Reviewerly/exHarmony huggingface dataset:
Privacy-Focused Blockchain Solution for Production Royalty Management

Abstract Oil and gas operators have a multitude of tasks to perform for smooth business operations. In addition to various engineering, technical, and operational decisions, there are back-office operations that should be efficient to keep business running smoothly, including production royalty management (PRM). The current system for PRM is extremely complex and costly, and involvesmultiple parties/contracts and often causes disputes. A blockchain-based solution allows the organizations to better navigate complex laws, courtrulings, and legal jargon that determine how billions of dollars are distributed every year. There are two aspects to PRM —royalties owed and royalties received—and the proposed solution caters to both operators and independent mineral owners. The operator should compile production and revenue data monthly and distribute it to all owners as per their individual lease. The expenses and marketing costs should be entered into the accounting system before revenue is distributed. Owners may be skeptical about data shared; therefore, the entire process can take up to two or more months. Furthermore, privacy is of utmost importance because theoperator can neither share data between owners nor disclose proprietary information. This paper proposes the entire royalty management system be hosted on a permissioned blockchain. Given their intrinsic nature of trustworthiness and transparent execution of transactions involving multiple parties, blockchain can streamline the royalty distribution process. The design and implementation of a smart contract for consent-driven and double-blind data sharingon a blockchain platform is presented. The system is designed for both inter- and intra-organizational transaction—a local peer interacts with an associated anchor peer within the globalnetwork. In addition to transparency and efficiency, this method provides important features ofprivacy and auditability while limiting ledger size. Further, peer identities are hiddenfrom each other while they collectively agree on transaction outcomes, and data will never be shared among the peers of the blockchain without explicit consent from the owners to ensure that the conflicting objectives of privacy and transparency are met. Blockchain is peer-to-peer decentralized digital ledger technology with promising potential. Even after all the recent innovations, the owner-relations department still operates using old methods. An innovative blockchain-based solution to reduce time and money spent on disbursing revenue for the operator and to increase the time value of information and trust in the system for the owner is presented.
Issues of Static and Dynamic Graphical Testing of Stochastic Transformations Results

The article deals with the pseudorandom number generators exploration using graphical methods. In this paper, we defined the necessary tools for building graphical tests and consider the features of static and dynamic PRNG research methods. Also, a method presented for numerical sequences visual inspection based on the joint use of the methods considered in this paper. Additionally, we presented comparison of the stochastic transformations results exploration using static and dynamic methods. Finally, we outline ways to improve the stochastic transformations research quality.
A Hybrid Path Planning Algorithm Based on Floyd and A* Theory

Automatic driving system mainly includes four modules: perception, decision-making, planning and control. Path planning system finds the best route between the starting point and the end point for autonomous vehicles, and its accuracy and real-time directly determine the efficiency and safety of unmanned driving. In this paper, a vehicle model and a high-precision map are built as the precondition of the algorithm. After analyzing and comparing the problems of Floyd algorithm and A* algorithm, a hybrid path planning algorithm is proposed, which can give consideration to both global optimality and real-time. Meanwhile, collision detection rules and trajectory generation strategies are designed. Through the simulation experiment of Matlab/Simulink software platform, it is confirmed that the algorithm can meet the practical application requirements and achieve the expected performance.
DPP-VSE: Constructing a variable selection ensemble by determinantal point processes

As an effective tool to analyze high-dimensional data, variable selection is playing an increasingly important role in many fields. In recent years, variable selection ensembles (VSEs) have gained much interest of researchers due to their great potential to improve selection accuracy and to stabilize the results of traditional selection methods. Inspired by one common practice of Bayesian methods, we propose in this paper a novel technique named DPP-VSE to build a VSE by utilizing determinantal point processes (DPP) to infer a distribution of model size. By sampling from this distribution, DPP-VSE has the advantage that the number of variables for a base learner to select can be automatically determined. In contrast to other VSE strategies, it has fewer parameters for users to specify. The experiments conducted with both synthetic and real data illustrate that DPP-VSE performs best under most circumstances when being evaluated with several metrics. Hence, DPP-VSE can be seen as an effective and easy to use method to solve variable selection problems.
Predicting Breast Density of Digital Breast Tomosynthesis from 2D Mammograms

