Reviewerly/exHarmony huggingface dataset:
AlCons : Deductive Synthesis of Sorting Algorithms in Theorema

We describe the principles and the implementation of AlCons (Algorithm Constructor), a system for the automatic proof–based synthesis of sorting algorithms on lists and on binary trees, in the frame of the Theorema system. The core of the system is a dedicated prover based on specific inference rules and strategies for constructive proofs over the domains of lists and of binary trees, aimed at the automatic synthesis of sorting algorithms and their auxiliary functions from logical specifications. The specific distinctive feature of our approach is the use of multisets for expressing the fact that two lists (trees) have the same elements. This allows a more natural expression of the properties related to sorting, compared to the classical approach using the permutation relation (a list is a permutation of another). Moreover, the use of multisets leads to special inference rules and strategies which make the proofs more efficient, as for instance: expand/compress multiset terms and solve meta-variables using multiset equalities. Additionally we use a Noetherian induction strategy based on the relation induced by the strict inclusion of multisets, which facilitates the synthesis of arbitrary recursion structures, without having to indicate the recursion schemes in advance. The necessary auxiliary algorithms (like, e.g., for insertion and merging) are generated by the same principles from the synthesis conjectures that are automatically produced during the main proof, using a “cascading” method, which in fact contributes to the automation of theory exploration. The prover is implemented in the frame of the Theorema system and works in natural style, while the generated algorithms can be immediately tested in the same system.
Optimization of University English Teachers’ Career Development Path in the Era of Big Data

With the progress of science and technology and the advent of the information age, general career planning has been unable to meet the times’ needs. In the era of big data, university English teachers’ career development is exceptionally critical than before. This paper focuses on the optimization of university English teachers’ career development path based on big data. In this study, altogether, 70 English teachers from the foreign languages department and the department of college English teaching and research in a Chinese university were selected and divided into two groups. The questionnaire was analyzed by SPASS 20.0 and incomplete induction. The results show that the experimental group's career development path optimization rate is as high as 63%, while that of the control group is 40%. The career development effect of the experimental group is higher than that of the control group. It also shows that the optimization of a career development path under big data can bring university English teachers’ value and potentials into full play.
Analysis of the development of educational competencies according to teaching modalities through the use of ICT

One of the main objectives of the convergence process within universities is that the design of study plans and teaching programs should be carried out taking student learning as a reference point. Hence, in the development of a plan, in addition to defining the contents of the training program, the procedures that will be applied in the development of the teaching-learning processes must specified to encourage the methodological change from teaching focused on the teacher's activity to another oriented to student learning. This implies that once the competencies to be developed by students in relation to an educational level are established, the next step is to design the activities that they must carry out to achieve knowledge as a result of this learning process. The design of a plan cannot be limited to the distribution of subjects and credits, but must specify the teaching methods that will allow an average student to achieve the proposed learning; that is to say, to specify the methods and methodologies of the teacher and the student that are considered appropriate according to the objectives. The purpose of this study is to define and characterize the main teaching methodologies based on the use of technologies that can be used in higher education, specifying the main activities and tasks to be developed by teachers and students for each of them.
Emotional Quotient Analysis: How the Students Adapted in the COVID-19 Periods

The research aims to know that success is influencing by intellectual intelligence and emotional intelligence. Emotional intelligence is a set of personal, emotional, and social abilities that can influence a person's ability to succeed. Emotional intelligence also allows individuals to feel and understand correctly, using emotional power and sensitivity as information energy and affecting learning outcomes. The method used in this research is to use a qualitative approach with a descriptive analysis method. Qualitative research an inquiry strategy that emphasizes the search for meaning, understanding, concepts, characteristics, symptoms, symbols, and descriptions of a phenomenon. The research used several natural and holistic methods, with prioritizing quality, which presented in a narrative. The result found that students recognizing self-emotion (self-awareness) are still low, where 75% stated that they prefer to go to school at home because they can play gadgets longer.
A Study on Algorithms Towards Data Security Issues in Cloud Computing Environment

This paper involves identification of issues in existing data security algorithms so that a hybrid algorithm can be developed, which will be useful in quantum computing. It has been seen that, data security breaches are increasing, making cloud computing a vulnerable system for online data storage and processing. This paper involves discussion on researches conducted on the aspects of cloud and quantum computing and issues in existing data security techniques. Moreover, it has also been seen that the best cloud-based security framework AES is not going to work effectively with upcoming quantum computing scenario. Hence, it is necessary to keep on reiterating on ideas for enhancing data security features using hybrid algorithm.
Automated Crime Tweets Classification and Geo-location Prediction using Big Data Framework

