Reviewerly/exHarmony huggingface dataset:
Cost Estimation Model Using Fifth Generation Language Technique for Software Maintenance Project

Software cost estimation is a standout among the huge demanding tasks in project management for new software. In any case, the procedure of estimation is unsure as it largely relies on certain qualities that are very hazy amid the beginning periods of improvement. This exploration is to give a method to software cost estimation that performs superior to different procedures on the precision of effort estimation. A soft computing procedure has been investigated to beat the vulnerability and error in estimation. This investigation is to expand the constructive cost model by intertwining the possibility of fuzziness into the estimation of size, method of improvement projects, and the cost drivers adding to the general advancement effort. The primary goal of the explorations is to examine the job of the fuzzy inference system method in enhancing the cost estimation precision utilizing COCOMO II by describing inputs variables utilizing fifth GL systems and contrasting their outcomes. The PROMISE dataset is utilized for the assessment of the fuzzy inference system (FIS) procedures. The examinations have been completed utilizing MATLAB simulation conditions.
Real-Time Human Action Recognition with Multimodal Dataset: A Study Review

Due to difficulties including a cluttered background, partial occlusion, and variations on dimensions, angle, illumination, or look, identifying human endeavors using video clips or still images is a challenging process. A numerous mechanism for recognizing activity is necessary for numerous applications, such as robotics, human–computer interaction, and video surveillance for characterizing human behavior. We outline a classification of human endeavor approaches and go through their benefits and drawbacks. In specifically, we classify categorization of human activity approaches into the two broad categories based on whether or not they make use of information from several modalities. This study covered a depth motion map-based approach to human recognizing an action. A motion map in depth created by adding up to the fullest differences with respect to the two following projections maps for each projection view over the course of the entire depth video series. The suggested approach is demonstrated to be computationally effective, enabling real-time operation. Results of the recognition using the dataset for Microsoft Research Action3D show that our method outperforms other methods.
Recommendation System Using Neural Collaborative Filtering and Deep Learning

Recommender systems have transformed the nature of the online service experience due to their quick growth and widespread use. In today’s world, the recommendation system plays a very vital role. At every point of our life, we use a recommendation system from shopping on Amazon to watching a movie on Netflix. A recommender system bases its predictions, like many machine learning algorithms, on past user behavior. The goal is to specifically forecast user preference for a group of items based on prior usage. The two most well-liked methods for developing recommender systems are collaborative filtering and content-based filtering. Somehow, we were using the traditional methods, named content-based filtering (CB) and collaborative-based filtering (CF), which are lacking behind because of some issues or problems like a cold start and scalability. The approach of this paper is to overcome the problems of CF as well as CB. We built an advanced recommendation system that is built with neural collaborative filtering which uses implicit feedback and finds the accuracy with the help of hit ratio which will be more accurate and efficient than the traditional recommendation system.
Addressing Role of Ambient Intelligence and Pervasive Computing in Today’s World

Intelligence refers to the ability to learn and apply knowledge in new innovative situations. Recent computational and electronic advances have increased the level of autonomous semi-intelligent behavior exhibited by systems, so the new terms like ambient intelligence and pervasive computing started to emerge. Artificial intelligence (AI) and ambient intelligence (AmI) are inextricably technology cooperation agreements with each other. Artificial is something made by human beings and ambience is something that surrounds us while ambient intelligence assumed to be something artificial. Pervasive computing is the growing trend of embedding computational capability into everyday objects to make them effectively communicate and perform useful tasks to satisfy end user’s resource needs. The paper provides a bibliometric information using VosViewer software about the present trends of ambient intelligence and pervasive computing technologies using Scopus and Web of Science databases, which will help researchers getting ideas toward research development in these domains. It also highlights a comprehensive study on ambient intelligence and pervasive computing covering the application areas that are dramatically affected by them in today’s world. Undoubtedly, both clean technologies have strongly influenced the recent developments in the past few years. Furthermore, we can expect that the scope will continue to multiply in upcoming years.
Aspect-Based Opinion Mining Framework for Product Rating Embedded with Fuzzy Decision