Breast density may be used as a predictor of breast cancer risk and can measure the condition of tissues on mammograms. This research developed a computer-aided diagnosis (CAD) system to predict breast density on digital breast tomosynthesis (DBT) images. We used two-dimensional (2D) mammograms to train the linear discriminant analysis (LDA) classifier. Then load the DBT projection image to predict breast density. Experimental results show that LDA is better than other classification methods, such as one rule, naive Bayes, decision tree and support vector machine. The breast density prediction accuracy of DBT is 80%.
Dual link distributed source coding scheme for the transmission of satellite hyperspectral imagery

Traditional lossless compression methods for satellite hyperspectral imagery focus on exploiting spatial and/or spectral redundancy. Those methods do not consider the temporal redundancy between images of the same area that are captured at different times. To exploit the temporal redundancy between hyperspectral images and reduce the amount of information to be transmitted from the space-satellite to the ground station via the downlink, this paper introduces a dual link distributed source coding (DLDSC) scheme for hyperspectral space-satellite communication. The proposed scheme employs the space-satellite dual link (i.e., the downlink and the uplink). The satellite onboard uses some side information from the ground station to calculate the hyperspectral image band coset values, and then, without syndrome coding, transmits to the ground station via the downlink. Coset coding is a typical technique used in distributed source coding (DSC), and here the coset values represent the timely hyperspectral image details. Typically, the coset values have lower entropy than that of the original source values. To exploit the temporal redundancy, the side information is computed in the ground station using the image captured at the previous time for the same area and transmitted to the space-satellite via the uplink. Hyperspectral images from the Hyperion satellite are used for the validation of the proposed scheme. The experimental results indicate that the proposed DLDSC scheme can reduce the original signal entropy by approximately 3.2 bits per sample (bps) and can achieve up to 1.0 bps and 1.6 bps gains over the lossless JPEG2000 standard and the state-of-art predictive CCSDS-123 method, respectively.
Hierarchic Clustering-Based Face Enhancement for Images Captured in Dark Fields

A hierarchic clustering-based enhancement is proposed to solve the luminance compensation of face recognition in the dark field. First, the face image is divided into five levels by a clustering method. Second, the results above are mapped into three hierarchies according to the histogram thresholds. A low, a middle, and a high-intensity block are found. Third, two kinds of linear transforms are performed to the high and the low-intensity blocks. Finally, a center wrap function-based enhancement is carried out. Experiment results show our method can improve both the face recognition accuracy and image quality.
A high order statistics based multipath interference detection method

Multipath interference commonly exists in wireless communication, navigation and radar systems, which may cause severe signal fading and bit error rate (BER) performance degradation. Therefore the detection of multipath interference is urgently needed. This paper proposes a high order statistics (HOS) based multipath interference detection method, since the HOS of the received signal show distinct difference when multipath interference exists. Moreover, the generalized theoretical values of the moments and cumulants, which release the demand for prior knowledge of timing information and symbol period, are deduced in this paper. It makes the proposed HOS features based detection method robust in blind environment. Computer simulations are performed to verify the proposed method.
Sentiment Analysis using Feature Generation And Machine Learning Approach

The study of opinion offers answers to what the most critical problems are. Since sentiment analysis can be automated, judgements can be taken based on a significant amount of data rather than plain intuition, which is not always accurate. This paper focuses on the feature generation using Bag-of-Words and TF-IDF and the build model using the machine learning approach for sentiment analysis. The dataset used contains review of trip advisor on various hotels. This dataset consists of 20k reviews. Word cloud had been formed using sentiment ratings. Data was cleaned and pre-processed, and then applied Bow and TF-IDF for feature extraction. After implementation of classifiers, training and evaluation was performed. Evaluation metrics is used for measuring the accuracy of classifier. MultinomialNB obtained the highest accuracy in the realm of Bag of Word features and random forest outperformed in the case of TF-IDF out of three classifiers used to determine accuracy. We got 82% of the classification rate of MultinomialNB in Bag of Word and 78% accuracy in TF-IDF Random Forest.
How Packaging of Information in Conversation Is Impacted by Communication Medium and Restrictions