This paper investigates with the automated classification of tweetswhich turns out to be a very complicated problem because of its nature,heterogeneity and the amount of data. According to internet live stats, nearly 500million tweets are tweeted per day, where the user’s opinion about different topicsis shared. An automated decision support system is developed to analyze thetweets related to crime against women and children. The problem is viewed in abig data perspective because of the nature of data. The proposed work focuses ondeveloping two systems: Hadoop MapReduce and Apache Spark framework forprogramming with Big Data. The algorithm based on hierarchical domain lexiconclassifies different types of crime in a parallel and distributed manner. Moreover,the crime classification tool is based on hybridized Machine Learning techniquescombined with Natural Language Processing techniques. To predict the location oftwitter users, multinomial Naive Bayes classifier trained on Location Indicativeterms and other vital parameters (such as city/country names, #hash tags and@mentions) is implemented. Our approach outperforms in terms of classificationaccuracy, mean and median error distance when compared with other algorithmsbased on parameters such as Location Indicative terms, #hash tags andcity/country names.
Applied Machine Learning to Predict the SQL Injection Attacks

In current days for any small scale or large scale enterprise companies, their data plays one of the important assets and this is very important for each and every enterprises. As we all know that as data is increasing day by day there are a lot of attackers who try to create some sort of attacks on that data by injecting some traits. One among the several attacks in enterprise level is SQL injection attack, which will be injecting the fake contents inside the business server and try to alter the queries based on intruder choice. In general it is very difficult for the network administrator to trace the difference between the normal SQL query and Injected SQL query which is triggered for the enterprise server. In this paper we try to design SQL injection detection based on SQL tainting method, which can greatly identify the intruders who try to create SQL attacks on the employee tuples and try to differentiate the genuine query and abnormal query easily in the run time. Our Experimental results clearly state that this approach is best in identifying the attackers dynamically and detect the type of injection strings applied to gather the sensitive information from the business server.
Formation Of Patient Communication Skills And Expressive Abilities In Future Doctors

This article discusses the issue of communication of medical personnel with patients, analyzes the specifics and types of communication, the significance, as well as taking into account age characteristics during communication, problems arising from non-compliance with the rules of communication, as well as issues of expressiveness in communication.
Multi-Level Visualization with the MLV-Viewer Prototype

Data visualization, especially if we are talking about a large volume, can, and should, be presented as a graphical and visual representation supported by a computer in an interactive way, and in this way it allows supporting the decision maker and increasing his cognition. The appropriate tools, methods and techniques can increase the understanding of data, with greater importance if they were large in volume and multidimensional. These visual and interactive representations, associated with analysis methods, enable decision makers to combine flexibility, creativity and human knowledge with the resources of computer storage and processing to obtain a more effective view of complex problems. Decision makers, too, should be allowed to interact directly with data analysis, adapting it to their tastes and needs. In this article, the mlv-viewer prototype will be presented, which, in short, consists of a universal decision support system, allowing multilevel data visualization, associating a set of data with a symbol.
COMPARATIVE PERFORMANCE OF RANDOM FOREST AND SUPPORT VECTOR MACHINE ON SENTIMENT ANALYSIS OF REVIEWS OF INDIAN TOURISM

Automatic sentiment extractions from social media reviews is new trend in business analysis. It visualizes and summarizes the sentiments extracted from millions of reviews in set of predefined three classes: positive, negative and neutral. Foundations of automated sentiment analysis lies in Natural language Processing (NLP) and Machine Learning (ML) algorithms. Through this paper we are presenting results of applying two robust supervised machine learningalgorithms on Indian tourism reviews: Random Forest(RF) and Support Vector Machine(SVM) and compare the performance of both on the 11K dataset collected through Tripadvisor.com. It is found that using a limited feature, RF outperforms SVM in terms of accuracy and execution time.
D-DARTS: Distributed Differentiable Architecture Search

