Reviewerly/exHarmony huggingface dataset:
Application of Deep Learning in Art Therapy

Art therapy is a non-verbal psychotherapy that diagnoses and treats human psychology through the medium of arts.It is focusing on the characteristics that human psychology, especially unconsciousness, appears directly through non-verbal forms rather than specific language.It is used in various fields such as psychotherapy and rehabilitation, and is mainly used for psychotherapy of children who have difficulty expressing their feelings in a specific language.Art therapists interpret symbolic meanings shown in the drawings to diagnose the psychological state of the counselee, and record them as text.But, during this process, interpretation and diagnosis may be affected by the therapist's subjectivity and experience.Therefore, it is necessary to improve the reliability and objectivity of therapy by automating some of process.For this purpose, in this paper, we propose a CNN(Convolutional Neural Network)-based deep learning method for art therapy.Researches that classify images and generate captions using deep learning models have been actively studied in the field of computer vision and natural language processing.Especially, state of the art has been achieved by applying CNN-based image deep learning models and transfer learning using pre-trained model on large amounts of data.In this paper, we present a CNN model that finds symbolic features in drawings that can be used as a clue in the process of art therapy.Specifically, we apply the image captioning and attention techniques of deep learning to identify psychological features in each drawing.After key features in drawings have been identified and summarized through the proposed methodology, a psychotherapist can make consistent and standardized interpretation based on this in more efficient way.We expect that the proposed methodology may contribute to increase of reliability and objectivity of art therapy.
DC MOTOR FUZZY MODEL BASED OPTIMAL CONTROLLER

This paper presents a method for the design of optimal fuzzy controller of a DC motor using a fuzzy model based approach with emphasis on minimal knowledge on the controlled system. In the first part of the paper we describe the method of the black-box fuzzy model design based only on the system´s measured input/output data without the necessity of preliminary knowledge of its internal structure and parameters. This fuzzy model is in the second part used in the design of optimal fuzzy controller based on finding the sequence of input signal values that will transfer the controlled system into the desired state in accordance with the selected optimality criterion. The realized simulation and experimental measurements performed on HIL platform have confirmed the correctness and effectiveness of the proposed design method and also its applicability to others dynamic systems with as little previous knowledge as possible.
A live comparison between Unity and Unreal game engines

A game engine is an ongoing thread that helps us in making and designing beautiful games with the simplest methods and least resources. Game engines support a wide variety of playing platforms that can translate the game designed into a playable game in different platforms like PlayStation, PC, Xbox, Android, IOS and Nintendo. There is a wide variety of game engines that suits every programmer and designer working on engines such as Unity game engine, Unreal game engine and construct game engine. In order to make a game one has to learn how to code in one of these engines. From our results, Unity has everything you need to create games in one place. It has an integrated development framework that creates rich solutions and out-of-box functionality to make games. This paper recommends making a decision one has to look at, such as what platforms one wants to target and how one can plan on monetising their product. Keywords: Game engine, compare engine, Unity game, Unreal game, platforms.
Description of e-Health Initiatives to Reduce Chronic Non-Communicable Disease Burden on Brazilian Health System

Chronic non-communicable diseases (NCD) account for 72% of the causes of death in Brazil. In 2013, 54 million Brazilians reported having at least one NCD. The implementation of e-Health in the Unified Health System (SUS) could fill gaps in access to health in primary health care (PHC).to demonstrate telehealth strategies carried out within the scope of the Institutional Development Support Program of the Unified Health System (PROADI-SUS) and developed by Hospital Alemão Oswaldo Cruz, between 2018 and 2021, on evaluation, supply, and problem-solving capacity for patients with NCDs.a prospective and descriptive study of three projects in the telehealth areas, using document analysis. The Brasil Redes project used availability, implementation, and cost-effectiveness analysis, TELEconsulta Diabetes is a randomized clinical trial, and Regula Mais Brasil is focused on the waiting list for regulation of specialties. All those strategies were developed within the scope of the SUS.161 patients were attended by endocrinology teleconsultation in one project and another two research projects, one evaluating Brazil's Telehealth Network Program, and another evaluating effectiveness and safety of teleconsultation in patients with diabetes mellitus referred from primary care to specialized care in SUS. Despite the discrepancy in the provision of telehealth services in the country, there was an increase in access to specialized care on the three projects and especially on the Regula Mais Brasil Collaborative project; we observed a reduction on waiting time and favored distance education processes.the three projects offered subsidies for decision-making by the Ministry of Health in e-Health and two developed technologies that could be incorporated into SUS.
The effectiveness of task-based language teaching to improve students' speaking skills

This study aims to find out the effectiveness of Task-Based English Teaching (TBLT) to improve college students’ speaking skills. Teaching speaking is considered to be difficult as there are various factors involved, both from the teachers’ part and the students’ as well. TBLT uses authentic sources of teaching materials with tasks as the essence of the teaching instructions. The research was done in a private college in Bogor. Sixty 1st semester-students of non-English department are the subjects of the research. They were divided into two groups: the control group and experimental group. Each group consists of 30 students. The control group was taught using a more conventional method, however the experimental group was taught using Task-based instructions. A pre and post test were administered to both groups to find out the effectiveness of TBLT to improve their speaking skills. The mean scores of pre and post English speaking tests were tabulated using SPSS to identify whether there was a difference in students’ achievement of both groups and whether the difference was significant or not. Questionnaires were also distributed to find out their perspectives towards the English lessons. The results of the tests were supported by the students’ answers in the questionnaires in which all students of the experimental group showed significant improvement in their speaking skills in term of accuracy and fluency. Their perspective after experiencing TBLT in the English lessons also demonstrates a significant change resulting in students’ higher self confidence in speaking.
Novel Comparison of Machine Learning Techniques for Predicting Photovoltaic Output Power

Connected objects, Big data and Artificial intelligence are the main drivers of the global industrial revolutions. Among these digital technologies, Artificial intelligence has become a must in recent years. If the shift towards artificial intelligence-based solutions is only happening now, it is thanks to the explosion of computing power of computers, the introduction of new algorithms enabling to take advantage of Machine Learning's analysis and prediction capabilities along with the volume of available data generated in real time by sensors and Internet of Things activators. Artificial intelligence is already revolutionizing several industrial sectors. In the energy field, its ability to learn from data allows for the optimization of both energy generation and consumption. Energy needs are better assessed in order to enhance the energy transition. Therefore, this study aims to explore the development potential of artificial intelligence solutions in the energy sector. A performance comparison of several up-to-date Machine learning algorithms is conducted for the hourly prediction of the resulting power of photovoltaic panels. The model offering the best accuracy is identified using the most common performance metrics including R-squared, Root Mean Square Error, and Mean Absolute Error. Moreover, this paper investigates the prevailing inputs affecting the PV power through using correlation analysis. The methods investigated are Partial Least squares regression, LASSO, Multivariate Adaptive Regression Splines, Support vector regression, K-nearest Neighbor, Gradient Boosting, Random Forest and Bayesian regularized neural networks. Finally, residual analysis and diagnostic techniques are carried out to visually examine the fit of regression models.
Real exchange rate effect on economic growth: comparison of fundamental equilibrium exchange rate and Balassa–Samuelson based Rodrik approach

This study reproduces the work of Dani Rodrik on real exchange rate undervaluation and economic growth for 93 countries over the period 1990–2018. While the empirical literature on the dynamics between the real exchange rate1 and economic growth is relatively comprehensive, little has been done to compare these dynamics within economies using the Balassa–Samuelson-based Rodrik approach (BS) and the fundamental equilibrium exchange rate model (FEER). This research, to the best of the authors’ knowledge, is one of the first to compare the fundamental equilibrium exchange rate model and Balassa–Samuelson-based Rodrik approach and use dynamic estimation on the Rodrik approach. The findings of the study support Rodrik’s conclusion that undervaluation has a significant impact on economic growth, although the results of FEER are more significant than those of BS. Furthermore, the first lag of undervaluation has a significant effect on economic growth.
Privacy preserving hybrid recommender system based on deep learning

Deep learning models are widely being used to provide relevant recommendations in hybrid recommender systems.These hybrid systems combine the advantages of both content based and collaborative filtering approaches.However, these learning systems hamper the user privacy and disclose sensitive information.This paper proposes a privacy preserving deep learning based hybrid recommender system.In hybrid deep neural network, user's side information such as age, location, occupation, zip code along with user rating is embedded and provided as input.These embedding's pose a severe threat to individual privacy.In order to eliminate this breach of privacy, we have proposed a private embedding scheme that protects user privacy while ensuring that the nonlinear latent factors are also learnt.In this paper, we address the privacy in hybrid system using differential privacy, a rigorous mathematical privacy mechanism in statistical and machine learning systems.In the reduced feature set, the proposed adaptive perturbation mechanism is used to achieve higher accuracy.The performance is evaluated using three datasets with root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), R squared, precision and recall.These evaluation metrics are compared with varying values of privacy parameter ϵ .The experimental results show that the proposed solution provides high user privacy with reasonable accuracy than the existing system.As the engine is generic, it can be used on any recommendation framework.
A Study of Future Opportunities and Challenges in Digital Healthcare Sector: Cyber security vs. crimes in digital healthcare sector

Objective: The current study is to understand the opportunities for and challenges faced by digital healthcare sector. The aim of the study is to suggest measures for securing data and information collected by the sector plus have safer online transactions. Design and Setting: The secondary data was collected from reliable sources. The primary data was collected online. The quantitative and qualitative analysis was done on data. The data was statistically analysed for this research article. Results: Even though healthcare sector has spent on digital health and cybersecurity but still the number of cybercrime cases have increased. An attempt is made to understand the factors that can influence the number of cybercrime cases. Conclusion: The healthcare sector has opportunities in the digital healthcare field in the current pandemic situation. At the same time, it has challenges to reduce the cybercrime cases as hackers are stealing confidential data and to take stringent measures to reduce the same.
PENGGUNAAN MEDIA PEMBELAJARAN BERBASIS ANDROID PADA PEMBELAJARAN TEKS EKSPOSISI

Learning media is important for the continuity of learning activities so that these activities jave variations that make students not bored. One of them is with android-based learing media. The android-based learning media is form of alignment in the field of education with advances in the field of technologu. This study aims to produce android-based learning media as a form of learning variation in exposition texts. In addition, this study also aims to see student responses to android-based learning applications. This research method uses a qualitative descriptive method with interview techniques. The object of this research is an application that is organized according to the subject matter and also a review of this android-based learning application. The results of this study indicate that this android-based learning media application has good feasibility to be used as a variation of new learning media in Indonesian lessons.Keywords: Media, learning, android, text, exposition
Some Classes of New Quantum MDS and Synchronizable Codes Constructed From Repeated-Root Cyclic Codes of Length 6<i>p<sup>s</sup> </i>

In this paper, we use the CSS and Steane’s constructions to establish quantum error-correcting codes (briefly, QEC codes) from cyclic codes of length 6ps over Fpm.We obtain several new classes of QEC codes in the sense that their parameters are different from all the previous constructions. Among them, we identify all quantum MDS (briefly, qMDS) codes, i.e., optimal quantum codes with respect to the quantum Singleton bound. In addition, we construct quantum synchronizable codes (briefly, QSCs) from cyclic codes of length 6ps over Fpm. Furthermore, we give many new QSCs to enrich the variety of available QSCs. A lot of them are QSCs codes with shorter lengths and much larger minimum distances than known non-primitive narrow-sense BCH codes.
Faster-LTN: A Neuro-Symbolic, End-to-End Object Detection Architecture

The detection of semantic relationships between objects represented in an image is one of the fundamental challenges in image interpretation. Neural-Symbolic techniques, such as Logic Tensor Networks (LTNs), allow the combination of semantic knowledge representation and reasoning with the ability to efficiently learn from examples typical of neural networks. We here propose Faster-LTN, an object detector composed of a convolutional backbone and an LTN. To the best of our knowledge, this is the first attempt to combine both frameworks in an end-to-end training setting. This architecture is trained by optimizing a grounded theory which combines labelled examples with prior knowledge, in the form of logical axioms. Experimental comparisons show competitive performance with respect to the traditional Faster R-CNN architecture.
Analytical Study of Some Selected Classification Algorithms and Crime Prediction

To prevent the crime these days police exercises particularly in the case of investigation, emphasis on Artificial Intelligence, data mining and Machine learning aspect. To prevent future crimes it is necessary to understand the crime behavior from the earlier crime records. The more numbers of recorded crime data approaches the research to analysis the data for prediction and prevention of crime. Most of the researcher use to cluster the data for further classification to predict the definitive of crime. To prevent the crime these tools are xeric applicable to predict the most sensitize zone in the city. Thesis concentrated on the methods to predict the crime and on his hidden arrays in the existing past records. The objectives of the thesis is to predict the certain possibility of crime by applying data ruining approach through WEKA are applied to confirm criminality y proclamation. three algorithms are referred from different groups of methods: SMO Zero R and J 48 decision trees. Over 10000 records from Indian police department are collected to predict the frequency of crime in overall and its behavior in which Naive algorithm shows the reliable prediction represent against crime frequency. This paper compares the three diverse order classification to be specific, SMO, Zero R and J 48 Decision Tree for anticipating Crime Category' for various states in India. The outcomes from the examination demonstrated that, Decision Tree calculation out performed calculation and sieved 41.44% and 73.33%% Accuracy in anticipating Crime Category for various conditions of India. order classification to be specific, SMO, Zero R and J 48 Decision Tree for anticipating Crime Category' for various states in India. The outcomes from the examination demonstrated that, Decision Tree calculation out performed calculation and sieved 41.44% and 73.33%% Accuracy in anticipating Crime Category for various conditions of India.
Impact of Social Media Influencer Marketing On Youth Purchase Intentions in UAE

The study analyses how youth perceive social media Influencers and whether they affect their purchasing intentions and buying decisions.It also measure s influencers' attractiveness, expertise, and trustworthiness while communicating with consumers.In addition, the study explores the types of digital interaction with influencers, measures the impact of their online marketing activities on purchasing intentions, and finds differences between youth demographic segments.A total of 350 youth participants in the United Arab Emirates (UAE) completed an online survey evaluating influencers' characteristics, promotional videos, types of youth interaction with their content, and their impacts on purchasing intentions.The findings show that youth in UAE consider that influencers have positive personal appeals, traits, and information support to attract their followers and encourage them to purchase.Furthermore, the results reveal that influencers push youth to search for more information about the products and visit marketers' websites.Influencers' social media activities also affect youth's interests, desires, and purchase intentions by helping them assess the products.
SAL-Net: Self-Supervised Attribute Learning for Object Recognition and Segmentation

Existing attribute learning methods rely on predefined attributes, which require manual annotations. Due to the limitation of human experience, the predefined attributes are not capable enough of providing enough description. This paper proposes a self-supervised attribute learning (SAL) method, which automatically generates attribute descriptions by differentially occluding the object region to deal with the above problems. The relationship between attributes is formulated with triplet loss functions and is utilized to supervise the CNN. Attribute learning is used as an auxiliary task of a multitask image classification and segmentation network, in which self-supervision of attributes motivates the CNN to learn more discriminative features for the main semantic tasks. Experimental results on public benchmarks CUB-2011 and Pascal VOC show that the proposed SAL-Net can obtain more accurate classification and segmentation results without additional annotations. Moreover, the SAL-Net is embedded into a multiobject recognition and segmentation system, which realizes instance-aware semantic segmentation with the help of a region proposal algorithm and a fusion nonmaximum suppression algorithm.
Implementation of K-Nearest Neighbors Algorithm for Predicting Heart Disease Using Python Flask

Heart disease is a non-communicable disease and the number 1 cause of death in Indonesia. According to WHO predictions, heart disease will cause 11 million deaths in 2020. Bad lifestyle and unhealthy consumption patterns of modern society are the causes of this disease experienced by many people. Lack of knowledge about heart conditions and the potential dangers cause heart disease attacks before any preventive measures are taken. This study aims to produce a system for Predicting Heart Disease, which benefits to prevent and reduce the number of deaths caused by heart disease. The use of technology in the health sector has been widely practiced in various places and one of the advanced technologies is machine learning. Machine learning technology can be used to predict the potential patients of heart disease by implementing the K-Nearest Neighbors (KNN). The algorithm results in 65.93% for its accuracy, which is then improved to 82.41% due to the z-score normalization. It shows that z-score can noticeably improve the accuracy of the KNN algorithm. The system is developed based on a website that uses the Flask micro-framework so that development is more efficient. Flask is a micro-framework based on the Python programming language that does not contain many tools and libraries, so it is more portable and does not utilize a lot of resources.
Quorum-based Blockchain Network with IPFS to Improve Data Security in IoT Network

The Internet of Things (IoT) is an emerging technology.It enables users and various IoT-enabled devices to be linked across the IoT network in order to exchange their data and resources, which would result in a more relaxed and connected lifestyle.There are still several challenges facing the new centralized IoT network.For illustrative purposes, the method for computing all connected nodes within the network relies on a centralized server.The centralized IoT model prompts a single point of failure when the central server is offline.The centralized IoT model is a clear target for privacy and security issues, as a centralized system completely handles all IoT data of several connected devices.The implementation of the Quorum Blockchain Network (QBN) with the InterPlanetary File System (IPFS) on the IoT network will solve these problems.It provides an unchangeable ledger that would ensure the reliability of transactions between linked nodes on a distributed blockchain network thereby replacing the central authority.This paper aims to propose a distributed IoT data store and a decentralized IoT network using a QBN model with an IPFS protocol.
Instagram Usage in Learning English: A Literature Review

The social media of Instagram, one of the most used mediums to connect online, is used to build relations and correlations among individuals to support long-distance interaction. Educators and researchers often apply it in global contexts for educational purposes like teaching and researching English vocabulary learning. Moreover, vocabulary is essential in order to enhance EFL learner’s skills. This study presents an analysis of Instagram usage in learning English Vocabulary. The purpose is to analyze how Instagram can assist EFL students in learning vocabulary and investigate whether or not Instagram improves vocabulary comprehension. The authors also review journals taken from several trustworthy sources like Research Gate, Turkish Online Journal of Educational Technology, Arab International English Journal, Springer link, Atlantis press, and Science Direct.