In team-based tasks, successful communication and mutual understanding are essential to facilitate team coordination and performance. It is well-established that an important component of human conversation (whether in speech, text, or any medium) is the maintenance of common ground. Maintaining common ground has a number of associated processes in which conversational participants engage. Many of these processes are lacking in current synthetic teammates, and it is unknown to what extent this lack of capabilities affects their ability to contribute during team-based tasks. We focused our research on how teams package information within a conversation, by which we mean specifically (1) whether information is explicitly mentioned or implied, and (2) how multiple pieces of information are ordered both within single communications and across multiple communications. We re-analyzed data collected from a simulated remotely-piloted aerial system (RPAS) task in which team members had to specify speed, altitude, and radius restrictions. The data came from three experiments: the “speech” experiment, the “text” experiment, and the “evaluation” experiment (which had a condition that included a synthetic teammate). We asked first whether teams settled on a specific routine for communicating the speed, altitude, and radius restrictions, and whether this process was different if the teams communicated in speech compared to text. We then asked how receiving special communication instructions in the evaluation experiment impacted the way the human teammates package information. We found that teams communicating in either speech or text tended to use a particular order for mentioning the speed, altitude, and radius. Different teams also chose different orders from one another. The teams in the evaluation experiment, however, showed unnaturally little variability in their information ordering and were also more likely to explicitly mention all restrictions even when they did not apply. Teams in the speech and text experiments were more likely to leave unnecessary restrictions unmentioned, and were also more likely to convey the restrictions across multiple communications. The option to converge on different packaging routines may have contributed to improved performance in the text experiment compared some of the conditions in the evaluation experiment.
Effects of Social Media on Academic Performance of High School Students under Pandemic (COVID-19) Situations

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An Application Framework for Quantum Computing using Artificial intelligence Techniques

Artificial Intelligence working along with quantum computers provides a whole new domain of smart systems to explore the world of computing. This paper aims at providing a close examination of the very principle applications of quantum computation along with Artificial Intelligence and to view the workings of an AI neural network along with the quantum theory. For a better understanding, a basic introduction to quantum computers along with a significant but straightforward quantum algorithm is addressed. Also, an unbalanced panorama of the field is given using a survey on quantum computation. The paper cites work and knowledge by previous researchers. Using the knowledge as base algorithms and predictions are added and checked for accessing a deeper connection between AI and quantum. Although, most of the pre-given maps are still very rough. Some parts are still unknown and empty. The ultimate aim is to provide a better and powerful method of tapping into the quantum technology. This research work provides an introduction and a glimpse of quantum computation along with examining the relation between quantum theory, AI and quantum computation for the researchers.
Leveraging Domain Knowledge for Robust Deep Reinforcement Learning in Networking

The past few years has witnessed a surge of interest towards deep reinforcement learning (Deep RL) in computer networks. With extraordinary ability of feature extraction, Deep RL has the potential to re-engineer the fundamental resource allocation problems in networking without relying on pre-programmed models or assumptions about dynamic environments. However, such black-box systems suffer from poor robustness, showing high performance variance and poor tail performance. In this work, we propose a unified Teacher-Student learning framework that harnesses rich domain knowledge to improve robustness. The domain-specific algorithms, less performant but more trustable than Deep RL, play the role of teachers providing advice at critical states; the student neural network is steered to maximize the expected reward as usual and mimic the teacher's advice meanwhile. The Teacher-Student method comprises of three modules where the confidence check module locates wrong decisions and risky decisions, the reward shaping module designs a new updating function to incentive the learning of student network, and the prioritized experience replay module to effectively utilize the advised actions. We further implement our Teacher-Student framework in existing video streaming (Pensieve), load balancing (DeepLB) and TCP congestion control (Aurora). Experimental results manifest that the proposed approach reduces the performance standard deviation of DeepLB by 37%; it improves the 90th, 95th and 99th tail performance of Pensieve by 7.6%, 8.8%, 10.7% respectively; and it accelerates the rate of growth of Aurora by 2x at the initial stage, and achieves a more stable performance in dynamic environments.
Natural Language Understanding (NLU) on the Edge

Today, chatbots have evolved to include artificial intelligence and machine learning, such as Natural Language Understanding (NLU). NLU models are trained and run on remote servers because the resource requirements are large and must be scalable. However, people are increasingly concerned about protecting their data. To be efficient, the current NLU models use the latest technologies, which are increasingly large and resource-intensive. These models must therefore run on powerful servers to function. The solution would therefore be to perform the inference part of the NLU model directly on edge, on the client’s browser. We used a pre-trained TensorFlow.js model, which allows us to embed this model in the client’s browser and run the NLU. The model achieved an accuracy of more than 80%. The primary outcomes of NLU on edge show an effective and possible foundation for further development.
Semi-supervised Co-ensembling for AutoML

