Reviewerly/exHarmony huggingface dataset:
Toward estimating standard enthalpy of combustion of pure chemical compounds: extreme learning machine approach

One of the effective thermochemical properties in the determination of heat process efficiency is the combustion enthalpy changes during complete combustion of the compounds. According to the importance of this property in different processes, the main aim of this work is selected as the development of extreme learning machine (ELM) approach to predict the combustion enthalpy in terms of functional groups. To achieve this goal, a comprehensive data set containing 4,590 experimental enthalpy points is used for the preparation and validation of ELM. To investigate the accuracy of the ELM approach in the estimation of the enthalpy, various visual and statistical comparisons are used. These comparisons lead into R2 value of one and low error values for overall phase. The standard deviation, root mean squared error, and mean relative error for overall phase are determined to be 11.18, 14.92, and 0.28, respectively. The relative deviations between the estimated and actual enthalpy points are below 8%. According to the statistical and graphical results, ELM algorithm has great potential in the prediction of enthalpy of combustion for pure chemical materials.
Usability Evaluations Focused on Children with Down Syndrome: A Systematic Literature Review

A previous literature review indicated the lack of studies about how to develop accessible games for children with Down syndrome and evaluate their interfaces. With that in mind, the research team decided to do a new one focusing only on finding usability methods that can be applied with this specific audience regarding their safety and their conditions. Therefore, this analysis used the Cochrane methodology organized into: research question, location and selection of the study, critical evaluation of the studies, data analysis, and content analysis. In total, 22 articles were selected from Scopus and Web of Science, in which the observational analysis method was the most used one, maybe because it does not require the user to communicate verbally a lot nor to remember his entire journey. Other methods found were heuristic evaluation, interview, focus group, and questionnaires. Some initiatives must be mentioned such as the research of Lazar et al. which used the assistance of an illustrator to help the volunteers in expressing their ideas during a workshop, and finally, the review permitted to create a list of precautions and recommendations before and during a test with children with Trisomy 21.
Applying Negative Association Rule Utilizing Unsupervised Machine Learning Models

Association rules are described as the relationships between objects in the database, and these rules are broadly investigated in the analysis of shopping baskets, to examine the influence of one item on the another in the same transaction. In this paper, a novel technique is suggested that can be the ability to mine thrilling negative association rules between items in the transaction database, by extracting knowledge from that database depending on the purchased quantities. Two clustering techniques are examined in this paper, which are the density-based spatial clustering and K-means of applications with noise (DBSCAN) techniques. The outcomes of these methods are compared to the outcomes of extracting negative association rules without any domain knowledge. The utilization of DBSCAN clustering technique has given better negative association rule mining outcomes of 40,862 rules, with a rate of 0.209% support and 91.840% confidence, when examining on a real-life transaction database. Mining negative association rules depends on the domain knowledge extracted utilizing the K-means clustering technique that has 17,801 rules with a rate of 0.189% support and 85.835% confidence, while mining negative rules without any domain knowledge results in 90,655 rules with rate support of 0.122% and rate confidence of 99.365%, utilizing the same database.
Logistic Regression for Prospectivity Modeling

Regression models are often employed in prospectivity modeling for the targeting of resources. Logistic regression has a well understood statistical foundation and uses an explicit model from which knowledge can be gained about the underlying phenomenon. In this paper, a model selection procedure based on logistic regression enhanced with nonlinearities is proposed. The method is designed to help the researcher in the model building process and can also be used as preprocessing step for other machine learning algorithms such as neural networks.
datasailr - An R Package for Row by Row Data Processing, Using DataSailr Script

Data processing and data cleaning are essential steps before applying statistical or machine learning procedures. R provides a flexible way for data processing using vectors. R packages also provide other ways for manipulating data such as using SQL and using chained functions. I present yet another way to process data in a row by row manner using data manipulation oriented script, DataSailr script. This article introduces datasailr package, and shows potential benefits of using domain specific language for data processing.
Learning Human Vehicle Interactions using Simulated Data

When a deep neural network based action classifier can be trained in an end-to-end manner using adequately large training datasets, it has shown to establish impressive benchmarks even without much alterations to the model architecture. However, it is yet unclear how to utilize such heavily parameterized models when it is difficult to obtain sufficient labeled training examples. In this work, we show that we can simulate training videos to improve performance of deep neural network based action classification models. We show that pretraining the model first using a large number of simulated examples and then later finetuning using available real instances leads to much better results. We evaluate our approach using a subset of the DIVA dataset which is a challenging benchmark containing fine-grained human-vehicle interactions captured from surveillance cameras. We show that pretraining with simulated data improves performance of state-of-the-art action classifiers by a considerable margin.
Feature Point Matching Based on Local Relative Velocity Consensus

Abstract Feature matching is still a crucial and challenging problem in image processing. In this paper, a novel feature matching algorithm based on local relative velocity consensus (LRVC) is proposed to find two accurate matched feature points. To remove the outliers accurately and robustly, the relative velocity between the putative matches and their corresponding neighbors are exploited. Based on the consensus of relative velocity and the consensus of neighborhood elements, the similarity of each putative matches is evaluated. With a two iteration outlier removal strategy, the feature points are matched accurately and robustly. In the experiment, VGG dataset are used to verify the accuracy and robustness of the proposed method LRVC. The matching results indicate that our method is superior to three other classic feature matching methods.
GWOA Model For Data Protection

The most important data we need to preserve is the patient's data. Nowadays every data is stored on the cloud and various techniques are used to protect the privacy of the data on the cloud but still protecting personal data on the cloud is a challenging task. It is necessary to implement or build a privacy protection policy to protect the medical data of the users. In this paper, the Glowworm Swarm based Whale Optimization Algorithm (GWOA) is implemented. Here, GWOA is a combination of the Glowworm Swarm Optimization and Whale Optimization algorithm. This privacy preservation model is applied to the registered Store data. Experiments are performed on a Hungarian dataset and the key size is used 128 bits and 256 bits. Results are compared with the existing algorithms. The results are good for the 256 key sizes as compare to the 128 key sizes.
Dusts Control operations in Ririwai Mines, Kano State, Nigeria

Respirable dust is everywhere (in the surface and underground mine) operations and its environs. Ririwai Tin mine is laden with dust of metallic particles of Arsenic (As), Chromium (Cr), Lead (Pb) and Zinc (Zn). These particles are also found in the soil. Continuous inhalation of dust could lead to irreversible diseases. This paper addresses the control of dust, using previous review of prevailing metallic concentrates. This research answers questions like which are the dust control strategies to be adopted by Ririwai tin mine to obtain best practice? What challenges will Ririwai tin mine face when applying dust control methods in their operation? What is the impact of dust laden with metallic particles on soil, water, vegetation and man in Ririwai tin mine?
EyeSee: Camera to Caption with Attention Mechanism

According to the WHO, there are currently around 2.2 billion people who are either visually impaired or blind in the world. Previously, these people had to rely only on classic aids such as the white cane and the guide dog for mobility and magnifiers and screen readers amongst others for reading. The massive use of smartphones has opened many new possibilities for the visually impaired and blind. They can now use their smartphones to help them navigate around cities and other places. In this project it is proposed to have an app for smartphones which automatically tells the blind user the objects around him. However, automatically identifying and describing the content of an image is not such a simple task. It involves tasks from 2 complex fields namely computer vision and natural language processing. The proposed application, EyeSee, takes images from a real-time environment, processes these frame by frame and tells the user what the image represents. The app also annotates the images with text. The app uses Deep Learning, more specifically, Show, Attend and Tell and GRU.
Population-Based Metaheuristics

A specialized thread of metaheuristic research, bordering and often overlapping with Artificial Intelligence, studied heuristics that evolved whole sets of candidate solutions, often named “populations” of solutions. Genetic algorithms were among the first results, and following their success it became common to get inspiration from some natural phenomenon to design the heuristic. This chapter considers three representative population-evolving metaheuristics, namely genetic algorithms, ant colony optimization, and scatter search (with path relinking) and shows how they have been complemented with mathematical programming modules to achieve better performance.
Analysis of determination of items ordering patterns by using apriori method (case study in University of Potensi Utama)

This study is entitled Analysis of the Determination of Ordering Patterns of Items Using Apriori Method Case Study of Major Potential Universities. The research conducted by the author uses data mining and uses the Apriori method which divides arranged items into two so that the more detailed types of items are often ordered so that it can help inventory to increase the stock of items needed by the staff of Potential Main University. Ordering items is one of the activities carried out at the Main Potential University which is usually used to process work activities in the office. The test results using a priori algorithm and a system built using 2 item sets showed 94% confidence results that have met the needs in determining the pattern of ordering items based on the tendency of ordering items carried out by each department .
Distortion based Watermark Extraction Technique Using 1D CNN

We have proposed a novel concept of a digital watermarking technique for music data that focuses on the use (a) of sound synthesis and sound effect techniques. The previous proposed technique was confirmed a vulnerability to high-pass filtering. This paper describes the details of the conventional embedding technique and the improved traction technique that employs the Deep Neural Networks. This paper describes the experimental results of evaluating the resistance of the proposed technique against high-pass filtering. It is demonstrated that the proposed technique in this paper has appropriate resistance (b) against high-pass filtering attack, which was not good at conventional technique.
Sedition Law and Freedom of Speech

In accordance with Article 19(1)(a) of the Indian Constitution, every citizen has been granted the right to speech and express their views. However, the freedom is not absolute and, under Article 19, some fair limits on freedom of speech and expression have been imposed (2). But if a person performs an act that is deemed to be disdainful to the Government of India by his speech, signs or representation, then that act is punishable under section 124-A of the Indian Penal Code, 1860. Sedition is an offense that criminalizes speech that is considered disloyal to the state or threatening it. The provisions of Section 124A are very broad and relate to the Government's act of defamation, excluding any criticism in good faith of any specific measures or acts of administration. Sedition is a controversial term that is thrown about in today's social dialogue rampantly and carelessly. With a distaste for the policies of the government growing in the general population, the manifestation of youth dissatisfaction is also branded as sedition. Many don't know what it really constitutes, though. Therefore, we have to ask ourselves first, what does sedition mean in law?In this review paper, we shall look at the various aspects related to the crime of Sedition in reference to freedom of speech. We will further look at the judicial pronouncement which helps in evolving and establishing the concept of the sedition.
크리에이티브 에이징의 관점에서 본 문화예술교육의 방향성

The pace of population change in Korea is more radical than in any other country in the world. The second half of life, which was considered a period of weakness and loss, is now a time of potential for a longer, healthier life with new meanings and goals. The shift of medical perspectives on aging from functional loss to the approach of ‘creative aging’, which focuses on the advantages and possibilities of a human being, constitutes a fertile ground for creativity and arts. From the perspective of creative aging, arts & culture education should have the following direction.First, it prescribes a change of perception from ‘passive aging’ to ‘creative aging’. Now it is important to pay attention to the possibilities created by aging, the creative things that can be achieved by aging. The issue of creative aging is not unique to older adults. All of us living at the same time should contemplate the words that divide our lives by life cycle from birth to aging, such as children, teenagers, middle-aged people, and old people.Second, starting with the story and life of ‘I’-the principal agent of arts & culture education- it should be a sustainable arts and culture education program that can create, restore and transform mutual life in the process of relating and interacting with others and communities.Third, a local community base that pursues creative aging as a common goal should be established. And within it, we need to support the active connection and exchange with cultural and artistic activities that encompass age and cultural differences between generations, and state policies so that a venue for social interaction and lifelong arts education can be established.Creative aging aims to create a healthy and meaningful life from an integrated perspective that encompasses the physical, mental, social and economic domains through artistic participation or creative activities. So it is necessary to set the direction for creative aging, not just a single task of arts and culture education, but to make efforts to change the perception of individuals, the paradigm of society as a whole, and policies together.
Usability and aesthetics of website design to measure Indonesian teacher competency 4.0

Abstract Website development that considers usability and aesthetics will have an impact on achieving the website development goals themselves. Ease of users in using the features provided and the appearance of beauty that will increase user comfort becomes an important factor that must be considered in the website development process. This research is an attempt to incorporate the concepts of usability and aesthetics in the development of the Teacher Competencies 4.0 website, which is a website aimed at Indonesian teachers in conducting self-assessment of their competencies whether they are in accordance with the competency needs of 4.0. This research used Neuroresearch and Website Development Life Cycle as the research methods. The results of the research are website designs that have taken into account the usability and aesthetics so that the website is expected to be able to be felt by the teacher and continue to continuously use the website to carry out continuous assessment.
Cancer Prediction Using Machine Learning

Breast cancer is a common form of cancer experienced in women across different age groups and accounts for 14% of cancers in Indian women. The high mortality rate is due to the lack of awareness of the disease and delay in screening and diagnosis. Early detection is the solution which implies that breast cancer can be treated, and patients have the chance to live a healthy life post-recovery. The proposed system aims at providing a diagnostic analysis of the cytological features derived from a biopsy-based image. The designed model attempts to predict cases of malignancy or benign on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The dataset used for analysis and implementation consists of attributes obtained from images of fine needle aspiration (FNA) tests on an affected breast tissue. The feature selection algorithm implemented was recursive feature elimination based on random forest approach. A comparative analysis was carried out against several other classification methods in an attempt to compare the accuracy of random forest approach against each of the other models. The neural network constructed using the features selected exhibited an accuracy of 96.51% while predicting malignant and benign cases in the test set. The main objective of this study was to devise an efficient model that a pathologist can effectively use in determining cases of malignancy based on the most critical cytological features. This ensures high accuracy, thereby enabling a patient to avail of the required medical attention at the earliest.
Better battery-powered lawn tools

If you're looking for lawn tools that pack a punch but omit less pollution and require less maintenance, the experts at Consumer Reports reveal top battery-powered tools.
Adversarial Neuro Encoding with Binary Neural Networks

Adversarial neuro encoding could provide new insights for ciphering information with different perspectives. Nevertheless, it is still underexplored with a handful of publications on the subject. This work proposes the implementation of neuroevolved binary neural networks based on boolean logic functions only (BiSUNA) that apply payload ciphering between two agents to disperse information from an observer. The BiSUNA framework provides three distinctive attributions: it uses an adversarial neural encoding environment to improve the system data transmission; one execution yields a diversity of results given its population heuristics; lastly, it is an unconventional proposal to employ binary neural networks for the solution of symmetric ciphered problems.
Edge-based Anomaly Data Detection Approach for Wireless Sensor Network-based Internet of Things

Nowadays, Internet of Things (IoT) has been widely employed in different applications, such as health care, manufacturing, and weather forecasting. However, due to sensor sensitivities, potential harsh environmental interference, and deception, IoT data is normally apt to be imperfect and erroneous. This paper presents an edge-based approach based on the Gaussian mixture model and fuzzy measure to detect anomalous data without prior knowledge or training to overcome such adverse issues. The experimental results demonstrate that the proposed approach is efficient and effective in detecting anomaly data and achieves detection accuracy ranging from 93% to 100%.
Examining Gender-Responsive Risk Factors That Predict Recidivism for People Convicted of Cybercrimes

This study sought to explore the role of gender-responsive approaches to reduce reoffending for women convicted of cybercrimes by comparing and contrasting the risk and needs assessment results among women and men convicted of cybercrimes in the United States. Assessments from 4457 individuals convicted of cybercrimes (both cyber-enabled and cyber-dependent) during 2005–2015 were included in this study. The domains from assessment results were used to examine the types of risk factors (or criminogenic needs) that predicted revocation by gender. Results demonstrated mixed support for gender-responsive risk factors. The criminal history domain was the strongest predictor for both genders, but the education and employment domain was not predictive for either gender. A measure related to mental health was found to predict risk reoffend among women and not men; however, findings provided support for other needs regardless of gender. Implications for gender-responsive policy and cybercrime are discussed.
CarfRCNN: A Two-stage Effective Model For Instance Segmentation

Abstract With the rapid development of deep learning, many instance segmentation models have achieved good results in accuracy and time. But here are still many problems. In this paper, we proposed a two-stage model CarfRCNN. We proposed CAResNet to change the structure of the backbone, making the feature extraction of the input image more refined. At the same time, we also added the CRF module to add smooth constraints to the pixels, so that the segmentation mask fits the target contour more closely. We train and test on the COCO datasets, and compare with the Mask R-CNN. The experimental results prove that the model we proposed can greatly improve the accuracy.
A Case Study to Validate Feasibility of Risk Proposal in the Deployment Process of Software Systems

Deployment is the process by which a software system is transferred to a business client. A risk is defined as the likelihood for a loss to occur. In a software project, a risk might imply decreased quality of the software product, increased costs, a delay in project completion or a flaw, among others. A case study is developed with the aim to refine the set of risks. Furthermore, procedures are proposed for their prevention, mitigation and/or transfer for the software system deployment process. This article presents the results of a case study which analyzed the documentation related to deployment of functionalities in a bank’s Human Resources Portal conducted by an Argentina based software Small and Medium Enterprise (SME (Presidencia de la Nación. (2018). https://www.argentina.gob.ar/noticias/nuevas-categorias-para-ser-pyme. Last updated on 09/05/2018.)).
Importancia del estudio de las actitudes para el aprendizaje de las matemáticas

This article presents a reflection on the importance of attitudes towards mathematics in the students’ academic results. Since attitudes are an essential part of the affective component, the article begins with a brief definition of the concepts involved and the attitude components in a tripartite model. Subsequently, an analysis conducted by several authors on the impact of attitudes towards mathematics on the students’ academic results is presented. Likewise, an explanation on how to measure attitudes and some suggestions to modify students’ attitudes towards mathematics are proposed.