Reviewerly/exHarmony huggingface dataset:
Pentium 4 Old School yet Modern in Engineering

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Security Framework for Cloud Computing using Fragmentation and Homomorphic Encryption

An invention of cloud computing technology comes with numerous benefits for IT industries and others. The data store in the cloud can be easily shared among stakeholders irrespective of their location, i.e. data availability is very good. Nowadays organizations are switching to cloud platform for storing and sharing data in a decentralized manner. This is significantly reduces the economically burden to the organization. As the data are accessible through network, so major concern is to maintain the data confidentiality. Data breach in any form organization losses their trustworthy, and this affect the reputation of the organization. This is very important to maintain the privacy and security of data all the time. There are so many works has been proposed by many researchers to secure data in the cloud by using various encryption techniques. In this paper, we proposed a security mechanism to maintain confidentiality of data. This method is combination of multiplicative homomorphic encryption algorithms along with vertical fragmentation of data. We have tested our scheme based on crypto delays, communication delays, and query processing delays with an existing work. The results obtained show that our method out-perform the existing work. The results obtained show improvement with the proposed method.
AKSESIBILITAS KEMAMPUAN LITERASI MEDIA SISWA SEKOLAH DASAR

In Indonesia Gerakan Literasi Sekolah (GLS) launched by the government, has aim to make learners become literary generation. One of the literacy skills that should be developed nowadays is media literacy. Information can be more accessible to children through their ability to use information technology and media. This research aimed to determine the needs of media literacy of elementary school students . The purpose of this research is to know students' accessibility the most to media, media influence, and the importance of media literacy ability to be owned by grade V elementary school students . Through a survey approach that has been conducted on 83 students of Class V primary schools in Purworejo, Central Java , The results of this study describe that students of grade V elementary school in Purworejo have been familiar with media technology. Most of them can access the media especially television . Therefore, it is necessary media literacy ability to increase students' awareness of various content in the media information, so that students can be wise in choosing the appropriate content with his age . T he development of media literacy can be implemented in GLS
An Optimal Warehouse Design for Crime Dataset

Data science and analytics represent one of the most emerging fields nowadays.Collecting, storing and analyzing the data are challenging issues in the field since they require the most advanced techniques and technologies.Data Warehouse and Data Marts represent some solutions for collecting, storing and accessing the data.Good Warehouse design leads to better analysis results.Among different application fields of the data, crime data is an important and complex discipline that contains a number of complex relationships between its contents, a wide range of applications and its crucial importance.The aim of the work in this paper is building an optimal Data warehouse for crime dataset using real crime data collected from the internet.Among the different DW modules available in this field galaxy module is used in this work.The data warehouse will support the decision-making process for lawmaker and police departments by understanding crime subjects, and statistics that allow them to track actions, foretell the probability of occurring crimes and efficiently use supplies which are inverted in this paper.The proposed design of the DW shows more reliability, better storing and accessing capabilities and lower anomalies among the other designs.The proposed design was supported with a crime database design to remove heterogonous of the data and to apply some preprocessing issues from which they require data is extracted, transformed and loaded (ETL) into the warehouse.Finally, more than six million high quality, clean, and preprocessed of crime records data are available for the researchers.
Case data-mining analysis for patients with oesophageal cancer

We are in an era of digital medicine in which physicians can generate copious patient data, but tools to analyse these data are limited. Thus, we used case data from patients with oesophageal cancer from a medical institution, removed incomplete information, and quantified the textual data according to recommendations from the corresponding physicians. We used different classification algorithms to process the data, predict patient survival, and compare accuracies across algorithms. Our experimental results show that the BayesNet algorithm was highly accurate and precise, and, thus, may represent a promising data-mining tool.
Keystroke Dynamics for User-Authentication on Mobile Devices using Ensemble Method

