Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Using Logistic Regression

As digitalization and online transactions are getting improving day by day the usage of credit card is getting increased. Credit card frauds are the most frequently happening problems in present society. Majorly this type of frauds occurs whenever the fraudster was using some another user credit card information. The system which can be used in order to detect the frauds in the credit card transactions is called credit card fraud detection system. In this project we will use various types of machine learning models and algorithms for identifying the fraud in the transactions. Here algorithm such as logistic regression are considered. By selecting the algorithm with best high accuracy, the fraud will be detected. The logistic regression algorithm accuracy will be nearer to 0.99% with this accuracy we can easily identify the frauds. In this web application the admin can login and the authentication will be performed, then the admin can easily upload a dataset for identifying the frauds in the given dataset.
A Machine Learning Framework for Job Failure Prediction in Cloud using Hyper Parameter Tuned MLP

Failures are inevitable in cloud computing systems (CCS) because of their enormous size and complexity, which results in reliability and efficiency losses. It is possible to take actions to increase the reliability and effectiveness of CCS through failure mitigation, fault tolerance, and recovery. Failure prediction and risk identification approaches could forecast such failure occurrences using data gathered during CCS operation. In order to handle the present state-of-the-art evolving computing systems, standard runtime fault-tolerance (FT) solutions like data replication and periodic check-pointing are not very successful. This has made it essential to have a robust method with a thorough knowledge of component and system failures as well as the capability to accurately predict probable system failures in the future. In this research, we develop a paradigm for improving the reliability and efficiency of cloud environment by risk assessment. This study starts with analyzing the failure task behavior and their related operational information. A predictive model is built using cat boost, genetic algorithm and hyper parameter tuned multilayer perceptron for finding the feature importance, selecting the most relevant features and to predict the high risk cloud tasks respectively. The present method is evaluated on Google Custer data with necessary performance metrics and compared with other machine learning approaches.
Conceptualization of Effective Algorithm for Minimizing Power Consumption in Cloud Servers

There is no adequate way to describe the vastness of today's Internet data. This shows that we are now in the midst of the shift to the age of data-intensive applications. Consequently, the cloud has become essential in the current condition of affairs where we find ourselves in order to deal with this unexpected increase in data. It's common knowledge that worldwide, cloud data centres account for less than 0.01% of all power use. Companies must entirely embrace renewable sources of energy as a source of electricity to stop environmental deterioration caused by the usage of large quantities of brown energy (energy from traditional fossil fuels, such as oil or coal), to power these server factories (i.e. data centres). In this piece, we detail an algorithm for planning out cloud data centre operations such that they are both profitable and environmentally friendly. With this approach, we want to find the optimal balance between a workload's energy needs and the renewable resources available in a cloud data centre.
LEGAL BASIS FOR COLLECTION AND ATTACHMENT OF DIGITAL INFORMATION AS PROOF IN CRIMINAL CASES IN THE REPUBLIC OF KAZAKHSTAN

The constantly increasing volume of natural science, technical and other knowledge at the present stage determines the technical saturation of the criminal process in the investigation of crimes. The increasing complexity of forensic tools used in the collection and consolidation of digital traces of crime and evidence, the extensive instrumentalization of expert research methods, determines the need for a set of issues related to the use of special knowledge by participants in the criminal process. The purpose of this study is to identify, generalize and analyze the theoretical and practical aspects of the legal grounds for collecting and consolidating digital information in proving criminal cases, taking into account world experience and the opinions of scientists. The issues of legal grounds for collecting, consolidating digital information in proving in criminal cases in the realities of the current legislation of the Republic of Kazakhstan is highlighted in this article. The historical aspects of the development of collecting evidence are also reviewed. In addition, the problems associated with the search, consolidation and application of digital information in evidence in criminal cases are addressed. Covert investigative actions aimed at collecting digital information are discussed. The author concludes that it is necessary to consolidate the concept of “Investigative actions” in the criminal procedural legislation, as well as investigative actions such as “Inspection” are not applicable to the collection of evidence in the form of digital information. Thus, authorization of investigative actions in the form of a search and seizure is required when receiving digital information.
USE OF NETWORK SERVICES WEB 2.0 FOR ICT COMPETENCE FORMATION OF STUDENTS IN ENGINEERING WITH APPLICATION OF PROJECT APPROACH