The technique of evaluating text on a subject jotted down in natural language and classifying it with polarity as neutral form, positive form, or negative form based on the sentiments, emotions, and expressions by humans in it is known as opinion mining or sentiment analysis. Analyzing and extracting opinions from such a large volume of reviews manually is probably impossible. A self-dependent automated opinion mining strategy is required to resolve this issue. It can primarily be carried out at three levels: document, sentence, and aspect level. Work proposed is an effort to rate a product and predict its demand based on features extracted from aspects of opinion expressed with fuzzy set decision boundary. Aspect identification, aspect-based opinion word identification, and inclination as positive, negative or neutral are the three major tasks in aspect-based opinion mining. Weights were assigned according to the polarity percentage of the words extracted. Polarity scores of the labeled dataset were then divided into 5 fuzzy set decision boundaries, confirmed positive, positive, neutral, negative, and confirmed negative, to accurately predict the correlation between polarity score obtained and product rating. Algorithm proposed was operated on aspect-based corpus for product rating which acquired from GitHub, comprised of 7563 rows of product aspects; the analysis suggests that maximum share 44% lies with positive range of fuzzy boundary, and this can be tapped for trend prediction of customer choice; the algorithm successfully provides a fuzzy-assisted prediction mechanism for product rating for the corpus with labeled aspects; algorithm performs at par with existing technique, and it also provides percentage share of products presence in market for future trends.
Political Elite Structures in Arab Uprisings (2011 And On) and Foreign Policy Ramifications

The Arab Spring phenomena seems like a case of the proverbial butterfly flapping its wings in one place that sends vibrations that expand into cyclonic turmoil elsewhere. The self-immolation of a Tunisian street vendor, the initial shock in a series of shocks, impacted the wider region and led to a large number of mass protests, the overthrow of regimes, long-lasting internal wars in Syria and Yemen, and difficult—but not impossible—processes of transitions away from old orders as with Libya. Responses in other parts of the region were arguably muted, such as in the Gulf; there, we do observe, however, incremental changes to governance in response to wide-ranging demands. These outcomes tell us something important about differential responses to the initial shocks. How governments and societies reacted depended considerably on how unified their elite structures were at the time of the initial shocks. The degree of unification also hinged on short-term vulnerabilities in the dominant patron-client coalition of each state. In these respects, responses were very similar to the earlier color revolutions that took place in parts of the former Soviet Union. In the Middle East and North Africa, however, interventions by external actors also played an important role in how events played out.
Authentication in P2P Environment Based on Multi Dimensional Administration Graph

Secure authentication in P2P environments has been a central point of attention, since centralized legal infrastructure don’t exist in those environments. Different works has been introduced providing required facilities to make such a process possible in a secure manner. Most of these concepts propose the use of Public/private encryption mechanism as a secure authentication approach. Public key cryptography is an established method, which enables secure communication between two parties in insecure environments. Any facility in P2P environment, which aims to use the Public key approach for a secured authentication and communication, must provide a substitute for the required appropriate services in P2P environment, as there is no certificate authority there. This work is concentrated on this matter and provides a concept for an administration graph maintaining a structure for secure disseminating Public key of peers in the whole network. In addition to this structure, required functionalities are provided to perform secured authentication between unknown and known peers in P2P environments using a multi criteria approach. The designed administration graph supports also creation of community and groups with hierarchical permissions for members.
Neural Networks in View of Explainable Artificial Intelligence

Artificial neural networks are just mathematical functions used for approximation purposes, and learning is a euphemism for determining the parameters of these functions. Experience from other areas of approximation theory shows that closed-form approximation functions determined globally such as neural networks tend to be quite rippled and are, thus, outperformed by approximation functions defined locally. With respect to artificial intelligence, neural networks lack explainability. Therefore, research in novel approaches for local approximators apt to replace neural networks and to outperform them with respect to accuracy, computational expense and explainability is suggested.
Taking Real-Time and Virtualization to Open Source Hardware

With the goal of using the open source RISC-V ISA in real-time processing, the porting of the real-time operating system “Marron” is described in detail. The result is then extended to a hypervisor so that multiple guest systems with different timing and resource requirements can be safely separated. Since both solutions are open source, they enhance the available open source infrastructure. They thus help to reduce supply chain problems and avoid dependencies in the future.
A Survey on Pedestrian Detection: Towards Integrating Vulnerable Road Users into Sensor Networks

This paper provides an overview of fundamental and recent 2D and 3D pedestrian detection methods. It is part of the ongoing investigation on integrating vulnerable road users into sensor networks. Besides the sensor-specific object detection methods based on LIDAR sensors, RADAR sensors, thermal imaging cameras, RGBD cameras, and RGB cameras, a selection of sensor fusion methods is presented. Shown methods have been developed to increase traffic safety for vulnerable road users, which include pedestrians and cyclists.
Exploring the practices of Extensive Reading in South Omo Secondary Schools. Selected Secondary Schools in focus

