Reviewerly/exHarmony huggingface dataset:
An analysis of code mixing and code switching used by teacher and students in english class

The purpose of conducting this study is to find out the kinds of code mixing and code switching used by teacher and students in English class and some factors that make the teacher and students use code mixing and code switching in English class. This research used qualitative method. The researcher use basic technique in analyzing the data which are recording the entire content, transcribing the utterances, classifying and organizing the data, grouping data and the last is analyzing data. Based on the data found by the researcher, the result of the research shows 4 types of code mixing and code switching namely outer code mixing (English with Indonesia and Indonesian with English) and external code switching (English into Indonesian and Indonesian into English). There are 51 utterances total of code mixing and switching in English class. And some factors that make they use code mixing and switching because the teacher often found out that the students confused or did not understand, the students lack of vocabulary and English knowledge, feel afraid of being wrong in using English and they are not used to talking only in English.
A deep reinforcement learning model with plan value network for join order selection

The existing optimisers and dynamic programming methods rely on the cardinality estimation and the cost model of the local database. The resulting join plan does not reflect the execution time, and the errors of cardinality estimation will also lead to the join plans with poor quality. We propose a new learning optimiser, called DVJ (Deep reinforcement learning with plan Value networks for Join order selection). Compared with the existing deep reinforcement learning method, DVJ has two improvements: (1) the plan value network is designed to improve the reward mechanism in the existing deep reinforcement learning method, and the join plan generated by DVJ can reflect the latency; (2) applying Deep Q-Network to reduce the optimisation time and increase the chance of finding the best connection plan. Extensive experiments are conducted and the experimental results demonstrate that DVJ outperforms traditional optimisers and the existing deep reinforcement learning methods.
A Secured e-Governance Enterprise Framework using Aadhaar Based eSign System

Over the wide expansion of internet and e-Governance, many services of government are nowadays provided online. In any online services, confidentiality, authentication and non-repudiation are of foremost priority in setting up any e-Governance framework. Strong cryptographic security protocols and framework must be deployed for large scale e-Governance transactions. We have proposed an open source based secured e-Governance enterprise framework, using Aadhaar based eSign system to integrate all available existing e-Governance services from different sources to seamlessly perform secured transactions in very large-scale and cost-effective manner.
Location-Aware Emergency Button for Blind Stick Navigator

Blind and visually impaired people require assistance in their movement.They normally walk using a stick to preserve the sense of direction.In emergencies, they have difficulties in asking for help or contacting their guardians and they have to depend on the people around them.Therefore, this project proposes to build a blink stick with an emergency response button that uses current technologies such as GPS, GSM, and Bluetooth to connect them to their guardians.Once they activate the emergency button, an SOS message with a link to the user's location is sent to the guardian, enabling them to respond to the SOS.Thus, the response time to the SOS message is crucial to ensure the distressed person receives help without undue delay.Most research on the blind stick is focused on navigation and seldom addresses the issue of response time to the emergency signal.Therefore, this study evaluates the response time of different service providers to the SOS messages to see how long it would take from the time it is activated from the blind stick to the time it reaches the guardians and caretakers of the blind.The analysis findings have led to recommending for the blind people suitable network service providers based on a selection technique.This research output can also be extended to people who are likely to face emergencies, such as heart attack, sudden hypertension, and diabetic coma.
Computational Algorithms for Deep and Shallow Word Search in Sanskrit with Case-inflected Forms of Feminine and Neuter Nouns

Effective and comprehensive word-search in Sanskrit E-text is a non-trivial problem in natural language processing that has been solved in this work for the first time in the literature. The problem is also one of great benefit to a variety of users. The complexity of the problem stems from the fact that words in Sanskrit can get completely metamorphosed on account of the phenomena of euphonic conjunctions and case-inflected forms. This work deals with the case-inflected forms of feminine and neuter gender nouns in particular, and their bearing on the search problem. The novel computational model presented here for generating case-inflected forms of Sanskrit nouns involves a categorization of word forms and a development of new formulae to achieve the generation of case-inflected forms. Pre-processing to improve the performance of the algorithm based on these formulae has also been developed. This model has been extended to include processing of euphonic conjunctions to enable a more elaborate search and to thereby produce meaningful search results. The dual paradigms of deep/slow and shallow/fast searching have been handled in this work and the efficiency of the fast search algorithm developed is shown to have an increased efficiency of 77-80%. The work also discusses the trade-off between search accuracy and speed in the context of word-search in Sanskrit E-text.
Sentiment on Twitter Data Set using Recurrent Neural Network - Long Short Term Memory

