Reviewerly/exHarmony huggingface dataset:
Aspect-Based Argument Mining

Computational Argumentation in general and Argument Mining in particular are important research fields. In previous works, many of the challenges to automatically extract and to some degree reason over natural language arguments were addressed. The tools to extract argument units are increasingly available and further open problems can be addressed. In this work, we are presenting the task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of Aspect Term Extraction (ATE) and Nested Segmentation (NS). At the first instance, we create and release an annotated corpus with aspect information on the token-level. We consider aspects as the main point(s) argument units are addressing. This information is important for further downstream tasks such as argument ranking, argument summarization and generation, as well as the search for counter-arguments on the aspect-level. We present several experiments using state-of-the-art supervised architectures and demonstrate their performance for both of the subtasks. The annotated benchmark is available at https://github.com/trtm/ABAM.
Contribution of the implementation of an alternative teaching model in the teaching of mathematics: solving arithmetic problems in primary basics

This article is the product of the research process carried out within the framework of doctoral thesis research entitled, "Alternative teaching model for the resolution of arithmetic problems in primary basic". Which seeks to provide the education system with a new alternative composed of a series of consecutive steps, with the general objective of "Strengthening the basic mathematical competencies of primary school students through a teaching model that facilitates the resolution of arithmetic problems".
Understanding the Tradeoff between Cost and Quality of Expert Annotations for Keyphrase Extraction

Generating expert ground truth annotations of documents can be a very expensive process. However, such annotations are essential for training domain-specific keyphrase extraction models, especially when utilizing data-intensive deep learning models in unique domains such as real-estate. Therefore, it is critical to optimize the manual annotation process to maximize the quality of the annotations while minimizing the cost of manual labor. To address this need, we explore multiple annotation strategies including self-review and peer-review as well as various methods of resolving annotator disagreements. We evaluate these annotation strategies with respect to their cost and on the task of learning keyphrase extraction models applied with an experimental dataset in the real-estate domain. The results demonstrate that different annotation strategies should be considered depending on specific metrics such as precision and recall.
Komparasi Model Prediksi Daftar Ulang Calon Mahasiswa Baru Menggunakan Metode Decision Tree Dan Adaboost

Every year, all the colleges hold new student enrollment. It is needed to start a new school academic year. Unfortunately, the number of students who resigned is considerably high to reach 837 students and caused 324 empty seats. The college’s stakeholders can minimize the resignation number if the selection phase of new students is done accurately. Making a machine learning-based model can be the answer. The model will help predict which candidates who potentially complete the enrollment process. By knowing it in the first place will help the management in the selection process. This prediction is based on historical data. Data is processed and used to train the model using the Adaboost algorithm. The performance comparison between Adaboost and Decision Tree model is performed to find the best model. To achieve the maximum performance of the model, feature selection is performed using chi-square calculation. The results of this research show that the performance of Decision Tree is lower than the performance of the Adaboost algorithm. The Adaboost model has f-measure score of 90.9%, precision 83.7%, and recall 99.5%. The process of analyzing the data distribution of prospective new students was also conducted. The results were obtained if prospective students who tended to finish the enrollment process had the following characteristics: graduated from an Islamic school, 19-21 years old, parents' income was IDR 1,000,000 to IDR. 5,000,000, and through the SBMPTN program.
Data Management Conceptual Algorithm of Transnational Digital Scientific Infrastructure as an Answer to the Globalization Challenges

Research background: The research background of the article has been formed by the analyses and expert assessments, made during the development of 10 different, strategic plans for the development of transnational digital distributed scientific infrastructure. This type of scientific network represents the answer of the regional research institutions, scientists, and businesses to the globalization challenges and emerging technologies. Purpose of the article: The purpose of the paper is to contribute to the enrichment of the limited amount of literature on project management of digital transnational science networks through presenting a conceptual algorithm of a starting-up scientific infrastructure project. Methods: The approach is based on defining the core inter-related features of a data management, developed for the needs of a newly- establishing virtual transnational scientific infrastructure. It is aiming to justify the defined principles for FAIR data, Data set description, Standards, and metadata, Data sharing, access and preservation, Restricted and Open research data, Data quality, laid down by the data management conceptual algorithm. Findings &amp; Value added: The algorithm has been presented as an integral part of a won project aiming to establish a virtual trans-national science infrastructure between partners from 5 EU countries. The findings are dealing with the definition of new organizational models based on a common mega-data system. The proper adaptation of the data management algorithm by each partner will improve the information management within its specific infrastructures as well as the visibility of their research outputs.
MODELLING AN INTELLIGENT TUTORING SYSTEM USING REINFORCEMENT LEARNING

