Reviewerly/exHarmony huggingface dataset:
ANFIS for Fraud Automobile Insurance Detection System

Fraud claims of automobile insurance are great loss for insurance companies as well as client with insurance policy. The main motive of this work is to devise a mechanism first as predictive model to classify whether a policy claim is classified as genuine or not and secondly what different kinds of parameters should be goaled to find fraud claims. To accomplish this motive, greatly precise prediction models are made by finding key features set through feature selection methods that are necessary for avoiding loss in future. The study of parametric and non-parametric algorithms that are statistical learning in nature reflects on to diminish unpredictability and escalates the possibilities of finding the accurate claims. The required feature set that is important for a framework is investigated by calculating feature relevant formed on the perceived specifics of a policy claim through a cross-validation. This is further tested for improvement of the efficiency through which claims of automobile fraudulent are precisely distinguished using adaptive neuro-fuzzy inference system. The accuracy of the model based on adaptive neuro-fuzzy inference system can reach more than 98% with an exact feature set chosen through a cross-validation. The endeavour attempted here would surely benefit not only insurance sector but also other fraud detection in other sectors like credit card, financial communication and many more in a way to get rid of increasing fraud in financial sector.
Face Recognition system using Extreme Learning Machines

Face recognition process is a computer technology which employs various calculations which identify exactly the individual anatomy in electronic graphics and establish the individual and verify the recorded pictures by evaluating them along with the decorative graphics stored at the database. Facial recognition can be a significant issue in computer vision, and numerous investigators have learned this specific issue in lots of manners; nevertheless, it really is crucial notably in a few applications like surveillance methods. Computational intelligence methods are use in vast tool. But it is understood both neural networks along with SVMs confront some hard problems like sluggish learning rate, insignificant human intervene, and inferior computational scalability. Multi-Scale Feature Pattern-based Extreme Learning Machine Algorithm (MSFP-ELM) as emerging technology that simplifies some struggles confronted by additional processes has just drawn the eye of a growing number of research workers. The prior is seen as single-hidden layer feedforward neural networks (SLFN). It chooses hidden pathways and analytically decides their result. So, in this specific paper, we will plan to give you a comprehensive opinion of those Multi-Scale Feature Pattern-based Extreme Learning Machine Algorithm (MSFP-ELM) advancements for various tool. Finally, MSFP-ELM weakness and strengths is going to be shown, together side its prospective viewpoints.
Deep Learning for Big Data

We live in a world where data is becoming increasingly valuable and increasingly abundant in volume. All companies produce data from sales, sensors, and various other sources. The main challenges are how can we extract insights from such a rich data environment and if Deep Learning is capable of circumventing Big Data’s challenges. To reach a conclusion, Social Network data is used as a case study for predicting sentiment changes in the Stock Market. The main objective of this paper is to develop a computational study and analyze its performance. Deep Learning was able to handle some challenges of Big Data, allowing results to be obtained and compared with real world situations. The outputs contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment Analysis.
Prediction of Chinese Semantic Word-Building Patterns Based on Complex Network Features

The research on prediction of Chinese semantic word-formation patterns based on complex network features has certain practical and theoretical significance in the field of natural language understanding. In this paper, complex networks are introduced into the prediction of Chinese semantic word-formation patterns, and a new prediction method of Chinese semantic word-formation patterns based on complex networks is proposed. And a solution that combines the semantic word-building rules of Chinese language with pattern recognition algorithm is put forward. Aiming at this scheme, a variety of pattern recognition algorithms are compared and analyzed, and the most suitable binary logistic regression model and naive Bayes model are found to predict Chinese semantic word-building patterns. The semantic loss is reduced, and the text classification model and corresponding classification algorithm are constructed, by introducing the maximum common subgraph theory to calculate text similarity under the complex network representation. The results of the experiments show that using complex networks to predict Chinese semantic word-formation patterns is both effective and feasible. The computer can judge the semantic word-formation pattern more accurately using the semantic word-formation pattern prediction model based on this theory.
Illumination-based Augmentation for Cuneiform Deep Neural Sign Classification