As the days go by, the need for security keeps increasing; whether for humans, data, climate and almost everything in existence.However, despite the increase in the need to protect data, information and the corresponding devices, the Personal Identification Number (PIN) is still the most widely used approach on mobile devices.This increased requirement for protection is evidenced by various issues that continue to arise based on intrusion, theft and unlawful access to classified information.It is important to note that apart from secretknowledge (that is PIN), biometrics and tokens are useful for security even though token seldom authenticates because it is expected to be with the authentic owner.Biometrics on the other hand makes use of the individual himself.This research sought to address the issue of securing data on mobile devices by employing some Machine Learning algorithms.The K-Nearest Neighbours (KNN), Decision Trees (DT) and Multinomial Logistic Regression (MLR) classification algorithms were used to train and test the typing patterns of several individuals who volunteered to type a static passphrase.After carrying out the different experiments, the predictions given by Decision Trees were the most accurate of all the three base classifiers used with an accuracy value of 99.92%.
Search Trees

Search trees, or in this chapter specifically binary search trees, are also recursively defined. A binary search tree is either an empty tree or a node containing a value and two trees, a left and a right subtree. Furthermore, we require that the value in a node is larger than all the values in its left subtree and smaller than all the values in its right subtree. It is the last property that makes search trees interesting for many algorithms. If we know that the smaller values are to the left, and the larger values are to the right, we can efficiently search in a tree, as we shall see.
Privacy Preservation Using (L, D) Inference Model Based On Dependency Identification Information Gain

The improvement of an information processing and Memory capacity, the vast amount of data is collected for various data analyses purposes. Data mining techniques are used to get knowledgeable information. The process of extraction of data by using data mining techniques the data get discovered publically and this leads to breaches of specific privacy data. Privacy-preserving data mining is used to provide to protection of sensitive information from unwanted or unsanctioned disclosure. In this paper, we analysis the problem of discovering similarity checks for functional dependencies from a given dataset such that application of algorithm (l, d) inference with generalization can anonymised the micro data without loss in utility. [8] This work has presented Functional dependency based perturbation approach which hides sensitive information from the user, by applying (l, d) inference model on the dependency attributes based on Information Gain. This approach works on both categorical and numerical attributes. The perturbed data set does not affects the original dataset it maintains the same or very comparable patterns as the original data set. Hence the utility of the application is always high, when compared to other data mining techniques. The accuracy of the original and perturbed datasets is compared and analysed using tools, data mining classification algorithm.
STUDENTS WILLINGNESS TO COMMUNICATE USING ENGLISH: A SURVEY STUDY

In Indonesia, one of the common problems encountered by many English teachers during the process of teaching and learning in the classroom is students unwillingness to communicate using English. Having learners who are willing to communicate using English in class is essential in a language classroom that following the communicative approach (Riasati, 2012). This study aims to investigate students perceptions towards willingness to communicate using English in the classroom. It employs a quantitative approach, survey research design. To know the students willingness to communicate using English, a well-known FLCAS (Foreign Language Classroom Anxiety Scale) developed by Horwitz, E.K., Horwitz, and M. B., Cope J. (1986) was adapted. 115 students of SMK Negeri 10 Malang participated as the respondents. Based on the findings, it is concluded that tenth and eleventh grade students at SMK Negeri 10 Malang have a positive opinion towards willingness to communicate using English in the classroom. They stated that learning and communicating using English is essential and beneficial. However, their willingness to communicate using English itself is quite low and it is quite a serious problem.
Text Mining

This chapter looks at a particular type of classification task, where the objects are text documents. A method of processing the documents for use by the classification algorithms given earlier in this book using a bag-of-words representation is described. An important special case of text classification arises when the documents are web pages. The automatic classification of web pages is known as hypertext categorisation. The differences between standard text classification and hypertext categorisation are illustrated and issues relating to the latter are discussed.
Optimised tags with time attenuation recommendation algorithm based on tripartite graphs network

Social recommendation has attracted increasing attention in recent years due to the potential value of social relations in recommender systems. Social tags play an important role in improving recommendation accuracy. However, garbage tags may lead to data matrix sparseness and affect the accuracy and performance of recommendation system. To optimise social tags in the recommendation system, tags are sorted by popularity ranking method with the time attention model in order to remove the garbage tags. The time attenuation model is used to consider the variation of tags with time change. Then a novel recommendation algorithm with optimised social tags is proposed based on complete tripartite graph network. This method considers the preference information of users and items and generates recommendation items for users based on collaborative filtering. Experimental results show that the proposed algorithm predicts recommendation items more accurately than other existing approaches.
Prediction of Emoji from News Headlines using Machine Learning Techniques

