Reviewerly/exHarmony huggingface dataset:
Automatic Extraction of Lithuanian Cybersecurity Terms Using Deep Learning Approaches

The paper presents the results of research on deep learning methods aiming to determine the most effective one for automatic extraction of Lithuanian terms from a specialized domain (cybersecurity) with very restricted resources. A semi-supervised approach to deep learning was chosen for the research as Lithuanian is a less resourced language and large amounts of data, necessary for unsupervised methods, are not available in the selected domain. The findings of the research show that Bi-LSTM network with Bidirectional Encoder Representations from Transformers (BERT) can achieve close to state-of-the-art results.
Automated Aggregator - Rewriting with the Counting Aggregate

Answer set programming is a leading declarative constraint programming paradigm with wide use for complex knowledge-intensive applications. Modern answer set programming languages support many equivalent ways to model constraints and specifications in a program. However, so far answer set programming has failed to develop systematic methodologies for building representations that would uniformly lend well to automated processing. This suggests that encoding selection, in the same way as algorithm selection and portfolio solving, may be a viable direction for improving performance of answer-set solving. The necessary precondition is automating the process of generating possible alternative encodings. Here we present an automated rewriting system, the Automated Aggregator or AAgg, that given a non-ground logic program, produces a family of equivalent programs with complementary performance when run under modern answer set programming solvers. We demonstrate this behavior through experimental analysis and propose the system's use in automated answer set programming solver selection tools.
The Impact of Teenagers’ Digital Literacy on the Use of Social Media

The biggest users of social media are teenagers.With a digital set, teenagers open up social media everywhere, so that, it is needed the strong and large controls from various parties.Besides that, in digital literacy knowledge, teenagers must understand the ability to use digital literacy because it will cause new problems by distributing content, sharing content or commenting on status.The purpose of this research is the involvement of teenagers in using social media and giving the problems when they share content, images, digital literacy, and desirable websites.Their involvement can cause big problems which can ultimately harm teenagers themselves, especially on digital literacy knowledge.This research aim is to investigate the involvement of teenagers on social media so that it can change all the order, language and content provided.The subject sample population numbered 273 teenagers aged 13-18 years.This research method is a mixed-method by using a questionnaire as a quantitative of research and observation and for qualitative using interviews conducted online or faces to face within 2-3 months to 22 teenagers.The result showed that teenagers are the subjects that use most social media, and also teenagers believe that social media helped them in solving the problem and also teenagers can change digital literacy and content to facilitate them in socializing.
A new model for predicting the attributes of suspects

In this paper, we propose a new model to predict the age and number of suspects through the feature modeling of historical data. We discrete the case information into values of 20 dimensions. After feature selection, we use 9 machine learning algorithms and Deep Neural Networks to extract the numerical features. In addition, we use Convolutional Neural Networks and Long Short- Term Memory to extract the text features of case description. These two types of features are fused and fed into fully connected layer and softmax layer. This work is an extension of our short conference proceeding paper. The experimental results show that the new model improved accuracy by 3% in predicting the number of suspects and improved accuracy by 12% in predicting the number of suspects. To the best of our knowledge, it is the first time to combine machine learning and deep learning in crime prediction.
Fractal Structures in Electrodeposition Process

In this work fractal dimension of the dendritic deposits was studied.These dendritic deposits were formed in the electrolysis process at various values of the applied voltage and various molar concentration of the CuSO4 or the ZnSO4 electrolytes.The formation of dendrites was determined by Diffusion Limited Aggregation process occurring during electrolysis of these solutions.Morphology of the obtained deposits was strongly dependent on the molar concentration and voltage applied to the electrodes.In addition, computer simulation was carried out for these deposits using an algorithm based on cellular automata.Theoretical calculations of the fractal dimension value gave the result of 1.7.Experimentally found average values of the fractal dimension were equal either to 1.55 for CuSO4 or to 1.59 for ZnSO4.
Searching for a universal constraint on the possible denotations of clause-embedding predicates

We propose a new universal constraint on the relationship between the meaning of a clause-embedding predicate when it takes an interrogative complement and its meaning when it takes a declarative complement. According to this proposal, every clause-embedding predicate V satisfies a constraint that we refer to as P-to-Q Entailment. That is, for any exhaustivity-neutral interrogative complement Q, if there is an answer p to Q such that "x Vs p" is true, then it follows that "x Vs Q" is true as well. We discuss empirical advantages of this proposal over existing proposals and explore potential counterexamples to P-to-Q Entailment.
Education Transformation in the Arctic Region of the Republic Of Sakha (Yakutia) Under the Impact of New Information Technologies in Modern Conditions

