Reviewerly/exHarmony huggingface dataset:
A Cloud Database based on AES 256 GCM Encryption Through Devolving Web application of Accounting Information System

The main objective of this research is to use AES 256 GCM encryption and decryption of a web application system database called Accounting Information System (AIS) for achieving more privacy and security in a cloud environment. A cloud environment provides many services such as software, platform, and infrastructure. AIS can use the cloud to store data to achieve accounting with more performance, efficiency, convenience, and cost reduction. On the other hand, cloud environment is not secure because data is kept away from the organization. This paper focuses on how we deal with secure sensitive data such as accounting data AIS web application at web level encryption by using AES 256 GCM encryption to store data as encrypted data at cloud in a secure manner? Accounting Information System (AIS) has very sensitive data and its need to be more secure and safe specially in cloud because it’s not saved at local servers but at another cloud service provider. The storage of encryption and decryption keys are stored in locations and devices different from those in which the database is stored in the cloud for ensuring more safety.
A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours

Lung cancer is a life-threatening disease and its diagnosis is of great significance. Data scarcity and unavailability of datasets is a major bottleneck in lung cancer research. In this paper, we introduce a dataset of pulmonary lesions for designing the computer-aided diagnosis (CAD) systems. The dataset has fine contour annotations and nine attribute annotations. We define the structure of the dataset in detail, and then discuss the relationship of the attributes and pathology, and the correlation between the nine attributes with the chi-square test. To demonstrate the contribution of our dataset to computer-aided system design, we define four tasks that can be developed using our dataset. Then, we use our dataset to model multi-attribute classification tasks. We discuss the performance in 2D, 2.5D, and 3D input modes of the classification model. To improve performance, we introduce two attention mechanisms and verify the principles of the attention mechanisms through visualization. Experimental results show the relationship between different models and different levels of attributes.
Designing a Model to Study Data Mining in Distributed Environment

To make business policy, market analysis, corporate decision, fraud detection, etc., we have to analyze and work with huge amount of data.Generally, such data are taken from different sources.Researchers are using data mining to perform such tasks.Data mining techniques are used to find hidden information from large data source.Data mining is using for various fields: Artificial intelligence, Bank, health and medical, corruption, legal issues, corporate business, marketing, etc. Special interest is given to associate rules, data mining algorithms, decision tree and distributed approach.Data is becoming larger and spreading geographically.So it is difficult to find better result from only a central data source.For knowledge discovery, we have to work with distributed database.On the other hand, security and privacy considerations are also another factor for de-motivation of working with centralized data.For this reason, distributed database is essential for future processing.In this paper, we have proposed a framework to study data mining in distributed environment.The paper presents a framework to bring out actionable knowledge.We have shown some level by which we can generate actionable knowledge.Possible tools and technique for these levels are discussed.
Application of Deep Learning Methods for Detection and Tracking of Players

This chapter deals with the application of deep learning methods in sports scenes for the purpose of detecting and tracking the athletes and recognizing their activities. The scenes recorded during handball games and training activities will be used as an example. Handball is a team sport played with the ball with well-defined goals and rules, with a given number of players who can participate in the game as well as their roles. Athletes move quickly throughout the field during the game, change position and roles from defensive to offensive, use different techniques and actions, and very often are partially or completely occluded by another athlete. If artificial lighting and cluttered background are additionally taken into account, it is clear that these are very challenging tasks for object detectors and trackers. The chapter will present the results of various experiments that include player and ball detection using state-of-the-art deep convolutional neural networks such as YOLO v3 or Mask R-CNN, player tracking using Deep Sort, key player determination using activity measures, and action recognition using LSTM. In the conclusion, open issues and challenges in applying deep learning methods in such a dynamic sports environment will be discussed.
Improved Estimation of Concentration Under ℓp-Norm Distance Metrics Using Half Spaces

Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. (2019) presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets. However, it remains unclear whether these lower bounds are tight enough to provide a useful approximation for the intrinsic robustness of a dataset. To gain a deeper understanding of the concentration of measure phenomenon, we first extend the Gaussian Isoperimetric Inequality to non-spherical Gaussian measures and arbitrary lp-norms (p≥2). We leverage these theoretical insights to design a method that uses half-spaces to estimate the concentration of any empirical dataset under lp-norm distance metrics. Our proposed algorithm is more efficient than Mahloujifar et al. (2019)'s, and experiments on synthetic datasets and image benchmarks demonstrate that it is able to find much tighter intrinsic robustness bounds. These tighter estimates provide further evidence that rules out intrinsic dataset concentration as a possible explanation for the adversarial vulnerability of state-of-the-art classifiers.
Unified framework for quantum classification

Quantum machine learning is an emerging field that combines machine learning with advances in quantum technologies. Many works have suggested great possibilities of using near-term quantum hardware in supervised learning. Motivated by these developments, we present an embedding-based framework for supervised learning with trainable quantum circuits. We introduce both explicit and implicit approaches. The aim of these approaches is to map data from different classes to separated locations in the Hilbert space via the quantum feature map. We will show that the implicit approach is a generalization of a recently introduced strategy, so-called \textit{quantum metric learning}. In particular, with the implicit approach, the number of separated classes (or their labels) in supervised learning problems can be arbitrarily high with respect to the number of given qubits, which surpasses the capacity of some current quantum machine learning models. Compared to the explicit method, this implicit approach exhibits certain advantages over small training sizes. Furthermore, we establish an intrinsic connection between the explicit approach and other quantum supervised learning models. Combined with the implicit approach, this connection provides a unified framework for quantum supervised learning. The utility of our framework is demonstrated by performing both noise-free and noisy numerical simulations. Moreover, we have conducted classification testing with both implicit and explicit approaches using several IBM Q devices.
Hamiltonian transformability, fast adiabatic dynamics and hidden adiabaticity

Abstract We prove the existence of a unitary transformation that enables two arbitrarily given Hamiltonians in the same Hilbert space to be transformed into one another. The result is straightforward yet, for example, it lays the foundation to implementing or mimicking dynamics with the most controllable Hamiltonian. As a promising application, this existence theorem allows for a rapidly evolving realization of adiabatic quantum computation by transforming a Hamiltonian where dynamics is in the adiabatic regime into a rapidly evolving one. We illustrate the theorem with examples.
An Extensive Online Examination System With Automatic Assessment Technique

An online examination system is a software solution, which allows any industry or institute to arrange, conduct, and manage examinations via an online environment. Online Examination is an essential ingredient in electronic and interactive learning; both teachers and students are benefited from this. It’s very much useful during the current situation of the global pandemic Novel Corona Virus (COVID-19). In this paper, we proposed a system with automatic assessment technique is generated. The algorithms for calculations word frequency, matching keywords, analyzing linguistics, generating grades are proposed in this system. The system is implemented by using PhpStrom and MySQL. The performances of the system is evaluated with a large number of students and questions as well as answers, and we found the absolute (about 0.3%) and relative error (about 3.57%) which is quite satisfactory.&#x0D; GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 6(1), Dec 2019 P 54-59
The impact of online learning and distance technologies on the educational process in modern circumstances

Subject. Practiced involuntarily to counteract the spread of the coronavirus in educational institutions, online learning turned to be the only effective solution ensuring the sustainable and uninterrupted educational process in the highly uncertain circumstances the world faced. The managerial decision on online learning induces many implications for everyone involved into the educational process and the educational system. Thus, it can be undertaken again in similar situations in the future. So, we need to have the profound understanding of the mechanism. Objectives. We study what students think about their educational process when they have to learn remotely. Therefore, we identify strengths and weaknesses of online learning. Methods. The methodological framework relies upon proceedings on remote learning and the impact of the digitalization on the educational process. The study involves methods of generalization, comparative analysis, sociological survey. Results. As the sociological survey reveals, students tend to new formats of learning during the pandemic. This is due to a lack of personal communication with teachers and their classmates. Conclusions and Relevance. Referring to most of the respondents, we conclude that the new form of learning proved to be rather effective in the current situation when people have to work and learn remotely during the coronavirus pandemic.
Two national classification systems – of the People's Republic of China and the Republic of Korea