Intelligent tutoring systems are computer systems that provide personalized instructions to the learner, replacing a human tutor by a machine. They aim to suggest personalized studying strategies taking into consideration student needs, level of knowledge and abilities. An ITS consists of four interacting components: the knowledge base that contains all information to be taught, the student model that contains information related to the student’s current knowledge status, the pedagogical module that decides what teaching strategy to use and the interface module that facilitates the communication between the ITS and the student. Researchers have built ITS that teach different subjects such as algebra, equation solving, chemistry, database design etc. To make and ITS more ‘intelligent’, in their design are applied different artificial intelligence principles. In this work we propose a model that uses reinforcement learning for building ITS that teaches concepts of the python programming language. Reinforcement learning is a form of unsupervised learning in which an agent learns the best policy to act by interacting with the environment through trial and error. After each action that the agent makes, the environment gives a reward that determines if that action was good or bad. The agent has no previous knowledge about the environment, but through interaction and reward it learns what are the best actions. We focus on the pedagogical module of the ITS and model it as a reinforcement learning agent, in order to learn the best strategy for presenting the learning materials to each individual student. The learning materials consist of different lessons related to python programming language. Each lesson teaches some concepts and may require prior knowledge of some other concept. The student decides what concepts wants to learn and may or may not have previous knowledge about those concepts. Taking into consideration this fact, the pedagogical module should determine a strategy to present lessons that teach concepts that the student wants to learn, and make sure that student has acquired knowledge of concepts that are precondition for learning some other concepts. The combination of the concepts that the student wants to learn and concepts that he already knows may be large, so for every student may be needed a different strategy. Because of these, we propose a model that uses reinforcement learning for the pedagogical module so it can be trained to learn the best strategy for presenting the learning materials to each student based on their knowledge and what they want to learn. We propose a way of how to organize the states, actions and rewards that can be used in training this system using reinforcement learning.
Person Re-identification Based on Attribute Hierarchy Recognition

In order to improve the accuracy rate of person re-identification, a pedestrian attribute hierarchy recognition neural network is proposed based on attention model. Compared with the existing algorithms, the model has the following three advantages. Firstly, the attention model is used in this paper to identify the pedestrian attributes, and to extract of pedestrian attribute information and degree of significance. Secondly, the attention model in used in this paper to classify the attributes according to the significance of the pedestrian attributes and the amount of informationcontained. Thirdly, this paper analyzes the correlation between attributes, and adjusts the next level identification strategy according to the recognition results of the upper level. It can improve the recognition accuracy of small target attributes, and the accuracy of pedestrian recognition is improved. The experimental results show that the proposed model can effectively improve the first accuracy rate (rank-1) of person re-identification compared with the existing methods. On the Market1501 dataset, the first accuracy rate is 93.1%, and the first accuracy rate is 81.7% on the DukeMTMC dataset.
MATHEMATICS FOR MACHINE LEARNING

Machine learning is a way to study the algorithm and statistical model that is used by computer to perform a specific task through pattern and deduction [1]. It builds a mathematical model from a sample data which may come under either supervised or unsupervised learning. It is closely related to computational statistics which is an interface between statistics and computer science. Also, linear algebra and probability theory are two tools of mathematics which form the basis of machine learning. In general, statistics is a science concerned with collecting, analysing, interpreting the data. Data are the facts and figure that can be classified as either quantitative or qualitative. From the given set of data, we can predict the expected observation, difference between the outcome of two observations and how data look like which can help in better decision making process [2]. Descriptive and inferential statistics are the two methods of data analysis. Descriptive statistics summarize the raw data into information through which common expectation and variation of data can be taken. It also provides graphical methods that can be used to visualize the sample of data and qualitative understanding of observation whereas inferential statistics refers to drawing conclusions from data. Inferences are made under the framework of probability theory. So, understanding of data and interpretation of result are two important aspects of machine learning. In this paper, we have reviewed the different methods of ML, mathematics behind ML, its application in day to day life and future aspects.
Spatial Analysis for Fire Risk Reduction in Kampung Ampel Cultural Heritage Area, Surabaya

