Reviewerly/exHarmony huggingface dataset:
Bibliometric Mapping of Research on User Training for Secure Use of Information Systems

Information systems are pervasive in organizations of all sizes. To use them securely, users must be properly trained. Because of the pervasiveness of information systems the number of scientific publications reporting on user training for secure use of information systems is increasing year by year. To overcome the issue of manually surveying such a vast body of knowledge and to keep up with research trends, we conducted bibliometric mapping of research on user training for secure use of information systems. A total of N = 1955 records published between 1991 and 2019 were retrieved from the Web of Science bibliographic database on 21 November 2019. Top contributing authors, organizations, countries and research field were identified with the Web of Science built-in results analysis tool. Additionally, keyword mapping was performed with VOSviewer software. The analysis of the network and overlay keyword maps revealed six clusters: healthcare, technology adoption, management, information security, technical solutions and physical security. The results of this study suggest attractive research directions to be pursued in the future, such as information security training in healthcare and individualized user training alternatives to one-size-fits-all user training approach.
On the Handwriting Tasks’ Analysis to Detect Fatigue

Practical determination of physical recovery after intense exercise is a challenging topic that must include mechanical aspects as well as cognitive ones because most of physical sport activities, as well as professional activities (including brain–computer interface-operated systems), require good shape in both of them. This paper presents a new online handwritten database of 20 healthy subjects. The main goal was to study the influence of several physical exercise stimuli in different handwritten tasks and to evaluate the recovery after strenuous exercise. To this aim, they performed different handwritten tasks before and after physical exercise as well as other measurements such as metabolic and mechanical fatigue assessment. Experimental results showed that although a fast mechanical recovery happens and can be measured by lactate concentrations and mechanical fatigue, this is not the case when cognitive effort is required. Handwriting analysis revealed that statistical differences exist on handwriting performance even after lactate concentration and mechanical assessment recovery. This points out a necessity of more recovering time in sport and professional activities than those measured in classic ways.
Enhancing business entrepreneurship through open government data

Small and medium enterprises play a very important role in today’s economy and contribute heavily on a national economy specially in developing countries like Jordan, but they are still facing many challenges, and need support from government specially in data availability. This study aimed to know the impact of open government data (OGD) on business entrepreneur-ship from Jordanian Irbid State entrepreneurs’ point view. To achieve this, (600) questionnaires were distributed to the sample of Jordanian entrepreneurs in Irbid state and (536) valid questionnaires were recovered. The study indicates that OGD and entrepreneurship had a moderate level. There is a significant statistical effect of OGD on business entrepreneurship (α ≤ 0.05) in Jordanian SMEs Irbid state. At the end researchers recommend government and entrepreneurs to adopt OGD as a strategy to maximize benefits gained from open government data strategy, improve the quality of published data, and recommended entrepreneurs to increase the level of relying on OGD to get information about materials, markets, competitors, legislations and so on to get benefits for their companies.
Hardware Implementation of Residue Multipliers based Signed RNS Processor for Cryptosystems

The Residue Number System (RNS) characterize large integer numbers into smaller residues using moduli sets to enhance the performance of digital cryptosystems. A parallel Signed Residue Multiplication (SRM) algorithm, VLSI hierarchical array architecture for balanced (2 n -1, 2 n , 2 n +1) and unbalanced (2 k -1, 2 k , 2 k +1) word-length moduli are proposed which is capable of handling signed input numbers. Balanced 2 n -1 SRM is used as a reference to design an unbalanced 2 k -1 and 2 k +1. The synthesized results show that the proposed 2 n -1 SRM architecture achieves 17% of the area, 26% of speed and 24% of Power Delay Product (PDP) improvement compared to the Modified Booth Encoded (MBE) architectures discussed in the literature. The proposed 2 n +1 SRM architecture achieves 23% of the area, 20% of speed and 22% of PDP improvement compared to recent counterparts. There is a significant improvement in the results due to the fully parallel hierarchical approach adopted for the design which is hardly attempted for signed numbers using array architectures. Finally, the proposed SRM modules are used to design {2 n -1, 2 n , 2 n +1} special moduli set based RNS processor and the real-time verification is performed on Zynq (XC7Z020CLG484-1) Field Programmable Gate Array (FPGA).
Automatic SARIMA Order Identification Convolutional Neural Network