Social media is a combination of different platforms where a huge amount of user-generated data is collected. People from various parts of the country express their opinions, reviews, feedback and marketing strategies through social media such as Twitter, Facebook, Instagram, and YouTube. It is vital to explore, gather data, analyze them and consolidate the people views for better decision making. Sentiment analysis is a natural language processing for information extraction that identifies the user’s views. It is used for extracting reviews and opinions about the satisfaction of products, the events, and people for understanding the current trends of product or user’s behavior. The paper reviews and analyses the existing general approaches and algorithms for sentiment analysis. The proposed system selected to perform sentiment analysis on Twitter data set is Long Short Term Memory [LSTM] and evaluated with Naive Bayes Approach.
Stream of traffic balance in active cloud infrastructure service virtual machines using ant colony

Cloud load balancing is the manner of distributing computing resources and workloads over a cloud computing infrastructure. It allows an enterprise to manage workloads through appropriate resource allocation in the cloud. Various load balancing techniques in cloud computing are reviewed and the work presented in this paper thoroughly analyses and compares two well-known algorithms in MATLAB, the ant colony optimisation (ACO) algorithm and genetic algorithm (GA). The objective is to produce an optimal solution for cost and execution time through balancing the workload. It is observed through experimental observations that ACO-based load balancing possess incurs low cost and low execution time as compared to the GA for a constant workload over a fixed number of cloud machines. However, the execution time follows a different trend when workload increases and more machines are utilised to handle the increased workload, it rises sharply in ACO as compared to the GA.
New Data Structure for Many-to-Many Relations to Reduce Data Size, Recording Time, and Search Time

With the aim of reducing data recording and search times, this paper proposes a new data structure that reduces data sizes for data in which two item types have many-to-many relations. The proposed data structure newly introduces container variables related to many values of both items, and these container variables record many-to-many relations between them. The proposed data structure maintains data normalization and integrity and is independent of indexing methods conventionally used for relational databases, allowing simultaneous use of both. Simulation results show that data sizes and recording times using the proposed data structure are smaller than those using conventional structures. Search times in the proposed method are much shorter than those under conventional methods when searching data in main memory, such as in the key–value systems often used for data searches in NoSQL, whereas search times under both methods are equivalent when searching data in a relational database. The reduced data size by the proposed data structure might show good performance in other data processing tasks, such as data transmission to other nodes or making copies.
Using Hypermedia to Assist Language Minority Learners in Achieving Academic Success

To facilitate the movement of hypermedia systems from presentation systems toward representation systems, more attention should be placed on the underlying processes required for human knowledge representation. It is not sufficient to present information on a computer screen and assume that this will be accurately, adequately, and completely transferred to the knowledge base of the learner. Nodes and links are also a common ground between artificial intelligence and linguistics researchers, but practitioners in these disciplines have been hesitant to claim that they are referring to the same entities. In fact, the field of cognitive science has evolved to reconcile the psychological, linguistic, and computer science conceptions of knowledge representation and to promote a more multidisciplinary approach to study in this important area. As hypermedia systems move from mere presenters of information to more sophisticated knowledge representational vehicles, the use of linkages is also a critical issue.
The steady state theory: Recalibrating the quiddity of ambient music

music beyond airports 84 bang' theory. 3 The latter has become firmly established as the most compatible with contemporary cosmological understanding, though arguments remain and further theories (including attempts to combine the steady state and big bang models) 4 continue to be proposed. 5 It is perhaps not too fanciful a parallel to say that musical compositions can be regarded as sonic universes.They contain objects arranged and positioned in a particular way, distributed with varying densities, according to the most strict or arbitrary organisational rules and schemes.They surround and envelop us, our role as listeners becoming akin to travellers drifting and exploring their inner worlds.Within and around these worlds we are able to perceive everything from the gigantic to the infinitesimal, events that happen in the blink of an eye or which play out over extended periods of time.And while music is inevitably limited by being temporal, bound to and by time, it can in both its objective behaviour and subjective character suggest and evoke the infinite.Just as our understanding and interpretation of the universe is by necessity subject to continual scrutiny, challenge and revision, the same is true of music, and particularly true, arguably belatedly so, of ambient music.In the four decades since Brian Eno first coined the term 'ambient' to describe a particular kind of musical outlook and practice, what that term connotes has become increasingly simplified, assumption-laden and formulaic.Greek musician Vangelis has complained that it "gave the opportunity for untalented people 10.5920/beyondairports.05the steady state theory
Scrutiny of Breast Cancer Detection Techniques of Deeplearning and Machine Learning

