Reviewerly/exHarmony huggingface dataset:
EMPATHY AND IDEA GENERATION: EXPLORING THE DESIGN OF A VIRTUAL REALITY CONTROLLER FOR REHABILITATION PURPOSES

The paper explores the ideation and design of a Virtual Reality (VR) proof-of-concept controller for rehabilitation of users with limited physical mobility (upper-limb disability).An existing tracker solution is used to map input (actions and movements) in VR.The main challenge was integrating some of the default functionalities existing in current commercial VR controllers, while providing an empathic setup and a use-case for disability rehabilitation, as well as keeping the controller compact, lightweight, and handheld.The prototyping process followed a human-centred explorative design idea generation.Only limited functionality of existing commercial controllers was maintained, with the feasibility and readiness for implementing additional functionalities to use the controller with existing applications and future use cases.An experiment was performed to investigate the usability of the system and the effectiveness and reliability of the controller in empathic remapping of real-life disability to VR.
Efficient Sky Dehazing by Atmospheric Light Fusion

In this article, we present a new method of dehazing based on the Koschmieder model, which aims to restore an image that has been affected by haze. The difficulty is to improve the estimation of the transmission and the atmospheric light that generally suffer from the nonhomogeneity and the random variability of the environment. The keypoint is to enhance the dehazing of very bright regions of the image in order to improve the treatment of the sky that is often overestimated or underestimated compared to the rest of the scene. The approach proposed in this paper is based on two main contributions: 1. an L0 gradient optimization function weighted by a set of Gaussian filters and based on an iterative algorithm for optimization convergence. Unlike the existing methods using a single value of the atmospheric light for the whole image, our method uses a set of values neighboring an initial estimated value. The fusion is then applied based on Laplacian and Gaussian pyramids to combine all the relevant information from the set of images constructed from atmospheric lights and improves the contrast to recover the colors of the sky without any artifacts. Finally, the results are validated by three criteria: an autocorrelation score (ZNCC), a similarity measure (SSIM) and a visual criterion. The experiments carried out on two datasets show that our approach allows a better dehazing of the images with higher SSIM and ZNCC measurements but also with better visual quality.
The Development and Application of Interactive Multimedia in Project-Based Learning to Enhance Students' Achievement for 2D Animation Making

Enhancing the quality of student competencies effectively cannot be separated from the development of learning resources through technological innovation. In-teractive multimedia is an innovative learning resource that can be integrated with Project-Based Learning, which is a solution to the problems that occur for 2D Animation Making. The purpose of this study is to develop interactive multime-dia and assess the effectiveness of its application in Project-Based Learning (PjBL) for 2D animation making. The researchers applied the Research and De-velopment method using the 4D model. This study was conducted at the Univer-sitas Muhammadiyah Sidoarjo, with a total of 66 students of Information Tech-nology Education. Based on the results of the study, validation by material and media experts showed that interactive multimedia learning media is excellent and feasible to use. The effectiveness of the application of interactive multimedia in PjBL has a significant positive effect on learning achievement for making 2D an-imations.
On the Digital Cohomology Modules

In this paper, we consider the digital cohomology modules of a digital image consisting of a bounded and finite subset of Zn and an adjacency relation. We construct a contravariant functor from the category of digital images and digital continuous functions to the category of unitary R-modules and R-module homomorphisms via the category of cochain complexes of R-modules and cochain maps, where R is a commutative ring with identity 1R. We also examine the digital primitive cohomology classes based on digital images and find the relationship between R-module homomorphisms of digital cohomology modules induced by the digital convolutions and digital continuous functions.
Machine Learning for Query Processing System and Query Response Time using Hadoop

Now a day's 1000 of companies using hadoop.Hadoop is used for storing and processing big data.It is actually storing huge amount of data and processing system.Here you can store any type of data like (Structured data, Unstructured data, Video and Audio data) here data storing process is not a problem.But data retrieving is low query processing then response time is also low.In previously we study some papers then we find some issues of retrieving query processing and response time also.We propose to machine learning for query processing system and response time using hadoop.In this paper are simple changes to retrieving query process in hadoop using some techniques and the queries performance will make a huge difference in the response times.
Classical simulation of boson sampling with sparse output

Abstract Boson sampling can simulate physical problems for which classical simulations are inefficient. However, not all problems simulated by boson sampling are classically intractable. We show explicit classical methods of finding boson sampling distributions when they are known to be highly sparse. In the methods, we first determine a few distributions from restricted number of detectors and then recover the full one using compressive sensing techniques. In general, the latter step could be of high complexity. However, we show that this problem can be reduced to solving an Ising model which under certain conditions can be done in polynomial time. Various extensions are discussed including a version involving quantum annealing. Hence, our results impact the understanding of the class of classically calculable problems. We indicate that boson samplers may be advantageous in dealing with problems which are not highly sparse. Finally, we suggest a hybrid method for problems of intermediate sparsity.
A Prostate MRI Segmentation Tool Based on Active Contour Models Using a Gradient Vector Flow

Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling the radiologists to use novel and easy to use semi-automatic segmentation techniques instead of manual segmentation. In this work, a detailed specification of the proposed segmentation algorithm using an Active Contour Models (ACM) aided with a Gradient Vector Flow (GVF) component is defined. The purpose is to help the manual segmentation process of the main ROIs of prostate gland zones. Finally, an experimental case of use and a discussion part of the results are presented.
Electric Network Frequency Based Audio Forensics Using Convolutional Neural Networks

Digital media forensics can exploit the electric network frequency of audio signals to detect tampering. However, current electric network based audio forensic schemes are limited by their inability to obtain concurrent electric network frequency reference datasets from power grids. In addition, most forensic algorithms do not provide high detection precision in adverse signal-to-noise conditions. This chapter proposes an automated electric network frequency based audio forensic scheme that monitors abrupt mutations of tampered frames and discontinuities in the variations of electric network frequency features. Specifically, the scheme utilizes the multiple signal classification, Hilbert linear prediction and Welch algorithms to extract electric network frequency features from audio signals; the extracted features are passed to a convolutional neural network classifier to detect audio tampering. The negative effects of low signal-to-noise ratios on electric network frequency extraction are addressed by employing extra low-rank filtering that removes voice activity and noise interference. Simulation results demonstrate that the proposed scheme provides better audio tampering detection accuracy compared with a benchmark method, especially under adverse signal-to-noise conditions.
Augmented reality in scientific visualization and communications: a new dawn of looking at antibody interactions

ABSTRACT The use of augmented reality (AR) in providing three-dimensional (3D) visual support and image depth have been applied in education, tourism, historical studies, and medical training. In research and development, there has been a slow but growing use of AR tools in chemical and drug discovery, but little has been implemented for whole 3D antibody structures (IgE, IgM, IgA, IgG, and IgD) and in communicating their interactions with the antigens or receptors in publications. Given that antibody interactions can vary significantly between different monoclonal antibodies, a convenient and easy to use 3D visualization can convey structural mechanisms clearer to readers, especially in how residues may interact with one another. While this was previously constrained to the use of stereo images on printed material or molecular visualization software on the computer, the revolution of smartphone and phablets now allows visualization of whole molecular structures on-the-go, allowing rotations, zooming in and out, and even animations without complex devices or the training of visual prowess. While not yet as versatile as molecular visualization software on the computer, such technology is an improvement from stereo-images and bridges the gap with molecular visualization tools. In this report, we discuss the use of AR and how they can be employed in the holistic view of antibodies and the future of the technology for better scientific communication.
Analyzing Grammatical Error in Students' Recount Text Writing in Junior High School

Having good grammar is one of important components in the mastery of writing skills. However, grammatical errors still become an important problem in writing, especially for EFL students. Therefore, this study aimed to find out the major types of grammatical errors; and to know the major source of grammatical errors in students’ recount text writing. This study was designed as a descriptive qualitative study. Recount texts written by the eighth-grade students of SMP Pelangi Dharma Nusantara in academic year 2019/2020 were used as the research subjects of this study. The data were identified and analyzed using theory of error proposed by Dulay et al. (1982) about surface strategy taxonomy and theory of source of grammatical error proposed by Brown (2007)). The findings showed that the major type of grammatical errors was omission, with the total of 171 errors or 37. 33% of the total errors. Meanwhile, the major source of grammatical errors was intralingual transfer which the total result was 248 or 43.43% error. Thus, it can be concluded that the most frequent grammatical errors that happened among the subject was error of omission, while the most common source of errors was intralingual transfer.
Agricultural Imports, Agriculture Productivity and Economic Growth in Sub-Saharan Africa

Debate on agricultural trade can be traced back from the negotiations on agricultural trade under the General Agreement on Tariffs and Trade, which came into force in 1948.Later, significant progress toward agricultural trade reforms were covered and continues to be negotiated upon by the World Trade Organization Agreement on Agriculture, which came into force in 1995 as a result of the 1986-1994 Uruguay Round of world trade negotiations supported by the General Agreement on Tariffs and Trade (WTO, 2016).Since 1994, agricultural trade has been liberalized, which has led to increased globalization and integration.World Trade Organization members commit themselves to general agricultural trade rules pertaining to market access, domestic support, and export competition within well-defined timelines.The ultimate goal of these negotiations is to improve the livelihoods of farmers around the world through implementing sound agricultural trade regulations and domestic policies that promote competition and reduce distortions in agricultural trade (WTO, 2016).The research on agricultural trade and economic growth has therefore been gaining attention in the recent past both in developed and developing countries.Most countries in sub-Saharan Africa (SSA) are agriculture-based, and agriculture accounts for about 32% of their Gross Domestic Product (GDP) on average (World Bank, 2008).In addition, more than 65% of the total population in the region is employed in the agriculture sector, although Africa in general has recently been experiencing rapid agricultural exits particularly in oil-producing countries (Headey et al., 2010).Nevertheless, Africa's agriculture has the potential to reduce rural and urban poverty, which explains why boosting agriculture productivity remains a critical priority (Mozumdar, 2012).In SSA, the sector's production remains relatively low compared with other regions in the world.Agriculture total factor productivity in SSA has been generally low and highly volatile, with an annual growth rate ranging between -0.05 and +0.05 for both low-and middle-income countries for the period 1962-2015 (USDA 2019).Stagnation in agriculture productivity in the region has resulted in low agricultural output and consequently low global share of Africa's agricultural
Longitudinal Cognitive Diagnostic Assessment Based on the HMM/ANN Model

Cognitive diagnostic assessment (CDA) can get some information about the student's cognitive processes and knowledge structure based on the psychometric model. Most of the previous studies using traditional cognitive diagnosis models (CDMs) based on the cross-section. This study aims to propose a new longitudinal cognitive diagnosis model, namely the HMM/ANN model. In this model, the artificial neural network (ANN) was used as the measurement model of the hidden markov model (HMM) to realize longitudinal tracking of students' cognitive skills. This study includes simulation study and empirical study. The results show that the HMM/ANN model can confirm high classification accuracy and correct conversion rate when the number of attributes is small. The combination of ANN and HMM can help to effectively track the development of students' cognitive skills in real educational situations. Moreover, the classification accuracy of HMM/ANN model is affected by the quality of items, the number of items, and the number of attributes examined, but not by sample size. The classification result and the correct transition probability of the HMM/ANN model were increased with the increase of the item quality and the number of items but decreased with the increase of the number of attributes.
VALUE-SEMANTIC BASES OF IDEAS ABOUT THE PROFESSION AND SATISFACTION WITH THE PROFESSION OF HIGHER SCHOOL TEACHERS

The business culture in the educational system remains conservative, despite the fact that the values of tolerance are wide-spread in the world. This affects the transformation of ideas about the profession of teachers. The article establishes the relationship of ideas about the profession and satisfaction with the profession with the values and meanings of university teachers. It is assumed that in modern conditions of higher education, values and life meanings may differ among teachers with different ideas about the profession. The study sample consists of 270 university teachers aged between 25 and 65 years, work experience from 3 to 25 years, 150 women and 120 men. We used the questionnaire of professional satisfaction (Kissel), the method of measuring values (Schwartz), the semantic differential (Osgood), and the method of identifying life meanings (Kotlyakov). We defined groups of teachers with a neutral view and a positive view of the profession, with varying degrees of satisfaction with the profession. As a result, it was found out that the group of teachers with a neutral attitude to their profession has the most manifested values: conformity, hedonism, security, and meanings: hedonistic, status, and family. The group of teachers with a positive view of the profession, has the values of independence, achievement and security, combined with the meanings: altruistic, self-realization, family and cognitive. It is concluded that university teachers with positive ideas about the profession are more altruistic about their activities, and teachers with neutral ideas about the profession are pragmatic about it.
A framework to implement AI-integrated chatbot in educational institutes

The purpose of this paper is to explore the usefulness of chatbot in educational institutes such as schools and colleges and to propose a chatbot development plan that meet the needs. Usually chatbots are built for one specific purpose, for example, to answer general queries prospective students might have regarding admission. This paper aims to provide an artificial-intelligence(AI) integrated chatbot framework that can help develop a multi-use chatbot. Study is based highly on qualitative data collected from case studies and journal articles. Primary data is also collected from interviews and questionnaires presented to appropriate staffs and students in college, in this case, Middle East College. Integrating AI into the chatbot to make it self-reliant, intelligent and learn from user interaction is necessary to make it deal with multiple fields. This requires complex algorithms, database management and extensive labor, thus making it very costly. However, if developed, this single chatbot can help students, faculties and other staffs greatly, not just as an assistant in answering frequently asked questions, but also in learning and teaching. The chatbot can be integrated with mobile app making it a part of daily life. Due to over complexity, the chatbot will first developed for use in one field then gradually expanded to other. A chatbot built for multiple purposes certainly holds more complexity than a single general purpose chatbot. This being said, having the software developed and tested in real life would have helped to better understand its flexibility and functionality.
An Efficient Person Re-Identification Model Based on New Regularization Technique

The aim of person re-identification (ReID) is to recognize the same persons across different scenes. Due to the many demanding applications that utilize large-scale data, more and more attention has been devoted to matching efficiency and accuracy. Many methods that are based on binary coding have been presented to reach efficient ReID. Those methods learn projections to map the high-dimensional features into deep neural networks or compact binary codes through simple insertion of an extra fully connected layer with tanh-like activation. Nevertheless, the former approach needs hand-crafted feature extraction that also wastes a lot of time and complex (discrete) optimizations. In contrast, the latter approach lacks the essential discriminative information to a large extent because of the straightforward activation functions. A ReID framework is proposed in the current work, and it is inspired by the adversarial framework depending on the new regularization approach (ABC-NReg). We embedded the discriminative network into adversarial binary coding (ABC) with our new regularization, which improved the discriminative power combined with the triplet network. ABC-NReg and triplet networks were optimized, and three large-scale benchmark datasets, namely CUHK03, Market-1501, and DukeMTMC-reID datasets, were utilized to test the performance of our proposed model. We further compared the simulation results with the present hashing and non-hashing algorithms. Our model provided better results than other present models using the Market-1501 and DukeMTMC-reID datasets when considering Rank-1. For CUHK03 dataset, the proposed model exceeded the performance of other works when considering Rank 5 and Rank 20.
The Impact of Human Resource Development (HRD) Practices on Organizational Performance: The Mediating Role of Human Capital

The objectives of this study are to examine the impact of Human Resources Development Practices (HRDP) on Organizational Performance (OP) and to examine the mediating role of Human Capital (HC) on the relationship between HRDP and OP.A coordinated research model is created joining the fundamental elements of the current writing.The information will be gathered through a questionnaire from workers and clients of an Iraqi telecommunication organization.The unwavering quality and legitimacy of the measurements were set up through the examination of the affi rmation factors.The result of this study demonstrates the eff ect of HRDP on HC.The article additionally broke down the interceding impact of HC in the connection between HRDP and OP.The review was directed in the telecommunications segment among its workers and clients.The result of this investigation is considered to possibly help leaders, partners, and the administration of an organization and direct them in embracing a right and all around explained HRDP, which will animate the positive conduct of workers and will infl uence insight, aptitudes, and capacities.Perspectives and, thusly, aff ect OP.This study adds to the HR research, incorporating HRDP and HC into a farreaching research model that impacts OP.
An Analysis and Evaluation of Open Source Capture the Flag Platforms as Cybersecurity e-Learning Tools

Capture the Flag (CTF) challenges are typically used for hosting competitions related to cybersecurity. Like any other event, CTF competitions vary in terms of context, topics and purpose and integrate various features and characteristics. This article presents the results of a comparative evaluation between 4 popular open source CTF platforms, regarding their use for learning purposes. We conducted this evaluation as part of the user-centered design process by demonstrating the platforms to the potential participants, in order to collect descriptive insights regarding the features of each platform. The results of this evaluation demonstrated that participants approved the high importance of the selected features and their significance for enhancing the learning process. This study may be useful for organizers of learning events to select the right platform, as well as for future researchers to upgrade and to extend any particular platform according to their needs.