Reviewerly/exHarmony huggingface dataset:
Data Security and Privacy of Individuals in Data Mining: A Critical Analysis of Data Mining in India

We live in world where every day vast amount of data flows in through various medium.Data mining could be considered because of the natural growth of information technology.The concept of data mining is widely used all over as an instrument to mine hidden information from the database of companies that can be used by the respective companies to deal with their complicated business affairs and improve their services accordingly.Despite that the information revealed by data mining applications, people have shown their increasing concern about the other side of the coin, specifically the privacy threats posed by data mining.An individual's privacy may be hampered or violated due to unauthorized access to their sensitive personal data, unwanted discovery of once embarrassing information etc.In this research paper the author desire to highlight the probable privacy issues that an individual must face due to data mining.
Internet of Everything: Macro &amp; Micro

Cryptocurrencies and nonfungible tokens (NFTs) may be all the rage right now, but they're overshadowing better uses for blockchain and other distributed-ledger technologies. Rather than using them to disrupt financial systems or the art world, distributed ledgers can be used to create trust among Internet of Things devices, which is essential for any successful IoT network.
Steganographic Tool Detection using Specific Composite Feature Set and Weighted Decision Function

Steganographic tools available in the internet and other commercial steganographic tools are preferred than customized steganographic tools developed from scratch by unlawful groups. Hence a clue regarding the steganographic tool deployed in the covert communication process can save time for the steganalyst in the crucial active steganalysis phase. Signature analysis can lead to success in targeted steganalysis but tool detection needs to be taken forward from a point with a suspicious stego image in hand with no additional details available. In such scenarios, statistical steganalysis comes to rescue but with issues to be addressed like huge dimensionality of feature sets and complex ensemble classifiers. This work accomplishes tool detection with a specific composite feature set identified to distinguish one stego tool from the others with a weighted decision function to enhance the role of the specific feature set when it votes for a particular class. A tool detection accuracy of 85.25% has been achieved simultaneously addressing feature set dimensionality and complexity of ensemble classifiers and a comparison with a benchmark procedure has been made
A Comparative Analysis of Software Development Models Based On Various Parameters

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.
Quantitative Analysis of Requirement Elicitation Techniques through FANP: Security Perspective

The last few decades have shown an incredible rise in the production of different types of software according to the user’s needs. Requirement Elicitation techniques for security requirements are one of the most crucial stage of software development life cycle. Based on the Analytical Network Process (ANP) process, this paper analyzes the weighting of elicitation techniques for security requirements in the production of software applications. In other words, the elicitation strategies of security requirements play an important role in the development of secure software. It also analyzes the relationship between security requirement elicitation techniques and their objectives through the use of ANP method and also demonstrates the application of fuzzy ANP method to achieve higher accuracy. When developing a secure software framework, the results provide a better platform. With these facts in mind, the proposed study will also clarify the priority weights of security requirement elicitation techniques that can be used to analyze trade-offs between competing software security requirement elicitation techniques and provide a new way for developers when constructing the secure software.
Fuzzy-based review rating prediction in e-commerce

Opinion mining is an ongoing research area in e-commerce which aims at analyzing the people's opinions, sentiments and emotions. Moreover, the existing e-commerce systems allow the users to share their feedback in the form of textual reviews regarding the products and services. It also allows the consumers to give ratings for products that help in future recommendation of products. In this research work, a computational framework for efficiently predicting the consumer review ratings on the products has been proposed. The proposed framework integrates dimensionality reduction, genetic algorithm, fuzzy c-means and adaptive neuro-fuzzy inference techniques to overcome the limitations of the existing systems. Experiments have been conducted in this work using Amazon dataset consisting of reviews for different products. This system provides better performance and prediction accuracy for review ratings when it is compared with the related work.
An Efficient Component based Analysis of Optical Character Recognition