The objective of the research is to improve fire risk reduction in Kampung Ampel Surabaya through: [1] identifying the current firefighting system in Surabaya; [2] identifying the characteristics and conditions of Kampung Ampel; [3] identifying structure of the problem; [4] proposing strategies for fire risk reduction in Kampung Ampel. The analysis will focus on determining risks and resources of Kampung Ampel to fire hazard using Geographic Information System (GIS) analysis. Risk and resources are combined to find out the areas that have the highest risk of fire hazard. The results of the analyses consist of challenges and possible solutions. The challenges can be concluded as follow: [1] resources for firefighting cannot cover the entire area of Kampung Ampel; and [2] resources for evacuation cannot accommodate all the population and visitors. The proposed solutions for those challenges are: [1] reactivation of inactive fire wells; [2] utilization on source of water in Ampel Mosque; [3] proposing wider road to connect roads which are wider than 3.5 meters but are blocked by narrower roads; [4] the purchase of adapters to connect different types of fire hoses; [5] adding the number of fire hoses brought to the site; [6] remodeling the vulnerable buildings using inflammable materials with keeping the value of cultural landscape; [7] keeping portable fire pump in Ampel Mosque to facilitate the fire handling by residents; and [8] preparing evacuation route to the closest open space areas. The application of those solutions can reduce the high-risk area from 26.6% to 0.2%.
Service Oriented Cloud Computing Trusted Evaluation Model

More and more cloud computing services are being applied in various fields; however, it is difficult for users and cloud computing service platforms to establish trust among each other. The trust value cannot be measured accurately or effectively. To solve this problem, we design a service-oriented cloud trust assessment model using a cloud model. We also design a subjective preference weight allocation (SPWA) algorithm. A flexible weight model is advanced by combining SPWA with the entropy method. Aiming at the fuzziness and subjectivity of trust, the cloud model is used to measure the trust value of various cloud computing services. The SPWA algorithm is used to integrate each evaluation result to obtain the trust evaluation value of the entire cloud service provider.
YerevaNN’s Systems for WMT20 Biomedical Translation Task: The Effect of Fixing Misaligned Sentence Pairs

This report describes YerevaNN’s neural machine translation systems and data processing pipelines developed for WMT20 biomedical translation task. We provide systems for English-Russian and English-German language pairs. For the English-Russian pair, our submissions achieve the best BLEU scores, with en→ru direction outperforming the other systems by a significant margin. We explain most of the improvements by our heavy data preprocessing pipeline which attempts to fix poorly aligned sentences in the parallel data.
TEACHING DURING DISTANCE LEARNING – SHARED EXPERIENCE OF BULGARIAN TEACHERS

ICT is increasingly used in education in order to improve student motivation and learning effectiveness. To access the impact of educational platforms and ICT on achieving state educational standards, we conducted a survey of teachers’ opinions on the manner and effectiveness of distance learning, the advantages and disadvantages they report. In this article we present the results of a survey conducted through an online anonymous survey among 68 teachers from the city of Plovdiv who work in an innovative school approved by the Ministry of Education and Science. The subject of the research are professional experience, used resources, technologies, methods and pedagogical approaches. Based on the results obtained, we found the effectiveness of the distance learning, combined with the application of innovative methods and web-based educational technologies together with a wide variety of technological tools.
Mapping of data communication networks on social media

Information technology is developing very fast, this has an impact on real changes in every element of life. In addition to hitting the information media industry, developments in information technology have brought updates to public spaces with easy access and an increasingly massive pattern of information distribution. Ease of access does not always present a positive side but there is a negative side, namely shifting communication patterns with the spread of false information or disinformation methods that invite public upheaval. This research uses the case study method, which is one way to investigate contemporary phenomena in the context of real life, where the boundaries between the phenomenon and the context are not clearly visible. Through the Social Network Analyzer (SNA) theoretical approach, this research will find three communication network patterns, namely a centralized network, a decentralized network and a distributed network.
Comparing Different Machine Learning Techniques for Classifying Multi Label Data

In recent years, multi-label classifications have become common. Multi label classification is a classification in which a collection of labels is associated with a single instance, which may be a variation of the classification of a single label. The problem of huge data is the classification in which each instance is of different kind which further can be identified with more than one class. The various machine learning strategies for classifying multi-label data are discussed in this paper. Many researches have been carried out that specify the grouping of multiple labels. Here we will compare various classification machine learning techniques that involve two approaches: the adapted algorithm approach and the method of problem transformation. Here we are using naive multinomial bayes and logistic regression. We use certain evaluation metrics to predict the differences as well. Better classification methods are discussed in this paper.
Multilateral Transparency for Securities Markets through Dlt