Differentiable ARchiTecture Search (DARTS) is one of the most trending Neural Architecture Search (NAS) methods. It drastically reduces search cost by resorting to weight-sharing. However, this approach also dramatically reduces the search space, thus excluding potential promising architectures. In this article, we propose D-DARTS, a solution that addresses this problem by nesting neural networks at the cell level instead of using weight-sharing to produce more diversified and specialized architectures. Moreover, we introduce a novel algorithm that can derive deeper architectures from a few trained cells, increasing performance and saving computation time. In addition, we introduce DARTOpti, an alternative search space in which we optimize existing handcrafted architectures instead of searching from scratch. Our solution reaches competitive performance on image classification tasks.
Toward Rapid Development and Deployment of Machine Learning Pipelines across Cloud-Edge

Building machine learning (ML) pipelines, as well as deep learning (DL) pipelines, require expertise, including ML algorithms, various choices of ML frameworks, data ingestion, processing, and visualization techniques. The operationalization of the ML pipeline in an efficient manner demands knowledge in system design and implementation in distributed systems across the cloud-edge spectrum. To address the development and deployment challenges of ML pipelines, we present Stratum, an ML-as-a-service, that provides several unique capabilities as follows. First, it offers a collaborative and version-controlled graphical environment for the developers to build and operationalize ML pipelines without domain expertise. Second, it provides a language for the automated development and operationalization of the ML pipeline using the desired ML frameworks on the target infrastructure. Third, we integrate a resource monitoring and management interface to facilitate pluggable resource management logic to scale the ML pipeline components across the cloud-edge spectrum. In this chapter, we outline ML pipeline design principles, system design principles, and the lessons learned in building ML-as-a-service using case studies. Lockheed Martin Advanced Technology Labs
Layer adaptive node selection in Bayesian neural networks: Statistical guarantees and implementation details

Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. Although several works have studied theoretical and numerical properties of sparse neural architectures, they have primarily focused on the edge selection. Sparsity through edge selection might be intuitively appealing; however, it does not necessarily reduce the structural complexity of a network. Instead pruning excessive nodes leads to a structurally sparse network with significant computational speedup during inference. To this end, we propose a Bayesian sparse solution using spike-and-slab Gaussian priors to allow for automatic node selection during training. The use of spike-and-slab prior alleviates the need of an ad-hoc thresholding rule for pruning. In addition, we adopt a variational Bayes approach to circumvent the computational challenges of traditional Markov Chain Monte Carlo (MCMC) implementation. In the context of node selection, we establish the fundamental result of variational posterior consistency together with the characterization of prior parameters. In contrast to the previous works, our theoretical development relaxes the assumptions of the equal number of nodes and uniform bounds on all network weights, thereby accommodating sparse networks with layer-dependent node structures or coefficient bounds. With a layer-wise characterization of prior inclusion probabilities, we discuss the optimal contraction rates of the variational posterior. We empirically demonstrate that our proposed approach outperforms the edge selection method in computational complexity with similar or better predictive performance. Our experimental evidence further substantiates that our theoretical work facilitates layer-wise optimal node recovery.
The Macroeconomic Determinants of Economic Growth in Uganda (1990–2019)

The study was based on Macroeconomic Determinants of Economic Growth in Uganda using cointegration approach. The researcher used statistical software of Eviews 11 student version for the analysis of the macroeconomic variables affecting Uganda’s economic growth. The main objective of this study was to examine the major macroeconomic determinants of economic growth in Uganda between the periods 1990 and 2019 applying the Johansen method of cointegration. All the variables are integrated at first order except real effective exchange rate as a result the Johansen's cointegration approach was used. The study found out that foreign direct investment (% of GDP), inflation (consumer prices) and real interest rate had a positive effect on growth in real gross domestic product per capita while real effective exchange rate has a negative effect on per capita growth in gross domestic product. In the long run, real effective exchange rate and real interest rate are the significant determinants of growth in real gross domestic product per capita in Uganda. In the short run, inflation (consumer prices) and real interest rate turned to be statistically significant determinants of growth in real gross domestic product per capita. Based on the findings, the following policy recommendations are made:The Bank of Uganda should make use of monetary policy to stimulate the economy through increased aggregate demand due to the positive relationship between inflation and gross domestic product per capita growth. Since there is a positive relationship between FDI and gross domestic product per capita growth, it is therefore recommendable that the government of Uganda continues to attract more international capital inflow through trade liberalization if it is to achieve its growth target of 8 percent growth rate per annum. However the most important policy implication from the findings of negative relationship between economic growth and exchange rate indicates that there is a need for exchange rate policy framework that compliments the existing inflation targeting regime in Uganda. Instead of completely liberalizing the exchange rate in the framework of inflation targeting strategy adopted, policymakers in Uganda have to prevent the upside movements in the exchange rate by taking into consideration its negative effect on economic growth.
An Effective Model Of Person Re- Identification From Spatio Temporal Features Using Hs Algorithm And Sm Method