Automated content-based search for arbitrary cuneiform signs in photographic reproductions is a challenging task in the analysis of ancient documents, a central component of which is a reliable cuneiform sign classification. We present an illumination-based approach to generate synthetic training data for cuneiform sign classification via deep neural networks to overcome common issues with the transferability of machine learning training results. Starting from an analysis of the negative impact of illumination variations in the processed cuneiform data, we employ an illumination augmentation to two-dimensional (2D) training data generated from annotated 3D datasets. We demonstrate that our method is able to overcome the high visual variance of most digitized 2D cuneiform reproductions and achieve an illumination invariant generalization. The effectiveness of our approach is evaluated by its successful application to several subsets of a cuneiform script dataset with an originally poor transferability of mutual training results. Furthermore, we show that a sufficient sampling of the illumination space mostly removes the necessity to match the training data to specific target illumination conditions. The practical applicability of our approach is validated by applying it to a larger dataset, raising the overall classification accuracy by 4 percentage points to 90%, resulting in a classification error reduction of 28.5% when compared to results without the proposed data augmentation.
Handwritten Character and Digit Recognition Using Convolutional Neural Network

The necessity for quick and precise content section on little handheld PCs has prompted a resurgence of interest in on-line word recognition utilizing counterfeit neural Networks. Old style strategies are consolidated and improved to give strong recognition of hand-printed English content. The focal idea of a neural net as a character classifier gives a legitimate base to are cognition framework; long-standing issues comparative with preparing, speculation, division, probabilistic formalisms, and so forth, need to settled, notwithstanding, to instigate astounding execution. assortment of developments in a manner to utilize a neural net as a classifier in a very word recognizer are introduced: negative preparing, stroke twisting, adjusting, standardized yield blunder, mistake accentuation, numerous portrayals, quantized loads, and incorporated word division all add to effective and hearty execution.
Building Trust with a Contact Tracing Application: A Blockchain Approach

On March 11, 2020, the novel coronavirus (COVID-19) was declared a global pandemic. With no treatment or vaccine available at the time, it was necessary to rely on non-pharmaceutical methods for case identification and contact tracing. This kind of approach has good results in detecting and preventing tuberculosis, sexually transmitted infections, and vaccine-preventable diseases. Contact tracing and keeping safe distances are crucial to containing the spread of COVID-19. Nonetheless, contact tracing is a complex intervention, it involves quarantining and investigating close contacts. Manual contact tracing methods are slow, require a large amount of effort, and more often than not rely on the memory or assumptions of individuals. To combat these downsides, contact tracing applications were developed, resulting in quicker and more reliable recognition of infected individuals. However, because of the complex nature of these applications and their lack of transparency, a large portion of the population started doubting the privacy of the data collected. Soon after, many of these applications started to dwindle in the user department, which caused a feedback loop. "If fewer people are using the application, the application itself becomes useless, and there is no longer a reason to use it." Is clear that the main issue behind their downfall was an overwhelming lack of trust. In response, this paper will analyze how the use of blockchain technology can help the development of a more transparent application. And describe how a proof of work based on this concept was implemented. On the same note, it will also approach why was Hyperledger Sawtooth chosen, instead of more popular solutions such as Bitcoin or Ethereum.
Advanced 256-Bit Aes Encyption With Plain Text Partitioning

Security consultants take into consideration AES is safe against brute-force attacks, within which all attainable key combinations are checked until the right key’s found. However, the key size utilized for encryption must be large enough in order that it can’t be cracked by modern computers. The most advantage of AES encryption lies in its key length choices. 256-bit key encryption is significantly more difficult for brute-force attacks to guess than 128-bit key encryption. Here in this paper, a new 256-bit key encryption scheme is developed by partitioning and mixing 256-bit plain text into 2 128-bit plain text blocks and then only proceeds with standard 128-bit AES encryption. This ensures greater security.
Digital Management of Department as a business-unit of educational institution

The article is dedicated to actual issues of implementing digital technologies in management of the educational, scientific, research and innovation activity at educational institution. In the modern economy the education is rather regarded as a market part, and educating is considered as an institute service. The market of educational services consists of the personally created educational programs as products for consumers. At the same time, on the labour market graduates (specialists, bachelors and masters) who were trained on a special educational program are regarded as an offer, as well as the results of scientific research and innovation activity in the forms of patents, know-how, program software etc. are regarded on the market of R&D products and services. In the current research we analyse the activity of a department as the initiatives of the business-unit of a higher education institute. The article analyzes the organization of department management and digital methods for assessing the effectiveness of departments at the university. A roadmap for assessing the effectiveness of the department has been developed. Through the implementation of this research, the problem of ensuring and improving competitiveness of a higher education institution is solved by enhancing the effectiveness of the department, as a business unit of a university, in the digital economy. This will ensure the market orientation of educational and research and innovation activities, meeting the needs of people in education, and needs of enterprises and companies in qualified personnel and high-quality research and development work (R&D) deliverables and services.
The Impact of the COVID-19 Pandemic