For decades, changing technology and policy choices have worked to fragment securities markets, rendering them so dark that neither ownership nor real-time price of securities are generally visible to all parties multilaterally. The policies behind these developments are found in the US National Market System and the EU Market in Financial Instruments Directive, together with universal adoption of the indirect holding system, and have painted Western securities markets into a corner from which escape to full transparency has seemed either impossible or prohibitively expensive. Although the reader has a right to skepticism given the exaggerated promises surrounding blockchain in recent years, we demonstrate in this paper that distributed ledger technology (DLT) contains the potential to lead fragmented securities markets back to multilateral transparency. Leading markets generally lack transparency in two ways that derive from their basic structure: multiple platforms on which trades in the same security are matched have separate bid/ask queues and are not consolidated in real time (fragmented pricing), and high-speed transfers of securities are enabled by placing ownership of the securities in financial institutions, preventing transparent ownership (depository or street name ownership). The distributed nature of DLT allows multiple copies of the same pricing queue to be held simultaneously by a large number of order-matching platforms, curing the problem of fragmented pricing. This same distributed nature of DLT would allow the issuers of securities to be nodes in a DLT network, returning control over securities ownership to those issuers and thus restoring transparent ownership through direct holding with the issuer. A serious objection to DLT is that its latency is very high – with a Bitcoin blockchain transaction taking up to 10 minutes. To cure this, we first propose a private network without cumbersome proof-of-work cryptography and, second, introduce into our model the quickly evolving technology of “lightning networks”, which are advanced two-layer off-chain networks conducting high-speed transacting with only periodic memorialization in the permanent DLT network. This paper demonstrates against the background of existing securities trading and settlement that a DLT network could bring multilateral transparency and thus represent the next step in evolution for markets in their current configuration.
Repercussions of Coronavirus on Cyber-Security Threats

Coronavirus outbreak has posed different challenges to society all over the world.The term 'new normal' has emerged and resulted in a change in the working style of society.Countries are using all of their resources to fight the virus.To keep the economy stable, to maintain the continuity, organizations are working from home with limited cyber-security.There is a sense of fear and hysteria among the people about the virus.Such situations are always taken into an advantage by cyber-criminals and the number of cyber-security threats has increased in the pandemic.This paper provides knowledge about cyber-security attacks on people during the COVID'19 pandemic.It provides details about various types of cyberattacks experienced by people, which people were affected by the threats, reasons behind their vulnerability.We throw light upon specifically the range of cyber attacks and their impact on the workforce.It provides details about how the attacks should be prevented in the future.This paper helps people understand the severity of the attacks, and how these are exercised.We aim to encourage people to remain vigilant against such breaches and prepare them to confront if they witness these types of attacks.The paper is also written with an intention to motivate and support further research related to cyber-crimes during the Coronavirus pandemic.
Variational Intrinsic Control Revisited

In this paper, we revisit variational intrinsic control (VIC), an unsupervised reinforcement learning method for finding the largest set of intrinsic options available to an agent. In the original work by Gregor et al. (2016), two VIC algorithms were proposed: one that represents the options explicitly, and the other that does it implicitly. We show that the intrinsic reward used in the latter is subject to bias in stochastic environments, causing convergence to suboptimal solutions. To correct this behavior, we propose two methods respectively based on the transitional probability model and Gaussian Mixture Model. We substantiate our claims through rigorous mathematical derivations and experimental analyses.
English Characters OCR Pertinent for Mobile Devices

Optical character recognition concerns translating character images into character text.Four main stages constitute any optical recognition system.They are pre-processing, feature extraction, character recognition, and post-processing.The two most important stages are the feature extraction method and the character recognition algorithm.The optical character recognition system for mobile application must fulfill two requirements, small system size and high speed.In this research, a lightweight footprint feature dataset was created based on the center and centroid of the character image together with other simple image statistics.Fast character recognition algorithm based on weighted Euclidean distance was adapted.A mobile phone application prototype was developed.An accuracy of over 99% was achieved.The execution time for recognizing one character was in millisecond scale when a mobile phone model was used.