Optical character acknowledgment alludes to the way toward understanding pictures of written by hand, typescript, or printed content into an arrangement comprehended by machines for the motivation behind modifying, ordering/looking, and to reduce size. Optical character acknowledgment is the understanding of pictures of written by hand, typescript or printed content into machine-editable content by mechanically or electronically. The purpose of the present hypothesis is to find the numbers and English letter sets picture of times new roman, Arial, Arial square size of 72, 48 by using imperative part examination. Head Components Analysis (PCA) is a functional and standard measurable instrument in current information examination that has discovered application in various zones, for example, face acknowledgment, picture pressure and neuroscience. It has been called one of the most valuable outcomes from connected straight polynomial math. PCA is a clear, non-parametric technique for splitting appropriate data from confounding instructive indexes.
Interpersonal Interactions in Engineering Teams: Findings from a Multi-year Mixed Methods Study at Three Institutions

Her research focuses what factors influence diverse
PEARL

Deep neural networks have demonstrated promising prediction and classification performance on many healthcare applications. However, the interpretability of those models is often lacking. In comparison, classical interpretable models such as decision rule learning do not lead to the same level of accuracy as deep neural networks and can be too complex to interpret (e.g., due to large tree depths). In this work, we present PEARL, Prototype LeArNing via Rule Learning, which iteratively constructs a decision rule list to guide a neural network to learn representative prototypes. The resulting prototype neural network provides accurate prediction, and the prediction can be easily explained by a prototype and its corresponding rules. Thanks to the prediction power of neural networks and interpretability associated with rules, PEARL demonstrates state of the art accuracy to various neural networks baselines and provides simple and interpretable decision rules to explain the prediction. Experimental results also show the resulting interpretation of PEARL is simpler than the standard decision rule list while achieving much higher accuracy.
Improved Content Based Medical Image Retrieval using PCA with SURF Features

In the computer era, the Content Based Image Retrieval system (CBIR) has most widely used in medical field and crime invention. During the last decade, CBIR emerged as powerful tool to efficiently retrieved images visually similar to query image. The basic process behind this concept is representation of image as feature vector and to measure the similarities between the images with distance between their corresponding feature vectors according to some metrics. The finding of correct features to represent images with, as well as the similarity metric that groups visually similar image together, are important milestone in construction of any CBIR system .The work in this paper focused on retrieve the correct query image from a huge number of medical image databases with the help of Principal Component Analysis (PCA) through SURF feature vector detection. The combination of this method produces an accurate and quick response than other conventional methods like SIFT and SURF feature vector based medical image retrieval.
Research on Artificial Intelligence Forecasting Method Integrating Data Mining and Statistical Analysis

For the science of the predicting methods of artificial intelligence, data mining and statistical analysis are two indispensable technologies. The core and essence of the technology is to analyze and process data, and select appropriate models and parameters to solve practical problems. In this paper, the input data dimension of time series is taken as the research object. Based on the two aspects of univariate and multivariable time series, the analysis and research of artificial intelligence prediction method integrating data mining and statistical analysis are carried out. The results show that in terms of single variable statistical model of artificial intelligence parameter estimation method, the prediction accuracy is not greatly improved and the deviation between the estimated value and the real value is also large. While at the level of multivariate statistical model of artificial intelligence parameter estimation method, it can not only fully improve the prediction accuracy, but also be similar to the estimated value and the real value.
One-dimensional deep learning firefly algorithm guided by the best particle

We propose the one-dimensional deep learning firefly algorithm guided by the best particle in order to increase the convergence speed and optimisation precision of the firefly algorithm. In each generation of optimisation process, the optimal particle is first updated in a fixed number of times according to the newly designed update formula. The update mode is defined as single-dimensional deep learning. After the optimal particle completes single-dimensional deep learning, other fireflies in the population keep the original evolutionary way to update the location and iteratively complete the optimisation task. Experiments with 12 benchmark functions show that the proposed algorithm has a higher optimisation capacity than the other six modified firefly algorithms.
BLOCKCHAIN BASED DIGITAL IDENTITY MANAGEMENT