Breast cancer is one of the most widely recognized tumors globally among ladies with the data available that one of every eight ladies is influenced by this illness during their lifetime. Mammography is the best imaging methodology for early location of the disease in beginning times. On account of poor complexity and low perceivability in the mammographic pictures, early discovery of the cancer malignant growth is a huge challenge to effective cure of the disease. Distinctive CAD (computer aided detection) supported algorithms have been developed to enable radiologists to give an exact determination. This paper highlights the study of the most widely recognized methodologies of image segmentation created for recognition of calcifications and masses. The principle focal point of this survey is on picture theof strategies and the factors utilized for early bosom disease identification. Surface investigation is the vital advance in any picture division strategies of image segmentation which depend on a nearby spatial variety of color or shading. Subsequently, different techniques for texture investigation for small scale calcification and mass identification in mammography are talked about in the mechanism of mammography. The point of this paper is to audit existing ways to deal with the segmentation of masses and automated detection in mammographic pictures, underlining the key-focuses and primary contrasts among the utilized systems. The key goal is to bring up the preferences and drawbacks of the different methodologies. Conversely with different surveys which just portray and think about various methodologies subjectively, this audit likewise gives a quantifiable examination.In proposed research use deep learning base network for classification of mammography images . In previous approaches use machine learning base learning. The Main drawback of machine learning is selection of features manualy or by functions but in deep learning automatic feature detect and its vary according to image. The demonstration of seven mass recognition techniques is thought about utilizing two distinctive databases of mammography: an open digitized database and a full-field (local) advanced digitized database. The outcomes are given as far as Free reaction Receiver Operating Characteristic (FROC) and Receiver Operating Characteristic (ROC) examination.
Data Mining and Business Intelligence in SME Customer Relationship Value Analysis

As a tool and system, business intelligence plays a vital role in the strategic planning process of a business company. Business intelligence provides companies with functions such as collecting, storing, accessing, and analyzing company data. In the traditional business model, most companies use human resources to track these large amounts of data and information. And business intelligence provides an automated data processing method that significantly improves efficiency. During a customer relationship management service system, a large amount of data will be generated and stored. With this natural data advantage, data analysis has more possibilities in customer relationship management service systems. Based on the business intelligence system, this paper analyzes the data of the customer system. It puts forward the K-means clustering method to analyze the customer quality, the fuzzy set clustering algorithm to examine whether the customer is active. The rough set algorithm to judge to improve the user activity decision analysis. The proposed intelligent business system brings new technical solutions to the maintenance of current corporate customer relationships.
A Method for Arabic Handwritten Diacritics Characters

An Optical Character Recognition (OCR) is the process of converting an image representation of a document into an editable format. In addition, people have the ability to recognize characters without difficulty as reading papers or books. However, developing an OCR system that has the ability to read and recognized Arabic diacritics characters as human still, remain a problem. More, specifically, poor recognition rate in most of optical diacritics characters recognition is mainly attributed to failing in segmenting a handwritten text correctly. To overcome this problem, we perform develop a method based on seven operations; it starts with searching the text-line height followed by reading words from the line. Then identify the diacritics regions. The segmentation is also applied during this operation by converting the text-line into a grayscale and binary image. Moreover, we introduced a new model based on k-nearest neighbors (KNN) algorithm to identify diacritics and characters segmentation. KNN is trained to directly predict the diacritic from the text-line. Finally, we offer an evaluation discussion on optical diacritics characters recognition.
Blind Image Forgery Detection by using DCT and SURF Based Algorithm