The purpose of the article is to provide general background information about two national classification systems (CS), about which there have been practically no publications in Russian professional literature. The author gives the definition of national CS, adopted by the International Society for Knowledge Organization (ISKO) and talks about the typology categories of CS. The first part considers the Library Classification of the People's Republic of China (CLC). The history of its creation, the available variants and publications are sequentially analized. The distribution boundaries of CLC are shown, its structure and features are considered. CLC is a completely original system created by Chinese experts. The second part of the article analyzes the Decimal Classification of the Republic of Korea (KDC). Widespread in the world of DDC schedules were taken as the basis of the KDC many years ago. Some of the main classes were used unchanged, while others were completely rearranged so that Korea and Korean language were in the first places in the schedules. Similar changes were made to the religion class. KDC has only one option (the last 6th edition is in 3 volumes). It is considered national, but not used in all scientific libraries in South Korea. Knowledge of the classification practices of foreign countries is an indisputable indicator of our classification culture.
Performance and energy task migration model for heterogeneous clusters

This article presents a set of linear regression models to predict the impact of task migration on different objectives, like performance and energy consumption. It allows to establish whether at a given moment the migration of a task is profitable in terms of performance or energy consumption. Also, it can be used to determine the best node to migrate a task depending on the objective. The model uses a small set of parameters that are easily measurable. It has been validated against a small heterogeneous cluster using the Slurm resource manager. The model captures the tendencies observed in the results of the experiments, with average relative errors below 3.5% in execution time and 2.5% in energy consumption.
Automatically Identifying Requirements-Oriented Reviews Using a Top-Down Feature Extraction Approach

Processing application user reviews has recently been recognized as an efficient approach to explore user requirements. However, most existing approaches focus on mining the reviews themselves without effectively associating the reviews with requirements concepts, limiting the effectiveness of review mining for requirements analysis tasks. In this paper, we propose to automatically identify Requirements-oriented Reviews (RoRs) from software application reviews by considering requirements specific domain knowledge and syntactic information of user reviews. Specifically, we first define a conceptual model of RoRs based on existing requirements ontology and user review categories, establishing connections between the concepts of requirements engineering and user reviews. We then systematically identify the textual features of RoRs by following a conceptual model-driven top-down strategy. Based on such features, we then train effective RoR classifiers to identify RoRs. To evaluate the performance of our approach, we have applied our approach to a real dataset of mobile application reviews, the results of which show that our approach can effectively identify RoRs with an F-measure of 0.8, outperforming than the baselines.
Privacy-Aware Real Estate Recommendation in Cloud for Elderly Care Based on Historical Consumption Behaviors

With the continuous progress of world economy and human society, people's life span is increasingly growing and as a result, the whole world is stepping into the aging society. In the aging society, the elder people generally call for specialized healthcare services that are often provisioned by their residential areas or houses or communities. In this situation, recommending appropriate houses or real estates to the elder people becomes a necessity to help create a healthy elderly care service community, which contribute much to the stability and health of the whole nation even all the world. However, the personalized preferences of the elder people are often hard to capture and profile as the requirements from the elder people are generally vague and undetermined. Moreover, the profiles of the elder people stored in different cloud platforms are often sensitive enough, which block the rational and full use of the valuable elder people profiles for better elder people preference prediction. Considering the above two challenging issues, in this research work, we propose a privacy-aware real estate recommendation method for elderly care based on the historical consumption behaviors of the elder people stored in cloud platforms. At last, the effectiveness and efficiency of the proposal are validated by a series of experiments based on a real-world dataset. Comparison results show the feasibility of the proposal in this research work.
THE NEWEST ANALYTICAL TECHNOLOGIES FOR DETERMINING RARE-EARTH ELEMENTS IN GRANITOIDS OF THE UKRAINIAN SHIELD

