Reviewerly/exHarmony huggingface dataset:
Validation Analysis of Scalable Vector Graphics (SVG) File Upload using Magic Number and Document Object Model (DOM)

The use of technology is increasing rapidly, such as applications or services connected to the Internet.Security is considered necessary because of the growing and increasing use of digital systems.With the number of threats to attacks on digital form or server systems is required to handle the risk of attacks on the server, the file upload feature.The system usually processes the file upload feature on a website or server with server-side (back-end) validation or filtering of digital object file types or a client-side (front-end) web browser in HTML or Javascript.Filtering techniques for Scalable Vector Graphics (SVG) usually files only see the file extension or Multipurpose Internet Mail Extension (MIME) type of an uploaded file.However, this filtering can still manipulate, for example, in ASCII prefix checking, which has two writes, namely "<?xml" and "<svg ".SVG files do not contain metadata such as image encoded in JPEG or PNG files.This problem can overcome by adding filtering techniques to check the validation of a file with validation of eXtensible Markup Language (XML) using magic numbers and the Document Object Model (DOM).This research developed using the waterfall method and black-box security testing refers to a software security testing method in which security controls, defense, and application design are tested.Handling of security validation for uploading SVG files using file extensions and MIME types has a success rate of 75 percent from the eight tested scenarios while handling using file extensions, magic numbers, and Document Object Model (DOM) produces a success rate of 100 percent from 8 test scenarios.Testing uses a black-box so that handling using the file extension, magic number, and Document Object Model (DOM) is better than using only file extensions and mime types.
Multinomial Naïve Bayes and Rapid Automatic Keywords Extraction for Taharah (Purify) Law Chatbot

The aim of this study is to utilize the Natural Language Processing (NLP) technology, one of them is in the form of a chatbot.Chatbot has the ability to answer the questions as a conversational search engine.The methods that used on chatbot's machine are Multinomial Naïve Bayes (MNB) with TF-IDF vectorization to classify the intent, and Rapid Automatic Keywords Extraction (RAKE) to classify the entity.The methods are implemented for thaharah (purify) law as one of Muslim's daily life that cannot be separated from Islamic law.It is important for Muslims to know the thaharah law.The experiments of the methods against chatbot have used a total of 132 data trains and 44 data tests.Results represented by the Confusion Matrix showed the implementation of methods has the overall accuracy 97% with an average precision 90% and recall 97%, which means MNB and RAKE can give the answer well.
An Ontology Based Approach for Next Generation Customer Relationship Management Systems

Customer relationship management - CRM systems aim to increase customer satisfaction and loyalty to the company through actions that can be performed by monitoring and analyzing customer behaviours for companies. In these systems, the data obtained from different sources such as sales process, service, and call center are interpreted by the CRM experts as a whole, and the necessary action is taken for the customer. With the rapid progress in information technologies, the diversity of channels that are used to collect data, the size and dynamism of the data have increased. This situation has made it impossible for experts to wholly interpret big-size data from a wide variety of sources in a short period of time. In the CRM area, there is a need for technologies that automatically integrate the data obtained from several sources and can interpret it semantically. In this study, an ontology-based approach is proposed, which allows automatic semantic interpretations on the data obtained from different sources. Based on the proposed approach, a tool has been developed that can unify data from different CRM systems in a common format and make customer-centric interpretations of this data. The tool continuously monitors the systems it is integrated with, and when it detects the conditions defined by the users on the data, it can automatically perform the actions determined by the users. The tool developed was experienced by integrating a CRM system of a company and the evaluation results obtained in this process were discussed.
Predictive analytics for spam email classification using machine learning techniques

Automated text classification is the most widely used approach to manage an enormous amount of unstructured text data in digital forms, which is continuously increasing across the globe. Machine learning techniques are applied for automatic email filtering effectively to detect the spam mail and prevent them from delivering into the user's inbox. This paper used Logistic regression, k-Nearest Neighbours (k-NN), Naive Bayes, Decision Trees, AdaBoost, ANNs, and SVMs for spam email classification. All the classifiers are learned, and the performance measured in terms of precision, recall, and accuracy using a set of systematic experiments conducted on the Spambase data set extracted from the UCI Machine Learning Repository. The effectiveness of each model is empirically illustrated to find a better and viable alternative model. The quantitative performance analysis of supervised and hybrid learning techniques is presented in detail. Experimental results indicate that ensemble methods outperform in terms of accuracy compared with other methods applied.
Development of Image Dataset Using Hand Gesture Recognition System for Progression of Sign Language Translator

