Reviewerly/exHarmony huggingface dataset:
An Effect of Using A Storage Medium in Dijkstra Algorithm Performance for Ideal Implicit Path Cost

The graph model is used widely for representing connected objects within a specific area.These objects are defined as nodes; where the connection is represented as arc called edges.The shortest path between two nodes is one of the most focus researchers' attentions.Many algorithms are developed with different structured approach for reducing the shortest path cost.The most widely used algorithm is Dijkstra algorithm.This algorithm has been represented with various structural developments in order to reduce the shortest path cost.This paper highlights the idea of using a storage medium to store the solution path from Dijkstra algorithm, then, uses it to find the implicit path in an ideal time cost.The performance of Dijkstra algorithm using an appropriate data structure is improved.Our results emphasize that the searching time through the given data structure is reduced within different graphs models.
Additional education in higher professional educational institution

With the active renewal and development of science and technology in society, there is an objective need for continuous improvement of competencies for the implementation of qualified professional activities. The issues of retraining and advanced training of specialists are becoming particularly important, which necessitates the development of additional education. The purpose of the article is to analyze the experience of additional education. Based on the necessary regulatory support, the University organizes the activities of the Institute of continuing education, which prepares students for programs of courses of different directions. The authors analyzed the program of additional training, highlighted its features. The methodological support of the courses is highlighted. Analysis of the experience of additional education courses showed their high level of demand among students, so it is planned to expand the scientific and methodological support for the implementation of additional areas.
An Approach Towards Image Edge Detection Based on Interval-Valued Fuzzy Mathematical Morphology and Admissible Orders

Edge detection is an important step for preprocessing digital images before more advanced methods of image analysis such as segmentation can be applied.There are an infinite number of edge detectors that can be derived from pairs of fuzzy dilation and erosion operators.Usually, an edge detector is based on the incorrect assumption that there is no uncertainty regarding the pixel values of the given digital image.The approaches presented in this paper do not rely on this assumption.Instead, the uncertainty regarding the pixel values is modelled in terms of an interval-valued image.After an application of an interval-valued fuzzy dilation and erosion, we are able to produce a binary edge image after a number of steps including an order-preserving transformation based on an admissible order.
VIRTUAL LIBRARY SPACE AS THE ENVIRONMENT OF SOCIO-CULTURAL COMMUNICATION (BY THE EXAMPLE OF THE LIBRARIES OF LUTSK BIBLIOTEK)

The purpose of the article is to study the representation of libraries in the modern virtual space, which is formed to meet the information needs of users, as well as a characteristic feature of the transformation of the existing library service system and the matching of libraries to new social needs. The methodology consists in the application of general scientific methods: analysis, synthesis, and generalization of the source information. This methodological approach allows us to reveal and generalize the main provisions regarding the presence of library institutions on the Internet. The scientific novelty is the expansion of representations about the representation of libraries in the virtual space as a medium of socio-cultural communication and their impact on individuals as well as on society as a whole. Conclusions. The study showed that using such virtual presentation tools as websites, social networks and blogs, librarians quickly inform visitors about current library news, provide round-the-clock access to information resources and provide remote information and information services.
Rotation-equivariant convolutional neural network ensembles in image processing

For the present engineering of neural networks, rotation invariant is hard to be obtained. Rotation symmetry is an important characteristic in our physical world. In image recognition, using rotated images would largely decrease the performance of neural networks. This situation seriously hindered the application of neural networks in the real-world, such as human tracking, self-driving cars, and intelligent surveillance. In this paper, we would like to present a rotation-equivariant design of convolutional neural network ensembles to counteract the problem of rotated image processing task. This convolutional neural network ensembles combine multiple convolutional neural networks trained by different ranges of rotation angles respectively. In our proposed theory, the model lowers the training difficulty by learning with smaller separations of random rotation angles instead of a huge one. Experiments are reported in this paper. The convolutional neural network ensembles could reach 96.35% on rotated MNIST datasets, 84.9% on rotated Fashion-MNIST datasets, and 91.35% on rotated KMNIST datasets. These results are comparable to current state-of-the-art performance.
GKR: Bridging the Gap between Symbolic/structural and Distributional Meaning Representations