The main objective of this research is to explore the practice of extensive reading in six secondary schools in the South Omo zone. To achieve the objective of the research, a mixed research design was used. In line with this, questionnaires, interviews and observation were the main data collection tools through which English language teachers, librarians and students participated. Likewise, the analysis of the collected data was made through simple descriptive statistics (i.e., mean, Standard deviation and frequency). The finding of the study shows that students mainly were dependent on the school library. It was also found that students read for academic gain than for their pleasure time. This is found to be out of the extensive reading principles. It was also found that English teachers were using and promoting the extensive reading approach in their language classes. It is recommended that schools and public libraries should prepare reading materials that are at the level of the student’s reading competencies. Furthermore, English language teachers should plan a free reading program along with their school curriculum which is designed and appropriate for extensive reading. Keywords: Extensive Reading, reading fluency, Challenges, teacher’s role,
Wajib Belajar Perfektif UU. No. 20 Tahun 2003

This research is motivated by the fact that there are still many children who do not continue their education in high school, some of them drop out of school because of the lack of family economy and children's interest in learning who follow their friends. The formulation of the problem in this research is how is the implementation of Law Number 20 of 2003 concerning Compulsory High School Education, what hinders public awareness of the importance of compulsory high school education. The purpose of this study is to find out how the Law Number 20 of 2003 concerning Compulsory High School Education is, and to find out what factors hinder the compulsory education program for school-age children. This study uses descriptive qualitative methods, namely research that is directed to describe the symptoms or events that occur in fact. Informants in this study were children aged 16-18 years as many as 20 people, parents as many as 15 people who were in Gunung Tua Village, Panyabungan District, Mandailing Natal Regency. The data collection techniques used in this study were observation and interviews. The results of the study stated that: the implementation of Law Number 20 of 2003 concerning Compulsory High School Education has not gone as expected, some children have followed the compulsory education program and some have not continued their education. Whereas this law was formed in order to improve the intelligence and ability of children in the nation and state. However, this law has not been able to improve the quality of children's education in Gunung Tua Village. What hinders public awareness of the importance of compulsory education is the lack of attention from parents to their children in the field of education, lack of public knowledge about existing education rules, and the lack of family economy in financing their children's education.
An Integrated Approach for Analysis of Electronic Health Records Using Blockchain and Deep Learning

Abstract: Blockchain is used to assess health records digitally, preserving the security and immutability of the records. The goal of this study is to make it easier for patients to access their medical records and to send them alert messages about important dates for their check-ups, healthy diet, and appointments. To achieve the above-mentioned objective, an integrated approach using Blockchain and Deep learning is initiated. The first approach is Hyperledger Fabric in Blockchain, i.e., private Blockchain, for storing the data in the medically documented ledger, which can be shared among hospitals as well as Health organizations. The second approach is incorporated with a deep learning algorithm. With the help of algorithms, we can analyse the ledger, after which an alert i.e. consultation, health diet, medication, etc., will be sent to the patient’s registered mobile device. The proposed work uses nine features from the dataset; the features are identification number, age, person gender, disease, weight, consultation date, medication, diagnosis, and diet specification. The study is conducted with several features to give accurate results. The integrated model used in this suggested piece of work automates the patient's alert system for a variety of activities. In terms of precision, recall, and F1 score, testing data demonstrate that the LSTM performs better than the other models. By working together with the calendar software on Android mobile devices, alert systems can be improved in the future.
Wikibase Model for Premodern Manuscript Metadata Harmonization, Linked Data Integration, and Discovery

To facilitate discovery of premodern manuscripts in U.S. memory institutions, Digital Scriptorium, a growing consortium of over 35 institutional members representing American libraries, museums, and other cultural heritage institutions, has developed a digital platform for an online national union catalog. The platform will allow low-barrier and efficient collection, aggregation, and enrichment of member metadata and sustainably publish it as Linked Open Data. This article describes the methods and principles behind the data model development and the decision to use Wikibase. The results of the prototype implementation and testing phase demonstrate the practicality and sustainability of Digital Scriptorium’s approach to building an online national union catalog based on Linked Open Data technologies and practices.
Learning a holistic and comprehensive code representation for code summarization