This article outlines the usage of a hand gesture recognition system using image processing for creating a unique image dataset to develop a sign language translator by using a deep learning approach that will decrease the communication gap with hearing-impaired people. The main focus of our research is to make progress of a sign language translator or electronic dictionary which will translate sign to text or vice versa depending on the users’ demand. The image dataset we are developing is original and consists of almost 1000 images. Here we are using the existing hand gesture recognition system algorithm in Matlab and image processing for further development. #COMESYSO1120 Please, keep this hash tag for conference identification.
Standards Conformance Metrics for Geospatial Linked Data

This paper describes a set of new Geospatial Linked Data (GLD) quality metrics based on ISO and W3C spatial standards for monitoring geospatial data production. The Luzzu quality assessment framework was employed to implement the metrics and evaluate a set of five public geospatial datasets. Despite the availability of metrics-based quality assessment tools for Linked Data, there is a lack of dedicated quality metrics for GLD, as well as, no metrics were found based on geospatial data standards and best practices. This paper provides nine new metrics and a first assessment of public datasets for geospatial standards compliance. Our approach also demonstrates the effectiveness of developing new quality metrics through analysis of the requirements defined in relevant standards.
Polar Codes for Quantum Reading

Quantum reading provides a general framework where to formulate the statistical discrimination of quantum channels. Several paths have been taken for such a problem. However, there is much to be done in the avenue of optimizing channel discrimination using classical codes. At least two open questions can be pointed to: how to construct low complexity encoding schemes that are interesting for channel discrimination and, more importantly, how to develop capacity-achieving protocols. The aim of this paper is to present a solution to these questions using polar codes. Firstly, we characterize the rate and reliability of the channels under polar encoding. We also show that the error probability of the scheme proposed decays exponentially with respect to the code length. Lastly, an analysis of the optimal quantum states to be used as probes is given.
Technological initiative for the "roadmap” implementation for the bachelors-designers’ creative development in art

The article discusses the possibilities of the pedagogical technologies’ initiative for the creative development of bachelors-designers, the use of the roadmap for the students’ creative potential realization, the possibilities of self-development and self-realization in artistic activity, the process of personality development on a personality-oriented basis, taking into account the formation of the students’ inner world, their motivation and system value relationships, educating and developing the artistic activity’s possibilities. The artistic activity’s functions in the learning process, art as a product of this activity are determined. The conditions for the internal spiritual transformation and harmonization of the student in the creative development context in the artistic activity process are discussed.
The Design of a Hybrid Model-Based Journal Recommendation System

There is currently an overload of information on the internet, and this makes information search a challenging task.Researchers spend a lot of man-hour searching for journals related to their areas of research interest that can publish their research output on time.In, this study, a recommender system that can assist researchers access relevant journals that can publish their research output on time based on their preferences is developed.This system uses the information provided by researchers and previous authors' research publications to recommend journals with similar preferences.Data were collected from 867 respondents through an online questionnaire and from existing publication sources and databases on the web.The scope of the research was narrowed down to computer sciencerelated journals.A hybrid model-based recommendation approach that combined Content-Based and Collaborative filtering was employed for the study.The Naive Bayes and Random Forest algorithms were used to model the recommender.WEKA, a machine learning tool, was used to implement the system.The result of the study showed that the Naïve Bayes produced a shorter training time (0.01s) and testing time (0.02s) than the Random forest training time (0.41) and testing time (0.09).On the other hand, the classification accuracy of the Random forest algorithm outperformed the naïve Bayes with % correctly classified instance of 89.73 and 72.66; kappa of 0.893 and 0.714; True Positive of 0.897 and 0.727 and ROC area of 0.998 and 0.977, respectively, among other metrics.The model derived in this work was used as a knowledge-base for the development of a web-based application, named "Journal Recommender" which allowed academic authors to input their preferences and obtain prompt journal recommendations.The developed system would help researchers to efficiently choose suitable journals to help their publication quest.
A Steganographic Embedding Scheme Using Improved-PSO Approach

