Reviewerly/exHarmony huggingface dataset:
A secured automated Attendance Management System implemented with Secret Sharing Algorithm

Attendance management system is always an important entity for any of the organization, a number of attendance system like barcode, RFID, finger-print recognition system are already available in the market. While various companies are using different types of attendance management system according to their budget and convenience. Few of the designed and adopted systems have their own drawbacks like data handling, data security and privacy of the information. In this pape, we have proposed and designed a secured attendance management system with Secret sharing algorithm. The main objective is to automate the attendance system and provide the complete authentic and secure database of all the employees or users. Due to Covid-19 maximum of the organizations are trying to adopt the attendance management system without any physical connectivity or manual intervention. Our proposed solution is the best feasible solution to handle the stated problems. Attendance can be marked by scanning the QR code with a mobile phone at distant locations while secret sharing security algorithm is providing the security by distributing the complete code into number of secret shares which can be only recovered by authentic entities so integrity and privacy of the information is maintained properly.
Emoji and Emoticon in Tweet Sentiment Classification

Indonesian Twitter's user tends to use emoji or emoticon when tweeting. Emoji and emoticon can help them for conveying their message efficiently. It is used for expressing their sentiment about a certain topic. For analyzing sentiment with classification technique, the use of emoji and emoticon might take into consideration. In this paper, we knew that Indonesian Twitter's User not only uses emoji in their tweets but also emoticon although emoji used is dominating tweets than emoticon. In some cases, both of them are used simultaneously in a tweet. Emoji and emoticon affect classification model performance positively so that emoji and emoticon might not remove in-text pre-processed and include in classification features. For included in classification feature emoji and emoticon convert into its Unicode Name, but this conversion slightly below classification model from conversion emoji and emoticon into its lexicon sentiment. Conversion emoji and emoticon into its lexicon sentiment make classification model performance more stable. But considering emoticon and emoji cannot simply be interpreted into certain sentiment class so conversion into Unicode Name is encouraging. SVM produces the best performance followed by Bernoulli Naïve Bayes and the last one is Multinomial Naïve Bayes.
Risk Prediction-Based Breast Cancer Diagnosis Using Personal Health Records and Machine Learning Models

Breast cancer is most common in middle-aged female population. It is the fourth most dangerous cancer compared to remaining cancers. In recent years, breast cancer patients are significantly increasing, so the early diagnosis of cancer has become a necessary task in the cancer research, to facilitate subsequent clinical management of patients. The prevention of the breast cancer tumor is early detection of the tumor. Early detection of cancer can stop increase in tumor and saves lives. In the field of machine learning classification, cancer patients are classified into two types as benign or malignant. Different preprocessing techniques like filling missing values, applying correlation coefficient, synthetic minority oversampling technique (SMOTE) and tenfold cross-validations are implemented and aptly used to obtain the accuracy. The main context of this study is to identify key features from the dataset and analyze the performance evaluation of different machine learning algorithms like random forest classifier, logistic regression, support vector machine, decision tree, Gaussian Naive Bayes and k-nearest neighbors. Based on the results, the classification model that gives highest accuracy will be used as the best model for cancer prediction.
Image Processing Based Approach for Crime Scene Investigation Using Drone

Time management at the crime scene is very difficult. It is necessary to make good use of time and examine the evidence at the scene without any damage and deterioration from life. There may be evidences that are overlooked in a short time and places where experts cannot reach the evidence or situations that may endanger the safety of experts. In this study, the crime scene investigation system with an autonomous drone was investigated. With the help of the drone, data was collected to help examine the evidence. Thus, in examining the evidence; time savings, securing the crime scene investigator and more data will be obtained. Remote control and autonomous driving feature has been written for the drone. The camera placed on the drone, the images of the evidence at the scene were taken thanks to the cameras. The inferences obtained from the images are transferred to the user with the artificial intelligence supported model created. In order to process these data, it was given as an introduction to the model created after preparation and the training of the model was provided. As a result of the training of the model, the images taken on the drone can be detected through the software developed with the support of real-time artificial intelligence. These detected objects are transferred to the scene investigation specialist on the interface screen in real time, thereby developing a system with both artificial intelligence control and user supervision.
A Robust Lightweight Algorithm for Securing Data in Internet of Things Networks

