Reviewerly/exHarmony huggingface dataset:
An Advanced IoT Based Frame Work to Save Electrical power in an Organization

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.
IoT Analytics/Data Science for IoT

With the advancement in technology and also with increasing demand of users in mobile communication, particularly during this pandemic situation, the data rate needed for the end users (customers) is in high demand. To cope with this demand in the data rate and technology innovations, the Internet of Things (IoT) provides a solution for this demand in the present and also in the future. IoT by itself cannot provide the required data rate. Hence, IoT with the concept of data science or certain algorithms is needed to balance the demand in data rate. There are various traditional methods to serve the customers in the world with the allocated bandwidth and data rate but this method cannot manage the velocity and speed of transmission. Thus, a special algorithmic approach has been proposed for IoT services. Data analytics depends on data with time series from IoT devices with additional techniques like deep learning, sensor fusion, and streaming. Deep learning is also considered as cameras are treated as sensors and also include many reinforcement neural networks for IoT devices. Ironically, due to the emphasis on data, the contents are concerned primarily with applying analytical learning algorithms on IoT datasets, and the dataset is personalized. In data science, research is static and limited. The data obtained cannot be updated, so the results obtained in the context of the preparation may not be usable. But, as IoT information is constantly being collected, research is supplemented by the latest market designs, making this research more important and smarter when it is routinely invested. Furthermore, as more and more layers of integrated or integrated with IoT, it is tricky to access and process a massive amount of information. So, really, data scientists upgrade their skills with the primary goal of understanding the information generated by the IoT. Because the amazing quality of the IoT develops the subsequent data flow, this is bound to change the way data science works for some time. The burst of information requires not only a better foundation, but also more sensible data scientists. Computer science for the Internet of Things can help overcome some far-reaching challenges and make better choices. The purpose of this chapter is to satisfy the reader and to provide an opportunity to define the effective use of data science as a safe path to data science as an opportunity for the Internet of Things in the coming era.
De Blend IPv7 Beginning Multi-Processors in Idleness

The development of the location-identity split has explored robots, and current trends suggest that the visualization of suffix trees will soon emerge. After years of extensive research into B-trees, we disprove the simulation of model checking, which embodies the confirmed prin- ciples of machine learning. This finding is usu- ally a theoretical objective. In this position paper we con- struct a novel application for the emulation of flip-flop gates (RopyRongeur), which we use to show that kernels [11] and robots can synchro- nize to address this quagmire.
Uncertainty of Mineral Prospectivity Mapping due to Random Negative Training Samples

This contribution explored the uncertainty of a mineral potential map because of random negative training samples.Both positive and negative training datasets are required for data-driven mineral prospectivity mapping (MPM).Positive training datasets can be generated using the locations of known mineral deposits, and negative training datasets can be created using the locations of random points.However, we cannot confirm that all the negative points are really true negative samples, meaning that there is uncertainty of MPM due to random selection of negative training points.Fifty negative training datasets were generated using the locations of random points, and then 50 mineral potential maps for skarn-type skarn Fe mineralization in southwestern Fujian of China can be obtained by logical regression.We observed that the weight of each evidence layer and the probability of the presence of mineralization for each location within the study area changed in terms of different training datasets, meaning that negative training datasets affected a mineral potential map.The risk and return analysis was used to explore the uncertainty of a mineral potential map due to random negative samples.The mean of the probability obtained through back-transforming the mean of the log odd ratio could be regarded as a final mineral potential map in which the uncertainty due to random negative samples has been considered.The areas having low risk-high return s compared with other locations should be considered firstly for mineral exploration.
Clustering-Based Approach for Private Chain Data Anonymization

Blockchain is a new type of distributed data storage technology.With the rapid development of blockchain, vast amounts of data have been accumulated in these applications, which provide researchers with unprecedented opportunities to analyze blockchain data.However, if blockchain data is published openly, it may cause privacy leaks.Owing to the characteristics of blockchain data, the traditional anonymous method based on data publishing cannot be directly applied to blockchain data.This paper proposes an anonymous method based on clustering named clustering partition based on Bisecting k-medoids (CP-BK).We treat the transaction data of the blockchain as table data and use the k-anonymity model to protect the identity privacy of blockchain users.Finally, we evaluated the information loss and efficiency of this algorithm in experiments.
A Novel Approach for Authorship Verification using Similarity Measure

