Reviewerly/exHarmony huggingface dataset:
KB-NLG: From Knowledge Base to Natural Language Generation

We perform the natural language generation (NLG) task by mapping sets of Resource Description Framework (RDF) triples into text. First we investigate the impact of increasing the number of entity types in delexicalisaiton on the generation quality. Second we conduct different experiments to evaluate two widely applied language generation systems, encoder-decoder with attention and the Transformer model on a large benchmark dataset. We evaluate different models on automatic metrics, as well as the training time. To our knowledge, we are the first to apply Transformer model to this task.
New upper bounds on the quantum capacity for general attenuator and amplifier

There have been several upper bounds on the quantum capacity of the single-mode Gaussian channels with thermal noise, such as thermal attenuator and amplifier. We consider a class of attenuator and amplifier with more general noises, including squeezing or even non-Gaussian cases. We introduce new upper bounds on the energy-constrained quantum capacity of those channels by using the conditional quantum entropy power inequality. Also, we obtain lower bounds for the same channels by means of Gaussian optimizer with fixed input entropy. They give narrow bounds when the transmissivity is near unity and the energy of input state is low.
Two Way Distributed Sequential Pattern Mining using Fruitfly Algorithm along with Hadoop and Map Reduce Frame Work

Data mining is an effective tool used to take out information from big data as it provides several benefits to conquer the restrictions in it. In this paper, we present an innovative procedure developed using an updown directed acyclic graph (UDDAG) with fruit fly optimisation algorithm (FFOA), which is based on sequential pattern (SP) mining algorithm. In this work, the distributed sequential model mining algorithm is used to diminish the scanning time and scalability and the transferred database is employed to optimise the memory storage. The proposed method is used to expand the sequences in both the ends (prefixes and suffixes) of the identified model thereby supplying the consequences in quicker model expansion resulting in fewer database projections when compared to conventional methods. Our proposed method is implemented in Hadoop distributed surroundings to resolve the scalability issues and executed on JAVA platform using big datasets with Hadoop and map reduce framework.
ANALYSIS THE IMPLEMENTATION OF SELF DIRECTED LEARNING IN LEARNING ENGLISH FOR SPECIFIC PURPOSE

Self Directed Learning is the actions taken by which the students independently take initiative to assess their own learning needs, set up learning goals with implementation of appropriate strategies and evaluate learning outcome. This study aims to find out how Students of Nursing at Unika Santu paulus Ruteng, Flores implement self Directed Learning in learning English for Specific Purposes (ESP). It’s mixed method research combining the use of quantitative and qualitative study. For the purpose of quantitative study, it’s used total sampling. Therefore, all 6th semester students numbered 32 were chosen as the samples of the research. Meanwhile for the purpose of qualitative study, it’s chosen 6 students taken purposively. The research result showed that most students of Nursing Unika Santu Paulus Ruteng 23 (71.9%) implemented medium Self Directed Learning in learning English for Specific Purpose (ESP). It’s furthermore supported by the findings of qualitative study which was elaborated and described based on Self Directed Learning processes namely 1) self learning awareness, 2) planning to learn, 3) learning goal, 4) being discipline to learn, 5) self confident, 6) hard working to learn, and 7) originality. The findings would be the suggestions for school authorities in particular English teacher to plan for applying Self Directed Learning in learning English for Specific Purpose at Nursing Study program Unika Santu Paulus Ruteng, Flores. By knowing earlier the concept and how to apply SDL, students of Nursing have a free will to implement it in learning English.Keywords: SDL, learning English, ESP, nursing students
Learning Trilingual Dictionaries for Urdu – Roman Urdu – English

In this paper, we present an effort to generate a joint Urdu, Roman Urdu and English trilingual lexicon using automated methods. We make a case for using statistical machine translation approaches and parallel corpora for dictionary creation. To this purpose, we use word alignment tools on the corpus and evaluate translations using human evaluators. Despite different writing script and considerable noise in the corpus our results show promise with over 85% accuracy of Roman Urdu–Urdu and 45% English–Urdu pairs.
A New Approach to Develop Collaborative Ontology based BPMN Case Study: Generation of Fiscal Code

Collaborative platforms are becoming a necessity for enterprises and organizations since they have become extended and have to work with other organizations in joint projects.Knowledge management is considered one of the critical successes of collaborative tools and platforms, especially collaborative ontologies.This paper aims to propose a new approach for developing a collaborative ontology that can be used to support a collaborative platform.Our approach begins with the idea of using a business process model of the collaborative situation, represented in BPMN notation, and to transform it into a collaborative ontology.Our collaborative process will be modeled collaboratively, and its transformation to an ontology will be done through a first transformation to Subject oriented BPM (S-BPM).We also proposed to validate our collaborative ontology using competency questions that will be formulated at the beginning by domain experts and the ontologist and verified at the end by the ontologist.Fiscal code generation of a newborn was adopted as a case study to prove the importance of this approach.Compared to other approaches cited in the literature, our approach allows the construction of a collaborative ontology, that does not need enrichment perspectives, and that involves all collaborative partners during its construction process.
Knowing Personality Traits on Facebook Status Using the Naïve Bayes Classifier

With the development of social media trends among students by using Facebook social media, students can communicate and pour out everything that is felt in the form of status. Personality is the character or various characters of a person - therefore, how a person to adjust to the surrounding environment for the achievement of communication smoothly. In the personality category, many things classify a person's category in the psychologist theory. In this exercise, the Big Five, the psychologist theory, is described in five codes, namely Openness, Conscientiousness, Extraversion, Agreeables, Neuroticism. Naive Bayes Classifier is used to determine the highest probability value with the aim to determine the highest value. The data used are two namely training data and testing data obtained from the Facebook status of students. From the data obtained can be tested in the system that the accuracy value is 88%.
FEATURE SELECTION USING MULTIOBJECTIVE MICRO-CHC GENETIC ALGORITHM: A HYBRID APPROACH

The dimensionality reduction problem can be handled by employing feature selection techniques.Feature selection (FS) is a key preprocessing technique which selects more informative and useful features from data based on some criterion.Researchers have suggested many feature selection algorithms that differ in their selection criteria.Furthermore, feature selection is intrinsically a multiobjective problem with several conflicting objectives like size, redundancy and error rate.Thus, Multi-Objective Genetic Algorithms (MOGAs) are an obvious choice for solving the feature selection problem.Moreover, Micro-GA is used with a very small size population in cooperated with different type of elitisms.This work presents a Multi-objective Micro-CHC based Algorithm (MO-Micro-CHC) for feature selection.The proposed hybrid algorithm exploits the peculiarities of CHC (Cross generational elitist selection, Heterogeneous recombination, and Cataclysmic mutation), NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) the most popular MOGA and Micro-GA to the advantage of arriving at better Pareto optimal solutions.The algorithm has been validated and compared to several other similar approaches on many datasets available from UCI data repository.The comparison endorses the superiority of the suggested approach.
DEVELOPMENT OF REFLECTIVE MECHANISM FOR PROFESSIONAL SELF-DETERMINATION AMONG STUDENTS WITHIN PROFESSIONAL TRAINING

Statement of the problem. The article discusses the concepts of professional self-determination and reflection in the system-activity approach as mechanisms for the development of students and arrangement of professional training within the framework of the psychological and pedagogical specialization. The authors specified and disclosed the structural components of reflection, developed and tested a fundamental method for their diagnosis. Students,&nbsp;self-determination in the framework of training occurs through the development of special logic for professional design in order to develop participants of the activity that is the basis for development of students,&nbsp;reflection. The purpose of the study is to carry out a theoretical analysis of the concepts of professional determination and reflection, to determine the components of the reflection development and diagnostic tools for their identification in the course of specially organized activities for professional self-determination of students during professional training. The research methodology consists of research works of international and Russian scientists in the field of pedagogy and developmental psychology, recognized by the scientific community, the experience of teaching and organizing professional training for students of psychological and pedagogical specialization. Research results. On the basis of the system-activity approach, the concepts of professional self-determination and reflection are clarified, the conditions for the development of reflection are defined in the framework of professional training for students of psychological and pedagogical specialization. A diagnostic procedure has been developed and tested to determine the components of reflection. Conclusion. In the course of the study, the components of the development of reflection as a mechanism of professional self-determination among students of psychological and pedagogical specialization during their professional training are theoretically grounded and experimentally tested. This allows us to draw conclusions about the effectiveness of professional training and makes it possible to improve this training for students from the 1st to the 4th years of study.
Bespoke Measurement based on Convolutional Neural Network using OpenCV

This paper introduces the smart measurement for assisting tailors.This project explores the pattern of scanning the accurate measurement of garment dimensions for bespoke tailoring purpose.Computer vision is used for measuring garments along with camera calibration technique using OpenCV and python through object detection.This reduces the manual work and can detect the corners of the garment image exactly, with increased efficiency and effectiveness.It is simple, robust and has fast processing speed.We seek in our project to provide an accurate measurement of garment dimensions for bespoke tailoring purpose.
Identification of Subjects and Bystanders in Photos with Feature-Based Machine Learning