One of the modern technologies that link millions of different devices together, is the technology called Internet of things. The amount of data exchanged between these devices is very large. Therefore, it requires high protection for that data. Also, those devices are small size and limited resources. Therefore, conventional cryptography will take long process on the internet of things due to complex mathematical operations and an increase in the number of rounds, which leads to energy resource consumption for devices. In this paper, a lightweight cryptographic algorithm is suggested. The algorithm uses simple mathematical operations (XOR, XNOR, shifting, swapping). The algorithm works on a combination of Feistel and SP architectural methods to increase the complexity of the encryption. The algorithm has been compared to other algorithms in terms of structure, security, and flexibility.
Design and Implementation of Second-generation ID Card Number Identification Model based on TensorFlow

This paper designs and implements a model base on tensorflow framework in Python language to complete the identification of ID card number. Firstly, the image of ID card is preprocessed by a computer vision library (OpenCV), including angle correction, grayscale and binarization and so on. Secondly, an algorithm is used to project the image, and obvious projection peak area is number area, meanwhile the number area of ID card is determined, character segmentation is carried out by using the fixed width height ratio and spacing of characters. And then the data set is trained by the convolutional neural network. Finally, the character segmentation is recognized and the ID card number is output. In the end, the model is tested to show that the training speed is fast, the recognition and identification accuracy is high, and the convolutional neural network(CNN) has a certain value.
The construction of Digital Teaching Skills: A challenge for Teacher Training

Teacher training is key for the construction of digital teaching skills and therefore must be proposed in alignment with the teacher training needs, the educational system and the demands from the digital society. When planning the training, it is key to acknowledge the new context, as well as the new educational models that allow us to break old paradigms towards a reflective and autonomous pedagogical practice. In this article, we analyze the relevance of teacher training, its importance for the construction of digital teaching skills of university teachers, establishing the emerging and necessary training needs for their professional development. We carried out a case study in a private university in Catalonia, using the life stories methodology, which has allowed us to have conversations with the teachers, get closer to their reality and obtain precious and subjective information.
Interdisciplinarity as Key Characteristic of Video Games Localisation Process

The purpose of the study is to characterise the process of software and video games localisation as a specific type of translation activity. The article provides a systemic description of tasks facing a localiser and dictating their own standards of working with the material. Scientific novelty of the research lies in clarifying and drawing a distinction between such notions as “translation” and “localisation”, as well as in giving a systemic description of software and video games localisation requirements, which determine their own standards of working with the material. As a result, it is proved that a localiser acts not only as a translator in the conventional sense but also as a multi-skilled specialist, who understands principles of working with a programme code and has a broad view on methods of working with cultural patterns.
Formal verification of masking countermeasures for arithmetic programs

Cryptographic algorithms are widely used to protect data privacy in many aspects of daily lives from smart card to cyber-physical systems. Unfortunately, programs implementing cryptographic algorithms may be vulnerable to practical power side-channel attacks, which may infer private data via statistical analysis of the correlation between power consumptions of an electronic device and private data. To thwart these attacks, several masking schemes have been proposed. However, programs that rely on secure masking schemes are not secure a priori. Although some techniques have been proposed for formally verifying masking countermeasures and for quantifying masking strength, they are currently limited to Boolean programs and suffer from low accuracy. In this work, we propose an approach for formally verifying masking countermeasures of arithmetic programs. Our approach is more accurate for arithmetic programs and more scalable for Boolean programs comparing to the existing approaches. We have implemented our methods in a verification tool QMVerif which has been extensively evaluated on cryptographic benchmarks including full AES, DES and MAC-Keccak. The experimental results demonstrate the effectiveness and efficiency of our approach, especially for compositional reasoning.
Breast cancer Analysis and Detection in Histopathological Images using CNN Approach

The detection of cancer from the histology images with hematoxylin and eosin stained is significant, requires more analysis, and often leads to disagreement among pathologists. Computerized diagnostic systems help pathologists to enhance diagnostic accuracy and efficiency. Recent advances in this field have led them to successfully process histology images using convolution neural networks (CNNs). The classification from histology images into malignant and benign depends upon the cell density, variability, and tissue structure. The state and art of review of various research works using histology images are detailed in this work. The VGG16 model is also used to classify the breast cancer histology images into benign and malignant which achieved 88% accuracy on the testing data. The findings are likewise compared to other approaches.
Machine Learning for Cloud Resources Management—An Overview