Authorship verification is a task of identifying whether two text documents are written by the same author or not by evaluating the veracity and authenticity of writings. Authorship Verification is used in various applications such as analysis of anonymous emails for forensic investigations, verification of historical literature, continuous authentication in cyber-security and detection of changes in writing styles. The Authorship Verification problem primarily depends on the similarity among the documents. In this work, a new approach is proposed based on the similarity between the known documents of the author and anonymous document. In this approach, extract the most frequent terms from the dataset for document vector representation. These most frequent terms are used to represent the train and test documents. The term weight measure is used to represent the term value in the vector representation. The Cosine similarity measure is used to determine the similarity among the training and test document. Based on the threshold value of similarity score, the author of a test document is verified whether the test document is written by the suspected author or not. The PAN competition 2014 Authorship Verification dataset is used in this experiment. The proposed approach achieved best results for Authorship verification when compared with various solutions proposed in this domain
Caching

Caching is the process of storing data into temporary memory so that it can be easily retrieved for later use if it is required again. As an example, a database system keeps data cached to avoid rereading the hard drive, and a web browser caches web pages (images and assets) to avoid redownloading the contents. Put simply, in caching, the goal is to maximize hits (an item is in the cache when requested) and minimize misses (an item is not in the cache when requested).
Implementation of interdisciplinary and interdepartmental interaction in the organization of support for a child with special needs in development

At the present stage of the development of the education system, the support of children with special needs of psychophysical development is an important condition for the organization of their education and upbringing, taking into account the development of integrative and inclusive approaches.
Inverse Analysis of Viscous Fluids with Physics-Informed Neural Networks to guarantee Physical Conditions

With the recent intensification of disasters, as exemplified by the 2011 off the Pacific coast of Japan earthquake, the need to strengthen existing damage countermeasures and damage estimation has become increasingly important. Although simulation is an effective method for damage estimation, it consumes enormous computational resources because the relevant parameters need to be determined empirically or by human iteration. In this study, we employed PINNs (Physics-Informed Neural-Networks), one of deep learning framework, to perform inverse analysis of parameters from observed physical quantities. PINNs is a deep learning framework in which physical validity is ensured by adding physical constraints such as governing equations, initial conditions, and boundary conditions. These properties are expressed by the two items "prediction loss" and "physical loss", which are unique to PINNs. "Prediction loss" evaluates the error between the observed data of each physical quantity and the physical quantity predicted by PINNs, while "physical loss" evaluates whether the predicted value of PINNs satisfies the governing equation. In this study, we examined how reliable the PINNs are based on observation data containing strong noise, which is expected in practice. As a result, it was shown that the accuracy of parameter estimation was significantly reduced when strong noise was included in the observed data.
Comparison and Analysis of the Positive and Negative words in the websites among Australia, UK and Canada

With the rapid development of the Internet, network public opinion forms more rapidly and spreads more widely, and has a great impact on the society. In the context of social network media, network public opinion has largely influenced and eventually changed people’s attitude towards the important topics such as government and national policies. For this reason, in this study, the paper analyzes how netizens are positive and negative to their countries in Australia, UK and Canada. The paper designs an efficient crawling system by embedding the two text corpora, and extracts the positive and negative words as well as the positive sentences by text mining. The results indicate the average of positive words in Australia is the largest, and the average of negative words in Canada is the smallest. Australia has the highest ratio of the positive words to negative words among the three countries. The average of positive sentences per website in Australia is 0.357, which is largest. The results reveal Australians are more positive to their country than British and Canadians. Finally, the suggestions to improve netizens positive to their countries are discussed.
Bug Severity Prediction using Class Imbalance Problem

