Reviewerly/exHarmony huggingface dataset:
Design Restaurant Information System (RIS) Cloud Based with the Zachman Framework Approach

Information system technology has become an especially important component in various companies in Indonesia, including the restaurant business.But some of the big problems that are often faced by the restaurant business in implementing SI have become increasingly clear, among which are systems that are running less than optimal due to lack of experience.This paper will examine a restaurant franchise in Indonesia called SS Restaurant.Currently, SS Restaurant is facing complaints against the restaurant information system (RIS) such as the RIS system which is no longer adequate and does not have the latest features and the research and development (R&D) process of the restaurant is not running due to slow data processing and not optimal control outlets.The purpose of this paper is to design and recommend new business processes in restaurant RIS and add new system models such as online ordering, real-time reporting, cloud-based systems and data analysis for restaurants.The model in the Zachman framework that will be used in this research framework becomes a 3x3 matrix, basedon 3 main perspectives required by Restaurant SS management that is: General Scope, Owner Perspective and Designer Perspective.By implementing this new RIS, Restaurant SS is expected to be able to further increase their business' performance through the benefits of improved operational efficiency and continual development of the implemented information system.
Structure of Formation of Intercultural Competence in Specialists of Socionomic Professions: Social-Pedagogical Aspect

Radchenko M
FAIR data and data management requirements in a comparative perspective: Horizon 2020 and FWF policies

Based on our input to the Data Management Workshop, held during the Austrian Citizen Science Conference 2019 in Obergurgl, we provide a comparative perspective on the open data and data management requirements in the European Union’s Horizon 2020 programme and those of a national funder, the Austrian FWF, in this paper. We conclude that, although there are some differences in terminology and specific requirements, both the FWF and Horizon 2020 DMPs essentially cover the same ground.
Understanding recurrent neural network for texts using English-Korean corpora

Deep Learning is the most important key to the development of Artificial Intelligence (AI). There are several distinguishable architectures of neural networks such as MLP, CNN, and RNN. Among them, we try to understand one of the main architectures called Recurrent Neural Network (RNN) that differs from other networks in handling sequential data, including time series and texts. As one of the main tasks recently in Natural Language Processing (NLP), we consider Neural Machine Translation (NMT) using RNNs. We also summarize fundamental structures of the recurrent networks, and some topics of representing natural words to reasonable numeric vectors. We organize topics to understand estimation procedures from representing input source sequences to predict target translated sequences. In addition, we apply multiple translation models with Gated Recurrent Unites (GRUs) in Keras on English-Korean sentences that contain about 26,000 pairwise sequences in total from two different corpora, colloquialism and news. We verified some crucial factors that influence the quality of training. We found that loss decreases with more recurrent dimensions and using bidirectional RNN in the encoder when dealing with short sequences. We also computed BLEU scores which are the main measures of the translation performance, and compared them with the score from Google Translate using the same test sentences. We sum up some difficulties when training a proper translation model as well as dealing with Korean language. The use of Keras in Python for overall tasks from processing raw texts to evaluating the translation model also allows us to include some useful functions and vocabulary libraries as well.
A New Digital Signature Scheme Using Tribonacci Matrices

Achieving security is the most important goal for any digital signature scheme. The security of RSA, the most widely used signature is based on the difficulty of factoring of large integers. The minimum key size required for RSA according to current technology is 1024 bits which can be increased with the advancement in technology. Representation of message in the form of matrix can reduce the key size and use of Tribonacci matrices can double the security of RSA. Recently M.Basu et.al introduced a new coding theorycalled Tribonacci coding theory based onTribonacci numbers, that are the generalization ofthe Fibonacci numbers. In this paper we present anew and efficient digital signature scheme usingTribonacci matrices and factoring.
Seq2Seq AI Chatbot with Attention Mechanism

Intelligent Conversational Agent development using Artificial Intelligence or Machine Learning technique is an interesting problem in the field of Natural Language Processing. With the rise of deep learning, these models were quickly replaced by end to end trainable neural networks.
Vulnerability analysis of landslide at Sibolangit using Geographic Information System (GIS) based on population density and slope gradient