Nowadays, an important topic that is considered a lot is how to integrate Machine Learning (ML) to cloud resources management. In this study, our goal is to explore the most important cloud resources management issues that have been combined with ML and which present many promising results. To accomplish this, we used chronological charts based on keywords that we considered important and tried to answer the question: is ML suitable for resources management problems in the cloud? Furthermore, a short discussion takes place on the data that are available and the open challenges on it. A big collection of researches is used to make sensible comparisons between the ML techniques that are used in the different kind of cloud resources management fields and we propose the most suitable ML model for each field.
Methods of Creating an Electronic University

The main goal of creating an electronic university is the development, production, collection of storage, content delivery and management and administration of the educational process of e- learning. The implementation of e-learning is based on integrated organizational and functional structures, IT infrastructures with their own rules, responsibilities, people, a system of interaction and relationships, i.e. a business structure that flows into the university's business architecture. The paper proposes an approach to the construction of electronic education, which is associated with the development of IT infrastructure of the university, business architecture and process-service approach. The business processes, services, IT infrastructure of the university providing e- learning are considered. The object of the study is the electronic educational environment of the university, which represents the electronic educational environment of the university in the form of services. The term “service” means a service for an educational business, a service that is measured by business indicators, a service that is accessible, continuous and repeatable. The result of the work is a reasonable catalog of university's electronic educational services that can be used as a guide to designing such environments in various educational institutions.
Node classification over bipartite graphs through projection

Many real-world large datasets correspond to bipartite graph data settings—think for example of users rating movies or people visiting locations. Although there has been some prior work on data analysis with such bigraphs, no general network-oriented methodology has been proposed yet to perform node classification. In this paper we propose a three-stage classification framework that effectively deals with the typical very large size of such datasets. The stages are: (1) top node weighting, (2) projection to a weighted unigraph, and (3) application of a relational classifier. This paper has two major contributions. Firstly, this general framework allows us to explore the design space, by applying different choices at the three stages, introducing new alternatives and mixing-and-matching to create new techniques. We present an empirical study of the predictive and run-time performances for different combinations of functions in the three stages over a large collection of bipartite datasets with sizes of up to $$20\,\hbox {million} \times 30\,\hbox {million}$$ nodes. Secondly, thinking of classification on bigraph data in terms of the three-stage framework opens up the design space of possible solutions, where existing and novel functions can be mixed and matched, and tailored to the problem at hand. Indeed, in this work a novel, fast, accurate and comprehensible method emerges, called the SW-transformation, as one of the best-performing combinations in the empirical study.
Comparative analysis of identification of dynamic objects by scale-invariant feature transform and deep neural networks

Abstract The article is devoted to the development and analysis of methods of identifying dynamic objects. A system for identifying information from a luggage tag based on several neural networks with the SSD InceptionV2 architecture has been developed. These neural networks work with sufficiently high accuracy 82-95% and speed 7-10fps. Advantages and disadvantages of application of method of scale-invariant feature transform for identification of luggage tags are considered. The operability of the methods on real images has been tested.
Financial Instruments Entail Liabilities: Ether, Bitcoin, and Litecoin Do Not

The financial assets that are subject to major European financial legislation (i.e. (designated types of) financial instruments) have traditionally been defined in a largely exemplary and circular manner. The recent proliferation of ‘non-traditional’ financial assets, such as cryptocurrencies and stablecoins, is increasingly challenging the viability of these pragmatic financial asset definitions. Through the analysis of the technologies and functionalities underpinning non-traditional financial assets, legal scholarship has aimed to categorize novel assets within the existing framework of financial asset definitions. Although a solid understanding of e.g. distributed ledger applications and cryptography appears a prerequisite for future policy and legislative interventions, contemporary European financial legislation is mostly indifferent to the technologies on which financial assets may be wired. Categorizations based on the purposes that non-traditional assets may serve (i.e. payment, utility, and investment) are more relevant to financial law, but suffer from subjectivity because they depend on the asset usage by the asset holder. Against this backdrop, this paper proposes a novel systematization of non-traditional assets that is based upon the conceptual substructure of the assets within scope of European financial legislation. More specifically, this paper submits that, irrespective of underlying technologies and functionalities, all assets that are subject to major European financial legislation have a conceptual common denominator: they entail the liability of an entity and, hence, have intrinsic value. The proposed categorization singles out a well-defined group of novel financial assets that is not subject to European financial law (i.e. assets that only have extrinsic value). Different from functionality- and technology-based categorizations, the suggested approach allows to eradicate ambiguities and potential overinclusiveness of functionality-based categorizations of non-traditional assets. By exploring the conceptual common denominator of the financial assets that are subject to European financial legislation, this paper aims to foster debate on the circular and exemplary character of financial asset definitions in European financial legislation in general and the relation of these definitions to novel types of financial assets in particular.
Invisible Dynamic Mechanic Adjustment in Virtual Reality Games