Three broad approaches have been attempted to combine distributional and structural/symbolic aspects to construct meaning representations: a) injecting linguistic features into distributional representations, b) injecting distributional features into symbolic representations or c) combining structural and distributional features in the final representation.This work focuses on an example of the third and less studied approach: it extends the Graphical Knowledge Representation (GKR) to include distributional features and proposes a division of semantic labour between the distributional and structural/symbolic features.We propose two extensions of GKR that clearly show this division and empirically test one of the proposals on an NLI dataset with hard compositional pairs.
An approach to vocabulary expansion for neural network language model by means of hierarchical clustering

Neural network language models become the main tool to solve tasks in NLP field.These models already have shown state-of-the-art results in classification, translation, named entity recognition and so on.Pre-trained models are distributed freely in the internet, and could be reused with help of transfer learning techniques.However, the real life problem's domain could differ from the origin domain which the network was trained.In this paper an approach to vocabulary expansion for neural network language model by means of hierarchical clustering is proposed.This technique allows to adopt prerained language model to a different domain.Firstly, tokens from the language model are hierarchically clustered.Then new words from problem's domain are matched to the tokens accordingly obtained hierarchy.In the experimental part the proposed approach is demonstrated on the slightly modified ULM-FiT language model.
Fuzzy logic-predicate network

In many Artrificial Intelligence problems an investigated object is considered as a set of its elements {ω 1 , . . ., ω t } and is characterized by properties of these elements and relations between them.These properties and relations may be set by predicates p 1 , . . ., p n .The problems appeared with such an approach become NP-complete or NP-hard ones.To decrease the computational complexity of these problems a hierarchical multi-level description of classes was suggested.A logicpredicate recognition network may be constructed according to such a multi-level description.Such a network recognizes only objects which have been presented in the training set, but it may be easily retrained by a new object.After retraining it may change its configuration i.e., the number of levels and the number of nodes in every level.A modification of such a network is offered in this paper.This modification allows to do a fuzzy recognition of a new object and to calculate the degree of certainty that this object or its part belongs to some class of objects.
VoipLoc: VoIP call provenance using acoustic side-channels

We develop a novel technique to determine call provenance in anonymous VoIP communications using acoustic side-channels. The technique exploits location-attributable information embedded within audio speech data. The victim’s speech is exploited as an excitation signal, which is modulated (acted upon) by the acoustic reflection characteristics of the victim’s location. We show that leading VoIP communication channels faithfully transfer this information between sender-receiver pairs, enabling passive receivers to extract a location fingerprint, to establish call provenance. To establish provenance, a fingerprint is compared against a database of labelled fingerprints to identify a match. The technique is fully passive and does not depend on any characteristic background sounds, is speaker independent, and is robust to lossy network conditions. Evaluation using a corpus of recordings of VoIP conversations, over the Tor network, confirms that recording locations can be fingerprinted and detected remotely with low false-positive rate.
Penentuan Tingkat Kerawanan Penyebaran Leptospirosis Menggunakan Inferensi Fuzzy Tsukamoto

Cases of leptospirosis in Indonesia mainly occur in areas that often experience floods and areas where the majority of its citizens work as farmers. Special Region of Yogyakarta (DIY) was the province with the most leptospirosis cases in Indonesia in 2011. In 2010-2011 an extraordinary event (KLB) of leptospirosis occurred in Bantul district and in 2014 the number of leptospirosis cases in Bantul district increased by 76 cases.. Based on Kementerian Kesehatan report, data shows that there has been an outbreak of leptospirosis in Bantul , so in addition to epidemiological data necessary case information is also needed to determine the geographic case risk factors and mitigation efforts.In the processing of digital maps for GIS , often found important objects that are not appropriate in its processing can not even be excluded because of uncertainty owned. Applications are made in this study was built and designed by the architectural Tsukamoto fuzzy inference method for handling uncertainty. The results of the application is the visualization of the spread of the disease leptospirosis vulnerability maps based determinants that also involves uncertainty factors that will be resolved with the Tsukamoto fuzzy inference method for use as detection and prevention against the spread of disease leptospirosis in the future
Counting with Analog Neurons

We refine the analysis of binary-state neural networks with $$\alpha $$ extra analog neurons ( $$\alpha $$ ANNs). For rational weights, it has been known that online 1ANNs accept context-sensitive languages including examples of non-context-free languages, while offline 3ANNs are Turing complete. We now prove that the deterministic (context-free) language containing the words of n zeros followed by n ones, cannot be recognized offline by any 1ANN with real weights. Hence, the offline 1ANNs are not Turing complete. On the other hand, we show that any deterministic language can be accepted by a 2ANN with rational weights. Thus, two extra analog units can count to any number which is not the case of one analog neuron.
Ball Direction Prediction for Wheeled Soccer Robot Goalkeeper Using Trigonometry Technique