A remarkable array of visual images surrounds us. Image forgery by using digital technology has eroded confidence in the integrity of imagery. Image forensics is an aid to re-establish the trust and originality of imagery. It is important to identify the type of forgery for application of relevant image forensic technique for optimal output. Based on the availability of the original image, image forensic algorithms are classified in the blind and non-blind algorithms. But the availability of original imagery is not always necessary for all the cases of image tampering. The blind forgery detection is particularly difficult. Key point scheme is introduced instead of block based Discrete Cosine Transformation (DCT) and after the validation, the state of the art blind image forgery techniques, i.e., DCT and proposed Speeded-Up Robust-Features (SURF) algorithms are evaluated. Average Accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) are used as parameters for performance evaluation on various images. Experimental results concluded the efficiency of SURF scheme in dealing with region rotation manipulations. The paper concluded with the efficiency of the key-point based scheme.
Development of Writing Teaching Materials with a Contextual Approach of Public Junior High School (SMPN 1), Pinang City, South Labuhan Batu, Indonesia

Based on observations from interviews with Indonesian language and literature teachers at SMPN 1, Pinang City, South Labuhan Batu, Indonesia, it is known that students' writing skills still need to be improved. Only 60% of students who are able to write instructions properly and correctly, the rest still have difficulty in writing instructions. In this study, there are two categories of research data sources. First, the data source for the prototype needs of teaching materials consists of students and teachers. Second, the prototype validation data source that will assess the prototype of teaching materials writing instructions with a contextual approach. Sources of data needs prototype teaching materials in this study are students and teachers. Sources of prototype validation data that assess the product development of this research are teachers and expert lecturers. The main finding of this study is the average value given by three teachers and two experts on the prototype of teaching materials for writing instructions with a contextual approach, namely (1) aspects of material presentation of 69.79 included in the good category, (2) aspects of content /material of 79.17 is included in the good category, and (3) aspects of language and readability of 80.42 are included in the good category. After making improvements based on the results of assessments and suggestions by teachers and experts, the teaching materials write instructions with the Contextual after repair contains material on the meaning of writing instructions, characteristics that need to be considered in writing instructions, steps for writing instructions, examples of writing instructions, and effective sentences.
Spatial SIRD model with different risk groups – multiagent approach for modelling an epidemic

This paper highlights the use of software agents and simulating real-world medical phenomena. We start with a brief overview of different approaches and tools for developing software agents and running simulations. One of the more recent tools was utilized in this paper to develop a model of disease spread in a population of agents and for performing experiments. We proposed an extension of the SIRD model, which includes a new state for agents, spatial component, and also different risk groups based on age. Multiple factors affect the longevity of a pathogen in a given population, for instance, its infectivity and deadliness. This paper shows interesting results of extensive experiments performed with novel, modified SIRD model and impact of key factors on disease spread.
THE ROLE OF A COMMUNICATIVE TASK IN THE DEVELOPMENT OF 21ST CENTURY SKILLS AT THE FOREIGN LANGUAGE CLASSES

Introduction. Modern higher education is aimed at the formation of students’ soft skills, giving them the opportunity to work successfully in a competitive environment and market instability of 21st century. The purpose of this study is to determine the role of a communicative task and reveal psychological and pedagogical conditions for the formation of the concept “4C” including such key competences as communication, creativity, critical thinking and cooperation.&#x0D; Research methods. The research was carried out by the analysis of scientific, educational and methodological literature and pedagogical experience in the formation of professional competences for undergraduates.&#x0D; Results. The article shows that during the process of dialogic foreign language speech the students get favorable conditions for the development of soft skills. Under this educational process foreign language speech-thinking activity is successfully realized through generation of ideas and arguments aimed at the joint communicative task solution. The obtained results contribute to the methodology and technology of modern professional education, expanding the understanding of the role of a communicative task in the formation of key competences. On the basis of theoretical analysis, the author has developed psychological and pedagogical conditions for the formation of soft skills at the process of foreign language teaching. These conditions are the formulation of a communicative task as a problem and a speech communication situation, teaching students the rules of cooperation and the establishment of trust relations between them. The communicative task plays the main role in the creation of conditions for the soft skills formation.&#x0D; Practical implications. The results of the study can be obtained in the practice of foreign language teaching in the university.&#x0D; Conclusion. It was revealed that the solution of a problem during the professional communication is the main condition of conscious study of the foreign language and leads to the development of soft skills.
Reflexive communication strategy in postnonclassical science (from the methodological experience of V.N. Borisov AND V.P. Fofanov)

The paradigmality of science is removed by reflexive communication. Vertical reflexive communication actualizes and revises the classical heritage. Horizontal reflexive communication takes into account the spiritual experience of other areas, disciplines and worldviews.