Reviewerly/exHarmony huggingface dataset:
Compressed Data Structures for Binary Relations in Practice

Binary relations are commonly used in Computer Science for modeling data. In addition to classical representations using matrices or lists, some compressed data structures have recently been proposed to represent binary relations in compact space, such as the k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -tree and the Binary Relation Wavelet Tree (BRWT). Knowing their storage needs, supported operations and time performance is key for enabling an appropriate choice of data representation given a domain or application, its data distribution and typical operations that are computed over the data. In this work, we present an empirical comparison among several compressed representations for binary relations. We analyze their space usage and the speed of their operations using different (synthetic and real) data distributions. We include both neighborhood and set operations, also proposing algorithms for set operations for the BRWT, which were not presented before in the literature. We conclude that there is not a clear choice that outperforms the rest, but we give some recommendations of usage of each compact representation depending on the data distribution and types of operations performed over the data. We also include a scalability study of the data representations.
Development of a flowchart-based English-speaking chatbot for Korean primary students’ negotiation of meaning

The increasing influence of artificial intelligence in human society has encouraged novel attempts in the domain of English language education. One such attempt involves using chatbots to provide learners with meaningful English-mediated conversations. Chatbots are conversational agent systems that can process natural languages (e.g., English) to perform various linguistic tasks including interviews and small talks. Recognizing the practical utility of chatbots, this paper introduces a flowchart-based English-speaking chatbot which has been developed for Korean primary students to engage in negotiation of meaning (Long, 1983), a famous interactional event known to facilitate second language acquisition. This paper first discusses the theoretical and technological bases of the chatbot and then demonstrates details in its design as well as the development process using Dialogflow API. This information should provide useful guidelines when another theoretical insight from second language research applies to the design of a new chatbot for young learners of English.
Sequential Covering Rule Learning for Language Rule-based Aspect Extraction

Aspect extraction in aspect-based sentiment analysis has become a very challenging and important study today. One of the aspect extraction approaches is based on language rules (grammar). Language rules on most methods are manually determined, so they are more vulnerable to error. In this paper, we propose a rule learning method for aspect extraction using the Sequential Covering algorithm. The language features used are part-of-speech, dependency and constituent parse tree from each review sentence. This method generates rules starting from the simplest rule with a length of dependency relationship 1 to a certain length of dependency. Tests are carried out on several datasets with different domains. The test results show that this method succeeded in increasing f-measure aspect extraction compared to the baseline.
Citizen Sentiment Analysis in Social Media Moroccan Dialect as Case Study

Smart cities have millions of sensors and innovative technologies in order to improve the quality of their citizens and to increase the competitiveness of urban infrastructure. Nowadays these citizens like to communicate using social media such as Facebook and Twitter, thus building a smart city is not free from these platforms that have changed citizen’s daily life and becoming a new source of real-time information. These data are named Big Data and are difficult to process with classical methods. To exploit this data, it must be well-processed to cover a wide range of smart city functions, including energy, transportation, environment, security and smart city management. The aim of this paper is to highlight the advantages of social media sentiment analytics to support smart city by detecting various events and concerns of citizens. Towards the end, an illustrative scenario analyses data on citizens’ concerns about traffic in three main cities in Morocco.
Study of the porous media random structure model

The random structure model of porous media is constructed by the gravity packed method, which can give a random packed structure that is similar to the actual packed bed structure by simulating the free fall, collision and stacking process of pellets via the OpenFOAM. Different from the existing ordered structure model of porous media, the structure model adopted in this paper has strong randomness since it simulates the actual generation process of the porous media material. Meanwhile, the following batch modeling instructions are completed with the MATLAB, which can ensure that the random structure model of porous media has the advantages of high similarity with the actual porous structure and rapid modeling.
PENENTUAN RUTE TERPENDEK MENUJU PUSAT PERBELANJAAN DI JAKARTA MENGGUNAKAN ALGORITMA DIJKSTRA

Abstract : Shopping centers are easy to find in big cities like Jakarta and because there are too many times people are often confused which one to go to, not to mention if people from outside Jakarta come and want to visit a shopping center. The problem to choose a shopping center that encourages researchers to conduct a study to develop applications to find the nearest shopping center using the Dijkstra algorithm that can be used on smartphones in order to make it easier to find and find shopping centers around users. The application was designed using flowchart and UML and was made using Android Studio with the help of the Google Maps API to display maps, display pins, find and display the nearest route using latitude and longitude from Google Maps. Algorithm to calculate and determine the route that must be traversed using the Dijkstra algorithm. The accuracy of the calculation results using the Dijkstra algorithm reaches 93%
Proactively managing clones inside an IDE: a systematic literature review