A significant function in video observation and forensics evidences areconsidered by Person re-identification. With the help of pictures and video clips so manyperson re-identification requirements cases are performed. The picture and video arecommonly done to speak to different highlights, and there regularly exist large variationsamong frames of each video. There is several public safety and security applications areused by Person re-identification. In the existing system the use of DR- KISS (DualRegularized KISS) is to suppress effect of large Eigen values in two evaluated covariancematrices. Anyhow the DR-KISS metric learning faces an issue. In order to solving thisexisting issue of matching performance with the matching rate and increased signal to noiseration an algorithm used namely Horn-Schunck algorithm through Optical flow energymodel is our proposed one by extract spatio features. At initial, the Optical Flow EnergyModel is offered to separate attribute vector that encodes the spatially and transientlyadjusted appearance of the person. In Horn-Schunck algorithm the error rate of changes inhuman movements and discover the speed of moving action are reduced. At the end,Individuals re-Matching method. So as to coordinate the person on foot pictures dependenton the striking nature likelihood map with appearance coordinating similarities. Pictures ofthe same person are mapped by reducing the salience matching cost. The presentation ofproposed spatio transient highlights extraction for Person Re- identification is examinedagainst with the accompanying measurements, for example, Person mapping ratio, Signalto-noise ratio and Person correlation time regarding number of tests people groups.
INSURANCE MARKETS IN RUSSIA AND KAZAKHSTAN: GENERAL FACTORS AND DEVELOPMENT POTENTIAL

The quality and depth of content Web of Science offers to researchers, authors, publishers, and institutions sets it apart from other research databases.The inclusion of Bulletin of NAS RK in the Emerging Sources Citation Index demonstrates our dedication to providing the most relevant and influential multidiscipline content to our community. Қазақстан Республикасы Ұлттық ғылым академиясы «ҚР ҰҒА Хабаршысы» ғылыми журналының Web of Science-тің жаңаланған нұсқасы Emerging Sources Citation Index-те индекстелуге қабылданғанын хабарлайды
KOMPARASI ALGORITMA CLUSTERING DENGAN DATASET PENYEBARAN COVID-19 DI INDONESIA PERIODE MARET-MEI 2020

In data mining, there is a predictive model, namely predicting the value of different sample data sets, and testing into three types such as classification, regression and time series. While descriptive models allow us to determine patterns in sample data and divide them into groups, summaries and association rules. Report on the results of experiments on algorithms that are quite widely used in the field of machine learning. This experiment aims to measure performance on commonly used datasets in machine learning studies. The main performance factor to be compared in this experiment is the level of accuracy of the independent experiments on the dataset used. This research uses clustering algorithm method to compare various clustering algorithms using Weka Tools to find out which algorithm will be more convenient for users to do clustering algorithm using the Covid-10 distribution map dataset in Indonesia from March-May 2020. K-means taking the points closest to the center whereas Farthest-First picks the furthest points. Farthest-First can complete the clustering process but with a lower quality than K-Means. And other experiments, on the method of Making Based on Clusterd Density and EM (Expectation-Maximization) prove the same accuracy. The EM grouping method proves low (less than 50%) of the results comparing the Clusterd Based Densitity Making Method, with a percentage reaching 74%.
Challenges in implementing Encrypted Keyword Search Techniques over Cloud and Feasible Solution

In Cloud Computing, search-theme-maintained on phrases where Bloom varies that is substantially faster than the current methods, necessitating merely one round of correspondence and Bloom filter confirmations. With Advanced Forward and Secure Conjunctive Keyword Search Encryption technique, fast verification, storage cost and computational cost for all types of applications is made practical by using a Bloom filter index only. This even can accommodate its facilities as much efficient as CKS technique detects the matched documents much faster. Customizing filter sizes for effective usage of Bloom filter indexes and overcoming resource-limited client problem also. To avoid the server from restoring undesirable results and prevent the blooms filter false positive probability, an encoded index for every keyword is to be stored in the server. All these issues are to be resolved by designing and implementing Framework for Forward Secure Connective Keyword Search (FS-CKS) Encryption technique to attain low redundant storage in Cloud.