, storage, and transmission require a technique for boosting security, integrity and privacy measures in telehealthcare services. In fact, in our previous works, we used polynomial decompositions such as Chebychev orthogonal polynomial transform in medical image watermarking. We then customise our tools for finding the best candidate area for embedding the watermark, always seeking to provide the best solution to this issue. In this research, a variation to medical image watermarking approach based on Hermite transform (HT) is suggested for reliable management of medical data. In this approach, the HT is employed as a preprocessing step so as to extract the texture component of the host medical image. Afterward, a sliding window technique is done to select the most suitable regions for watermark embedding. Lastly, the Arnold transform is utilized for encrypting the watermark to strengthen the security of our scheme. Experiments were conducted on various modalities of medical images. Results indicate that the proposed scheme is robust when subjected to various attacks while preserving a high level of security and invisibility factors. Also, our method preserves the quality of medical images with a good embedding capacity. The obtained results support the use of Hermite Polynomials for the implementation of watermarking in the medical imaging context.
TANGENT-FILLING PLANE CURVES OVER FINITE FIELDS

Abstract We study plane curves over finite fields whose tangent lines at smooth $\mathbb {F}_q$ -points together cover all the points of $\mathbb {P}^2(\mathbb {F}_q)$ .
Differential evolution with objective and dimension knowledge utilization

Performance of differential evolution, which is one of the most competitive evolutionary algorithms, heavily depends on the utilization of feedback information. The feedback information can be from objective, solution and dimension spaces. To facilitate the better utilization of feedbacks for performance enhancements, this paper proposes an objective-dimension feedback (ODF) method with two novel mechanisms to, respectively take advantages of dimension and objective space knowledge. The first mechanism, named “Small diversity dimensions exploit, Large diversity dimensions explore” classifies dimensions into exploitation and exploration dimensions according to their diversity rankings and assigns them with collective and single dimensional learning strategies, respectively. The second mechanism, named “number of dimensions automatic configuring” automatically configures the number of dimensions performing exploitation and exploration in each solution according to its fitness ranking. Experiments on 29 benchmark functions confirm the effectiveness of ODF by performance comparisons with single utilization of objective and dimension space knowledge, single utilization of dimensional learning strategies and several objective, solution and dimension space knowledge-based methods from literatures.
DaGzang: a synthetic data generator for cross-domain recommendation services

Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations. Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge to generate recommendations in CDRS. In addition, finding these overlapping associations in the real world is generally tricky, and it makes its application to actual services hard. Considering these issues, this study aims to present a synthetic data generation platform (called DaGzang) for cross-domain recommendation systems. The DaGzang platform works according to the complete loop, and it consists of the following three steps: (i) detecting the overlap association (data distribution pattern) between the real-world datasets, (ii) generating synthetic datasets based on these overlap associations, and (iii) evaluating the quality of the generated synthetic datasets. The real-world datasets in our experiments were collected from Amazon's e-commercial website. To validate the usefulness of the synthetic datasets generated from DaGzang, we embed these datasets into our cross-domain recommender system, called DakGalBi. We then evaluate the recommendations generated from DakGalBi with collaborative filtering (CF) algorithms, user-based CF, and item-based CF. Mean absolute error (MAE) and root mean square error (RMSE) metrics are measured to evaluate the performance of collaborative filtering (CF) CDRS. In particular, the highest performance of the three recommendation methods is user-based CF when using 10 synthetic datasets generated from DaGzang (0.437 at MAE and 0.465 at RMSE).
Effects of the 4th Industrial Revolution and VR Recognition on VR Education Application

This research empirically investigates the recognition of beauty major learners' perception of VR and their expectations for VR application education, because it is an efficient educational process that utilizes quaternary industry technology and VR in beauty education. A total of 61 learners from skin beauty, makeup &amp; nail, cosmetics, and hair beauty related subjects participated in this research. Analysis results based on general characteristics It was found that the greater the learning experience with VR, the higher the overall VR awareness, the degree of VR awareness by sub-factor, and the degree of utilization of VR (p&lt;.05). The higher the degree of interest in the latest technology of the 4th Industrial Revolution, the higher the degree of VR awareness (VR awareness, degree of utilization of VR), and the higher the degree of VR awareness, the higher the expectation of VR education application (p&lt;.05). It was confirmed that beauty major learners expect visually sensitive reactions from major classes, and that the use of VR has a positive effect on education. In addition, the expansion of beauty major classes using VR is expected to be effective in education in a wide range of skin beauty, make-up, nail beauty, cosmetics, and hair beauty.
Cryptocurrency estate planning: the challenges, suggested solutions and Malaysia’s future directions