The article describes the experience of increasing efficiency of competence formation of students of applied information science. Authors underline the necessity of improving theoretical materials of disciplines and elaborating new methodical approaches to teaching technical disciplines for students in engineering of higher education institutions. One of the primary problems of future software engineers training is the lack of efficient approaches to ICT competence formation and particularly in development of its creative and research components. As a solution to the problem, application of project activities for teaching disciplines which are related to information and communication technologies is proposed as well as the active use of web 2.0 services. The authors point out the following advantages of using web 2.0 in the educational process: services allow increasing students' cognitive interest to the problems of the academic discipline, diversifying the main activities during the project, presenting design solutions in a visual, interactive form, forming their own unique digital content. The article presents a mental map of web 2.0 services with categories and particular resources that will be useful for carrying out applied informatics bachelors' projects. The authors offered examples of project topics in the disciplines of computer science, application programming, information security, design activities, and development of Internet applications. In addition, the pedagogical capabilities of such services as comics creation service (Pixton), development of mental maps (mindmup), creation of infographics (Piktochart), organization of teamwork (Yougile) are described. The research results showed that the use of web 2.0 network services while applying the project approach in the process of developing ICT competence among applied informatics bachelors allows not only increasing interest in technical disciplines study, enhancing information exchange, communication among students, but also developing ability of cooperate, teamwork, as well as creative and research activities. The article reveals methods of applying network services Web 2.0 for organizing educational projects. The review of services according to categories is offered and specific examples of their application for organizing project activities while studying ICT disciplines are described.
An Overview of Stochastic Quasi-Newton Methods for Large-Scale Machine Learning

Abstract Numerous intriguing optimization problems arise as a result of the advancement of machine learning. The stochastic first-order method is the predominant choice for those problems due to its high efficiency. However, the negative effects of noisy gradient estimates and high nonlinearity of the loss function result in a slow convergence rate. Second-order algorithms have their typical advantages in dealing with highly nonlinear and ill-conditioning problems. This paper provides a review on recent developments in stochastic variants of quasi-Newton methods, which construct the Hessian approximations using only gradient information. We concentrate on BFGS-based methods in stochastic settings and highlight the algorithmic improvements that enable the algorithm to work in various scenarios. Future research on stochastic quasi-Newton methods should focus on enhancing its applicability, lowering the computational and storage costs, and improving the convergence rate.
Comparative Analyses of Multilingual Sentiment Analysis Systems for News and Social Media

In this paper, we present evaluation of three in-house sentiment analysis (SA) systems originally designed for three distinct SA tasks, in a highly multilingual setting. For the evaluation, we collected a large number of available gold standard datasets, in different languages and varied text types. The aim of using different domain datasets was to achieve a clear snapshot of the level of overall performance of the systems and thus obtain a better quality of an evaluation. We compare the results obtained with the best performing systems evaluated on their basis and performed an in-depth error analysis. Based on the results, we can see that some systems perform better for different datasets and tasks than the ones they were designed for, showing that we could replace one system with another and gain an improvement in performance. Our results are hardly comparable with the original dataset results because the datasets often contain a different number of polarity classes than we used, and for some datasets, there are even no basic results. For the cases in which a comparison was possible, our results show that our systems perform very well in view of multilinguality.
Sensitivity considerations on denoising series of spectra by singular value decomposition

Abstract When acquiring series of spectra ( , CP buildup curves, etc.) on samples with poor SNR, we are usually faced with choosing between taking a few points with a large number of scans to maximize the SNR or more points with a smaller number of scans to maximize the information content. In this Letter , we show how low‐rank decomposition can be used to denoise a series of spectra, reducing the trade‐off between the number of scans and the number of experiments.
Blockchain and smart contract architecture for notaries services under civil law: a Brazilian experience

This paper proposes a blockchain solution for some activities currently performed by notary offices under the Civil Law judiciary that is technically viable. The architecture is also planned to accommodate Brazil's legal, political, and economic requirements. Notaries are responsible for providing various intermediation services for civil transactions, where their primary role is to be the trusted party capable of guaranteeing the authenticity of these transactions. This type of intermediation is common and demanded in Latin American countries, such as Brazil, which is regulated by a Civil Law judiciary. The lack of adequate technology to meet such legal demands leads to an excess of bureaucracy, dependence on manual document and signature checks, and centralized and face-to-face actions in the physical dependence of the notary. To deal with this scenario, this work presents a blockchain-based solution to make some of the activities performed by notaries automatic, guaranteeing non-modification and adherence to civil laws. Thus, the suggested framework was evaluated in accordance with Brazilian legislation and provides an economic evaluation of the proposed solution.
SEAL: Integrating Program Analysis and Repository Mining