Identity is a tool that identifies a person or group and ensures that they are recognized by others. Personalidentification cards issued by the states to people contain specific information about the person given.Identity systems used for centuries are now digitalized, ID cards with chips and passports with chipshave entered our lives. In the past, only information such as name, surname and place of birth wereincluded in ID cards with chips and passports. But today, in addition to our personal information, itincludes our biometric information such as fingerprints, iris, digital signatures. Blockchain technology,which has entered our lives with the financial sector, offers application areas in different sectors andsubjects. Some of these are IoT (internet of things), security and reliability systems, copyrights, publicand health sectors. In this study, it has been mentioned about the advantages and the features obtainedby using blockchain technology in identity management. The purpose of this study; It is to ensure thesafe use of identity information thanks to the features provided by the block chain such as distributeddatabase called DLT (distributed ledger technology), peer-to-peer transmission, transparency andirreversible records. Also in this study, a software that can simulate blockchain technology was createdand an Android application that reads data with NFC (Near Field Communication) technology wasdeveloped. Thus, the process of adding the data in the ID card or passport to the block chain by readingthe data from NFC with the Android application can be performed.
Ebara invests in AI specialist Ridge-i and forms business alliance

Ebara Corp has invested ¥100 million in Japanese AI (Artificial Intelligence) consultancy and developer Ridge-i Inc.
Drivable path detection based on image fusion for unmanned ground vehicles

Autonomous vehicles are used for a range of tasks, such as automated highway driving, transporting work, etc. These vehicles are used both in structured and unstructured environments. This work presents an effective method for path detection using statistical texture features extracted from fused LIDAR sensor and visual camera images. An edge-based feature detection approach is adopted for image registration. The Grey Level Co-occurrence Matrix (GLCM)-based texture features are extracted from the fused image. Classification performance of K-NN and Support Vector Machine (SVM) classifiers are analysed in this work. For experimentation, the data available in Ford Campus Vision data set are used. The results of this new approach are very promising for path detection problem of unmanned ground vehicles.
PneuModule: Using Inflatable Pin Arrays for Reconfigurable Physical Controls on Pressure-Sensitive Touch Surfaces

We present PneuModule, a tangible interface platform that enables users to reconfigure physical controls on pressure-sensitive touch surfaces using pneumatically-actuated inflatable pin arrays. PneuModule consists of two types of different passive modules: a main module and extension modules. The main module can be customized by attaching extension modules that have distinct physical input modalities. The extension modules are hot-swappable, enable users to quickly customize the interface layout. We showcase the feasibility of PneuModule through a series of interactive demonstrations.
Deep Learning Based Indian Currency Detection for Visually Challenged using VGG16

Banknote recognition is a major problem faced by visually Challenged people. So we propose a system to help the visually Challenged people to identify the different types of Indian currencies through deep learning technique. In our proposed project, bank notes with different positions are directly fed into VGG 16, a pretrained model of convolution neural network which extracts deep features. From our work the visually impaired people will be able to recognize different types if Indian Currencies.
Autonomous Data hiding in an Encrypted Image using KM-DH Algorithm

K Means Clustering (KM) based data hiding algorithm (KM-DH) is to perform cluster operation which means split pixels into chunks from already encrypted image. Under Consideration of splitted pixels find place to allocate information Authenticated person encrypt the image pixels with their key to prepare envelope image then flow goes by grouping the pixels to stuff the already encrypted image of lowest bits to generate place to allocate data using K means Clustering methods with the help of secret key it form split up matrix.In Receiver side after getting the encrypted data they can withdraw data and image separately without overlapping each other using data and image encryption key.At the same time receiver can also get both the image and data without any bug by dimensional link.
Creative differences

As artificial intelligence algorithms play an increasing role in producing media, what will happen to the idea of copyright, asks Leah Crane
Image Ranking

This chapter deals with image rankingImage ranking , a technique to determine the similarity between images in the database so that only relevant images are retrieved. A number of important and commonly used similarity measuresSimilarity measures are discussed including the Lp distance, mass-based distanceMass-based distance , cosine distanceCosine distance , χ2 statistics statistics , HI, quadratic distanceQuadratic distance , and Mahalanobis distanceMahalanobis distance . The pros and cons of each of the distances are highlighted. The next part of the chapter covers performance measuresPerformance measures such as RPPRecall and Precision Pair (RPP) , F-measureF-measure , PWHPercentage of Weighted Hits (PWH) , PSRPercentage of Similarity Ranking (PSR) , and Bullseye. The pros, cons, and relationships between them are also explained in details. Readers are shown how similarity measuresSimilarity measures and performance measuresPerformance measures work together to retrieve images correctly.
FBI makes 179 arrests in dark web sting