Abstract Sibolangit is one of districts in Deli Serdang that has wavy and hilly relief and has high gradient of slope. This condition causes Sibolangit is vulnerable towards natural landslide. One of the causes of landslide is population density and slope gradient / Based on BPS data in 2018, the population growth/year from 2016 to 2016 is 2.42% and in 2015 to 2016 is 2.37%. It shows that the growth in Sibolanngit increases where the population densitiy affects the changes of land use. People living on a land with high gradient is most likely to be one of the causes of slide. This research is to determine the vulnaribility level in Sibolangit based on population density and gradient. The analysis is done using Geographic System Information. Parameter used is gradient, population density, working factor, number of children, number of the elderly, and number of women to map the vulnaribility. Risk Map with citizenship as parameter has three classes such as low, high and very high.
LazyBum: Decision Tree Learning Using Lazy Propositionalization

Propositionalization is the process of summarizing relational data into a tabular (attribute-value) format. The resulting table can next be used by any propositional learner. This approach makes it possible to apply a wide variety of learning methods to relational data. However, the transformation from relational to propositional format is generally not lossless: different relational structures may be mapped onto the same feature vector. At the same time, features may be introduced that are not needed for the learning task at hand. In general, it is hard to define a feature space that contains all and only those features that are needed for the learning task. This paper presents LazyBum, a system that can be considered a lazy version of the recently proposed OneBM method for propositionalization. LazyBum interleaves OneBM's feature construction method with a decision tree learner. This learner both uses and guides the propositionalization process. It indicates when and where to look for new features. This approach is similar to what has elsewhere been called dynamic propositionalization. In an experimental comparison with the original OneBM and with two other recently proposed propositionalization methods (nFOIL and MODL, which respectively perform dynamic and static propositionalization), LazyBum achieves a comparable accuracy with a lower execution time on most of the datasets.
Life plans of students – future engineers

Е. А. Коган, Л. В
PLANS: Robust Program Learning from Neurally Inferred Specifications

Recent years have seen the rise of statistical program learning based on neural models as an alternative to traditional rule-based systems for programming by example. Rule-based approaches offer correctness guarantees in an unsupervised way as they inherently capture logical rules, while neural models are more realistically scalable to raw, high-dimensional input, and provide resistance to noisy I/O specifications. We introduce PLANS (Program LeArning from Neurally inferred Specifications), a hybrid model for program synthesis from visual observations that gets the best of both worlds, relying on (i) a neural architecture trained to extract abstract, high-level information from each raw individual input (ii) a rule-based system using the extracted information as I/O specifications to synthesize a program capturing the different observations. In order to address the key challenge of making PLANS resistant to noise in the network's output, we introduce a filtering heuristic for I/O specifications based on selective classification techniques. We obtain state-of-the-art performance at program synthesis from diverse demonstration videos in the Karel and ViZDoom environments, while requiring no ground-truth program for training. We make our implementation available at github.com/rdang-nhu/PLANS.
Towards Situated User-Driven Interaction Design of Ambient Smart Objects in Domestic Settings

In recent years, involving users in the design of Internet of Things (IoT)-based solutions for everyday settings has received significant attention in Human-Computer Interaction (HCI) studies. This paper aims to contribute to that line of research by proposing an interaction design approach, which extends the scope of user-driven design beyond concept generation to support interaction design activities within the entire task-artifact cycle. We analyze key challenges for creating IoT-based solutions for domestic environments, and argue that dealing with such challenges requires adopting an approach characterized by (a) a focus on users’ activity spaces rather than individual objects and tasks, (b) support and assessment of the actual, and sufficiently extended, use experience of new solutions, (c) augmenting existing everyday objects, to transform them into smart ones, rather than developing entirely new smart objects, and, last but not least, (d) combining IoT prototyping and digital fabrication to ensure physical and esthetical integration of new designs into concrete everyday settings.
Quality Assessment of Modular Educational Resources for Smart Education System

Modular educational resource is defined as an autonomous set of studying and training materials that consist of information, practical, and controlling components for the discipline and created by teaching staff according to the thematic elements of subjects claimed to the professional curriculums. Modular educational resources are known as being complete interactive multimedia products aimed at solving and dealing with educational issues. The unified information model for metadata based on the Learning Object Metadata (LOM) standard implementation provides an effective usage of different electronic educational resources in its full compliance with the modern federal program’s requirements targeted the educational environment for digitalization. The problem of the study is to represent the existing expert methods chosen as the basic ones for modular resources’ quality assessment. The methods of modular educational resources and the algorithm of its implementation, as well as a set of assessing quality indicators are revealed. The suggested methods have been tested on the Togliatti State University’s sites and proved their reliability and effectiveness.
CLuAI -- Conversational Learning using Artificial Intelligence