Software projects are complex technical and organizational systems involving large numbers of artifacts and developers. To understand and tame software complexity, a wide variety of program analysis techniques have been developed for bug detection, program comprehension, verification, and more. At the same time, repository mining techniques aim at obtaining insights into the inner socio-technical workings of software projects at a larger scale. While both program analysis and repository mining have been successful on their own, they are largely isolated, which leaves considerable potential for synergies untapped. We present SEAL, the first integrated approach that combines low-level program analysis with high-level repository information. SEAL maps repository information, mined from the development history of a project, onto a low-level intermediate program representation, making it available for state-of-the-art program analysis. SEAL’s integrated approach allows us to efficiently address software engineering problems that span multiple levels of abstraction, from low-level data flow to high-level organizational information. To demonstrate its merits and practicality, we use SEAL to determine which code changes modify central parts of a given software project, how authors interact (indirectly) with each other through code, and we demonstrate that putting static analysis’ results into a socio-technical context improves their expressiveness and interpretability.
Virtual Reality at a Prehistoric Museum: Exploring the Influence of System Quality and Personality on User Intentions

Virtual Reality (VR) promises many benefits for the tourism industry. However, a review of tourism-related VR research shows that the roles of system quality and user personality remain largely unexplored. This study examines the causal relation underlying VR quality (information quality, interactivity, and visual attractiveness) and the user's personality (openness to experience, conscientiousness, and social influence) in conjunction with usability, attitude, and behavioural intention. We collected user data from a VR tourism experience of the Sangiran museum at Surakarta, Indonesia using a Head Mounted Device VR. The Sangiran museum is an archaeological excavation site recognised as a world heritage site by UNESCO. Two hundred eighteen valid responses were analysed using Structural Equation Modelling. The result suggests that only visual attractiveness positively impacts usability from a VR quality perspective, while openness to experience and social influence show significant positive evidence of attitude. These findings are discussed based on the practical and theoretical implications, including future research opportunities into VR tourism.
A novel spectrogram visual security encryption algorithm based on block compressed sensing and five-dimensional chaotic system

Based on block compressed sensing theory, combined with a five-dimensional chaotic system, we propose and analyze a novel spectrogram visual security encryption algorithm. This research is devoted to solving the compression, encryption and steganography problems of spectrograms involving large data volumes and high complexity. First, the discrete wavelet transform is applied to the spectrogram to generate the coefficient matrix. Then, block compressed sensing is applied to compress and preencrypt the spectrogram. Second, we design a new five-dimensional chaotic system. Then, several typical evaluation methods, such as the phase diagram, Lyapunov exponent, bifurcation diagram and sample entropy, are applied to deeply analyze the chaotic behavior and dynamic performance of the system. Moreover, the corresponding Simulink model has been built, which proves the realizability of the chaotic system. Importantly, the measurement matrix required for compressed sensing is constructed by the chaotic sequence. Third, dynamic Josephus scrambling and annular diffusion are performed on the secret image to obtain the cipher image. Finally, an improved least significant bit embedding method and alpha channel synchronous embedding are designed to obtain a steganographic image with visual security properties. To make the initial keys of each image completely different from other images, the required keys are produced using the SHA-256 algorithm. The experimental results confirm that the visual security cryptosystem designed in this study has better compression performance, visual security and reconstruction quality. Furthermore, it is able to effectively defend against a variety of conventional attack methods, such as statistical attacks and entropy attacks.
Understanding dynamics of ground movement based on seismic monitoring