Deep learning concept is popularly used to solve a classification problem including the model identification in time series analysis.For the time series models such as the autoregressive integrated moving average (ARIMA) model and the seasonal autoregressive integrated moving average (SARIMA) model, statisticians mostly identify the ARIMA/SARIMA orders before building the forecasting model.To identify them, they investigate sample ACF and PACF to extract p, d, q while most researchers automate this step using the likelihood based-method via AIC ignoring the residual diagnostic.This paper uses ACF, PACF and differencing time series images as inputs to the convolutional neural network architecture that automatically identifies the ARIMA/SARIMA orders, called the automatic ARIMA order identification convolutional neural network (AOC).The performance of AOC outperforms the likelihood based-method in terms of identifying ARIMA order via precision, recall and f1-score.Moreover, AOC is extended to identify the SARIMA order, called the automatic SARIMA order identification convolutional neural network (ASOC).The performance of the ASOC model provides better performance than the likelihood method via precision, recall and f1-score.
MAVSCOT: A fuzzy logic-based HIV diagnostic system with indigenous multi-lingual interfaces for rural Africa

HIV still constitutes a major public health problem in Africa, where the highest incidence and prevalence of the disease can be found in many rural areas, with multiple indigenous languages being used for communication by locals. In many rural areas of the KwaZulu-Natal (KZN) in South Africa, for instance, the most widely used languages include Zulu and Xhosa, with only limited comprehension in English and Afrikaans. Health care practitioners for HIV diagnosis and treatment, often, cannot communicate efficiently with their indigenous ethnic patients. An informatics tool is urgently needed to facilitate these health care professionals for better communication with their patients during HIV diagnosis. Here, we apply fuzzy logic and speech technology and develop a fuzzy logic HIV diagnostic system with indigenous multi-lingual interfaces, named Multi-linguAl HIV indigenouS fuzzy logiC - based diagnOstic sysTem (MAVSCOT). This HIV multilingual informatics software can facilitate the diagnosis in underprivileged rural African communities. We provide examples on how MAVSCOT can be applied towards HIV diagnosis by using existing data from the literature. Compared to other similar tools, MAVSCOT can perform better due to its implementation of the fuzzy logic. We hope MAVSCOT would help health care practitioners working in indigenous communities of many African countries, to efficiently diagnose HIV and ultimately control its transmission.
Scrutinizing EFL learners' online reading strategy use across proficiency levels

This study aimed at investigating the difference in online reading strategy use between EFL students with high and low English proficiency level and knowing the relationship between online strategy use and reading comprehension. Ninety-six university students were involved in this research, and they were distributed into two groups; high and low proficiency levels based on English proficiency test scores. SORS was administered to collect the data after completing three reading sessions. Analyses of the data, using the Chi-square test to see the differences and the Spearman test, to check the relationship between strategy use and reading achievement. The results showed that the reading strategy that was most frequently used by the EFL learners was a problem-solving strategy, and the least frequently used one was a socio-affective strategy. Additionally, there was no significant difference in the online reading strategy use between students in high and low proficiency levels. Therefore, there was a negative relationship between strategy use and reading achievement. The more strategies they used, the lower score they got.
Leveraging Social Relationship-Based Graph Attention Model for Group Event Recommendation

Recently, event-based social networks(EBSN) such as Meetup, Plancast, and Douban have become popular. As users in the networks usually take groups as an unit to participate in events, it is necessary and meaningful to study effective strategies for recommending events to groups. Existing research on group event recommendation either has the problems of data sparse and cold start due to without considering of social relationships in the networks or makes the assumption that the influence weights between any pair of nodes in the user social graph are equal. In this paper, inspired by the graph neural network and attention mechanism, we propose a novel recommendation model named leveraging social relationship-based graph attention model (SRGAM) for group event recommendation. Specifically, we not only construct a user-event interaction graph and an event-user interaction graph, but also build a user-user social graph and an event-event social graph, to alleviate the problems of data sparse and cold start. In addition, by using a graph attention neural network to learn graph data, we can calculate the influence weight of each node in the graph, thereby generating more reasonable user latent vectors and event latent vectors. Furthermore, we use an attention mechanism to fuse multiple user vectors in a group, so as to generate a high-level group latent vector for rating prediction. Extensive experiments on real-world Meetup datasets demonstrate the effectiveness of the proposed model.
A customer feedback sentiment dictionary: Towards automatic assessment of online reviews