In this research Trigonometry Technique was implemented to predict the ball movement direction for Wheeled Soccer Robot Goalkeeper. The performance of goalkeeper robot in Wheeled Soccer Robot Contest is very important. The crucial problem with goalkeeper robot is the delay in ball detection by the camera because the results of the camera images captured are always slower than the pictures that have been captured. This causes the robot's response to blocking the opponent's kick ball being late. Trigonometry Technique is one technique that can be used to predict the direction of the ball movement based on trigonometry mathematical formulas. The input data used is the location of the last ball position (x–last ball and y-last ball) and the location of the current ball position (x-current ball and y-current ball). The outputs are the prediction of the next ball location (x-predict ball and y-predict ball) and the prediction of ball movement direction prediction. The results are the goalkeeper's robot successfully predicts the opponent's kick direction with 90% accuracy and can predict the location of the next ball very well. By implementing this method, it is expected to optimize the performance of the goalkeeper robot in saving the goal.
The Use of Mind Mapping Technique to Increase EFL Students’ Motivation in Writing (A Case Study at the Eighth Grade of A Junior High School in Brebes)

This article focuses on describing the implementation of mind mapping technique to increase students’ motivation in writing descriptive text. An English teacher and 36 students in a Junior high school in Brebes participated in this study. A qualitative design was used, and data were collected through classroom observation, questionnaire and interview. This study found that the teacher did three steps in implementing mind mapping: giving students image of mind mapping, designing the writing task, and asking students to finish the writing task. Meanwhile, interview data indicated that most students enjoyed learning descriptive text by means of mind mapping. Regarding students’ motivation, data from observation revealed that most students enjoyed the activities, as seen from their enthusiasm. Likewise, questionnaire data indicated that most students gave positive perceptions towards the use of mind mapping. Most of them strongly agree with the statement that this strategy can make the learning process become imaginative. In short, it can be concluded that the teacher has successfully applied mind mapping technique. It can make the students enjoy and more interested in learning descriptive text. Therefore, the writers suggest that teachers apply various strategies in teaching and learning process, especially mind mapping, to create an enjoyable classroom atmosphere. Meanwhile, other researchers are recommended to discover the use of mind mapping in teaching other text types
Extreme Speech| A Comparative Approach to Social Media Extreme Speech: Online Hate Speech as Media Commentary

By exploring lessons learned from Ethiopia and Finland, this article challenges two assumptions about online hate speech research. First, it challenges the assumption that the best way to understand controversial concepts such as online hate speech is to determine how closely they represent or mirror some underlying set of facts or state of affairs online or in social media. Second, it challenges the assumption that academic research should be seen as separate from the many controversies that surround online hate speech debates globally. In its place, the article proposes the theory of “commentary” as a comparative research framework aimed at explaining how the messy and complex world of online and social media practices is articulated as hate speech over other ways of imagining this growing problem in global digital media environments.
Extracting and Tagging Unstructured Citation of a Hebrew Religious Document

Aim/Purpose: Finding and tagging citation on an ancient Hebrew religious document. These documents have no structured citations and have no bibliography. Background: We look for common patterns within Hebrew religious texts. Methodology: We developed a method that goes over the texts and extracts sentences con-taining the names of three famous authors. Within these sentences we find common ways of addressing those three authors and with these patterns we find references to various other authors. Contribution: This type of text is rich in citations and references to authors, but because there is no structure of references it is very difficult for a computer to automatically identify the references. We hope that with the method we have developed it will be easier for a computer to identify references and even turn them into hyper-links. Findings: We have provided an algorithm to solve the problem of non-structured cita-tions in an old Hebrew plain text. The algorithm definitely was able to find many citations but it has missed out some types of citations. Impact on Society: When the computer recognizes references, it will be able to build (at least par-tially) a bibliography that currently does not exist in such texts at all. Over time, OCR scans more and more ancient texts. This method can make people's access and understanding much. Future Research: After we identify the references, we plan to automatically create a bibliography for these texts and even transform those references into hyperlinks.
PEMANFAATAN E-LEARNING BAGI PARA PENDIDIK DI ERA DIGITAL 4.0 UTILIZATION OF E-LEARNING FOR EDUCATORS IN DIGITAL ERA 4.0