An FBI-led operation lasting nine months has resulted in major drug busts on dark web marketplaces.
Laws

In this chapter, current legal and policy responses to digital punishment are discussed, documenting the government’s regulatory ineffectiveness with regard to policing these operations and the ways it has chosen, instead, to place the onus of responsibility on the individuals impacted by digital punishment. The testimonies of the hundreds of people interviewed for this book demonstrate that government must regain control over how its data are monetized by third parties. At the same time, government should be held to better transparency standards that provide more insight into criminal justice agency activity rather than disclosing personal information about arrestees and defendants. This final chapter returns to the book’s core questions and suggests a series of policy reforms, asking what information should be public and what information should stay private.
Security firm: deepfakes are ‘fraud’s next frontier’

A growing number of criminals worldwide are using deepfake facial images and voice content to commit fraud, according to cyber-security firm Recorded Future.
Abnormal Human Activity Detection using Unsupervised Machine Learning Techniques

Nowadays there is a significant study effort due to the popularity of CCTV to enhance analysis methods for surveillance videos and video-based images in conjunction with machine learning techniques for the purpose of independent assessment of such information sources. Although recognition of human intervention in computer vision is extremely attained subject, abnormal behavior detection is lately attracting more research attention. In this paper, we are interested in the studying the two main steps that compose abnormal human activity detection system which are the behavior representation and modelling. And we use different techniques, related to feature extraction and description for behavior representation as well as unsupervised classification methods for behavior modelling. In addition, available datasets and metrics for performance evaluation will be presented. Finally, this paper will be aimed to detect abnormal behaved object in crowd, such as fast motion in a crowd of walking people
Prediction of Euclidean distance between existing and target product for software product line testing using FeatureIDE

Software product line (SPL) is a paradigm that consists a family of products, all these products have some common features. SPL contains enormous products due to variable features, to test all of them is unfeasible. For this reason, several methods have been introduced to test the product line. These methods are used to prioritise products because they are based on feature interaction and do not provide information regarding products' validity. To check the validity of products, we introduce a method based on Euclidean distance, which verifies the product based on the calculated distance between the real and desired product features. In addition, we compare the proposed method with the existing interaction-based method for product lines of different sizes. The results show that the proposed method takes less time to test the effectiveness of the product and increases the impact of the method in terms of time.
Use of Social Networking Sites by Lis Research Scholar of Gauhati University

Purpose: The library works as an access point of information, knowledge and services to fulfill the requirement of learning research activities of its users. In the modern age, information technologies become the gateway of knowledge access. Various social networking sites have been progressedin addiction to development of computer technology as well as meet the users with their changing needs. Thus social networking site become the tool of connect people for common purpose. The people of today’s world vastly use SNS in their daily life. Libraries have improved their collection and services in electronic form with rapid growth of ICT. SNS helps the library to work as a user friendly center. This paper describes the different use of SNS by LIS Research scholar, their purposes and effectiveness of SNS forenhancing library services. Methodology: The primary data for this study has been collected through questionnaire, interview and observation method from LIS Research scholar of Gauhati University. The secondary data has been imported from the research article, paper published in different journal, conference proceedings and from books and Wikipedia. Findings: Social Networking sites can be used as an effective way for access information and knowledge for their research and academic purpose. It will be helpful for library professional to provide different kinds of information with least time.
Design of OBE Based Closed-loop Teaching Mode for University Course

This article improves the traditional closed-loop teaching mode with outcome based education (OBE) idea. First, the outcome which is the core of OBE is relined in two dimensions, and a formalized learning outcome model is established; then, the exercise library is revised to support the learning outcome model. On this basis, an exact model is designed for OBE based closed-loop teaching mode, which adopts a four-level test and teacher-student two-level feedback operating mechanism. And the specific strategies of OBE based plan, OBE based test, OBE based evaluation, and feedback instruction ensure the closed-loop mode and OBE idea. OBE based closed- loop teaching mode combining two excellent modes is beneficial to the university course. Our research takes object-oriented programming course as the object, supports stable operation of the mode and also provides a foundation for informatization construction of the mode.