The purpose of this publication is twofold. Firstly, we would like to present an over-arching research proposal idea concerning the development of an interactive and adaptive learning framework that harnesses powerful tools from Artificial Intelligence, such as Data Mining, Natural Language Processing and Dynamic Difficulty Adjustment, in order to adapt the learning experience to the individual user's needs. Secondly, we would like to outline some of the preliminary work that we have carried out to date. This involved developing a prototype system to interactively teach Java programming and was developed as a Final Year Project by Michael Gavin, a fourth-year undergraduate computer science student at NUI Galway. This prototype system, developed using the Spring boot framework with a Rest API and MySQL backend, acts as an early "\emphproof of concept '' for developing these ideas further.
Arabic Speaker Identification System Using Multi Features

The performance regarding the Speaker Identification Systems (SIS) has enhanced because of the current developments in speech processing methods, however, an improvement is still required with regard to text-independent speaker identification in the Arabic language. In spite of tremendous progress in applied technology for SIS, it is limited to English and some other languages. This paper aims to design an efficient SIS (text-independent) for the Arabic language. The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is training, in this phase a corpus of reference database is built which will serve as a reference for comparing and identifying the speaker for the second phase. The second phase is testing, which searches the identification of the speaker. In this system, the features will be extracted according to: Mel Frequency Cepstrum Coefficient (MFCC), mathematical calculations of voice frequency and voice fundamental frequency. Machine learning classification techniques: K-nearest neighbors, Sequential Minimum Optimization and Logistic Model Tree are used in the classification process. The best classification technique is a K-nearest neighbors, where it gives higher precision 94.8%.
Peculiarities of SMS Abbreviations in Messages (by the Example of the Germanic Languages)

The study aims to determine peculiarities of making SMS abbreviations in messages using examples from English and German. The article considers the following models of SMS-abbreviations: initial abbreviations, syllabic abbreviations, replacement of a part of a word with a digit, morpheme contractions, omission of the subject in the sentence and grapheme identity. Scientific novelty lies in identification of common models that contribute to compilation of SMS abbreviations in both English and German. As a result, general rules for making SMS abbreviations for the two foreign languages are determined, and results of a survey are presented confirming significance and practical need for the research topic.
Fuzzy Inference System as an Aggregation Operator - Application to the Design of a Soil Chemical Quality Index

Fuzzy logic is widely used in linguistic modeling. In this work, fuzzy logic is used in a multicriteria decision making framework in two different ways. First, fuzzy sets are used to model an expert preference relation for each of the individual information sources to turn raw data into satisfaction degrees. Second, fuzzy rules are used to model the interaction between sources to aggregate the individual degrees into a global score. The whole framework is implemented as an open source software called GeoFIS. The potential of the method is illustrated using an agronomic case study to design a soil chemical quality index from expert knowledge for cacao production systems. The data come from three municipalities of Tolima department in Colombia. The output inferred by the fuzzy inference system was used as a target to learn the weights of classical numerical aggregation operators. Only the Choquet Integral proved to have a similar modeling ability, but the weights would have been difficult to set from expert knowledge without learning.
Key aspects of implementing the help desk system in an educational institution

The article summarizes the results of a practical study on the problem of automating the incident management process in the IT - infrastructure of an educational organization. Implementing an incident management system can lead to a complete overhaul of all processes. The cost-effectiveness of implementing the system in an educational organization can be reflected in saving working time when processing requests for maintenance. The organizational effectiveness of the project implementation will be reflected in a significant improvement in the quality of services provided by the organization's it infrastructure and their maintenance.
Self-Explanatory Interface: Embedding Visual and Aural Syntax to Improve Usability