Currently, education is considered as a multidimensional and comprehensive process of personality formation. Among other things, it contributes to the development of basic abilities such as critical thinking, problem solving ability, networking, adaptability, and initiative. Learning is continuous and widely accessible, both with regard to adaption and personalization. Nowadays, teachers are required to search for innovative technologies and techniques providing qualitative acquirement of the material and the development of key subject competencies. The objective of the study is to introduce information and communication technologies (ICT) in education. The authors use the methods of testing, questionnaire, study of documents, and reflection. The survey involves 312 full-time and part-time students of M. K. Ammosov North-Eastern Federal University (NEFU), among which 178 people represent the Arctic Region. The survey’s results are introduced in Tables 1 and 2. The results of the study are positive, with quantitative and qualitative indicators confirming the purpose of the study
Enhanced of Attendance Records Technology used Geospatial Retrieval based on Crossing Number

&lt;span lang="EN-US"&gt;Nowadays, the fingerprint scanner widely used to records attendance. However, this technology has a weakness. Much research has done to improve the attendance system by utilizing mobile technology, like usage a fingerprint smartphone and location by GPS sensor to validate user location manually. In this research, we developed an application to enhance the records attendance system with a smartphone by crossing numbers to verify user position automatically, which implemented in a mobile app. This application using the PNPOLY method for detecting the location of the user inside of the polygon area predetermined. This method is part of the crossing number algorithm for increasing x and fixed y from point P, which x is latitude, and y is a longitude. The result of the experiment demonstrated that the percentage of successful validate user coordinate inside edges of the polygon boundary is 87%, depending on the GPS sensor embedded into a mobile device.&lt;/span&gt;
Creation of Language Resources for the Development of a Medical Speech Recognition System for Latvian

This paper describes an ongoing work on the creation of Latvian language resources for the medical domain focusing on digital imaging to develop a medical speech recognition system for Latvian. The language resources include a pronunciation lexicon, a text corpus for language modelling, and an orthographically transcribed speech corpus for the (i) adaptation of the acoustic model, (ii) evaluation of the speech recognition accuracy, (iii) development and testing of rewrite rules for automatic text conversion to the spoken form and back to the written form. This work is part of a larger industry-driven research project which aims at the development of specific Latvian speech recognition systems for the medical domain.
Understanding Lua’s Garbage Collection

We provide the semantics of garbage collection (GC) for the Lua programming language. Of interest are the inclusion of finalizers (akin to destructors in object-oriented languages) and weak tables (a particular implementation of weak references). The model expresses several aspects relevant to GC that are not covered in Lua’s documentation but that, nevertheless, affect the observable behavior of programs.
Meningkatkan Keterampilan Berbicara Menggunakan Metode Contextual Teaching and Learning Siswa Kelas XI SMK BSI

The purpose of this Internship research aims to analyze: 1). speaking skills of XI Tourism Hospitality class students. 2). learning using the Contextual Teaching and Learning method which is carried out in class XI Hospitality Tourism so that students have good and correct Indonesian language skills. 3). how to improve students' speaking ability after the lesson using the Contextual Teaching and Learning method.Qualitative methods are used in the research approach undertaken. Where this qualitative research seeks to provide an explanation, describe, and outline the main issues that will be discussed in the study then draw conclusions deductively. Based on the results of the study indicate that the object of research is a number of 15 students of this study from pre-cycle to cycle 1 shows that there is an increase in the average value of students that is 64.33 to 73.67. While from pre-cycle to cycle 2 shows that there is an increase in the average value of students that is 64.33 to 78. From the results above indicate that there has been a change from pre-cycle to cycles 1 and 2. These conditions indicate that there has been a change the value of students who showed an increase in students' abilities since using the CTL method. So from here it shows that the application of the CTL method has a good impact on improving students' ability to speak.
Fuzzy Modelling using Butterfly Optimization Algorithm for Phishing Detection”