In this paper, we are interested by tackling the steganography task as an optimization problem carried out by a bio-inspired approach. Indeed, a novel embedding scheme using the substitution principle of LSB and related to an improved version of particle swarm optimization algorithm is proposed. Therefore, to fulfill the requirement of a good steganographic tool, some updating at the evolution rules of a particle were done. The improved-PSO embedding scheme looks for the best pixels locations, and eventually the best pixels bits to hide secret messages -both text and image- without degrading the quality of the original image. Thus, the robustness of the proposed steganographic scheme relates strongly on the strength of the improved-PSO algorithm in order to achieve higher imperceptibility. Three test cases were undertaken to highlight the performance of the improved-PSO embedding scheme.
Experience in Development and Implementation of an Information Management System in a Technical University

This article discusses the experience of developing and implementing a university management information system, as part of the implementation tasks in accordance with the Regulation on the credit system of education in higher professional educational institutions in Republic of Tajikistan, which was approved by Ministry of education and science of Republic Tajikistan from 30.12.2016 г., with number № 19/24. This study presents the general results obtained during the automation of document management (workflow of documents) in university based on a management information system of Khujand Polytechnic institute of Tajik technical university (KPITTU). It is described the functionality of the system based on the conceptual document management scheme at the university. It is given the descriptions of the proposed multi-module system architecture with a brief description of each module. The main tasks pursued by the management information system are considered: ensuring effective management and transparency of the organization activity at all levels, information storage, data management and access regulation, formalization of the activities of each employee, optimization of business processes, saving of resources by reducing the cost of managing paper records. Optimization of educational processes and decision making for university management is analysed. Determining the efficiency from embedding automation in KPITTU is given.
Statistical Characteristics of Decisions Made by a Neural Network Molecule with Quadrant Quantization and a Molecule with Data Quantization by Two Ellipses

The paper discusses the statistical characteristics of decisions made by a neural network molecule with quadrant quantization and a molecule with data quantization by two ellipses. Examples of software for a numerical experiment to model the states of two neurons-molecules in MathCAD are given. #COMESYSO1120.
Pemetaan Kebutuhan Pengembangan Bahan Ajar Digital

This study intends to map the needs of the PGSD FKIP UMSU academic community on the development of digital teaching materials that can be accessed by students at any time because they are integrated with Android. This research is part of the research development (R&amp;D). This type of research is an expo facto research where the researcher collects data directly from the respondent through a data collection instrument. The data collection process is a very important stage in research. The data to be collected will later be used to test the hypotheses proposed in the study where the data is obtained through interviews, questionnaires, and observations. The data analysis technique was done by using quantitative descriptive method. The results showed that there was a positive trend towards the need for digital-based teaching materials development in PGSD FKIP UMSU Study Program. So far, there has not been a single teaching material packaged by lecturers in digital form in the study program. The results of a questionnaire to students showed that 90% of students needed teaching materials that could be accessed by them at any time and wherever they were.
On Hagelbarger’s and Shannon’s matching pennies playing machines

Abstract In the 1950s, Hagelbarger’s Sequence Extrapolating Robot (SEER) and Shannon’s Mind-Reading Machine (MRM) were the state-of-the-art research results in playing the well-known “matching pennies” game. In our research we perform a software implementation for both machines in order to test the common statement that MRM, even simpler, beats SEER. Also, we propose a simple contextual predictor (SCP) and use it to compete with SEER and MRM. As expected, experimental results proves the claimed MRM superiority over SEER and even the SCP’s superiority over both SEER and MRM. At the end, we draw some conclusions and propose further research ideas, like the use of mixing models methods and the use of Hidden Markov Model for modelling player’s behaviour.
Coordinate transitivity of a class of extended perfect codes and their SQS

We continue the study of the class of binary extended perfect propelinear codes constructed in the previous paper and consider their permutation automorphism (symmetry) groups and Steiner quadruple systems. We show that the automorphism group of the SQS of any such code coincides with the permutation automorphism group of the code. In particular, the SQS of these codes are complete invariants for the isomorphism classes of these codes. We obtain a criterion for the point transitivity of the automorphism group of SQS of proposed codes in terms of GL-equivalence (similar to EA-type equivalence for permutations of F^r). Based on these results we suggest a new construction for coordinate transitive and neighbor transitive extended perfect codes.
Modern Education: Development Trends and Prospects