A key part of managing a player's virtual reality experience is ensuring that the environment behaves consistently to the player's interaction. In some instances, however, it is important to change how the world behaves-i.e. the world's simulation rules or mechanics-because doing so preserves the virtual environment's intended quality. Mechanics changes must be done carefully; if too overt, they may be perceivable and potentially thwart a player's sense of presence or agency.This paper reports the result of a study, which demonstrates the widely-held but heretofore-untested belief that changing an environment's mechanics without considering what the player knows is visible to the player. The study's findings motivate the paper's second contribution: an automated method to perform invisible dynamic mechanics adjustment, which affords shifting a game's previously-established mechanics in a manner that is not perceivably inconsistent to players. This method depends on a knowledge-tracking strategy and two such strategies are presented: (1) a conservative one, relevant to a wide variety of virtual environments, and (2) a more nuanced one, relevant to environments that will be experienced via head-mounted virtual reality displays. The paper concludes with a variety of design-centered considerations for the use of this artificial intelligence system within virtual reality.
Fault-tolerant detectors for distinguishing sets in cubic graphs

Assume that G models a facility with a possible "intruder" (or a multiprocessor network with a possible malfunctioning processor). We consider placing (the minimum number of) detectors at vertices in G to precisely determine the location of the intruder. Various distinguishing set parameters have been defined based on the functionality of the detector. We characterize several types of fault-tolerant detectors identified for the open-locating-dominating sets and determine the bounds on the minimum size of these sets for cubic graphs.
Android Software for Testing in Audio and 2D User Interaction Environment

Software has been developed that implements a digital form of remote knowledge control in Java for the Android platform. The testing system uses visual, audio and graphic generation of questions and answers, which ensures the use of traditional forms of training in remote mode. In addition to the offline mode, an automatic testing system suggests the possibility of a network interaction between a teacher and a group of students. An example of an automatic system for remote assessment of musical literacy of students of music colleges is given.
Analyzing Functional Magnetic Resonance Brain Images with OpenCV2

Functional Magnetic Resonance Imaging (fMRI) researchers are currently using various techniques for analyzing the cognitive states in brain images. Every time, it remains a challenge for such researchers to decode the brain information about stimuli affecting the related Region of Interest (ROI) from voxels of Brain Networks. In this paper, the authors used OpenCV library of Python to analyze various states of brain images. The images of each volume in the dataset are grouped into principal planes of fMRI views. Then operations of Dilation, Erosion, and Gaussian Blur are applied to all images for smoothening purposes. The authors believe that the procedure followed in this paper will be the optimal method for extracting brain images' features, which will improve the classification accuracy of the decoding of brain images in a much better way.
The Universal Research Portal

In recent times there are plenty of research projects with grants from major Government Organizations, Industrial Organizations and other Institutions. But a lack of universal research portal where Professors can motivate students interested in the domain to work with them on high scale research work and projects sponsored by Industries and Government Organizations hampers the growth of research community and also hinders great research opportunities which could be offered to young intelligent minds. Also huge availability of research projects with no customization and recommendation which can enhance students' knowledge in his/her field of interest makes very less students to take up research activities. Our solution aims at satisfying the above need by giving professors, a platform where they are continuously notified about the list of the projects with grants from major organizations for which they can offer research internships to students and float various research projects for students to apply. It also includes monitoring status of the research work and collaborative filtering a nd machine learning based recommendation with highly scale-able notification system making research recommendation completely customized according to data obtained from student's contribution in different version control systems.
Econometrics Meets Sentiment: An Overview of Methodology and Applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software.