Reviewerly/exHarmony huggingface dataset:
On the Data Security of Information Systems: Comparison of Approaches and Challenges

Information Systems (IS) are in constant evolution to cope with new technologies like cloud computing and Big data. Nevertheless, these new technologies bring several security issues, some caused by the numerous access points which make companies prone to an increasing number of attacks. The architecture of an information system can be structured in three main layers: data layer, application layer, and technology layer. Security must be reinforced in each of the previous layers to prevent internal and external attacks. In this paper, we focus on the security of the data layer, thus we survey the research work dealing with security related to this layer. For that, we propose a data meta-model with a high level of abstraction that shows the relationship between data, security issues, and security mechanisms covering the three aspects of security (confidentiality, integrity, availability) and considering different types of data and novel technologies (big data and cloud computing). We use the main concepts of this meta-model to classify security mechanisms according to security goals, to compare the different approaches proposed in the literature, and to identify open challenges in the data security field. Our data meta-model can be easily extended and serves as a reference to compare other approaches in the literature.
Canonical Extension of Possibility Measures to Boolean Algebras of Conditionals

In this paper we study conditional possibility measures within the algebraic setting of Boolean algebras of conditional events. More precisely, we focus on the possibilistic version of the Strong Conditional Event Problem, introduced for probabilities by Goodman and Nguyen, and solved in finitary terms in a recent paper by introducing the so-called Boolean algebras of conditionals. Our main result shows that every possibility measure on a finite Boolean algebra can be canonically extended to an unconditional possibility measure on the resulting Boolean algebra of conditionals, in such a way that the canonical extension and the conditional possibility, determined in usual terms by any continuous t-norm, coincide on every basic conditional expression.
Difficulties in using correct English prepositions among EFL students

The objectives of this study are to find out the reasons of this problem and find some solutions. The participants were ten students at levels seven and eight, at the Faculty of Sciences and Arts. The researcher used the Descriptive Statistical Method and collected the data via a written test as the main research tool. The data were then analyzed using a Statistical Package of Social Sciences Programme (SPSS). The results were as follows: Saudi students faced problems with using the correct prepositions after certain verbs. Students encountered difficulties in identifying the correct prepositional phrase. Students experienced the same problem with the use of English prepositions in writing tasks and in daily communication. The recommendations of this study are that students need to revise the use of English prepositions in general and need more practice in the use of prepositions after certain verbs. They also need to make a greater effort to use English prepositional phrases in their written work. Regarding writing courses, students need to focus more on the use of prepositions in general. Curriculum designers need more attention about teaching prepositions in their textbooks and curriculums.&#x0D; HIGHLIGHTS:&#x0D; &#x0D; Saudi students faced problems with using the correct prepositions after certain verbs.&#x0D; Students encountered difficulties in identifying the correct prepositional phrase.&#x0D; Students experienced the same problem with the use of English prepositions in writing tasks and in daily communication. &#x0D;
Comparison of PV and BESS Sizing Methods for Autonomous Street Lighting Systems

There are different calculation paradigms applied in the sizing of photovoltaic systems, which can be sorted accordingly to existent classifications. These methods vary regarding the amount of information needed as the input data, the computational resources required for their achievement, as well as the precision and complexity obtained in their results. Based on an existent classification for such methods, and presenting some works which can be sorted into this classification, it was noticed the lack of pointers for guiding professionals choosing the fittest method for a given project. As a first evaluation in this regard, a hypothetical case for different real-world conditions was considered as a comparison basis for two different sizing methods. The results of these methods are compared, founding the need for special attention in choosing the method depending on the application, due to the variables considered in each approach.
RESEARCH OF FEATURES OF EDUCATIONAL-AND-PROFESSIONAL MINDSET IN THE STRUCTURE OF PROFESSIONAL CONSCIOUSNESS OF STUDENTS