Introduction. To date, rare earth elements (REE) are used to manufacture most high-tech goods and are crucial in defense technologies (lasers, radars, and electromagnetic weapons), nuclear engineering, metallurgy, and others. All this determines the relevance of their study to assess the rare earth mineral resource base of Ukraine. Problem Statement. The determination of REE in rocks and minerals is a fundamental problem in geochemistry and petrology for understanding the processes of rock formation. However, it is a complex analytical task related to the similar chemical properties of these elements, which are caused by the "lanthanide compression effect". Purpose. The purpose is to develop analytical technologies for determining REE content by the ICP-MS method, to evaluate their content and distribution in granitoids of the Ukrainian Shield. Materials and Methods. The hybrid method of ICP-MS analysis and microwave decomposition of rocks and minerals has been used to measure the REE content. This technique has been tested and used to estimate the content and distribution of REE in fluorites and rare-metal granitoids of the Rusko-Polyanskyi massif of the Korsun-Novomirgorod pluton of the Ukrainian Shield. Results. Analytical technologies for determination of REE in granites and minerals have been developed. The method for determining REE in fluorites and granites without their prior concentration in the range from 0.01 to 1000 ppm with a relative standard deviation of 0.01–0.10 has been described. The content of rare earth elements in the Rusko-Polianskyi granites increases (218–797 g/t), the main concentrator of these elements is fluorite (692–26933 g/t REE). An inverse relationship has been observed between the REE content in fluorites and granites. Conclusions. The developed analytical technologies are the basis for establishing quality assessment criteria and developing principles for the rational use of rare-earth granitoids to create a rare-earth mineral resource base in Ukraine.
Enhancing Robustness of On-line Learning Models on Highly Noisy Data

Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we extend a two-layer on-line data selection framework: Robust Anomaly Detector (RAD) with a newly designed ensemble prediction where both layers contribute to the final anomaly detection decision. To adapt to the on-line nature of anomaly detection, we consider additional features of conflicting opinions of classifiers, repetitive cleaning, and oracle knowledge. We on-line learn from incoming data streams and continuously cleanse the data, so as to adapt to the increasing learning capacity from the larger accumulated data set. Moreover, we explore the concept of oracle learning that provides additional information of true labels for difficult data points. We specifically focus on three use cases, (i) detecting 10 classes of IoT attacks, (ii) predicting 4 classes of task failures of big data jobs, and (iii) recognising 100 celebrities faces. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98.95% for IoT device attacks (i.e., +7%), up to 85.03% for cloud task failures (i.e., +14%) under 40% label noise, and for its extension, it can reach up to 77.51% for face recognition (i.e., +39%) under 30% label noise. The proposed RAD and its extensions are general and can be applied to different anomaly detection algorithms.
Evaluating the DevOps Reference Architecture for Multi-cloud IoT-Applications

Abstract DevOps originated in the context of the agile development aims to enable the continuous deployment of software applications in small releases. Most recently, organizations are taking a significant interest in adopting DevOps for automatically deploying IoT applications to multi-cloud. The interest is there; however, the challenge is how to achieve this. A new DevOps reference architecture (DRA) framework has been iteratively developed to address this challenge. This paper presents the updated DRA framework and its empirical evaluation results. The empirical evaluation is composed of two phases: (1) industry case study evaluation conducted in an IT organization, and (2) industry field survey with a cohort of experts in software engineering. The evaluation aims to determine the applicability and novelty of the DRA framework. The evaluation results indicate that the DRA is fit for its intended purpose and is expected to help both researchers and practitioners. The DRA uses can architect complex design models for the purpose of supporting the automated development stage of software applications. Software engineers can implement the DRA models for deploying complex IoT application to the multi-cloud environment in the current organization development parameters.
RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety

Automated monitoring and analysis of passenger movement in safety-critical parts of transport infrastructures represent a relevant visual surveillance task. Recent breakthroughs in visual representation learning and spatial sensing opened up new possibilities for detecting and tracking humans and objects within a 3D spatial context. This paper proposes a flexible analysis scheme and a thorough evaluation of various processing pipelines to detect and track humans on a ground plane, calibrated automatically via stereo depth and pedestrian detection. We consider multiple combinations within a set of RGB- and depth-based detection and tracking modalities. We exploit the modular concepts of Meshroom [2] and demonstrate its use as a generic vision processing pipeline and scalable evaluation framework. Furthermore, we introduce a novel open RGB-D railway platform dataset with annotations to support research activities in automated RGB-D surveillance. We present quantitative results for multiple object detection and tracking for various algorithmic combinations on our dataset. Results indicate that the combined use of depth-based spatial information and learned representations yields substantially enhanced detection and tracking accuracies. As demonstrated, these enhancements are especially pronounced in adverse situations when occlusions and objects not captured by learned representations are present.