Emojis are generated from the news headlines of Malayalam language using Naive Bayes (NB) and Support Vector Machine(SVM) classifiers. The human brain processes visual data faster than text data. Assigning emoji to the text helps the people easily categorize the news based on the emotion without reading the entire sentence. Six different emojis are assigned to the news headlines based on the emotional contents of the text. These emojis are used for representing emotions like sad, angry, fear, happy, love, and neutral. The dataset contains 3111 sentences which are retrieved from the tweets of Manorama Online. Both Bag-of-Words (BOW) and Term Frequency versus Inverse Document Frequency (TFIDF) features are used for feature vector formation of the dataset. The SVM shows better accuracy compared with the NB classifier.
Analisis Perbandingan Kompresi Suara Menggunakan Principal Component Analysis dan Transformasi Wavelet

One of the requirements faced as a result of information technology development is memory and transmission efficiency. This requirement can be overcome with data compression. Compression is a method to obtain compact data with a smaller size but still maintaining similarity to the original data. Principal Component Analysis (PCA) is an algorithm in machine learning that is used to reduce dimensions. Dimensional reduction is a process of transforming high-dimensional data into new subspaces with lower dimensions. The goal is to use some principal components to represents the original data. Wavelet transformation represents a signal into a set of basic functions through filter analysis. Wavelets concentrate information into coefficients of approximation and coefficients of detail. Wavelet transform produces a lot of zero or close to zero coefficients that can be neglected so it can reduce storage space. In this research, we will propose the implementation of PCA and Wavelet for digital audio compression. The audio was performed with the .wav format. The compressed audio will be evaluated based on Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The mean PSNR obtained when using a wavelet is 47.61601 dB with an average MSE of 3.76 x 10-5. Meanwhile, when using PCA, the PSNR average was 57.3962772 dB and the average MSE obtained was 4.59 x 10-5. Four out of five compressed audio had a larger PSNR and smaller MSE when using PCA. Thus, the Principal Component Analysis algorithm can be better used for audio compression than the level 1 of Symlet Wavelet Transformation.
Negation in Musgum

This paper studies the negation construction in musgum language.We collect the musgum data on negation from native users and analyse them in terms of Kayne’s (1994) antisymmetry and Rizzi’s (1997) split CP approaches. We identify the free negation element (á:à) and several negation markers (kài, kirkài, kài tiŋ and kirkài tiŋ) that close independent and complex clauses. In complex structures with completive and relative clauses, the main clause cannot contain a negation marker. In complex structure with adverbial clause, negation marker can be present in main and adverbial clauses. We discover that Negation Phrase is the highest projection, higher than Force Phrase, rejecting the split‐CP projections order of Rizzi (1997). When the negation head is generated, Inflexion Phrase is subject to heavy pied‐piping. It occupies the specifier of Negation Phrase.
<i>Learning Disabilities Research and Practice</i> Updates

Learning Disabilities Research & PracticeVolume 34, Issue 3 p. 123-123 Editorial Learning Disabilities Research and Practice Updates First published: 01 August 2019 https://doi.org/10.1111/ldrp.12205Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume34, Issue3August 2019Pages 123-123 RelatedInformation
Automatic Vibration Device for Stroke Patients

Permanent paralysis due to stroke could be avoided if they do some exercises on the affected limbs to keep the flowing of the blood and soften the muscle. Every treatment or exercise needs to be done periodically and requires the help of a normal person such as nurses or physiologists. However, a caretaker may not be able to provide 100% of the assistance to those stroke patients. Therefore, an automatic device that can provide a periodic exercise could help stroke patients in their recovery process with minimal participation of caretaker. However, the available machine that can accommodate such services is bulky and costly. It exercises the whole body, and this is not beneficial if the stroke only effect to some part of the body. Hence, this study had shown that Theory of Inventive Problem Solving approach is able to reduce the size of the machine but gives significant impact on the specific limbs. The findings could be used as foundation for stroke rehabilitation development system.
THE RELATION BETWEEN PERSONALITY AND LEARNING PREFERENCE