Automated machine learning (AutoML) is an increasingly popular approach for selecting learning algorithms and for configuring their hyperparameters in an effective, principled and fully automated way. So far, AutoML techniques have been focussed primarily on supervised learning scenarios. In this work, we extend AutoML to semi-supervised learning. Specifically, we propose co-ensembling, a generic procedure that uses unlabelled data to enhance the performance of high-quality ensembles of learners obtained from a state-of-the-art supervised AutoML system. Co-ensembling can be applied to any set of two or more base learners, and bypasses direct exploration of the large design space of semi-supervised learning algorithms. We demonstrate substantial performance improvements on multiclass classification problems by applying a single iteration of co-ensembling. On a large, diverse suite of multiclass data sets, single-step co-ensembling yields improved performance on 75% of the multiclass problems we considered, and achieves an average relative reduction in error rate of 7%.
Pseudo-marginal Bayesian inference for Gaussian process latent variable models

A Bayesian inference framework for supervised Gaussian process latent variable models is introduced. The framework overcomes the high correlations between latent variables and hyperparameters by collapsing the statistical model through approximate integration of the latent variables. Using an unbiased pseudo estimate for the marginal likelihood, the exact hyperparameter posterior can then be explored using collapsed Gibbs sampling and, conditional on these samples, the exact latent posterior can be explored through elliptical slice sampling. The framework is tested on both simulated and real examples. When compared with the standard approach based on variational inference, this approach leads to significant improvements in the predictive accuracy and quantification of uncertainty, as well as a deeper insight into the challenges of performing inference in this class of models.
The Heuristic Algorithm For Symmetric Horizontal Data Distribution

The article considers one algorithm for the optimal distribution of "objects" of an arbitrary nature among "storages", the essence of which is determined by the subject area. Some subject areas for which the optimal distribution problem is relevant are considered. Authors considers the problem of accelerating of the Join operation is considered. In the case of big data parallel processing, the Join operation requires uniform distribution of data between the cluster processors. In this case, parallel implementation of the Join operation will be effective only when the computational complexities of its execution in all database fragments will be minimally different from each other. The optimality criterion should ensure uniform distribution of data. A detailed description of the heuristic optimal distribution algorithm is given. Objective functions for the problems under consideration are proposed. A description is given of the experiments that made it possible to assess the quality of the heuristic greedy optimal distribution algorithm. As a result of these experiments, the dependences of the execution time of the algorithm on the number of distributed objects and the quality of distribution (the difference between the maximum and minimum storage capacity) on the number of stores and the interval of the values of the objects weight. It is shown that the algorithm is quite simple and can be easily implemented in any programming language. The running time of the algorithm, even for big data, is small, which allows it to be effectively used in the preparation of data for parallel solving problems with high computational complexity. The algorithm shows good results when distributing ables-operands across data warehouses. The largest storage capacity differs from the smallest by a small amount.
DEVELOPMENT OF A MODERN E-LEARNING SYSTEM IN THE UNIVERSITY

This paper analyzes the world sources and considers the evolution of the e-learning system in the world, which are necessary for the formation of the e-learning system of the educational institution. The object of research is modern information pedagogical technologies that change the educational process. Subject of research – various educational technologies (distance, mixed, mobile, micro-) learning that influence the effectiveness of the educational process. The purpose is to identify the main elements that create the global e-learning system and to define the structure of the system for use in educational institutions. It is shown that in the first stage distance courses played the role of delivery of educational materials to the student. The development of web 2.0 technology and the advent of LMS have changed the way we learn. Pedagogical theories, the pedagogical instructional model ADDIE, and the use of Bloom's taxonomy allowed for student collaboration and active communication. The distance learning course is focused on achieving and measuring learning outcomes. In the third stage, all components of the world eLearning system are present: methodical, informational, technological, software and technical, personnel, regulatory, planning and development, monitoring and control. The components of the subsystems are variable in nature. Such a system is the basis for the strategy and tactics of the development of the e-learning system in the educational organization and its structural units. The most important is the planning and development subsystem, which defines the ways of development of the educational organization, the strategy and tactics of development of the e-learning system of the university, the required staff potential and the system of professional development of teachers. For its effective work it is necessary to have trained content curators who follow the most influential content curators of the world. Members of this team provide certification for distance courses in accordance with the organization standard. National Technical University "Kharkiv Polytechnic Institute" conducts systematic work in this area, conducts open distance courses for developers, tutors, content curators and experts for teachers of educational institutions of Ukraine. Developed distance courses that measure the competence of trained professionals ensure high efficiency of dual learning. More than 2,100 teachers of educational establishments of Ukraine have enrolled in the open distance courses, more than 320 teachers have successfully completed the courses.