Fuzzy system is a rule-based system by using human experts which hold the truth values or membership values to make a particular decision while fuzzy modelling is a process of identifying the fuzzy parameter.However, it is difficult to generate fuzzy parameter manually when applied on complex problem.To generate the fuzzy parameter automatically, an optimization method is required and Butterfly Optimization Algorithm (BOA) is one of the good methods to be applied.The proposed method will be utilized to produce the optimal solution in finding fuzzy parameter before implementing the fuzzy technique in the dataset chosen.Two datasets were used, namely Website Phishing Dataset (WPD) and Phishing Websites Dataset (PWD).As the result, average accuracy for WPD and PWD is 96.80% and 94.65% respectively.To be conclude, BOA shows promising results to be applied to measure the accuracy of the fuzzy modelling in phishing detection.
Analisis Isi dalam Penelitian Pembelajaran Bahasa dan Sastra

Content analysis can be widely applied in language and literature learning in schools, for example to: describe learning trends (trends); know learning patterns / models; uniqueness of the school / teacher in overcoming problems; predict attitudes, values and school cultural patterns; describe teacher perceptions; and describe learning management ideas. In addition, it can also be used for language and literary research, such as critical discourse analysis, describing elements of literary works, values of literary works, and readers' perceptions of literary works. Content analysis requires researchers who have and can take advantage of their analytical acumen to knit the content of communication phenomena into socially readable phenomena.
Assessment Instruments of Social Attitudes and Social Studies Learning Outcomes for Class V SD on Theme of Lingkungan Sekitar Kita

The assessment instrument is an important component needed in the learning assessment process. However, in practice, there are still many teachers who have not been able to produce quality assessment instruments. The purpose of this study was to produce valid and reliable assessment instruments for social attitudes and social studies learning outcomes in class V on theme of lingkungan sekitar kita. In this study using a 4D development model, namely define, design, develop, disseminate. In this development research, it is only up to the development stage. The instrument used for the assessment of social attitudes was a questionnaire (questionnaire), while for the assessment of social studies learning outcomes used was a multiple-choice test. The data obtained would be analyzed the validity and reliability of each assessment instrument. The results of the analysis of the validity of the social attitude assessment instrument and social studies learning outcomes obtained an average value of 1, which means that the two assessment instruments have very high validity, while the results of the analysis of the reliability of the social attitude assessment instrument are 0,89 and the reliability of the social studies learning outcomes assessment instrument is 0,89. 0,85 which means that these two instruments have a high level of reliability. From these results, it can be concluded that the development of an assessment instrument for social attitudes and social studies learning outcomes is feasible to use in assessing students.
Experimenting with Hotword Detection: The Pao-Pal

Hotword detection and voice command are implemented in many of today’s current technological devices. In this paper, the implementations using hotword detection as well as the improvements in hotword technology will be discussed including the Pao-Pal. The Pao-Pal was made using Google’s AIY Voice Kit which contains a Raspberry Pi Zero WH, voice bonnet, and a speaker that allows it to playback pre-recorded voice lines. These components allow the Pao-Pal to act as a switch for a Keurig machine to make coffee when certain phrases are said. To allow the Pao-Pal to always listen for hotwords, it uses a hotword detection engine called Snowboy, which also allows it to playback the pre-recorded voice lines. Snowboy allows users to create and customize their own special hotword. Other projects concerning voice commands will be discussed along with the application of that kind of technology.
The S-measure for algebraic integers having all their conjugates in a sector

Let α be a nonzero algebraic integer of degree d, all of whose conjugates α1=α,α2,…,αd lie in a sector |argz|≤𝜃, 0<𝜃≤90∘. We define the S-measure of α by S(α)= ∑i=1d|αi| and the absolute S-measure of α by s(α)=S(α)∕d. We compute the greatest lower bound c(𝜃) of s(α) for α belonging to twelve subintervals of (0,𝜃). Among these subintervals, three are complete. These computations use the principle of explicit auxiliary functions and our recursive algorithm.
GAIT: Gradient Adjusted Unsupervised Image-To-Image Translation

Image-to-image translation (IIT) has made much progress recently with the development of adversarial learning. In most of the recent work, an adversarial loss is utilized to match the distributions of the translated and target image sets. However, this may create artifacts if two domains have different marginal distributions, for example, in uniform areas. In this work, we propose an unsupervised IIT method that preserves the uniform regions after the translation. The gradient adjustment loss, which is the L2 norm between the Sobel response of the target image and the adjusted Sobel response of the source images, is utilized. The proposed method is validated on the jellyfish-to-Haeckel dataset, which is prepared to demonstrate the mentioned problem, which contains images with different background distributions. We demonstrate that our method obtained a performance gain compared to the baseline method qualitatively and quantitatively, showing the effectiveness of the proposed method.
Zigzag Expanded Navigation Plots in <i>R</i>: The <i>R</i> Package <b>zenplots</b>