This paper aims at creating a tool to automatically extract online customer reviews of hospitality businesses and to assign a reliable score to them, based on a specifically created sentiment dictionary for this purpose by means of a statistical learning method. The effect of the amount of available training data and their resulting dictionaries is investigated. As such, a practical approach for applying LASSO regression in the context of online hospitality reviews is presented resulting in a sentiment dictionary of 778 terms with their associated weights trained on 20 000 reviews. It is shown that the created dictionary is able to accurately predict online review scores set by consumers, therefore highlighting the practical relevance of the proposed approach.
Sum-Product Networks for Robust Automatic Speaker Identification

We introduce sum-product networks (SPNs) for robust speech processing through a simple robust automatic speaker identification (ASI) task *. SPNs are deep probabilistic graphical models capable of answering multiple probabilistic queries.We show that SPNs are able to remain robust by using the marginal probability density function (PDF) of the spectral features that reliably represent speech.Though current SPN toolkits and learning algorithms are in their infancy, we aim to show that SPNs have the potential to become a useful tool for robust speech processing in the future.SPN speaker models are evaluated here on real-world non-stationary and coloured noise sources at multiple signal-to-noise ratio (SNR) levels.In terms of ASI accuracy, we find that SPN speaker models are more robust than two recent convolutional neural network (CNN)-based ASI systems.Additionally, SPN speaker models consist of significantly fewer parameters than their CNN-based counterparts.The results indicate that SPN speaker models could be a robust, parameterefficient alternative for ASI.Additionally, this work demonstrates that SPNs have potential in related tasks, such as robust automatic speech recognition (ASR) and automatic speaker verification (ASV).
Penerapan Firebase Realtime Database pada Aplikasi E-Tilang Smartphone berbasis Mobile Android

Traffic violations are familiar things on the highway. For road users who commit traffic violations will be given sanctions such as reprimands, warnings or given a ticket. The speeding ticket is a medium for the police to write fines and violations against road users who commit traffic violations. In this modern era ideas and ideas have emerged to develop a system or application that can facilitate the performance, needs and human activities through an intermediary smartphone. E-ticketing is a digitization of the manual ticketing process that fills offenders data on the ticketing sheet. The e-ticket helps the police in the ticketing process by entering data in the application, utilizing the Firebase Realtime Database technology through smartphone intermediaries. The purpose of this study is to create a traffic violator or E-ticketing system using the Firebase Realtime Database technology. Then provide attachments to offenders in the form of notifications through the E-ticket application, and assist the police in an effort to deal with traffic offenders. The results of this study showed 80.5% of respondents rated the Application of Firebase Realtime Database on the Android-based Smartphone Mobile E-Ticket Application in accordance with the needs of the police in carrying out the ticketing process.
‘MAPAGI’ video game upgraded breakfast attitude among urban elementary school children in West Jakarta, Indonesia

Introduction: Skipping breakfast may contribute to poor academic and nutritional status among school children. The current study aimed to determine the effect of the ‘MAPAGI’ (Makan Pagi Bergizi) interactive video game on elementary student’s breakfast knowledge and attitude. Methods: This was a quasi-experimental equivalent group study with a pre-post test control group design. In total, 228 students aged 9-11 years were drawn by proportional stratified random sampling from two urban schools and were distributed equally into intervention (IG) and control groups (CG). Both groups were administered with similar printed media for 10-15 minutes. Video game was delivered only to IG for two consecutive days, 30 minutes each day. Pre-test (PT0) was performed a day before leaflet administration, while post-test was performed twice – after leaflet administration (post-test 1/ PT1) and a week after the last video game (post-test 2/PT2). Paired, independent t-test and analysis of covariance (ANCOVA) were employed to answer the research questions. Results: The mean changes (PT1-PT0 and PT2-PT0) in knowledge score for IG (4.82±2.53 and 3.25±2.47) significantly differed compared to CG (1.75±2.66 and 1.25±2.69). The mean changes in attitude score for IG (7.16±7.17 and 7.34±7.11) also significantly differed compared to CG (2.83±6.58 and 2.64±6.90). After adjustment for potential confounding factors, there was still a significantly greater score in children’s knowledge and attitude after a week’s administration of MAPAGI video game in IG. Conclusion: ‘MAPAGI’ improved school children’s knowledge and attitude, which may lead to the good behaviour of having breakfast.
Needs and Intentionality

A thorough understanding of what needs are is fundamental for designing well-behaved information systems for many social applications and in particular for public services. Talking about needs pervades indeed the jargon of Public Administrations when motivating their service offering. In this paper, we propose an ontological analysis of needs, aiming at a principled disentangling of the different uses of the term. We leverage philosophical tradition on intentionality, for its rich understanding of mental entities, we compare it with the well-established BDI (Belief-Desire-Intention) tradition in knowledge representation, and we propose a formalisation of needs within the foundational ontology DOLCE. Throughout the paper, we motivate our analysis focusing on needs in public services.
Treebanking user-generated content: a UD based overview of guidelines, corpora and unified recommendations

Abstract This article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this article is twofold: (1) to provide a condensed, though comprehensive, overview of such treebanks—based on available literature—along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The overarching goal of this article is to provide a common framework for researchers interested in developing similar resources in UD, thus promoting cross-linguistic consistency, which is a principle that has always been central to the spirit of UD.
Co-Creation of Understanding. On a Literature Course in the Developmental Education System

The article describes the theoretical grounds and structure of a literature course as a part of aesthetic education at school, based on the concept of developmental learning by D.B. Elkonin and V.V Davydov. This course became a first attempt to create a program from the first to the final year at school, which required finding a solution to a complicated problem of transferring the practice of developmental learning from primary to secondary school. The conception was innovative in that it considered literature as a part of Arts rather than Philology. The purpose of the course is to educate a cultured reader who would be able to engage in a text-mediated dialogue with an author. The course is based on the ‘author – literary text – reader’ relation (M.Bakhtin). It is shown that a child’s experience of creative work in literature (as the author) is essential for fostering the cultured reader. Learning happens through acquiring and interchanging the positions of the author, reader, critic and theorist, and furthermore, on different educational stages these positions play different roles in literature development of schoolchildren. The main educational paths are: “Readers’ practice and creative work of children” and “History of the world literature”.
Implementasi Haversine Formula untuk Pembuatan SIG Jarak Terdekat ke RS Rujukan COVID-19

Haversine formula-based GIS has been created to find closest location to referral hospital handling COVID-19 in Semarang City. The objectives of this study were (1) to determine closest distance and compare the results with the calculation of Find Nearest Tool and Google Maps and (2) to design GIS. It was done through (i) primary and secondary data creation and processing, (ii) accuracy measurement using Haversine formula. GIS is built after the calculation results are obtained. Calculation of the distance from user’s starting point to referral hospital can be generated using Haversine formula. Comparison of measurement results between Haversine formula-based GIS and Find Nearest Tool, the average differences is 13 meters, the smallest difference is 3 meters and the largest difference is 40 meters. The differences between the calculation results of Haversine formula and Google Maps, the smallest difference is 0 meters, the largest difference is 5 meters, and the average differences is 3 meters. GIS creation obtained through designing use case, activity, class diagram, and user interface. The conclusion is Haversine formula-based GIS can be used as "Geographic Information System for the Search of Referral Hospital Handling COVID-19 in Semarang City" based on the closest distance from user's location.&#x0D;
HLNet: A Unified Framework for Real-Time Segmentation and Facial Skin Tones Evaluation

Real-time semantic segmentation plays a crucial role in industrial applications, such as autonomous driving, the beauty industry, and so on. It is a challenging problem to balance the relationship between speed and segmentation performance. To address such a complex task, this paper introduces an efficient convolutional neural network (CNN) architecture named HLNet for devices with limited resources. Based on high-quality design modules, HLNet better integrates high-dimensional and low-dimensional information while obtaining sufficient receptive fields, which achieves remarkable results on three benchmark datasets. To our knowledge, the accuracy of skin tone classification is usually unsatisfactory due to the influence of external environmental factors such as illumination and background impurities. Therefore, we use HLNet to obtain accurate face regions, and further use color moment algorithm to extract its color features. Specifically, for a 224×224 input, using our HLNet, we achieve 78.39% mean IoU on Figaro1k dataset at over 17 FPS in the case of the CPU environment. We further use the masked color moment for skin tone grade evaluation and approximate 80% classification accuracy demonstrate the feasibility of the proposed method.
Expansion trees with cut

Abstract Herbrand’s theorem is one of the most fundamental insights in logic. From the syntactic point of view, it suggests a compact representation of proofs in classical first- and higher-order logics by recording the information of which instances have been chosen for which quantifiers. This compact representation is known in the literature as Miller’s expansion tree proof. It is inherently analytic and hence corresponds to a cut-free sequent calculus proof. Recently several extensions of such proof representations to proofs with cuts have been proposed. These extensions are based on graphical formalisms similar to proof nets and are limited to prenex formulas. In this paper, we present a new syntactic approach that directly extends Miller’s expansion trees by cuts and also covers non-prenex formulas. We describe a cut-elimination procedure for our expansion trees with cut that is based on the natural reduction steps and shows that it is weakly normalizing.