Reviewerly/exHarmony huggingface dataset:
Diagnosis of Sepsis Using Ratio Based Features

Early prediction of sepsis is of utmost importance to provide optimal care at an early stage.This work aims to use machine learning for early prediction of sepsis using ratio and power-based feature transformation.The feature transformation and feature selection process is optimized by applying a genetic algorithm (GA) based approach to extract the information specific to the sepsis from the given raw patient covariates that maximizes the underlying classification performance in terms of utility score.The proposed method begins with filling the missing values in the training dataset.Then, GA is applied strategically to identify influential ratio and power-based features from the raw patient covariates.The utility score is maximized as an objective of the optimization.RusBoost is used with default settings for underlying classification during optimization.Subsequently, an optimal RusBoost model is developed with a set of 55 identified features.Independent performance evaluation of the proposed method with the 2019 PhysioNet/CinC Challenge dataset has officially achieved 19 th rank with a utility score of 30.9% on the full hidden test data.This work appears as Shivpatidar on the leaderboard.The proposed early warning system has potential clinical value in critical care clinics.
A Systematic Judgment to Automated Programming Contracts Generation

Contracts provide a pre-emptive approach in identifying programming errors at run-time using assertions or by formal Static analysis tool or Manual source code reviews. They describe the expected software behavior. Contracts written by developers have a greater error detection ability than the generic ones that are created automatically but may involve strenuous efforts for larger sized source codes. The intent of this paper is a concise study of prevalent approaches in the generation of contracts and to put forward an approach to derive programming rules for real-time concurrent Java source code automatically with reduced efforts. The proposed method extracts the scalar variables and computed constants from Static program code analysis, then identifies various dependencies dynamically and generates the declarative contracts automatically by Decision tree modeling of computed dependencies. These rules can then be utilized for software Verification.
Audio Signal Preparation Process for Deep Learning Application Using Python

This paper was created as a part of the project "Development of software to improve communication, academic and social skills of children with disabilities [1]." Artificial Intelligence today represents a wide area of different computer algorithms and systems with one main goal -to mimic and eventually replace human thinking and logic.One of the most important parts of the mentioned software in development is correct and meaningful collection and preparation of relevant data.Deep learning model structure, model training and results heavily depend on thoughtful identification and processing of relevant data.As a part of a wider project, this paper is representing a short overview of sound record digitalization and recommended steps required in data preparation for use in artificial intelligence applications.
An Ensemble of Bagged Decision Trees for Early Prediction of Sepsis

Sepsis is a serious medical condition caused by the body's response to an infection.Early prediction and treatment of sepsis are critical.In response to the PhysioNet/CinC Challenge 2019, we developed an algorithm for early prediction of sepsis.Three datasets provided by the challenge are from ICU patients in three separate hospitals, two of which are publicly available to the participants, but the third is hidden and used for scoring.Data are highly unbalanced and contain many missing values.Each patient's data comprises hourly collected samples of 40 features.We preprocessed the data by a plausibility filter eliminating the outliers, forward filling of the missing data and replacing the remaining by population mean, and standardization of the numerical data.We developed an ensemble of bagged decision trees with a highly unbalanced misclassification cost to predict the sepsis for each sample of features in a patient.The classifier was trained on the first hospital dataset and validated on the second hospital dataset.A total of 15 important features and a set of hyperparameters were selected in an iterative training approach.Our team (AlgTeam, ranking 39) submitted nine entries for evaluation on the subset of the hidden data and selected the entry with highest utility score which resulted in the final utility score of 0.24 on the full test dataset.
Teaching and Learning Music Theory in the Age of AI and Mobile Technologies

Mobile technologies coupled with the power of artificial intelligence offer many affordances for educators and learners alike.This paper presents a case of a music mobile app, Harmonia-on-the-Go, created for the purpose of teaching and learning an area in music theory that deals with chord grammar.As a kind of personal tutoring tool, the app generates automated feedback that reinforces the harmonic theory to be learnt.Student users can practise creating chord progressions anytime, anywhere, with immediate feedback given.If registered as a user, they can seek supplementary asynchronous feedback from the music teacher; learning analytics will also be generated for their self-monitoring of their own learning.For the teacher, the app reduces mundane marking but more invaluably, it frees up time for them to focus on monitoring students' learning as reflected in the learning analytics generated.They can in turn offer remedial teaching if necessary.The "Consult" function in the app also gives the teacher an additional platform, outside of class, to address students' questions.The article presents some brief observations from the pilot testing of the app, conducted with fifty high school students.It ends by articulating how this app can potentially transform the teaching and learning of music theory.
Blockchain governance