We describe the features and implementation of the R package zenplots (zigzag expanded navigation plots) for displaying high-dimensional data according to the recently proposed zenplots.By default, zenplots lay out alternating one-and two-dimensional plots in a zigzag-like pattern where adjacent axes share the same variate.Zenplots are especially useful when subsets of pairs can be identified as of particular interest by some measure, or as not meaningfully comparable, or when pairs of variates can be ordered in terms of potential interest to view, or the number of pairs is too large for more traditional layouts such as a scatterplot matrix.They also allow an essentially arbitrary layout of plots.A highdimensional space can be explored in a zenplot (zenplot()) by navigating through lower dimensional subspaces along a zenpath (zenpath()) which orders the dimensions (i.e., variates) visited according to some measure of interestingness; see Hofert and Oldford (2018) for an application to S&P 500 constituent data.The R package zenplots provides compact displays for high-dimensional data via the notion of zenplots, grouping of variates, and customizable displays of zigzag layouts.It accommodates different graphical systems including the base graphics package of R Core Team (2020b), the package grid of R Core Team (2020a) (and hence packages like ggplot2 of Wickham et al. 2020), and the interactive graphical package loon of Waddell and Oldford (2020).zenplots handles groups of variates, partial and fully missing data, and more.One important feature is that zenplot() and its auxiliary functions in zenplots distinguish layout from plotting which allows one to freely choose and create one-and twodimensional plot functions; predefined functions are exported for all graphical systems.All R plots in this paper are reproducible with the vignette "selected_features" (available in zenplots ≥ 0.0-2).
GEP-based classifier with drift detection for mining imbalanced data streams

Mining data streams require to cope with time, data size and possible concept drift constraints. Even more challenging is the case where, apart from the above, one has to deal with imbalanced data. Mining non stationary and imbalanced data streams is a relatively new area of research. In this paper, we propose the Gene Expression Programming (GEP) classifier with drift detection and data reuse for mining imbalanced data streams. GEP is used to evolve a complex expression tree returning predictions. Drift detector role is to signal the occurrence of drift which triggers inducing a new learner. Data reuse mechanism allows for improving the balance between minority and majority instances in a subset of data used for evolving the learner. The proposed approach is validated experimentally. The experiment results confirm that our classifier produces high-quality predictions.
A Novel Semantic Segmentation Model for Chinese Characters

Character segmentation plays an important role in optical character recognition (OCR). Due to the limitations of feature representation, traditional image analyzing based methods cannot well segment characters with connected or broken strokes, especially for the Chinese characters which usually have complex structures. To solve this issue, this paper proposes a novel segmentation model based on fully convolutional neural networks (FCN). The model first uses convolutional neural networks to extract spatial features, then shares them throughout the whole model. Two FCNs are used to extract character information to form a score map. Finally, character features are reused to adjust the accurate segmentation points in the score map. What's more, to strengthen the ability of feature representation, a novel compound character feature which can well describe the characters' outline is also proposed. The proposed method is validated on two datasets: GBSD and CASIA-HWDB-MT, against the methods proposed in the literature. Experimental results show that the proposed model outperforms state-of-the-art methods.
Dampak Penerapan Akuntansi Forensik dan Audit Investigasi dalam Mendeteksi Fraud Pengadaan Barang/Jasa

The purpose of this study was to determine the effect of the forensic accounting and audit investigations in detecting fraud procurement of goods and services. The rise of fraud cases in Indonesia, one of which is fraud related to public needs, namely the procurement of goods and services. This is reminiscent of the real threat of fraud in the form of corruption in the procurement of goods and services that can cause poor quality of goods / services produced so that they cannot serve the public interest effectively and efficiently. In this context, the community is the most disadvantaged party. Collecting data in this study through the distribution of questionnaires with a total sample of 34 auditor respondents in the field of Investigation of the Badan Pemeriksa Keuangan dan Pembangunan DKI Jakarta. This research uses PLS (Partial Least Square) analysis technique, using SmartPLS software testing tool version 3.2.8. The results of this study indicate that the results of the analysis of the application of forensic accounting and investigative audits have a significant effect in detecting fraud procurement of goods / services.
Real-time Arabic scene text detection using fully convolutional neural networks

The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed.