Reviewerly/exHarmony huggingface dataset:
Implementation of heuristic learning technologies for theoretic concepts formation by students mastering agricultural professions

The paper studies nature and specific features of heuristic learning technologies, significant for the formation of theoretical knowledge by students of agrarian specialties in their course of humanities.Analysis of up-todate scientific works reveals the efficiency of the process of humanitarian theoretical knowledge formation by students due to the involvement of a set of important pedagogical factors, such as: motivation of students by setting ties between humanitarian theoretical concepts and their future professional activities; creation and competent defense of personally essential educational products; teaching students to apply heuristic learning technologies (methods for defining theoretical concepts, methods and tools of dialogue interaction in solving complex professional problems, reflexive conclusions, heuristic analytical and comparative learning activities, prediction of the possible outcome of the educational activity and visual representation of its image etc.).The experimental survey was aimed to determine the stages of a humanitarian theoretic concept formation based on the heuristic learning technologies.The first motivational stage provides students' awareness of necessity of the humanitarian theoretical concept that is being formed for their future professional activity.The next searching-creative stage is carried out with various methods and mechanisms of heuristic learning technologies, mastered by students under the conditions of individual creation of educational products.At the reflection stage a teacher and students use pedagogical diagnostic mechanisms to assess the level of formation of the theoretical concept, the level of mastering basic theoretical and practical aspects of the topic as well as the development of critical and creative thinking and professional competences of future specialists in agriculture, their ability to form the necessary knowledge independently.So, heuristic learning activities enable students to segment and study the specific areas of their future professional activity from humanitarian perspectives and establish personal motives for learning humanities.
Medical Health Record Protection Using Ciphertext-Policy Attribute-Based Encryption and Elliptic Curve Digital Signature Algorithm

Information on medical record is very sensitive data due to the number of confidential information about a patient's condition. Therefore, a secure and reliable storage mechanism is needed so that the data remains original without any changes during it was stored in the data center. The user must go through an authentication process to ensure that not an attacker and verify to ensure the authenticity and accuracy of the data received. In this research, we proposed a solution to secure medical data using the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Elliptic Curve Digital Signature Algorithm (ECDSA) methods. Our system can secure data centers from illegal access because the uploaded data has patient control over access rights based on attributes that have been embedded during the data encryption process. Encrypted data was added to the digital signature to pass the authentication process before being sent to the data center. The results of our experiments serve efficient system security and secure with low overhead. We compare the proposed system performance with the same CP-ABE method but donâ€™t add user revocation to this system and for our computing times are shorter than the previous time for 0.06 seconds and 0.1 seconds to verify the signature. The total time in the system that we propose requires 0.6 seconds.
Mimicking User Behavior to Improve In-House Test Suites

Testing is today the most widely used software quality assurance approach. However, it is well known that the necessarily limited number of tests developed and run in-house are not representative of the rich variety of user executions in the field. In order to bridge this gap between in-house tests and field executions, we need a way to (1) identify the behaviors exercised in the field that were not exercised in-house and (2) generate new tests that exercise such behaviors. In this context, we propose Replica, a technique that uses field execution data to guide test generation. Replica instruments the software before deploying it, so that field data collection is triggered when a user exercises an untested behavior B, currently expressed as the violation of an invariant. When it receives the collected field data, Replica uses guided symbolic execution to generate one or more executions that exercise the previously untested behavior B. Our initial empirical evaluation, performed on a set of real user executions, shows that Replica can successfully generate tests that mirror field behaviors and have similar fault-detection capability. Our results also show that Replica can outperform a traditional input generation approach that does not use field-data guidance.
An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository.Texture, Color and Shape are among the most prominent features of any CBIR system.Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet transform (DWT) have been utilized here for the formation of a hybrid texture descriptor, denoted as (Co-DGLCM).To enhance the retrieval accuracy of the proposed system, a framework of an Extreme learning machine (ELM) with Relevance feedback (RF) has also been used.This technique provides simultaneously spatial relationship and information related to frequency in co-occuring local patterns of an image.Two benchmark texture databases namely Brodatz and MIT-Vistex have been tested and results are obtained in terms of accuracy, total average recall and total average precision which is 96.35% and 97.34% respectively on the two databases.
Zeyad at SemEval-2019 Task 6: That’s Offensive! An All-Out Search For An Ensemble To Identify And Categorize Offense in Tweets.

The objective of this paper is to provide a description for a classification system built for SemEval-2019 Task 6: OffensEval.This system classifies a tweet as either offensive or not offensive (Sub-task A) and further classifies offensive tweets into categories (Sub-tasks B -C).The system consists of two phases; a brute-force grid search to find the best learners amongst a given set and an ensemble of a subset of these best learners.The system achieved an F1-score of 0.728, ranking in subtask A, an F1-score score of 0.616 in subtask B and an F1-score of 0.509 in subtask C.
Singapore’s Open Digital Token Offering Embrace: Context &amp; Consequences

The overall global public's ability to purchase some portion of a digital token project's initial batch of tokens is the defining feature of an open digital token offering.Using a dataset that differentiates this token distribution model from other varieties -a distinction often underemphasised in regional analyses of digital token sale trends -this research estimates 2017-18 open digital token offering activity by jurisdiction, finding that Singapore-registered projects accounted for 21 percent of Q3/Q4 2018 dollar-volume, more than any other country.Conversely, by late 2018, previous hubs of this distribution model represented a much smaller share.Reasons for Singapore's rise as a global hub of the open digital token offering are explored, with a particular focus on examining contrasting regulatory approaches to distinguishing between this token distribution model and traditional securities offerings.Notably, 11 percent of Singapore-registered Q3/Q4 2018 token offering dollar-volume was purely-private, versus 94 percent in the U.S. Policy considerations related to this distribution method and the open digital token offering are presented, as are contrasting outcomes: this research estimates that over 70 percent of Singapore's one-to-two-year-old open token offerings resulted in operational networks or minimum-viable-products, versus fewer than 40 percent of U.S. private sales.Also, about 40 percent of smart contract platform projects that conducted 2017-18 token sales were Singapore-registered -many more than in any other country.For reasons explored in this research, these findings support the view that open digital token offerings benefit projects aiming to concurrently raise funds, build up a user-base, and incentivise technologists to contribute to project development.Moreover, risks to retail participants posed by this distribution method are manageable.Singapore's policy approach towards open digital token offerings has benefited the Lion City, which was likely home to more digital token projects that conducted 2018 token sales than any other city in the world.
On Learning Word Embeddings From Linguistically Augmented Text Corpora

Word embedding learning is a technique in Natural Language Processing (NLP) to map words into vector space representations, is one of the most popular research directions in modern NLP by virtue of its potential to boost the performance of many NLP downstream tasks.Nevertheless, most of the underlying word embedding methods such as word2vec and GloVe fail to produce high-quality representations if the text corpus is small and sparse.This paper proposes a method to generate effective word embeddings from limited data.Empirically, we show that the proposed model outperforms existing works for the classical word similarity task and for a domain-specific application.
The Impact of Pay Level Satisfaction and Company Reputation on Work Engagement: A Study on Employees Graduated Between 2010 and 2017 working in Saudi Arabia Universities

Purpose: the main purpose of this study is to investigate and understand the impact of pay level satisfaction and company reputation on work engagement. Design/methodology/approach: the study considered an empirical study that uses a qualitative method and probability sampling technique. The questionnaire was conduct using internet- based survey where the sample includes random male and female working in both public and privet Saudi universities with 72 retrieved.Findings: the study shows that pay level satisfaction and company reputation have a significant statistical impact on work engagement.
Comparison of Manual Versus Automated Data Collection Method for Haematological Parameters

Shamila Fathima S, Packirisamy Meenatchi and Ayyakkannu Purushothaman* Author Affiliations Department of Biochemistry, Mohamed Sathak College of Arts and Science, India Received: February 19, 2019 | Published: March 01, 2019 Corresponding author: Ayyakkannu Purushothaman, Department of Biochemistry, Mohamed Sathak College of Arts and Science, India DOI: 10.26717/BJSTR.2019.15.002702
Comparison Study of BOD &amp; COD of Leachate Quality (Case Study in Air Dingin Landfill and Jatibarang Landfill

&lt;p&gt;&lt;strong&gt;Abstract: &lt;/strong&gt;Leachate is one of the products that produced by a landfill. Leachate is extremely potential to pollute the environment, such as soil, groundwater and surface water. Air Dingin and Jatibarang landfill have a treatment to treat the leachate produced by the landfill before it is discharged to the environment because it has to be in accordance with Regulation of Ministry of Environment and Forestry of Republic of Indonesia Number P.59/Menlhk/Setjen/Kum.1/7/2016 concern Leachate Standard Quality for Businesses and/or Activities of Landfill. The leachate management in Air Dingin Landfill is controlled landfill, while in Jatibarang landfill, the the leachate management is coagulation-flocculation system. COD and BOD contained in Air Dingin Landfill leachate is lower than Jatibarang Landfill. In order to make leachate more environmental friendly, some innovative methods for leachate management have been developed, such as biofilter, wetlands, coagulation-fluctuation, and electrocoagulation.&lt;/p&gt;
Comparison of Pathogenic Bacteria in Water and Fecal-Source Samples in the Kathmandu Valley, Nepal, Using High-Throughput DNA Microarray

Rapid population growth and unplanned urbanization are causing an insufficient drinking water supply, which is a major problem in the Kathmandu Valley, Nepal. People are therefore using groundwater for drinking, cooking, bathing, and laundry purposes [1].
Appliance of Agile Methodology at Software Industry in Developing Countries: Perspective in Bangladesh

To choose an appropriate methodology for software development is one of the primary concerns for those countries where the ICT industry will play a leading role in the near future. Agile methodology has already received enough interest in developed nations. However, deficiencies of experimental studies have been observed in developing countries. These issues are required to be addressed properly since developing nations are currently playing the lead role in software outsourcing and will also continue. Our idea is to consider this agile methodology as one of the services and receive maximum benefits out of it through lessening the challenges and making it popular in the software industry. Based on the survey, Scrum (58%), XP (24%) and DSDM (18%) are found to be the most accepted software projects development methodology in Bangladesh. It can also be stated that the agile method can be considered as the most admired design model and its popularity can be enhanced by 81% through merging with ICTization framework. The results achieved from this paper are anticipated to notify the software developers on the utilization and practice of software development model from the context of Bangladesh.
Trading Accuracy for Performance in Data Processing Applications.

Applications that need to process large volumes of data to make informed decisions, such as stream processing systems or those dealing with social networks, usually impose strong requirements on resources like memory, processing time, disk or network latency.One approach to address this problem is to reduce the amount of data to be processed, at the cost of decreasing the confidence on the results.Estimating the errors of such approximate solutions becomes a critical issue.In this paper, we explore different approximation possibilities depending on how the data is organized, and what information needs to be obtained from them.We propose an approach to estimate the accuracy of these approximate solutions in the context of data processing systems, in terms of the precision and recall of the results obtained.A case study is used to validate the proposal and to evaluate the performance of different types of approximations.
Text Mining: Classification of Text Documents using Granular Hybrid Classification Technique

Since past many years, a large amount of raw data is getting converted into digital data within the information era.Maintaining and procuring the data is busy task for all the users who are willing to access the information in line with the requirements, however, the digital knowledge that's unbroken throughout this globe is not relevant in line with the need of the users.To overcome this problem classification process plays a major role to classify the data according to the need of the customer and provide relevant information.The classification algorithm is the process of extracting the information from the large data set and classifying the data which helps the customer to get the relevant information.Multi-class classification is the process of classifying more than two outcomes.Most of the algorithms produce good results when the target classes are few but as the target classes increase the accuracy reduces.There are also cases in classification where instead of classifying a category in the target function, we classify a code.Imagine we want to classify a product code from a large corpus based on the text written by a user.In our paper, we study the repercussions of a corpus which outgrows memory after vectorizing and perform a comparative analysis of various algorithms used during the process with our algorithm.We have represented the Granular Hybrid Model algorithm to classify the ocean ship food catalogue data set based on the user need and product code at a granular level and also by taking care of memory constraints which is a major drawback of normal classification algorithms.Our algorithm has represented a good accuracy of around 75% compared to other algorithms by considering the memory constraints of a huge data set of Ocean ship food catalogue.
Mobile Service Delivery Mechanism in Smart Education: Conceptual Framework

Higher education institutions around the world have invested heavily in ICT development to enhance teaching and learning processes and also to meet the needs of students in improving knowledge.As mobile learning has been successful and wider use in developed countries such as Finland, Korea, Japan and so on, so many of higher education institutions in developing countries such as Malaysia have embarked on several mobile learning initiatives to support their traditional learning mods.The objective of this paper is to build a conceptual framework of m-learning involving the integration of current practices, benefits and challenges to know their effectiveness in ensuring the quality of teaching and learning.In addition, the proposed framework focuses on case studies for the service delivery mechanism in smart education at four of the nine branches of Kolej Universiti Poly-Tech MARA (KUPTM) / Kolej Poly-Tech MARA (KPTM) in the context of private higher education institutions in Malaysia.This framework will help students, lecturers and administrators in higher education to tailor educational resources from their mobile devices that can be used anytime and anywhere.
Machine translation and author keywords: A viable search strategy for scholars with limited English proficiency?

Author keywords are valuable for indexing articles and for information retrieval (IR). Most scientific literature is published in English. Can machine translation (MT) help researchers with limited English proficiency to search for information? We used two MT systems (Google Translate, DeepL Translator) to translate into English 71 Spanish keywords and 43 French keywords from articles in the domain of Library and Information Science. We then used the English translations to search the Library, Information Science and Technology Abstracts (LISTA) database. Half of the translated keywords returned relevant results. Of the half that did not, 34% were well translated but did not align with LISTA descriptors. Translation-related problems stemming from orthographic variation, synonymy, differing syntactic preferences, and semantic field coverage interfered with IR in just 16% of cases. Some of the MT errors are relatively “predictable” and if knowledge organization systems could be augmented to deal with them, then MT may prove even more useful for searching.
Assessing solar resource and photovoltaic production in Tahiti from ground-based measurements

This study focuses on the solar resource available at Faaa, Tahiti (17.5°S, 149.5°W) thanks to 10 year-long solar irradiance time series. Faaa’s global horizontal irradiance ranges from 14 MJ.m -2 .day -1 (June) to 21 MJ.m -2 .day -1 (November) in agreement with the sun’s annual path, while clearness index ranges from 0.5 (January) to 0.67 (July), in agreement with the wet and dry seasons. The Global Solar Atlas satellite-derived dataset shows acceptable relative error when compared to Faaa in situ measurements. This product could then be used for other coastal areas of Tahiti. The annual energy output of a single PV module is 256.7 kWh, which corresponds to 7 % of the annual consumption of a typical household in Tahiti. The capacity factor reaches 22.5 %, which makes Faaa a good site for harnessing solar resource.
Medical Image Analysis with Semantic Segmentation and Active Learning

We address object detection using semantic segmentation and&nbsp;apply it for prostate detection in an MRI data-set. Our detection pipeline&nbsp;uses first a segmentation step followed by a classifier with a convolutional&nbsp;neural network (CNN). Since the segmentation provides a set of unbalanced&nbsp;data-sets – where a high accuracy is difficult to obtain – we leverage the&nbsp;prospect of improving detection accuracy using a Bayesian treatment of&nbsp;deep networks and the possibility of better exploiting the data using active&nbsp;learning. The resulting algorithm is both adaptive and data-efficient: by&nbsp;assuming that from a large pool of data only a few are segmented, the&nbsp;active learning module of the algorithm finds the image that improves&nbsp;most detection accuracy. We test our algorithm on a prostate medical&nbsp;image data-set and show that the active learning-based algorithm performs&nbsp;well in the prostate detection class. The resulting system is invariant to&nbsp;translations within the image and the results show improvements when&nbsp;using the pipeline that includes active learning and CNNs.
METHODOLOGICAL SYSTEM OF A HIGHER EDUCATIONAL ESTABLISHMENT AIMED AT DEVELOPING FUTURE SOCIAL CARE TEACHERS’ COMMITMENT TO WORK IN CENTERS OF EARLY SOCIAL REHABILITATION

Scholarly grounds of the systemic approach allowed the author to analyze the pedagogical phenomenon of methodological system from the point of view of the totality of interrelated hierarchal components determining the processes aimed at higher education quality maintenance. The article discusses methodological system of students training for work in early rehabilitation establishments. This system is viewed as a cluster of three components: methodological and purposeful, content-related and procedural, and monitoring and impactful. The system-shaping element of the mentioned system is the aim – the development of future social care teachers’ commitment, which is implemented by means of developing worldview and positional, operational and cognitive, and personal and resourceful components of the commitment. The article proves that the content of future social care teachers’ training determines the selection of organizational forms, methods and means of education, as well as methodological framework. The efficacy of the elaborated system is researched from the point of view of functional and personally valuable results. The author emphasizes that the achievement of systematicity and complexity implies that the process of methodological system integration into the work of an educational establishment has to be prolonged for all the period of training, from the 1st to the 4th year (bachelor’s degree). The article presents the following conventional stages of methodological system implementation: propaedeutic (terms 1-4), guiding (terms 5-6), fundamental (terms 7-8). Specific components of the research and methodological support of the system are presented in the context of the development of future social care teachers’ commitment to work in early rehabilitation establishments.