Blockchain governance can be regarded as the integration of norms and culture, the laws and the code, the people and the institutions that facilitate coordination and together determine a given organisation. Issue 2This article belongs to the Glossary of decentralised technosocial systems, a special section of Internet Policy Review. DefinitionBlockchain governance can be regarded as the integration of norms and culture, the laws and the code, the people and the institutions that facilitate coordination and together determine a given organisation.
Differential Hiring using a Combination of NER and Word Embedding

In this data-driven world, AI is being used in almost all the tasks to automate processes and make human life more comfortable. One such industry where Artificial Intelligence (AI) importance is growing is the recruiting industry. This paper aims to propose a new and a better method to match the most suitable talent to jobs, which has been incorporated using two methods – suggesting top resumes to a job opening from a talent pool to a recruiter, recommending top jobs which match to a candidate based on the candidate's resume. Natural Language Processing techniques have been used in implementing this approach – Named Entity Recognition (NER), Word embedding model, and Cosine similarity using which a resume and job will be matched. The NER model is used to extract useful entities from documents, which is enhanced by the word2vec model by making the system more generic and the similarity is calculated using the cosine similarity algorithm.
Radiological and Cytological Correlation of Breast Lesions with Histopathological Findings in a Tertiary Care Hospital in Costal Karnataka

Introduction: Breast cancer is the leading cause of cancer deaths among women worldwide.Breast cancer clinically presents as breast lump.Fine needle aspiration cytology (FNAC) and breast ultrasound (sonomammography) is pivotal in characterization of a breast lump as benign and malignant lesion.There is a need to study radiological and cytological correlation of breast lesions with histopathological findings to evaluate the diagnostic accuracy of sonomammography and FNAC. Material and Methods:A prospective, quantitative and descriptive study was conducted in the department of Radiology, Father Muller Medical College Hospital, Mangalore.Patients who were evaluated for suspicious breast lump with a breast ultrasound, FNAC and biopsy from January 2017 to December 2018 were included in the study. Results:In this study, diagnostic accuracy for breast FNAC (98.7%) was found to be better than that of sonomammography (92.5%).Fine needle aspiration cytology of breast lump plays an important role when it is difficult to determine the nature of breast lump by clinical and radiological examination.In this study breast FNAC was found to have sensitivity of 98.4% and specificity of 95.7%.Sonomammography had a sensitivity of 90.6%and specificity of 97.8. Conclusion:Present study confirms the higher combined sensitivity, specificity and accuracy for ultrasonography and fine needle aspiration cytology in categorisation of breast lump as benign and malignant.USG is better indetection of benign lesions whereas cytology is better in case of malignancies.The triple assessment is an accurate, simple and cost-effective method for the evaluation of breast cancer and can be applied at our centre as a safe alternative for open biopsy when it is concordant and open biopsy must be reserved only for discordant results.This will subsequently reduce the number ofbreast biopsies significantly.
English text to Indian Sign Language Machine Translation: A Rule Based Method

The hearing challenged community all over world face difficulties to communicate with others. Machine translation has been one of the prominent technologies to facilitate a two way communication to the deaf and hard of hearing community all over the world. We have explored and formulated the fundamental rules of Indian Sign Language and implemented as a translation mechanism of English Text to Indian sign Language glosses. The structure of the source text is identified and transferred to the target language according to the formulated rules and sub rules. The intermediate phases of the transfer process is also mentioned in this research work.
Artificial Intelligence on the Move: A Revolutionary Technology

People have used technology to improve themselves throughout the human history. From the ancient times, human beings tried to get their work done by human slaves or inanimate machines. To build intelligent agents each new technology has been exploited. Clockwise, hydraulics, telephone switching systems, holograms, analog computers and digital computers have all been suggested both as mechanisms for intelligent agent and as technological metaphors for intelligence. A new invention of computer system is known as Artificial Intelligence that can perform tasks with the help of human intelligence. Artificial Intelligence associated with computer systems which includes various types of intelligence: systems that understand new concepts and tasks, systems that are able to give reason and draw useful conclusion about the world around us, systems which can learn a natural language and comprehend a visual scene. Artificial Intelligence means intelligence that is demonstrated by machines. This is a device that recognizes its environment and takes action that increases the chances of achieving its goal. The research goal of Artificial Intelligence is to create technology which helps computers and machines to perform various tasks in an intelligent manner. Artificial Intelligence analysis the intelligent acts of computational agents. Computational agent is one whose decisions about his/her actions can be explained in terms of computation. His actions, firstly, may be broken down into primary operation that further can be applied in a physical device. Computations have many forms, for example: In humans, it is in the form of “wetware” and in computers it is in the form of “hardware”. Greatest advances have occurred in the field of game playing. Super computer named Deep Blue defeated world chess champion Gary Kasparov in May, 1997. This research article explains history, features and goals of artificial intelligence. It also explains various types of artificial intelligence like reactive machine,limited memory, theory of mind and self- awareness. This article focus on application of artificial intelligence in many fields like literacy, finance, heavy industries, hospitals, news, publishing, transportations, telecommunication maintenance, telephone and online customer services etc.
Influence of Demographic Factors on Business Performance in Ethiopia

Ethiopia, as a developing country has a higher level of youth unemployment combined with a higher level of growth in the economy, in the recent past. In Ethiopia, the youth entrepreneurship has been regarded as a tool for employment creation. However, the country is faced with the challenge of high youth unemployment rates. Still, the country is one of the lowest rates of youth entrepreneurial activity and business survival in the sub-Saharan African region. This research is supposed to fill the gaps by investigating the major effect of demographic variables with youth owned business performance. Further, the research employed both a descriptive survey and exploratory research design with a sample size of 350 youth entrepreneurs. The results obtained from a multiple regression model indicate that age, educational level and prior work experience of the youth entrepreneur were found to be positive and significantly important in explaining the perceived business performance. The implications of this study are that specific policy measures are necessary to encourage the many well trained but unemployed young people to engage in businesses. Additionally it also implies that specific training programmes are necessary to equip the practitioners with necessary theoretical and practical capacities to enhance performance of their firms.
The Impact of Online Learning on the Learning Motivation of Junior High School Students

Online learning is a basic need of education in the 21st century. The benefits of online learning are felt in the continuity of education during the Covid-19 pandemic. This study aims to examine the impact of online learning on the learning motivation of Junior High School students. The results of the research discussion indicate that online learning has advantages, namely 1) as a medium to encourage independent learning; 2) as an effort to modernize the concept of education in the 21st century; 3) increasing the intensity of the interaction between teachers and students; 3) allows for an interaction that is not limited to space and time, and 4) meaningful learning. The impact of online learning on student learning motivation tends to be negative due to the unpreparedness of various parties, including teachers, parents, students, and schools. Early adolescents who need direct guidance, direction, and motivation must lose control due to reduced direct interaction with the teacher. The monotonous implementation of online learning, lack of direction and supervision, pressure for students to study from home, and low teacher innovation are the main causes of decreased learning motivation due to online learning.
Improving Performance using Relational and Graph Database

Paper Relational database model (also called SQL databases) are one of the prevalent databases that are used with structured data. Currently news demands are arising owing to the magnitude with which the internet and social networks are getting used which brought importance to graph-structured data. Graph database (a nosql database) deal more naturally with highly connected data and are thus becoming popular and efficient choice. Due to limitations faced by relational databases in handling relationships (highly connected data), enterprise information systems find graph database as a promising alternative. According to the form of queries and property of data both relational and graph databases have vitality and flaws. Since most of the data is available in relational schema in this context, the conversion of an application from a relational to a graph format is very beneficial. Thus, this paper develops a dual database system through migration, which unifies the strengths of both relational databases and graph databases. Experimental results have shown that, this hybrid system has efficient performance.
Random forest-based active learning for content-based image retrieval

The classification-based relevance feedback approach suffers from the problem of imbalanced training dataset, which causes instability and degradation in the retrieval results. In order to tackle with this problem, a novel active learning approach based on random forest classifier and feature reweighting technique is proposed in this paper. Initially, a random forest classifier is used to learn the user's retrieval intention. Then, in active learning the most informative classified samples are selected for manual labelling and added in training dataset, for retraining the classifier. Also, a feature reweighting technique based on Hebbian learning is embedded in the retrieval loop to find the weights of most perceptive features used for image representation. These techniques are combined together to form a hypothesised solution for the image retrieval problem. The experimental evaluation of the proposed system is carried out on two different databases and shows a noteworthy enhancement in retrieval results.
Predictive Tool for Dermatology Disease Diagnosis using Machine Learning Techniques

Prediction of skin diseases is more complex as many diseases have the same symptoms at the early stage but may vary at the later stages while the disease becomes incurable. So we can use data mining algorithms to classify the diseases based on the input symptoms. In this paper, the best algorithm suitable for classification of data into six dermatological diseases is determined by comparison with few other algorithms. Naive Bayes tends to show higher accuracy of 99.31% , Random forest exhibits 97.80% and SVM reveals 94.35% when test size is 40% in jupyter notebook. Linear regression and K Nearest Neighbors when trained with 80% of the data displays 82.14% and 94.44% accuracy respectively. Naive Bayes can be used for the prediction of several other diseases and is best for classification of data and thus helps doctors predict the disease more accurately and with comparatively lesser time.
AirLine Tweets Sentiment Analysis using RNN and LSTM Techniques

With the progression of web innovation and its development, there is an enormous volume of information present on the web for web clients, what's more, a great deal of information is created as well.The web has gotten a stage for internet picking up, trading thoughts, and sharing assessments.Lots of data generated from Facebook, WhatsApp, Twitter.All these data are in the form of text format.So, text processing plays a major role in decision making.In this paper, we applied RNN, LSTM models on airline tweets data.We applied sentiment analysis for airline customers and classified the reviews as positive or negative.The dataset is taken from a Kaggle repository and we performed airline sentiment analysis using a deep learning LSTM model.We applied various types of LSTM models to better classify the tweets.We achieved an accuracy of 90% with our proposed LSTM model.
Software Defect Prediction Via Deep Learning

Existing models on defect prediction are trained on historical limited data which has been studied from a variety of pioneering and researchers. Cross-project defect prediction, which is often reuse data from other projects, works well when the data of training models is completely sufficient to meet the project demands. However, current studies on software defect prediction require some degree of heterogeneity of metric values that does not always lead to accurate predictions. Inspired by the current research studies, this paper takes the benefit with the state-of-the-art of deep learning and random forest to perform various experiments using five different datasets. Our model is ideal for predicting of defects with 90% accuracy using 10-fold cross-validation. The achieved results show that Random Forest and Deep learning are giving more accurate predictions with compared to Bayes network and SVM on all five datasets. We also derived Deep Learning that can be competitive classifiers and provide more robust for detecting defect prediction.
The Effect of Kahoot on Developing EFL Saudi Students' Vocabulary Acquisition, Reading Comprehension, and their Attitudes towards such a Strategy

The study investigated the effect of kahoot on developing EFL Saudi students’ vocabulary acquisition, reading comprehension, and their attitudes towards it. To achieve this aim, the researcher adopted the experimental approach and employed a sample consisting of seventy seven male EFL Saudi students at Imam Mohammed Ibn Saud Islamic University at Deanship of Preparatory Programs. The researcher purposefully chose two scientific stream classes assigned one class consisting of (38) students as an experimental group and the other consisting of (39) students as a control group. The traditional method was used in teaching the control group, while kahoot was used with the experimental one in the second term of the academic year (2018-2019). In the current study, three instruments were used: a vocabulary test, a reading comprehension test, and a questionnaire. The findings of the study showed that there are statistically significant differences at (a= 0.05) between the means of both groups on the students' post vocabulary acquisition and reading comprehension tests, in favor of the experimental group. This could be attributed to using kahoot. This indicated that using kahoot as an instructional strategy had a positive effect on developing student’s vocabulary acquisition and reading comprehension.
A Research on Automatic Handwritten Devnagari Text Generation in Different Styles Using Recurrent Neural Network (Deep Learning) Especially for Marathi Script

The point of handwritten numeral reputation (HNR) framework is to order input numeral all in all of k classifications. There are standard HNR frameworks have 2 elements: handwritten numeral popularity. In spotlight exam step, data relevant as an example classifier. the example arrangement step names the numeral by means of and large of k classifications exploitation the class models. in the course of the years, right savvy amount of labor has been allotted in the space of HNR. Fluctuated methods are arranged within the writing for characterization of composed numerals. those hold close Hough changes, visible diagram methods, head element research, and bolster vector machines, closest neighbor methods, neural figuring and fluffy essentially based totally methodologies
The Fluctuations of Bitcoin Price during the Hacks

Security breaches of the cryptocurrency exchanges usually cause the price fluctuation in the market.Approximately one hundred cryptocurrency thefts, including hacks and scams, has occurred since 2012 to 2018, half of which are hacks of Bitcoins.Based on the thirty Bitcoin hacks, this study portrays the general price pattern during the hack.And it illustrates the link between the size of the hack and the subsequent price change of Bitcoin.The tests reveal that the larger the volume of the hack, the stronger the price drop.However, a similar obvious relationship does not exist for the recovery of the price.The study might be the first piece of research focus on the hacks and the price pattern in a short time period.