Managing clones proactively is one of the hot research areas under Software Cloning domain. Researchers of the Clone Research Community argue that it is better to avoid clones rather than to refactor them. Integrated Clone Management Systems are the ways of implementing preventive (proactive) clone management approaches and are basically implemented as an add-on to the Integrated Development Environments. There are different techniques that are implemented as an add-on to IDEs, thus, literature has a corpus of plug-in tools that need to be organized and analyzed to have an efficient plug-in in the future. To identify the related literature, the well-known methodology proposed by Kitchenham [Guidelines for Performing Systematic Literature Reviews in Software Engineering. Keele University and University of Durham, EBSE Technical Report; 2007.; Procedures for performing systematic reviews. School of Computer Science and Mathematics, Keele University, Keele, Technical Report; 2004] for conducting systematic literature review is adopted in this paper along with the snowballing technique. Utilizing these techniques, near about 33 IDE based real-time code clone detection tools are identified. This paper presents a comprehensive and systematic literature review of all the clone management techniques found in the literature that are integrated with the software development environments as a plug-in tool. An in-depth investigation and comparative analysis of all the plug-ins is carried out in this survey paper so that the future work can be identified for the further research under this domain.
Exploring Learners' Perception on Improving Their Willingness to Communicate in English through Experiential Learning among Undergraduate Students

The purpose of this study is to explore the expectations and goals of students when participating in language activities, and how they perceive their willingness to communicate.The language activities involved in this study are LAX activities which are compulsory for all undergraduate students within this institution.Throughout the LAX activities, the students go through a form of experiential learning where they will have to complete a task, reflect on their work, and apply what they have learned.The students are interviewed regarding their expectations and goals prior to doing the language activities and their willingness to communicate during and after having done the activities.The findings show that students did set up positive goals before participating in the language activities, but they had negative expectations of the experience.It was also found that the students perceive an improvement in their willingness to communicate after participating in the activities.In the future, experiential learning could be implemented in language learning for positive outcomes, and more studies can be done on the topic to improve second language learning.
Risk Analysis of Flight Hard Landing Based on Data Statistics

Hard landing is one of the key issues among the civil aviation. Hard landing will cause the aircraft structure to bear greater loads, especially for landing gear, aircraft wings and other components, that will cause damage to the Aircraft airframe structure. Based on flight QAR data of China civil aviation transport fleet collected by China FOQA station, this article carries out the risk analysis, gives some corresponding improvement measures and suggestions.
Identification of Ontologies of Prediabetes Using SVM Sentiment Analysis

Sentiment analysis is considered as a classification task as it classifies the polarity of a text into positive or negative. Different methods of sentiment analysis can be applied for the health domain, especially for prediabetes domain which has not been completely explored yet. And there is a lack of approaches for analyzing positive and negative tweets separately to identify the positive and negative ontologies for modeling the features in a domain of interest. Here in my work, proposed domain and sub-domains are Health and Prediabetes, respectively. Prediabetes defines the condition of blood sugar levels that are higher than normal but not high enough to be diabetes like a pre-warning call for diabetes. The proposed methodology is the deployment of original ontology-based techniques toward a more efficient sentiment analysis of Twitter posts on prediabetes. As part of experimentation, sentiment analysis uses the SVM algorithm with term frequency as a feature extraction method to train and test a large and sub-data set of tweet text. Negative ontologies are constructed for a better understanding of the aspects identified through semantic annotations. The results of the classification method are evaluated using the performance metrics accuracy, precision, recall, and F-measure for effective evaluation of the proposed method.
Detection of Variable Misuse Using Static Analysis Combined with Machine Learning

Industrial static analyzers are able to detect only several narrow classes of algorithmic errors, for example actual arguments order swapped with formal parameters, forgotten renaming of variable after copy-paste. However, even for these categories essential part of errors is lost because of heuristical design of a checker. We propose the generalization of specified errors in the form of variable misuse problem and deal with it using machine learning. The proposed method uses message propagation through the program model represented as a graph, combining data from multiple analysis levels, including AST, dataflow. We introduce several error criteria, which were evaluated on the set of open source projects with millions of LoC. Testing in close to industrial conditions shows good false positive and missed errors ratio comparable with remaining detectors and allows to include developed checker (after a minor rework) into a general purpose production static analyzer for error detection.
Short Text Classification Based on Keywords Extension