In this digital era or Industrial 4.0 revolution, technological advances become things that cannot be solved in human life. Teachers must learn better in learning, need to utilize optimal learning resources, this is important, because the effectiveness of learning is also included by the willingness and ability to utilize learning media, such as the web (google class). In this era students are required to have certain skills, besides those educators are also required to have several competencies that must be possessed so that the e-learning program that they run can run well. An educator or teacher must be able to learn to be there to learn, be creative and fun.
Modifying Playfair Cipher Algorithm by using Legendre Symbol

In recent years, transmitting data on the Internet became a daily usage of people, such as sending emails, online shopping and so on.Generally, this data should be confidential and hence it should be secured by using cryptosystem algorithms.A new algorithm of securing the transmitted data by combining Playfair Cipher with Legendre symbol is presented in this paper.In this algorithm a large prime number can be chosen by both of sender and the receiver in order to calculate Legendre Symbol ( ) , ∀ 1 ≤ ≤ -1 and then sorting them in a random order set.Moreover, as Legendre symbol is either 1 or -1, then this helps them in the proposed algorithm to use two tables with two keywords for the encryption and decryption processes instead of using only one table and one keyword as in the standard Playfair cipher algorithm.Thus, our proposed algorithm increases the security level of the transmitted data on the insecure channels comparing with the standard algorithm.
Minimal Session Types

Session types are a type-based approach to the verification of message-passing programs. They have been much studied as type systems for the pi-calculus and for languages such as Java. A session type specifies what and when should be exchanged through a channel. Central to session-typed languages are constructs in types and processes that specify sequencing in protocols.Here we study minimal session types, session types without sequencing. This is arguably the simplest form of session types. By relying on a core process calculus with sessions and higher-order concurrency (abstraction-passing), we prove that every process typable with standard (non minimal) session types can be compiled down into a process typed with minimal session types. This means that having sequencing constructs in both processes and session types is redundant; only sequentiality in processes is indispensable, as it can precisely codify sequentiality in types.Our developments draw inspiration from work by Parrow on behavior-preserving decompositions of untyped processes. By casting Parrow's results in the realm of typed processes, our results reveal a conceptually simple formulation of session types and a principled avenue to the integration of session types into languages without sequencing in types.
A Novel Ensemble Approach for Anomaly Detection in Wireless Sensor Networks Using Time-overlapped Sliding Windows

One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data mining.  Our proposed algorithm, at first applies a fuzzy clustering approach using the well-known C-means clustering method to create the clusters. In the classification step, we created some base classifiers, each of which utilizes the data of overlapping windows to utilize the correlation among data over time by creating time-overlapped batches of data. By aggregating these batches, the classifier proceeds to find an appropriate label for future incoming instance. The concept of “Ensemble of Classifiers” with majority voting scheme has been used in order to combine the judgment of all classifiers. The results of our implementation with MATLAB toolboxes shows that the proposed majority-based ensemble learning method attains more efficiency compared to the case of the single classifier method. Our proposed method enhances the performance of the system in terms of major criteria such as False Positive Rate, True Positive Rate, False Negative Rate, True Negative Rate, Sensitivity, Specificity and also the ROC curve.
Embedding-based system for the Text part of CALL v3 shared task

This paper presents a scoring system that has shown the top result on the text subset of CALL v3 shared task. The presented system is based on text embeddings, namely NNLM~\cite{nnlm} and BERT~\cite{Bert}. The distinguishing feature of the given approach is that it does not rely on the reference grammar file for scoring. The model is compared against approaches that use the grammar file and proves the possibility to achieve similar and even higher results without a predefined set of correct answers. The paper describes the model itself and the data preparation process that played a crucial role in the model training.
Preliminary Study on Biometric Recognition Based on Drawing Tasks

In this paper, we analyze the possibility to identify people using online handwritten tasks different from the classic ones (signature and text). Our preliminary results reveal that some drawings offer a reasonably good identification rate, which is 78.5% in the case of multi-sectional vector quantization applied to classify circles and house drawing tasks. To the best of our knowledge, this is the first paper devoted to biometric recognition based on drawing tasks.