The article is devoted to the presentation of the results of an empirical research of educational-and-professional mindset in the structure of professional consciousness of students. The specificity and the structural organization of the educational-and-professional mindset of students have been shown. A review of scientific literature has allowed to define educational-and-professional mindset as a multilevel motivational-semantic entity that appears as an individual willingness to perceive activity and make it properly; it permeates all aspects of the formation of the professional activity from professional consciousness and self-awareness up to action or deed. We have grounded a place of educational-and-professional mindset in the structure of professional consciousness. As a component of professional consciousness, educational-and-professional mindset is shown in the personal sense. As a kind of semantic mindset, it is updated by the motives of professional activity and appears as a form of personal expression of readiness to professional activity in general. Structural organization of educational and professional mindset includes the following components: professional-cognitive (goal setting, challenges in the future work), emotional (assessment of training for professional activity), subjective (involvement of students in the educational and professional activities), cognitive-personal (students’ representation of professionally significant personal qualities). The sample of respondents consisted of higher education students of various profiles – humanitarian (specialty “Psychology”) and natural sciences (specialty “Biology” and “Chemistry”). The results of the empirical research have revealed that psychological characteristics of the content of educational and professional mindset is partially determined by the education profile, and in part are common. The differences in the severity of the components of educational and professional mindset concern cognitive-professional component (professional goal setting); emotional component (professional expectations); cognitive and personality component (self-assessment of own negative qualities); subjective component (lack of time, lack of interest in the learning process, a lack of understanding of educational material).
PENGARUH STRATEGI MEMBACA DIRECT READING THINKING ACTIVITY (DRTA) TERHADAP SISWA SEKOLAH DASAR

Learning DRTA strategy is a suitable strategy in reading, so that research is conducted using a quasi-experiment method with nonequivalent group pretest-posttest design to see the effect seen from the pretest data in the experimental class of 10, 34, and the control class of 10.43 with an ideal score 20. In the posttest data, the average experimental class is 15.22, and the control class is 13.5, seen from the results of the pretest and posttest data, the experimental class has a higher increase in comprehension reading ability. To be clearer whether there is a difference in the ability of elementary school students, it was first tested by calculating the t-test statistic on pretest data  = 0.13 ≤ = 2.00 it can be concluded that there is no significant difference. Posttest data in the t-test is obtained  = 1.79 ≤  = 2.00 it can be concluded that there is no significant difference. So it can be concluded that this study has an influence on the DRTA strategy seen in the increase in the average pretest and posttest data of elementary school students. Keywords: Strateg y DRTA , Elementary School Students, Nonequivalent Group
VIETNAMESE ENGINEERING STUDENTS’ PERCEPTIONS OF THE USE OF GOOGLE TRANSLATION TOOL

The study looked at how Google Translate was used among a group of engineering students to learn TOEIC at a university in Southern Vietnam. This research took a form of a qualitative case study with semi-structured interviews and observations being the data collection tools. Findings suggested that this translation tool, in essence, was perceived to offer certain benefits to the students. For example, it was fast, convenient and helped students catch the gist or the general meaning of the source language quickly. Google Translate, however, failed to support reliable and understandable translation if the source texts contained technical terms, complex words, uncommon texts and meanings which perplexed the students. These limitations of this machine translation tool should be fully aware by engineering students and general language users who are not intermediate or advanced learners of English if it is to be used in translating tasks in the future. Suggestions on how to improve the limitations of Google Translate are mentioned in this study.
The Effect of Project Based Learning Assisted by Mobile Learning Applications and Learning Motivation on the Competence and Performance of Teachers

One learning model that can be used to improve teachers' quality, pedagogic competence, and personality is the Project Based Learning (PjBL) learning mod-el. The development in this research carried out the Project Based Learning (PjBL) learning model in collaboration with applying the mobile learning model. The use of project-based learning using mobile applications focuses on optimiz-ing teacher performance. The results of this study are: (1) there is an effect of the PjBL strategy assisted by the Mobile Learning Application on the pedagogical competence of teachers. It shows by the significant p-value 0.05; (2) there is an effect of the PjBL strategy assisted by mobile learning on teacher performance which is shown by the significant p-value 0.05; (3) there is an effect of the ped-agogic competence of teachers in students with high motivation compared to low learning motivation shown by the significant difference of p-value 0.05; (4) The average value shows teacher performance for high learning motivation students toward students with low learning motivation with a significant difference with the p-value 0.05; (5) there was an interaction between the implementation of PjBL strategy assisted by mobile learning and high learning motivation on the teacher's pedagogic competence. It can be seen from the significant value of p 0.05; (6) there was an interaction between the implementation of PjBL strategy assisted by mobile learning and high learning motivation on teacher performance from the significant value of p 0.05
Marketing Improvement of Chinese Original Picture Books From Dissatisfaction Evaluation - Text Mining Based on LDA Model