Due to the number of short texts in news is small, the traditional text processing method often causes the lack of semantic information when analyzing the news text, which becomes one of the bottlenecks that restrict the performance of short text classification. This paper uses the external corpus to train the Word2Vec model, expands the keywords extracted by the traditional keyword extraction algorithm based on external semantic information, and studies the feasibility of extending short text keywords based on external semantic information according to different extension methods. Finally, KNN (K-Nearest Neighbor) algorithm is used to verify that the proposed method improves the performance of short text classification in news compared with the classical algorithm, which is close to the current mainstream text classification algorithm.
A Microservice Architecture for Access Control Based on Long-Distance Facial Recognition

With each new year, the world’s population is more aged and the birth rate decreases. That is why, in a not distant future, we will encounter the challenge that there will be many elderly people in our population and few young people to care for them. To help facilitate this task, the use of new technologies is increasing. Therefore, this paper proposes the use of a not intrusive system that, using a microservice architecture and long-distance, non-intrusive facial recognition, allows to control elderly assistance to their daily tasks in residences, facilitating the work of nurses. For example, to check that all the elderly regularly go to the dining room at meal times or to the gym if they should do some type of rehabilitation exercises. For this, the system uses security cameras that are installed in the access of the different zones to be controlled.
Constructing a Knowledge-Based Quality Attributes Relationship Matrix to Identify Conflicts in Non-Functional Requirements

A critical success factor in requirements engineering involves recognizing conflicts in Non-functional requirements (NFRs). The recent approaches use Quality attributes relationship matrix (QARM) to identify conflicts in NFRs that represents how one quality attribute undermine (-) or support (+) achieving other types of quality attributes. However, the static QARMs are not always obvious and may vary in the context of available standards, development and the involved stakeholders. In addition, these matrixes do not contain relations for emergent quality attributes viz. Recoverability, context awareness, mobility among others. Furthermore, identifying the conflicts in NFRs using the matrix without knowing the purpose of NFRs in the system may produce the false conflict identification. Hence, the aim of our research is to identify relations and influences between NFR attributes (a.k.a. Quality attributes) from available unconstrained natural language documents through automated natural language processing and machine learning that helps to deal with false conflict identification.
Generalizing Design of Support Measures for Counting Frequent Patterns in Graphs

Frequent subgraph mining (FSM) from graphs is an active subject in computer science research. One major challenge in FSM is the development of support measures, which are basically functions that map a pattern to its frequency count in a database. Current state-of-the-art in this topic features a hypergraph-based framework for modeling pattern occurrences which unifies the two main flavors of support measures: the overlap-graph based maximum independent set measure (MIS) and minimum image/instance based (MNI) measures. For the purpose of exploring the middle ground between these two groups and guiding the development of new support measures, we present general sufficient conditions for designing new support measures in hypergraph framework, which can be applied to MNI and other support measures that are not included in the overlap graph framework. We utilize the sufficient conditions to generalize MNI and minimum-instance measure (MI) for designing user-defined linear-time measures. Furthermore, we show that a maximum independent subedge set (MISS) measure developed from the sufficient conditions can fill the gap between MIS and MI in computation complexity and support count.
Natural Language Inference for Portuguese Using BERT and Multilingual Information

Recognizing Textual Entailment, also known as inference recognition, aims to identify when the meaning of a piece of text contains the meaning of another fragment of text. In this work, we investigate multiples approaches for recognizing inference in the ASSIN dataset, an entailment recognition corpus for Portuguese. We also investigate the consequences of adding external data to improve training in two different forms: multilingual data and automatically translated corpus. Our results outperform, using the multilingual pre-trained BERT model, the current state-of-the-art for the ASSIN corpus. Finally, we show that using external data did not improve the performance of the model or the improvements are not significant.
Evaluation of Deep Learning Approaches to Text-to-Speech Systems for European Portuguese