The purpose of the study is to explore the role of service quality in reviving the United Arab Emirates hospitality sector post COVID-19 pandemic. Various factors contribute to the success of the hospitality industry in UAE; some of them are strategic location, leisure facilities, and business-friendly policies. Nevertheless, the COVID-19 pandemic has impacted the hospitality industry all over the world. Therefore, it is necessary to find ways to revive the hospitality sector during and post pandemic. Thus, the findings of this research give insight into customer preference and expectations as well as the need to invest and implement technology to deliver exceptional service. Therefore, it emphasizes that organizations operating in the hospitality sector should understand the customer needs, develop appropriate strategies, and invest in technology. Specifically, this research stresses that service quality dimensions improve customer satisfaction, enhances customer loyalty, and regain trust with consumers.
Research on an Intelligent Driving Algorithm Based on the Double Super-Resolution Network

Semantic segmentation plays a very important role in image processing, and has been widely used in intelligent driving, medicine, and other fields. With the development of semantic segmentation, the model has become more and more complex and the resolution of training pictures is higher and higher, so the requirements for required hardware facilities have become higher and higher. Many high-precision networks are difficult to apply in intelligent driving vehicles with limited hardware conditions, and will bring delay to recognition, which is not allowed in practical application. Based on the Dual Super-Resolution Learning (DSRL) network, this paper proposes a network model for training high-resolution pictures, adding a high-resolution convolution module which improves segmentation accuracy and speed while reducing computation. In a CamVid dataset, taking the road category as an example, IOU is 95.23%, which is 4% higher than DSRL, the real-time segmentation time of the same video is reduced by 46% from 120 s to 65 s, and the segmentation effect is better and faster, which greatly alleviates the recognition delay caused by high-resolution input.
Matching and Recovering 3D People from Multiple Views

This paper introduces an approach to simultaneously match and recover 3D people from multiple calibrated cameras. To this end, we present an affinity measure between 2D detections across different views that enforces an uncertainty geometric consistency. This similarity is then exploited by a novel multi-view matching algorithm to cluster the detections, being robust against partial observations as well as bad detections and without assuming any prior about the number of people in the scene. After that, the multi-view correspondences are used in order to efficiently infer the 3D pose of each body by means of a 3D pictorial structure model in combination with physico-geometric constraints. Our algorithm is thoroughly evaluated on challenging scenarios where several human bodies are performing different activities which involve complex motions, producing large occlusions in some views and noisy observations. We outperform state-of-the-art results in terms of matching and 3D reconstruction.
Approaches to Uncertainty Quantification in Federated Deep Learning

Trustworthy machine learning allows data privacy and a robust assessment of the uncertainty of predictions. Methods for quantifying uncertainty in deep learning have recently gained attention, while federated deep learning allows to utilize distributed data sources in a privacy-preserving manner. In this paper, we integrate several approaches for uncertainty quantification in federated deep learning. In particular, we show that prominent approaches such as MC-dropout and stochastic weight averaging Gaussian (SWAG) can be extended efficiently to federated setup. Moreover, we demonstrate that deep ensembles allow for natural integration in the federated learning framework. Our empirical evaluation confirms that a trustworthy uncertainty quantification on out-of-distribution data is possible in federated learning with little (SWAG) to no (MC-dropout, ensembles) additional communication. While all methods perform well in our empirical analysis and should serve as baselines in future developments in this field, deep ensembles and MC-dropout allow for better uncertainty based identification of out-of-distribution data and wrong classified data.
Single-view calibration of a catadioptric camera based on a theodolite model