Chinese original picture books play an important role in inheriting traditional culture and forming cultural identity, which is very important for children. We analyzes the dissatisfaction evaluation of Chinese original picture books by using the topic model of Latent Dirichlet Allocation (LDA). It is found that the dissatisfaction of consumers mainly focuses on smell, AR function, preaching, quality, picture, content, painting style and so on. In the future, we should take quality as the bottom line, focus on the content creation, and carry out integrated marketing through social media.
Words Algorithm Collection - finding closely related open access books using text mining techniques

Open access platforms and retail websites are both trying to present the most relevant offerings to their patrons. Retail websites deploy recommender systems that collect data about their customers. These systems are successful but intrude on privacy. As an alternative, this paper presents an algorithm that uses text mining techniques to find the most important themes of an open access book or chapter. By locating other publications that share one or more of these themes, it is possible to recommend closely related books or chapters.&#x0D; The algorithm splits the full text in trigrams. It removes all trigrams containing words that are commonly used in everyday language and in (open access) book publishing. The most occurring remaining trigrams are distinctive to the publication and indicate the themes of the book. The next step is finding publications that share one or more of the trigrams. The strength of the connection can be measured by counting – and ranking – the number of shared trigrams. The algorithm was used to find connections between 10,997 titles: 67% in English, 29% in German and 6% in Dutch or a combination of languages. The algorithm is able to find connected books across languages.&#x0D; It is possible use the algorithm for several use cases, not just recommender systems. Creating benchmarks for publishers or creating a collection of connected titles for libraries are other possibilities. Apart from the OAPEN Library, the algorithm can be applied to other collections of open access books or even open access journal articles. Combining the results across multiple collections will enhance its effectiveness.
Fraud Detection in Healthcare System using Symbolic Data Analysis

In the era of digitization the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or distortion for acquiring some pitiful advantage in the form of health expenditures. Bigdata analysis can be utilized to recognize fraud in large sets of insurance claim data. In light of a couple of cases that are known or suspected to be false, the anomaly detection technique computes the closeness of each record to be fake by investigating the previous insurance claims. The investigators would then be able to have a nearer examination for the cases that have been set apart by data mining programming. One of the issues is the abuse of the medical insurance systems. Manual detection of frauds in the healthcare industry is strenuous work. Fraud and Abuse in the Health care system have become a significant concern and that too inside health insurance organizations, from the most recent couple of years because of the expanding misfortunes in incomes, handling medical claims have become a debilitating manual assignment, which is done by a couple of clinical specialists who have the duty of endorsing, adjusting, or dismissing the appropriations mentioned inside a restricted period from their gathering. Standard data mining techniques at this point do not sufficiently address the intricacy of the world. In this way, utilizing Symbolic Data Analysis is another sort of data analysis that permits us to address the intricacy of the real world and to recognize misrepresentation in the dataset.
WHAT PERSONALISATION CAN DO FOR YOU! OR, HOW TO DO RACIAL DISCRIMINATION WITHOUT RACE

Between 2016 and 2020, Facebook allowed advertisers in the United States to target their advertisements using three broad “ethnic affinity” categories: “African American,” “U.S.-Hispanic,” and “Asian American.” This paper uses the life and death of these “ethnic affinity” categories to argue that they exemplify a novel mode of racialisation made possible by machine learning techniques. These categories worked by analysing users’ preferences and behaviour: they were supposed to capture an “affinity” for a broad demographic group, rather than registering membership of that group. That is, they were supposed to allow advertisers to “personalise” content for users depending on behaviourally determined affinities. We argue that, in effect, Facebook’s ethnic affinity categories were supposed to operationalise a “post-racial” mode of categorising users. But the paradox of personalisation is that in order to apprehend users as individuals, platforms must first assemble them into groups based on their likenesses with other individuals. This article uses an analysis of these categories to argue that even in the absence of data on a user’s race—even after the demise of the categories themselves—users can still be subject to techniques of inclusion or exclusion for discriminatory ends. The inductive machine learning techniques that platforms like Facebook employ to classify users generate “proxies,” like racialised preferences or language use, as racialising substitutes. This article concludes by arguing that Facebook’s ethnic affinity categories in fact typify novel modes of racialisation today.
Recent Trends in Blockchain Technology

In recent decades, blockchain is a leading central concept similar to the internet’s growth in 1990. However, it has some drawbacks, particularly in the security area. In this paper, the blockchain security structure had been summarized by conducting a detailed survey. The discussion on how to defend against particular security threats and challenges with blockchain technology’s security mechanism has been analyzed and presented. Moreover, the applications of blockchain system and their different security problems are also presented in this paper. Even though the blockchain technology concept is more consistent and provides trusted services, this novel approach regarding security and challenges plays a significant role.
A Comparative Study of Word Embeddings for the Construction of a Social Media Expert Filter