Deep Learning models are considered state-of-the-art regarding Text-to-Speech, displaying very natural and realistic results. However, it is known that these machine learning methods usually require large amounts of data to operate properly. Due to this, an assessment of the system’s ability to generalize to different instances becomes relevant, specially when learning from small data sets to create new voices. This study describes the assessment of a deep learning approach to TTS for European Portuguese. We show that we can use transfer learning techniques to fine-tune a Tacotron-2 model to a specific voice, while preserving speaker identity, without requiring large amounts of data. We also perform a comparison between the developed model and a statistical parametric speech synthesizer enhanced by deep learning, concluding that Tacotron-2 provided an overall better word pronunciation, naturalness and intonation.
The economics of e-books

E-books are complements to and not substitutes for paper books, but the development of a market for e-books can disrupt the publishing industry by changing the value chain and challenging regulations. The future of e-books depends on the ability of traditional publishers and newcomers to implement new business models with prices consistent with the willingness-to-pay of e-consumers.
Comparative Analysis of Deep Learning Models for Myanmar Text Classification

Text classification is one of the major research areas for Natural Language Processing (NLP). Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), and their combination models have been applied in many NLP tasks. This paper presents a joint CNN with no max-polling layer and Bidirectional LSTM to fulfill the requirements of each model. The proposed model takes advantage of CNN to extract features and Bi-LSTM to capture long term contextual information from past and future contexts. The proposed model is compared with CNN, Bi-LSTM, RNN, and CNN-LSTM models with pre-trained word embedding on five article datasets in Myanmar language.
A Management Decision Support System for Evaluating Information Security Behaviour

Information security management is a difficult task in organisations. Owing to skills shortages and the like, there are relatively few managerial staff that possess the required expertise to confidently make security decisions. The purpose of this paper is to present a decision support system that can analyse, and provide insight into, information security behaviour in an organisation, all the while supporting management decision-making for organisational factors. Behavioural threshold analysis is employed by the system to predict eventual information security behaviour for different groupings in an organisation. The decision support system that is presented here, which is the first of its kind, can be helpful in understanding the current state of organisational information security behaviour, and what can be done to improve upon the current state. After intervention measures the system may be used to test whether these measures had the intended positive effect. The system is discussed in terms of the different information areas that are used to allow insight into the security behaviours. A critical reflection provides an analysis of the contributions and limitations of the system.
Multi-Class Detection of Abusive Language Using Automated Machine Learning

Abusive language detection online is a daunting task for moderators. We propose Automated Machine Learning (Auto-ML) to semi-automate abusive language detection and to assist moderators. In this paper, we show that multi-class classification powered by Auto-ML is successful in detecting abusive language in English and German as well as and better than the state-ofthe- art machine learning models. We also highlight how we combatted the imbalanced data problem in our data-sets through feature selection and undersampling methods. We propose Auto-ML as a promising approach to the field of abusive language detection, especially for small companies who may have little machine learning knowledge and computing resources.
Research on Differential Privacy for Medical Health Big Data Processing

As big data, medical digitalization, and wearable devices continue to evolve, these technologies are driving the advancement of clinical medicine, genomics, and wearable health while also posing a risk of privacy breaches. For the disclosure of medical privacy issues, some methods and systems combining differential privacy protection applied in the clinical, genomic and wearable fields have been summarized, analyzed and compared. Finally, through the investigation and research, the assumptions and prospects for future research hotspots are given.
Prototype of Automatic Essay Assessment and Plagiarism Detection on Mobile Learning "Molearn" Application Using GLSA Method

In evaluating the student’s learning outcomes, essay exams were commonly used by teachers to measure the level of student’s understanding of the learning material. However assessing essay answers was more difficult in reality because it contained teacher’s subjectivity and required a longer correction time. In addition, detecting similarity in essay answers between students also required more teacher’s efforts. In previous studies, a prototype of essay answer assessment and plagiarism detection had been successfully created. However, the prototype display still needed an improvement based on the evaluation results given by biology teachers in East Java Province as the application users. The previous prototype also still carried the Latent Semantic Analysis (LSA) method which had several weaknesses. Therefore, this study aimed to produce prototypes that had better display and text similarity methods. The Generalized Latent Semantic Analysis (GLSA) method was chosen because it was able to cover the weaknesses of the LSA method. GLSA was able to detect sentences that had syntactic errors or missing common words. Based on the evaluation results, this study succeeded in producing a prototype with a better display value. The level of user satisfaction increased by 6.12%. In addition, the study succeeded in using the GLSA method as a substitute for LSA for creating better prototype essay assessment and automatic plagiarism detection.