Given the unique geology, topography, and hydrology of the Himalayas, it is imperative that a long-term strategy be developed to reduce the destructive potential of landslides in the region. Monitoring and an early warning system for landslides are the best non-structural measures for preventing landslide disasters. The study's overarching objective is to highlight why it's crucial to keep an eye out for landslides and how seismic sensor arrays can be used to issue early warnings. The implications of large mass flows in the study area must be carefully considered for sustainable hydropower and other socio-economic development projects. Seismic data, satellite imagery data, Time-Frequency analysis (TFA), and videos and photos taken by eyewitnesses form the basis of our investigation.First, we gather precise event data, and then we collect signals from the seismology observatory at the Indian Institute of Technology Roorkee for that time frame. The signals from the collection have been processed with signal processing methods like STA/LTA, Filtering, and TFA. One synthetic signal, two landslide events, and two local earthquakes were analyzed to better comprehend the dynamics and behavior of a natural distractive&amp;#160;event.Seismic records reveal that various types of events have distinctive dynamic properties. There are three distinct stages to a landslide event: (1) the detachment of slope-forming materials, (2) the debris flow, and (3) the flood flow. The P and S waves, the onset and end times, and the duration of an earthquake have all been determined. We have used synthetic signals to learn about TFA and have found the method that works best for interpreting seismic signals. We use the classification method developed by Provost et al. in 2017. Time-domain amplitude levels are a feature that can be easily extracted and classified, but they are also vulnerable to noise. Energy concentration in the time-frequency domain is one such method that, while requiring more complex operations, can lead to more trustworthy feature extraction and accurate classification. The absence of the distinct P- and S-wave arrival time, as is typical of earthquakes, is another feature of the seismic waveform that is indicative of a landslide. The results of the seismic record analysis also shed light on the breadth of monitoring for slope-moving disaster events in the North-West Himalayas.
Boosting semantic segmentation via feature enhancement

Semantic segmentation aims to map each pixel of an image into its corresponding semantic label. Most existing methods either mainly concentrate on high-level features or simple combination of low-level and high-level features from backbone convolutional networks, which may weaken or even ignore the compensation between different levels. To effectively take advantages from both shallow (textural) and deep (semantic) features, this paper proposes a novel plug-and-play module, namely feature enhancement module (FEM). The proposed FEM first uses an information extractor to extract the desired details or semantics from different stages, and then enhances target features by taking in the extracted message. Two types of FEM, i.e., detail FEM and semantic FEM, can be customized. Concretely, the former type strengthens textural information to protect key but tiny/low-contrast details from suppression/removal, while the other one highlights structural information to boost segmentation performance. By equipping a given backbone network with FEMs, there might contain two information flows, i.e., detail flow and semantic flow. Extensive experiments on the Cityscapes, ADE20K and PASCAL Context datasets are conducted to validate the effectiveness of our design. The code has been released at https://github.com/SuperZ-Liu/FENet.
Dynamic co-movement between economic growth and language: A new perspective of technological progress

To investigate whether language complexity has significant effects on technological progress and economic growth, this paper not only improves a concise endogenous growth model with open economy in which reducing the degree of official language complexity can help one country gain rapid technological progress and economic growth in balanced growth path, but also applies stochastic frontier analysis (SFA) and two-way fixed effect model to investigate our hypothesis. Using the database ethnologue distributed by Lewis et al. (2014) and the Penn World Table 9.1 about 89 countries and regions from 1990 to 2018, we consider the number of verb conjugations of official language to be the indicator of language complexity, and examine its effects on technological change and economic growth. The empirical findings demonstrate that reducing language complexity has a significant positive effect and time-lag influence on technological change and economic output. Meanwhile, the positive effect of linguistic complexity on technological progress and economic growth is stronger for these countries with isolating languages than those with other language families, as well as for middle and low-income countries than high-income countries. For developing countries and these countries with isolating and agglutinative languages, they are urged to simplify their native language and reform the second language learning institution in schools in order to achieve a high level of development.
Toward denoising of 3D CT scans with few data

We propose a new method for denoising of 3D CT scans with few data. Like any other form of imaging data, CT scans are susceptible to noise and artifacts. Noise in CT scan images is not only stochastic, but can be frequency dependent and introduced by the measuring device itself or by signal processing algorithms. Unfortunately, most state-of-the-art Deep Learning methods are mainly focusing on denoising random noise only. Therefore, we propose a new method for denoising 3D CT scans, which is based on a 3D AutoEncoder, a GAN, and self-supervised learning. Our method works not only on random noise but also the frequency dependent noise. It exploits the fact that, in a CT scan image, an object’s features are similar between different regions and easy to be encoded. In contrast, the features of structured noise are very different from the object, while the random noise is pixel-wise independent. Our method can be trained on few data, with or without ground truth, and is computationally inexpensive. Our experiments show that our method outperforms other methods in terms of several metrics, and outperforms most state-of-the-art methods in terms of computational efficiency.