The catadioptric panoramic imaging system may provide 360° panoramic imaging by employing the convex surface of a quadric surface with rotational symmetry as the reflector, which effectively compensates for the disadvantages of the narrow field of view in typical camera systems. First, this paper proposes a theodolite-based catadioptric camera image model based on the rotational symmetry of a catadioptric camera mirror, which simplifies the 2D modeling problem to a 1D problem. Simultaneously, the equivalence of the theodolite imaging model and the standard spherical imaging model also is demonstrated in this work. Second, this paper presents a method to calibrate the theodolite model parameters using only a single view and explains the calculation of model parameter initialization and iterative optimization steps in detail. Then, this paper demonstrates how to calibrate the theodolite model parameters using only a single view, as well as how to calculate the model parameter initialization and iterative optimization steps. Finally, simulation experiments and actual experiments have confirmed the correctness and stability of the method. The experimental results show that the average and standard deviation of the back-projection error are, respectively, 0.1983125 pixels and 0.0006153 pixels, in this model. We believe the theodolite model suggested in this paper offers a viable approach to catadioptric camera image modeling.
PSYCHOLOGICAL AND PEDAGOGICAL CONDITIONS FOR THE FORMATION OF MEDIA LITERACY IN CADETS OF THE NATIONAL ACADEMY OF THE NATIONAL GUARD OF UKRAINE

The scientific article is devoted to the problem of determining the psychological and pedagogical conditions of media literacy formation in the cadets of the National Academy of the National Guard of Ukraine during their professional training. A brief analysis of the concept «media literacy» revealed that it includes the skills to analyze, assess and use media communication in different forms and genres. A classification of objective and subjective psychological and pedagogical conditions for the formation of media literacy for cadets has been proposed. It has been established that the objective psychological and pedagogical conditions for the formation of media literacy in cadets include: diagnostics of the media literacy level, formation of subject-subject relationships in the educational process, conducting trainings on media literacy and the development of the ability for critical thinking, compliance with the psychological and pedagogical structure in the study of media literacy in the process of professional training, stimulating the process of self-development of cadets in the direction of mastering the skills of media literacy, prevention of mental health issues in cadets due to the influence of media on the psyche. Subjective conditions are the following: social responsibility, critical thinking, locus of control, emotional intelligence. The attention is focused on the formation of conscious behavior in the Internet, in particular, understanding of the causes and consequences of destructive information and psychological threats, compliance with the critical distance to various genres of media texts in media communications, recognition of psychological mechanisms of impact on the identity of the cadet through emotions. The described psychological and pedagogical conditions for the formation of media literacy in the cadets of the National Academy of the National Guard of Ukraine will contribute to increasing the resistance of future officers to the negative impact of media, increase the level of media literacy, the ability to control the consumption of media products; it is easier to navigate the media space, to distinguish information from official sources from destructive information, which may harm both a personality and the state as a whole.
Security Risk Assessment of Server Hardware Architectures Using Graph Analysis

The growing complexity of server architectures, which incorporate several components with state, has necessitated rigorous assessment of the security risk both during design and operation. In this paper, we propose a novel technique to model the security risk of servers by mapping their architectures to graphs. This allows us to leverage tools from computational graph theory, which we combine with probability theory for deriving quantitative metrics for risk assessment. Probability of attack is derived for server components, with prior probabilities assigned based on knowledge of existing vulnerabilities and countermeasures. The resulting analysis is further used to compute measures of impact and exploitability of attack. The proposed methods are demonstrated on two open-source server designs with different architectures.
Revisiting orthogonal lattice attacks on approximate common divisor problems

In this paper, we revisit three existing types of orthogonal lattice (OL) attacks and propose optimized cases to solve approximate common divisor (ACD) problems. In order to reduce both space and time costs, we also make an improved lattice using the rounding technique. Further, we present asymptotic formulas of the time complexities on our optimizations as well as three known OL attacks. Besides, we give specific conditions that the optimized OL attacks can work and show how the attack ability depends on the blocksize β in the BKZ-β algorithm.
Design of simulation-based pilot training systems using machine learning agents

Abstract The high operational cost of aircraft, limited availability of air space, and strict safety regulations make training of fighter pilots increasingly challenging. By integrating Live, Virtual, and Constructive simulation resources, efficiency and effectiveness can be improved. In particular, if constructive simulations, which provide synthetic agents operating synthetic vehicles, were used to a higher degree, complex training scenarios could be realised at low cost, the need for support personnel could be reduced, and training availability could be improved. In this work, inspired by the recent improvements of techniques for artificial intelligence, we take a user perspective and investigate how intelligent, learning agents could help build future training systems. Through a domain analysis, a user study, and practical experiments, we identify important agent capabilities and characteristics, and then discuss design approaches and solution concepts for training systems to utilise learning agents for improved training value.