Code summarization is the task of describing the function of code snippets in natural language, which benefits program comprehension and boosts software productivity. Despite lots of effort made by previous studies, existing models are not comprehensive enough to represent code, and yet the literature does not consider to model source code with API usage from a holistic perspective. To this end, this paper proposes a novel multi-modal code summarization approach called HCCS (Learning a Holistic and Comprehensive code representation for Code Summarization). We first design a neural network based on the graph attention mechanism to encode API Context Graph (ACG), which highlights holistic information of source code. Then, a multi-modal framework with a tree encoder for Abstract Syntax Tree (AST) and a code encoder for code tokens is incorporated to learn a more comprehensive code representation. Afterwards, we propose a fusing layer to integrate the encodings, which are then passed to a joint-decoder to generate summaries. The experimental results show that HCCS achieves better performance than the state of the arts (i.e., HCCS scores 9.5% higher in terms of BLEU metric and 11.46% in terms of BERTScore). As a result, HCCS is an effective approach to generate high-quality summaries.
Electric Motorcycle Design for City Touring Activities for Neo-Classical Motorcycle Enthusiasts

With increasing awareness of clean energy globally, the automotive industry is starting to produce electric cars and motorcycles. In Indonesia, sales of electric motorcycles are growing slowly. This is due to several factors, for example the lack of consumer knowledge about electric vehicles, high prices, limited sales network, uneven access to electricity in Indonesia, and also design limitations. This study discusses the process of designing an electric motorcycle for city touring activities for fans of classic motorbikes and classic styles, especially in the city of Bandung. This research is motivated by the possibility that the majority of domestic electric motorcycle designs are still in the form of scooters or mopeds. It creates a vacuum in the market. The purpose of this study is to produce an electric motorcycle design with a classic image for city touring activities in the city of Bandung. The resulting design has a novelty value that is offered, namely the use of classic design elements on electric motorcycles, and applying new functions to motorcycle parts that were previously closely related to the internal combustion function of the motorcycle. The design objectives were achieved through an online literature study analysis process accompanied by interviews and proofing the feasibility of the designs produced using the Stuart Pugh design method. At the end of the process, a design is produced in the form of a final concept sketch and a 3D model of an electric motorcycle.
Left Ventricle Segmentation of 2D Echocardiography Using Deep Learning

To identify the heart-related issues the very first step used by clinicians in diagnosis is to correctly identify the clinical indices which are possible by accurate Left Ventricle (LV) segmentation. Our work is related to building a deep learning model that automatically segments the cardiac left ventricle in an echocardiographic image into epicardium, endocardium, and left atrium. We propose the Vgg16 U-Net architecture for LV segmentation in this paper. On the CAMUS dataset, the Vgg16 Unet model is new, and it has demonstrated promising results for endocardium segmentation. The dice metric values achieved for endocardium, epicardium, and left atrium are 0.9412 $$\pmb {\pm }$$ 0.0289, 0.8786 $$\pmb {\pm }$$ 0.0420, and 0.9020 $$\pmb {\pm }$$ 0.0908 respectively.
FACTORS AFFECTING TEACHING, LEARNING AND ACADEMIC PERFORMANCE IN THE DEPARTMENT OF GRAPHIC DESIGN TECHNOLOGY: TAKORADI TECHNICAL UNIVERSITY – GHANA

This study was carried out to evaluate factors affecting teaching, learning and academic performance of the Graphic Design Students in the Department of Graphic Design Technology, Faculty of Applied Arts and Technology, Takoradi Technical University in Ghana. The objectives of the study were to identify the factors and analyse their effect on teaching, learning and academic performance of the Graphic Design Students. An expert purposive sampling method was employed. The research design employed was qualitative since the nature of this study is dependent solely on a wide range of observations, document reviews, verbal data from experienced, course lecturers and experts well-knowledgeable in Graphic Design and Students. Data for this study were collected from two key sources, namely: primary sources through interaction and observation and secondary sources through literary sources. Thematic and descriptive analyses were employed to present and analyse the data. The study concluded that lack of adequate spacious studios, laboratories equipped with state of the art equipment, materials, Course Lecturers and Technicians who are experts in their areas of specializations are the key factors affecting teaching, learning and academic performance of the Graphic Design Students. The study recommended that Takoradi Technical University Governing Council, in collaboration with the Takoradi Technical University Management should resource the Department of Graphic Design Technology with spacious studios and laboratories, equipped with state of the art equipment and materials, provide modern printing equipment to aid in acquisition of hands-on skills, competences and managerial aptitudes by the students, offer employment to Course Lecturers and Technicians who are experts in their areas of specialization and source funding from the Government of Ghana through the Ministry of Education in order to augment Takoradi Technical University’s effort in the provision of adequate material, physical and human resources needed to train Graphic Design Students holistically to improve their academic performance.