Class imbalance problem is often observed when instances of major class exceed instances of minor class. The performance of machine learning techniques is immensely afflicted by imbalanced data in several fields. The skewed distribution either predicts the majority class with high error rate or will not foresee the minority class. To solve the problem of imbalanced data of software bugs, Synthetic minority oversampling technique (SMOTE) is used which balances the imbalanced datasets of Apache Projects. It is applied on summary of bugs to balance the dataset and predicts severity at system and component level. Several machine learning techniques are applied on imbalanced as well as balanced datasets to predict the severity of software bugs using textual description. Test outcomes and statistical analysis shows improved results on balanced datasets in respect to Gmean and balance metrics instead of machine learning techniques applied on imbalanced data. Evaluation metrics Gmean improves by 34% and balance by 11% at system level and by 42% and 62% at component level. Further, it was observed that solving class imbalance problem on textual data is helpful in augmenting the performance.
The Topology of Disaster Notification System in a College Utilizing Smartphone Devices

Through this paper, we want to propose a disaster notification system in a college utilizing Smartphone devices. The proposed disaster notification system is still in the form of preliminary topology as a first step in designing the system. In this study, the topology of the disaster notification system intended for a college environment, with students as end-users. In an ideal condition, by using the proposed topology, the notification system can reach 100% accuracy in providing disaster information to the students. But if there is much disturbance, it could be a different case. We will discuss some obstacles and shortcomings from the proposed topology in future research.
SISTEM INFORMASI INVENTORY PADA YAYASAN GENERASI SEHAT INDONESIA SEHAT (SIIGESIT) TANGERANG SELATAN

Yayasan Generasi Sehat Indonesia Sehat (GESIT) is a foundation that provides health equipment such as medicines to schools that have programs Klinik Sekolahku is a program from Yayasan GESIT. Schools are collaborating to make Yayasan GESIT need an information system that can simplify, curb the recording of inventory data, therefore an information system is created that can handle these problems. This system is called SIIGESIT or Sistem Informasi Inventory Yayasan Generasi Sehat Indonesia Sehat. SIIGESIT is designed using the RAD Model (Rapid Application Development) which is one of the software development models. The advantages of the RAD Model are that in the User Analysis stage it is involved in analyzing the needs and if the user agrees the second stage, namely the new system can be implemented so that the possibility of a miss occurs between analysts, programmers and users.
Skin Lesion Image Segmentation Based on C-Means Clustering Algorithm

A skin lesion is an abnormal lump, bump, and ulcer, sore or colored area on the skin. There are many types of skin segmentation. In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super-pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. In this ipaper using C-Means Clustering approach for skin lesion image segmentation so that detection and recognition of skin decease will easy to understand by patient and biomedical industries.
Public Awareness on Cyber Crime with Special Reference to Chennai

With the rapid advancements in technology, our lives have become completely digitalised. We sit in the comfort of our home, and carry out our day-to-day activities like grocery shopping, education, banking, through the Internet. However, this has given rise to several threats to our security, called the cyber-crime which has become a global concern. Cybercrimes affect the lives of millions of people all around the world, including businesses, organisations and governments of different nations. This paper aims to determine the association between cybercrime awareness and the age, and to identify the difference between main cause of cybercrime and the gender. Descriptive research has been carried out which helps us to understand the characteristics of an individual or a group. The sampling method used here is convenient sampling where the samples have been chosen based on ease of access of availability. The sample size is 1540. Age, Gender and Occupation are the independent variables. Cyber-crime awareness, the main cause of cybercrime and the most popular cyber-crime are the dependent variables used here. The tools used here are Chi-Square, Independent Sample t test and ANOVA. It was found that there is a significant association between the cyber-crime awareness and the age of the respondent and that there is no significant difference between the main cause of cyber-crime and gender. With the rapid rise in the rate of cyber-crimes, it is imperative that the government provide the people with more effective ways of cyber security in order to protect the society.
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Application of Machine Learning on Determination of gem Provenance – Peridot as Example