With the proliferation of fake news and misinformation on social media, being able to differentiate a reliable source of information has become increasingly important. In this paper we present a new algorithm for filtering expert users in social networks according to a certain topic under study. For the algorithm fine-tuning, a comparative study of results according to different word embeddings as well as different representation models, such as Skip-Gram and CBOW, is provided alongside the paper.
Internal and external frontier‐based algorithm for autonomous mobile robot exploration in unknown environment

Navigation in the absence of initial environmental information is a situation in which a robot is faced with the difficulty of traversing an unknown area for exploration with obtaining the environmental information simultaneously. Therefore, to complete and optimize the exploration efficiently, the robot needs an autonomous path-planning algorithm. This work proposes a new autonomous path-planning algorithm for exploration in an unknown environment based on paired frontiers, which we call internal and external frontiers algorithm (IEFA), that defines extended area for navigation of the mobile robot. For each exploration round, the robot defines external frontiers using the maximum range of sensors. Then, the robot generates internal frontiers, that is, pairs of external frontiers by varying the range of sensors. According to the size of each pair of frontiers, the algorithm generates the target point for robot navigation. The frontiers of internal layer are utilized as a main parameter for generation of next exploration point. We evaluated the proposed algorithm in simulation environments using the ROS toolbox of MATLAB and compared it with two previous exploration algorithms. From the experimental results, the proposed algorithm showed from 31% to 85% better performance in the path distance than previous algorithms.
Unsupervised cross-lingual model transfer for named entity recognition with contextualized word representations

Named entity recognition (NER) is one fundamental task in the natural language processing (NLP) community. Supervised neural network models based on contextualized word representations can achieve highly-competitive performance, which requires a large-scale manually-annotated corpus for training. While for the resource-scarce languages, the construction of such as corpus is always expensive and time-consuming. Thus, unsupervised cross-lingual transfer is one good solution to address the problem. In this work, we investigate the unsupervised cross-lingual NER with model transfer based on contextualized word representations, which greatly advances the cross-lingual NER performance. We study several model transfer settings of the unsupervised cross-lingual NER, including (1) different types of the pretrained transformer-based language models as input, (2) the exploration strategies of the multilingual contextualized word representations, and (3) multi-source adaption. In particular, we propose an adapter-based word representation method combining with parameter generation network (PGN) better to capture the relationship between the source and target languages. We conduct experiments on a benchmark ConLL dataset involving four languages to simulate the cross-lingual setting. Results show that we can obtain highly-competitive performance by cross-lingual model transfer. In particular, our proposed adapter-based PGN model can lead to significant improvements for cross-lingual NER.
Image Denoising Based on Improved Gaussian Mixture Model

An image denoising method is proposed based on the improved Gaussian mixture model to reduce the noises and enhance the image quality. Unlike the traditional image denoising methods, the proposed method models the pixel information in the neighborhood around each pixel in the image. The Gaussian mixture model is employed to measure the similarity between pixels by calculating the L2 norm between the Gaussian mixture models corresponding to the two pixels. The Gaussian mixture model can model the statistical information such as the mean and variance of the pixel information in the image area. The L2 norm between the two Gaussian mixture models represents the difference in the local grayscale intensity and the richness of the details of the pixel information around the two pixels. In this sense, the L2 norm between Gaussian mixture models can more accurately measure the similarity between pixels. The experimental results show that the proposed method can improve the denoising performance of the images while retaining the detailed information of the image.
Integrasi Pemanfaatan Data Administrasi Kependudukan Dalam Rangka Mewujudkan Single Identity Number

The integration of population data and information implies the unification of population data and information in a system, the system in question is an application developed by the Ministry of Home Affairs for all Regency/City Population and Civil Registration Offices. With this integration, public service units are no longer problematic because this integration will create a population database where each resident only has one population data. The use of data and information held by the Population and Civil Registration Office of Parepare City has not been implemented properly, where each existing public service unit has its own database. The purpose of this paper is that the existing data and information can be utilized by all service units in Parepare. The methodology used is Observation, Interview and Literature Study. From the results of the research conducted, it is known that the utilization of population data and information through the Population Administration Information System Application has not been implemented in the form of cooperation in the use of SIAK data and information.