User Interface (UI) acts as a mediator between human and computer or any other system or sub-systems which exhibit its function, structure, and behavior. The complexities of UI are open issues that make it formidable to explore it. Supervision makes any task easier by explaining the elements and intentions of UI. Self-Explanatory User Interfaces (SEUI) traps the complexities of UI and provides assistive tools with it in order to supervise users addressing usability using visual and aural syntax. UIs require adapting to the mental model of users and support them during their task. The study presented in this paper has been administered to adult users (age group: 50-64 years) with minimum exposure to technologies and often requires support to get accustomed to interfaces. To complete a task successfully, it is essential to justify the intent of the design, purpose of any textual entry, the usability of the buttons, importance of the labels, visibility, or blurriness of certain items to end-users. Literature associated with SEUI highlight the intervention of Model-Driven Engineering in UI. But very few studies in this area have investigated concatenation of visual and aural syntax in UI. The visual aspect aims at explaining a clear and concise diagrammatic representation of the hierarchy of UI and the significance of each user entry while the aural dimension intends to provide audio instructions to the users. The results include evidence of significant growth in task completion rate and improvement in the task completion time of the novice users assisted with combined visual and aural syntax tool embedded in an interface. The results of the current study are significant in light of the designing and development of UIs that address adults who are a novice and lack exposure to UIs. The solution presented in this paper would support them to migrate from being a novice to an expert.
Targeted awareness: promoting niche resources using confirmation emails

Purpose The purpose of this paper is to examine the effectiveness of a targeted email advertising method that informs university students about library resources relevant to their major. Design/methodology/approach Over the course of one semester, students with business and communications majors who reserved group study rooms received customized confirmation emails that included targeted advertisements for library databases. These advertisements invited students to click a link that led to a database related to a student's major. Near the end of the semester, students were invited to complete a short survey about the advertisements. Survey questions were designed to discover whether the advertisements were seen and to measure database relevancy. The survey also gathered participant perceptions about the value of this targeted awareness advertising approach. Findings Overall, the technique of appending database advertisements to reservation emails proved ineffective; very few students recalled seeing the advertisements or clicking the links. On the positive side, this study showed that very few of the survey respondents disapproved of receiving advertisements. Another positive result was that a majority indicated the selected library databases were relevant to their needs. Respondents also offered suggestions for improving this marketing technique. Originality/value This research introduces a novel, unobtrusive approach to advertising library resources to a targeted audience. The results can be used by library administrators as a starting point for experimenting with similar (yet improved) methods of sharing information about relevant library resources with specific user populations.
Research on the Application of Virtual Simulation Technology and Vocational Education Teaching

Abstract Practical Teaching is Very Important to Vocational Education, But There Are Many Difficulties in Vocational Education Teaching. on the One Hand, It is Impossible for the School to Set Up Its Own Internship Factory for Its Own Major Due to the Restrictions of Capital Investment and Teaching Funds, Venues and Professionals. on the Other Hand, How to Find the Best Teaching Mode between the High Expectations of the Society for Vocational Education and the Students with Low General Scores?, Education Simulation Technology Can Meet the Requirements, the Simulation Technology in Vocational Education, the Author of This Paper How the Product Development, Design, Development Method and the Application of Simulation Technology in the Vocational Education Mode, Application Characteristic and Using Effect Were Discussed.
Blockchain-Based Differential Privacy Cost Management System

Privacy preservation is a big concern for various sectors. To protect individual user data, one emerging technology is differential privacy. However, it still has limitations for datasets with frequent queries, such as the fast accumulation of privacy cost. To tackle this limitation, this paper explores the integration of a secured decentralised ledger, blockchain. Blockchain will be able to keep track of all noisy responses generated with differential privacy algorithm and allow for certain queries to reuse old responses. In this paper, a demo of a proposed blockchain-based privacy management system is designed as an interactive decentralised web application (DApp). The demo created illustrates that leveraging on blockchain will allow the total privacy cost accumulated to decrease significantly.
A Robust Scheme to Improving Security of Data using Graph Theory

With the incredible growth of using internet and other new telecommunication technologies, cryptography has become an absolute necessity for securing communications between two or more entities, particularly in the case of transferring confidential data.In the literature, many encryption systems have been proposed against attack threats.These schemes should normally overcome the concerns by ensuring confidentiality, integrity and authenticity of transmitted data.However, several of them have shown weaknesses in terms of security and complexity.Hence the need for a robust and powerful non-standard encryption algorithm to prevent any traditional opportunity to sniff data.In this work, we propose a new encryption system that perfectly meets the security requirements.The scheme is based essentially on the principles of graph theory which are very promising at plain text representations.Our approach proposes another use of the concept of Hamiltonian circuit and adjacency matrix using a shared key and a pseudo-random generator.After analysis of the experimental results, which were very promising, the technique was found to be both efficient and robust.