Aim. The presented study aims to analyze and critically re-evaluate current trends in the development of education. Tasks. The authors examine the concept of lifelong learning; identify the strengths and weaknesses of modern digital technologies such as gamification, virtual reality (VR), and augmented reality (AR); show the prospects for the development of education through distance learning. Methods. This study is based on a systems approach using methods of comparative and structural analysis, analytical and theoretical generalization. Results. Current trends in the development of education associated with the introduction of digital technologies are considered, and the prospects for the development of the industry are determined. Conclusions. The concepts of lifelong learning and practice-oriented education are becoming increasingly widespread in the context of the digital economy and labor market transformation. The forced transition to distance learning driven by the COVID-19 pandemic has become a catalyst for rapid changes in education. Digital educational technologies such as VR and AR expand the possibilities of the natural educational environment, while also bearing certain risks.
Aggregation, Storing, Multidimensional Representation and Processing of COVID-19 Data

An outbreak of a new coronavirus disease (COVID-19) has spread rapidly around the world, which in turn has necessitated the prompt detection of people with symptoms in public. To do this, it is necessary to develop information systems for the accumulation and processing of large data collections obtained from various information sources. The article presents a prototype of a subsystem for storing an extensive list of information about COVID-19. The developed prototype includes the structure of the subsystem for storage, retrieval and processing of information on the detection, dissemination, rapid response to COVID-19 outbreaks. The multidimensional representation of information on COVID-19 formed on the basis of data hypercubes is considered. Describes the composition and purpose of measurements to provide information on devices used to detect and identify individuals with COVID-19 symptoms. Measurements for storing information of the mobile operator, which are used to establish the trajectories of citizens in order to identify cases of intersection of trajectories with a person infected with COVID-19, are considered. Based on the proposed information model of detection processes in public places of persons with suspected COVID-19 infection, a method of search and identification of such persons has been developed. The method is formed using procedures for finding intersections of trajectories of citizens.
Accuracy Analysis on 360° Virtual Reality Video Quality Assessment Methods

Due to the rapid development of telecommunication technology, the application of 360° video is increasingly gaining attention. For the design of the 360° video transmission mechanism, quality of experience (QoE) from the customer's perspective is very important. This study can help the readers to understand advantages/limitations of different 360° video quality assessment methods and be able to make suitable choices for various systems. To make it clearer, this paper performs experiments with two steps. Experiment I compares different assessment methods for evaluating 360° video quality selected from online and offline methods, respectively. Experiment II studies the performance of these assessment methods on different video quality levels. The results show that both offline and online test results have a relatively good correlation with the subjective test results. This paper statistically evaluates and compares the accuracy of different 360° video QoE assessment methods and the conclusion drawn from this paper can be used as a guideline when designing adaptive 360° video streaming systems and can also be applied to cloud computing in the future work.
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering

Interpretability and explainability of deep neural networks are challenging due to their scale, complexity, and the agreeable notions on which the explaining process rests.Previous work, in particular, has focused on representing internal components of neural networks through humanfriendly visuals and concepts.On the other hand, in real life, when making a decision, human tends to rely on similar situations and/or associations in the past.Hence arguably, a promising approach to make the model transparent is to design it in a way such that the model explicitly connects the current sample with the seen ones, and bases its decision on these samples.Grounded on that principle, we propose in this paper an explainable, evidence-based memory network architecture, which learns to summarize the dataset and extract supporting evidences to make its decision.Our model achieves state-of-the-art performance on two popular question answering datasets (i.e.TrecQA and WikiQA).Via further analysis, we show that this model can reliably trace the errors it has made in the validation step to the training instances that might have caused these errors.We believe that this error-tracing capability provides significant benefit in improving dataset quality in many applications.
Foreign languages communicative skills in Secretarial Studies

The aim of the foreign languages teaching is to enable students to further develop and use English language skills in general and specific contexts and this aim is always achieved through the practice of the four skills - reading, writing, listening and speaking. As far as speaking skills are concerned, oral assessment and role-play situations are often used by English teachers to assess students’ skills. Students pursuing a degree in Secretarial Studies and Administrative Assistance at ESTGL and want to become successful administrative professionals must be aware of the importance of English in their future jobs and must be prepared to communicate in real contexts. This paper, using a descriptive humanistic-interpretative methodology, aims to acknowledge that role-play is one technique a language teacher can explore and to substantiate the Communicative Language Teaching approach used in the Bachelor of Secretarial Studies and Administrative Assistance. We concluded that it boosts students’ confidence and their personal interaction skills. and a maximum of 150 words.