Purpose This paper aims to collect evidence on the issues and challenges in cryptocurrency estate planning and the possible approaches or methods to address them. Then, this paper also aims to understand Malaysia’s situation compared to global situations and determine how the country could address those challenges. Design/methodology/approach This study adapted a standard systematic review protocol named preferred reporting items for systematic reviews and meta-analyses, guided by three research questions: what are the challenges of cryptocurrency estate planning, how do researchers and practitioners address the issues or challenges and how could Malaysians move towards sustainable cryptocurrency estate planning? Relevant documents reported on cryptocurrency estate planning were analysed using thematic analysis. Findings The results of this review suggested that the issues and challenges in cryptocurrency estate planning can be described from six aspects: awareness, financial aspects, law, process, security and taxation. On the other hand, the approaches or methods to address these challenges may be categorised into laws, processes and technology. Further, a framework for cryptocurrency estate planning in Malaysia is proposed as an initial step towards a sustainable economy and society in Malaysia. Research limitations/implications The state and the use of cryptocurrency are still considered new in most countries globally. Hence, people are yet to be aware of the issues that may arise from it. Nevertheless, cryptocurrency estate planning will soon become an area of interest in which this study could enhance the literature and contribute knowledge to understanding the situation. Originality/value This study collected evidence on the issues and the approaches to address cryptocurrency estate planning. This study then focused on the Malaysian perspective by proposing a framework that was not covered by existing studies.
Review on data replication strategies in single vs. interconnected cloud systems: Focus on data correlation‐aware strategies

Summary Data replication is a well‐known technique in cloud systems for enhancing availability and performance. Various strategies and surveys have been proposed in this respect. These surveys include comprehensive analysis and classifications. However, to the best of the authors' knowledge, there is no survey concentrating on strategies designed for interconnected cloud systems. In this article, we provide an in‐depth analysis of existing data replication strategies in cloud systems, covering single and interconnected clouds. We also highlight data correlation‐aware strategies as well as their key steps. Furthermore, we examine the major strategies' features such as: () addressed replication issues, () orientation towards the provider and the consumer, () consideration of the service level agreement, () consideration of cost and economic aspects, and () evaluation tools. Finally, we provide a performance analysis through extensive simulations of several replication strategies dedicated for single and interconnected clouds.
It Takes Two: Using Co-creation to Facilitate Child-Robot Co-regulation

While interacting with a social robot, children have a need to express themselves and have their expressions acknowledged by the robot—a need that is often unaddressed by the robot, due to its limitations in understanding the expressions of children. To keep the child-robot interaction manageable, the robot takes control, undermining children’s ability to co-regulate the interaction. Co-regulation is important for having a fulfilling social interaction. We developed a co-creation activity that aims to facilitate more co-regulation. Children are enabled to create sound effects, gestures, and light animations for the robot to use during their conversation. A crucial additional feature is that children are able to coordinate their involvement of the co-creation process. Results from a user study ( n = 59 school children, 7–11 years old) showed that the co-creation activity successfully facilitated co-regulation by improving children’s agency. It also positively affected the acceptance of the robot. We furthermore identified five distinct profiles detailing the different needs and motivations children have for the level of involvement they chose during the co-creation process.
Information System Web Design For Class English Proficiency Test Using Fisher Yates Shuffle Method

Information technology has been rapidly developing over the past few decades, and the COVID-19 pandemic has further accelerated the adoption and implementation of technology in various aspects of our lives, particularly in the business sector and education., where improvements in the learning system that were originally offline become online, practical, and easy. Currently, information regarding the English Test at Faculty of Social Sains and Humanities Buddhi Dharma University (UBD) is still running manually starting from registration, scheduling, exam, and announcement of English test results which of course requires more time for both participants and faculty staff. In order to boost students English knowledge, an English comprehension exam test is needed as organized by the faculty, that is English Proficiency Test (EPT). The design of this EPT information system, a part of being able to change the manual method, and also to simplified the EPT procedure. The new system is using website which combined with Fisher-Yates Shuffle method for randomization the exam test to reduce fraudulent actions in the process of conducting English training tests, being followed by 94 respondents as English training participants, system tested using the User Acceptance Test (UAT) method with five aspects of quality components. The results obtained that the system built had a good criteria with a value of 75.93%.
The meta attack language - a formal description

Nowadays, IT infrastructures are involved in making innumerable aspects of our lives convenient, starting with water or energy distribution systems, and ending with e-commerce solutions and online banking services. In the worst case, cyberattacks on such infrastructures can paralyze whole states and lead to losses in terms of both human lives and money. One of the approaches to increase security of IT infrastructures relies on modeling possible ways of compromising them by potential attackers. To facilitate creation and reusability of such models, domain specific languages (DSLs) can be created. Ideally, a user will employ a DSL for modeling their infrastructure of interest, with the domain-specific threats and attack logic being already encoded in the DSL by the domain experts. The Meta Attack Language (MAL) has been introduced previously as a meta-DSL for development of security-oriented DSLs. In this work, we define formally the syntax and a semantics of MAL to ease a common understanding of MAL’s functionalities and enable reference implementations on different technical platforms. It’s applicability for modeling and analysis of security of IT infrastructures is illustrated with an example.