In this study, 106 women (M age = 23 years) completed a series of questionnaires online assessing personality traits and facets (subscales), learning preferences (Activist, Reflector, Theorist, Pragmatist), and attitudes toward learning preferences.The results of several hierarchical regression analyses found that a large proportion of variance in learning style was accounted for by personality traits or facets.For example, 43% of the variance in the Activist Learning Style was accounted for by higher scores on Extraversion, and lower scores on Conscientiousness and Negative Emotionality.When personality facet scores were used as predictors, the proportion of variance jumped to 55%.Similarly, between 27-31% of the variance in Reflector, Theorist and Pragmatist Learning Style was accounted for by personality facet scores alone.The results are discussed in terms of attitudes and the need for evidence-based practices.
Lexicon in Syntactic Frameworks

What is the lexicon, what does it contain, and how is it structured? What principles determine the functioning of the lexicon as a component of natural language grammar? What role does lexical information play in linguistic theory? This accessible introduction aims to answer these questions, and explores the relation of the lexicon to grammar as a whole. It includes a critical overview of major theoretical frameworks, and puts forward a unified treatment of lexical structure and design. The text can be used for introductory and advanced courses, and for courses that touch upon different aspects of the lexicon, such as lexical semantics, lexicography, syntax, general linguistics, computational lexicology and ontology design. The book provides students with a set of tools which will enable them to work with lexical data for all kinds of purposes, including an abundance of exercises and in-class activities designed to ensure that students are actively engaged with the content and effectively acquire the necessary knowledge and skills they need.
Digital Literacy, Competence, Identity and Intelligence: The Four Teachers Essential Skills in 21st Century

These days, digital literacy, digital competence, digital identity and digital intelligence are becoming essential skills for everyone, especially teachers. Understanding their concepts and components is essential for teachers even more than learners in the process of these digital skills development. If teachers have not capabilities in these four skills, they couldn’t transfer any knowledge to learners in online environments undoubtedly. Even proper, on time, enough and complete knowledge transfer from teachers to leaners never happened. The results of this study show that teachers had low awareness about 4D skills and couldn’t understand and define 4D skills very well. The present qualitative study measured teachers’ awareness about these digital skills by semi-structured interviews and based on the retrieved data, proposed a Quality 4D (digital literacy, digital competence, digital intelligence) skills framework for measuring and developing of teachers 4D skills.
MORE ON THE DEVELOPMENT OF PROFESSIONAL SELF-CONSCIOUSNESS OF FUTURE LAWYERS

The issues of developing the professional self-consciousness of a lawyer are becoming particularly relevant due to the aggravation of contradictions between the existing level of practical training of future lawyers and the objective needs of improving higher legal education.In this regard, an important social and psychological task of the higher law school at the present stage of society development is the formation of a professionallyoriented self-consciousness of future lawyers as a prerequisite for their further successful activity.The problem of creating such conditions that would allow constant improvement of lawyers' self-consciousness is of great importance.Practice shows that only those specialists who are able to include these changes in the context of the cognitive, emotional and behavioral components of their personality, having meaningfully and instrumentally mastered them at the level of understanding, attitude and activity, can adapt to diverse, cardinal changes in the conditions of professional activity.The efficiency of the professional activity of future lawyers is determined by the peculiarities of their professional self-consciousness, which cannot be developed without purposeful work on the formation of knowledge and skills necessary for young professionals in the conditions of changing reality, on the development of professionally significant qualities that enable young specialists not only to adapt to the conditions of professional activity, but also to engage in it with the greatest benefit for their personal development and professional success.The choice of the topic for this study was based on the need to determine the features of the manifestation of personal self-consciousness in the activity and to develop diagnostic tools for their assessment, search and determination of the conditions and factors for the development of general characteristics of professional self-consciousness of future lawyers that determine the efficiency of their activity.
RFID Enabled Virtual Guided Assistance System for Foreign Tourists

The language of the traveler might not always be known to the tourist guides, or the information available about the place might not be right, and moreover the charges of the guides might be too high. In the end, the travel experience and the place does not satisfy the tourists. In this paper, the development of a portable device is described, so that the tourists can experience a hassle-free tour. The implementation and demonstration of a complete system is discussed, which provides accurate information to the tourists along with visual guidance and assists them during their tour. This system makes use of two concepts, namely RFID technology and context aware information. This system can be used in several different locations like museum, zoo, historic monuments, tourist spots, government offices to provide the user with directions and information relevant to their location. The system can also be used as a monitoring device at research centers and hospitals.