With the proliferation of smartphones among everyday users, digital photography in public places has exploded. While this trend brings the hobby of photography to millions of consumers, it also reduces the privacy of individuals in public settings as people are inadvertently included in strangers’ photographs. These photos can then be uploaded to publicly accessible forums such as social media platforms without any knowledge of the bystanders in the image who might not want their location or surroundings disclosed. To combat these scenarios, we propose an automated system for detecting and distinguishing subjects from bystanders in digital images which will later be used with visual obfuscating techniques to protect bystander privacy. Techniques such as gaze direction tracking, facial size detection, and head pose estimation are all explored as methods of extracting useful features for training various classifiers. Specifically, Random Forest, Gradient Boosted Decision Tree, Multilayer Perceptron, and Support Vector Machine classifiers are trained and evaluated for accuracy.
Extending the GLOBDEF framework with support for semantic enhancement of various data formats

Semantic enhancement links sections of data files with well-described concepts from some knowledge domain. This allows for further automated reasoning about that data and can be especially useful for extracting value from Big Data. Most of the available enhancement tools focus on specific enhancement needs and data types. In this paper we present our efforts to expand the GLOBDEF framework, introduced in an earlier work, which aims to find a way for processing of large amounts of data and enhancing the data automatically. The framework is designed to leverage a variety of external enhancement tools and has no limitations on the format of the enhanced data. We demonstrate how the framework behaves on a mixed data set of texts and images and explain how an image can be semantically enhanced with a simple automated combination of an object recogniser and a text-based automated enhancer.
Konke, Gravity, Particle Relationships

The stars and the particle colliders prove that all things are made up of the same thing. This same thing has been called Konke. This was discussed in the paper “The Algorithm of Information and the Origin of Basic Particles.” This paper will show that this konke must get into relationships to become basic mass, particles. It will show how we categorize particles by mass and the behavior of that mass in the same class of particles. Because we know everything is in a relationship and all relationships take energy to maintain, resulting in a loss of freedom giving that relationship structure, there must be another variable that provides the energy for konke to get into a variable. This other variable is called V2, or variable 2. This V2 would be considered the original force as it allows konke to get into relationships; it thus gives other forces the ability to exist. Theoretically, this V2 behaves in many ways to what has been termed gravity. A collider is proof enough for konke given the first law of thermodynamics; energy can neither be created nor destroyed.
Towards Interaction Design for Mobile Devices in Collocated Mixed-Focus Collaboration

In collocated collaboration, applied methods and technologies to support the collaboration process mainly comprise either analog paper and pen methods, large display applications or the usage of several laptops. Whereas paper and pen are easy to use, they impair the digital documentation and further editing. Large displays are expensive, stationary, and depend on specific environments. Furthermore, laptops build physical barriers between people, which impedes face-to-face communication. This leads to the fact that direct digitization is still not often performed in collocated collaborative scenarios, although it would be useful for further processing or permanent storing of created content. To address advantages of analog media, especially small size and high ubiquity, and eliminate the disadvantages, namely the lack of direct digitization, we aim at applying mobile devices to collocated collaboration. To contribute to the development of future collaboration tools, we derive and propose concrete design goals for applying mobile devices in collocated mixed-focus collaboration.
Improved CLAHE Enhancement Technique for Underwater Images

In recent days, a wide range of research has been going on visual enhancement of underwater images, under images in submarine and military operations to discover the submerged structural designing and sea explorations.But the difficulties are increased by diving in the deep ocean for a long time for underwater images.The factors such as scattering resulting from presence of particles inside the water and blurring effects reduce the quality of images being captured by underwater optic camera.There are several algorithms are introduced to improve the visual quality of deep under water images.Therefore, in this project a novel algorithm White balanced CLAHE is to enhance the visual quality of the underwater images will be implemented andcomparison of data with different conventional enhancement algorithms will be illustrated.And by comparing with the different quality metrics of each algorithm we can conclude which method is best to enhance the underwater images for bothdatabase images and real time images.This can be implemented by using MATLAB software.
Design Adaptive AI for RTS Game by Learning Player's Build Order

Digital games have proven to be valuable simulation environments for plan and goal recognition. Though, goal recognition is a hard problem, especially in the field of digital games where players unintentionally achieve goals through exploratory actions, abandon goals with little warning, or adopt new goals based upon recent or prior events. In this paper, a method using simulation and bayesian programming to infer the player's strategy in a Real-Time-Strategy game (RTS) is described, as well as how we could use it to make more adaptive AI for this kind of game and thus make more challenging and entertaining games for the players.
Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL

Linked Open Data (LOD) refers to freely available data on the World Wide Web that are typically represented using the Resource Description Framework (RDF) and standards built on it. LOD is an invaluable resource of information due to its richness and openness, which create new opportunities for many areas of application. In this paper, we address the exploitation of LOD by utilizing SPARQL queries in order to extract social networks among entities. This enables the application of de-facto techniques from Social Network Analysis (SNA) to study social relations and interactions among entities, providing deep insights into their latent social structure.
HEALTH CARE DIAGNOSIS USING TRANSFER LEARNING

Traditionally, the method of detection and prediction of diseases like lung cancer, skin cancer, breast cancer, and Alzheimer's disease was lengthy and time consuming. It is observed that, over the world a majority of the population is suffering from these diseases and many deaths occur due to the same. So, to make this detection and prediction process quick, accurate and efficient, various modern techniques like machine learning, deep learning ,etc. can be applied. In various learning techniques, the model to be created must be created from scratch. But, in order to avoid model creation from scratch and training a large number of images, we make use of the method called transfer learning. This method can provide early detection along with greater accuracy in prediction of diseases mentioned above. So, a detailed review of breast cancer, lung cancer, skin cancer and Alzheimers disease using transfer learning is given in this paper
Sarcasm Detection Beyond using Lexical Features

In current time, social media plateforms such as facebook, twitter, and so forth have improved and received substantial importance.These websites have grown into huge environments wherein users explicit their thoughts, perspectives and reviews evidently.Organizations leverage this environment to tap into people's opinion on their services and to make a quick feedback.This research seeks to keep away from using grammatical words as the only features for sarcasm detection however also the contextual features, which are theories explaining when, how and why sarcasm is expressed.A deep neural network architecture model was employed to carry out this task, which is a bidirectional long short-term memory with conditional random fields (Bi-LSTM-CRF), two stages were employed to classify if a reply or comment to a tweet is sarcastic or not-sarcastic.The performance of the models was evaluated using the following metrics: Accuracy, Precision, Recall, F-measure.
DRIVERS OF BITCOIN PRICES: AN EMPIRICAL ANALYSIS OF INDIA

Bitcoin, the first per-to-peer based cryptocurrency, has gained attention globally since its inception. The currency has experienced an exponential increment in its market capitalization over the years, highlighting the growing investors interest in the decentralized currency. However, despite its leading position compared to other cryptocurrencies such as Etheruem and XRP, the number of studies conducted previously to identify the determinants of Bitcoin prices is scare. Additionally, studies with a focus on the drivers of Bitcoin prices in the Indian macroeconomic environment are few. With this backdrop, the presented study is aimed at empirically analysing the determinants of Bitcoin prices in the India for a period 156 weeks spanning 2017-2019 using VECM. The empirical findings indicate that Bitcoin supply, gold prices, money supply, and Bitcoin trade volume significantly impact the Bitcoin prices to remove the deviations from its equilibrium price.
Modelagem de falhas em nuvem Cloud Fault Modeling

In this work, fault modeling for simulators that work in the infrastructure as a service of the cloud computing architecture is presented. Due to the increased demand for online services and solutions, cloud computing has grown exponentially in recent years, as has the storage of your information, its services delivered and offered online. These factors have collaborated so that, increasingly, the services offered are fault-tolerant, whether they are related to software or hardware. For that, it is necessary that the cloud environments, be prepared with techniques for the detection and treatment of failures if they occur. In this way, this work studied the possible flaws in the cloud computing architecture and their respective techniques for detection and treatment in cloud environment simulators. This study was based on the main failures and fault tolerance techniques existing in relevant cloud managers today. Thus concluding that, in order to be successful in fault-tolerant systems, it is necessary to create libraries that allow their detection and their respective treatment.
Virtual platforms that recognize learning styles and allow the deployment of Problem Based Learning methodology -ABP-

This article shows the results of a research project developed by the University of Medellín, Kuepa company, Minciencias and the Government of Antioquia, which starts with the problem of designing virtual courses based on traditional pedagogical approaches, that they do not promote active learning and do not take into account student learning styles. For this, a project is developed in which new functionalities are added to a virtual learning platform to allow the detection of student learning styles, and the configuration of courses under ABP methodology (Problem Based Learning). To achieve this, artificial intelligence (AI) techniques are used. In the development of the article it is shown that the model used to determine learning styles was Kolb, this one was used as input to train a SOM (Self-Organizing Maps) neural network. In this way, the material and problems assignment are customized according to the student characteristics. This article shows the modules that make up the platform and the built-in neural network structure to provide intelligence to the system are shown.