Geographic origin determination is an important aspect in gem identification and evaluation.It is commonly done by geochemical methods such as trace element chemistry.Several 'fingerprint' elements are plotted and those from the same provenance tend to cluster.However, with more newly discovered gem deposits, these 'fingerprint' plots often showed overlap among different localities, making determination difficult.Machine learning (ML) can be used to solve this problem.ML can process more variables than plotting.All chemical components can be used in ML to reduce the overlap among localities.Furthermore, with more data, ML model can improve accuracy.Among all ML algorithms, linear discriminant analysis (LDA) is most commonly used.However, LDA might not be the best model.When the boundaries among different categories of data points in highdimensional space are not hyperplanes, but curve surfaces, a non-linear classification algorithm would perform better.For data with different distribution features and boundary shapes, choosing a suitable ML algorithm can improve the accuracy of discrimination.In this study, we used the LA-ICP-MS data of 262 peridot samples (62 from Hebei, China; 100 from Jilin, China and 100 from Changyon District, N. Korea) and six ML algorithms -LDA, decision tree, random forest, k nearest neighbour, support vector machine (SVM) and logistic regression.The samples are randomly divided into a training set and a testing set according to a ratio of 7:3.Training set is used to build ML models, while testing set is used to simulate samples of unknown localities.Different models are established by using Python and packages of Python like Numpy and Scikit-learn.Any sample can be predicted by the models, and the result can be compared with its actual locality to verify models' accuracy.The accuracy of LDA on training set is 0.825, while its score of testing set is 0.797.Other five algorithms perform better than LDA.Among these, SVM gains the highest scores on testing set, and shows the best generalization, which means the difference of accuracy is narrow between testing set and traing set.
Career Realization Model: Perspective of Individual Values and Attitudes, as an Educational-Developmental Tool for Teachers and Advisors

This article presents a theoretical model for career realization, the basis for which is the assumption that an individual independently shapes their career, where career is interpreted as the implementation of accepted values that determine a person’s attitudes and behaviors. It characterizes contemporary careers and analyzes the issue of values as the basis for action in relation to career realization. A career model based on accepted values and demonstrated attitudes illustrates certain regularities in the methods of career realization with regard to the possibility of change and the actualization. The model indicates an axiological scope of the career realization process, constituting a proposition for discussion and thought on the methods of interpretation of career, its course, and its determinants. It can prove useful in career counseling for students, adults, and active professionals, and in diagnosing their preferences, interests, attitudes, and value systems. It can also be used in designing curricula for pedagogy, psychology, sociology, human resources administration, and so forth, because it facilitates understanding of motives underlying human behaviors and actions in different situations in private and professional life.
Clustering Dissimilar Tuples: A Stronger Notion of Privacy

Identity disclosure and attribute disclosure have always been a major concern while publishing data.k-anonymity tries to solve identity disclosure but doesn't prevent attribute disclosure which leads to homogeneity and background knowledge attack.Preserving privacy of an individual is becoming more challenging due to increasing number of homogeneity and background knowledge attacks.l-diversity model has been proposed to thwart these attacks but it doesn't fulfil its obligations.Several authors found l-diversity model to be inadequate, hence they put forth another model called t-closeness.Over the years, many investigations and experimentations conducted by various researchers shows that t-closeness does not provide a clear relationship between the threshold value t and information gain and it also shows that Earth mover's distance, a distance metric used by t-closeness model, becomes complex with multiple sensitive attributes.In view of this challenge, we propose a stronger notion of privacy called Clustering Dissimilar Tuples (CDT) to thwart homogeneity and background knowledge attack by formalizing the idea of processing the original dataset initially wherever these attacks possibly occur.These attacks are found to occur in the tuples of sensitive attributes.Hence CDT processes the tuples of sensitive attributes to form equivalence classes consisting of dissimilar tuples.Through experimental evaluations, we show that CDT is practical and can be implemented efficiently with minimum utility loss and maximum privacy gain.
Evolutionary computation for digital art

tutorial Evolutionary computation for digital art Share on Authors: Aneta Neumann The University of Adelaide, Australia The University of Adelaide, AustraliaView Profile , Frank Neumann The University of Adelaide, Australia The University of Adelaide, AustraliaView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 901–926https://doi.org/10.1145/3377929.3389853Online:08 July 2020Publication History 0citation69DownloadsMetricsTotal Citations0Total Downloads69Last 12 Months17Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
In the News

Welcome to the April 2020 issue of C&amp;RL News. Academic libraries and librarians are increasingly prioritizing outreach to vulnerable populations. In this issue’s Perspectives column, Clare Kuntz Balcer writes about her experiences teaching “The Framework for Information Literacy (in prison)” through a program working with incarcerated students.