Reviewerly/exHarmony huggingface dataset:
Some applications of the GRAN1 to analyze two-dimensional continuous probability distributions

Abstract The article considers the use of GRAN1 software in the analysis of two-dimensional continuous probability distributions. The learning process can be provided by freely distributable software, through the use of cloud technologies, in particular software from the GRAN package. However, the use of any technology in the educational process, including modern information and communication technologies, as well as the content of education, must be pedagogically balanced, which will avoid any negative consequences for the formation of the future member of society, his mental and physical development. Attention is drawn to the fact that children are increasingly looking for entertainment, using mobile devices, sharing new products with their peers and spending time together, playing a variety of online games, and parents cannot control this process. At the same time, teachers conduct lessons using mobile devices to draw attention to the subject, they are teaching. In the process of using computer technology, students can form a special type of thinking, the so-called “clip consciousness”, which leads to fragmentary thinking and the irreversibility of changes in consciousness. Therefore, the problem of excessive use of mobile devices by children requires constant analysis and coverage. The article discusses some examples of using the GRAN1 software to solve problems in probability theory. Examples of approximate calculation of the value of the double integral are given. Tables of values of the corresponding integrals are described. The use of modern information and communication technologies during mathematics lessons is considered.
Arabic Topic Labeling using Naïve Bayes (NB)

The Artificial Intelligence (AI) has widely spread out in the last decades. It represents a reliable system for different problem solving. In this paper, one of the most used machine learning algorithms; Naïve Bayes (NB) is used to classify over than 7000 articles according to their topics. NB is compared to other machine learning algorithms with respect to the accuracy that results from applying both validation data and test data.
Towards a Trustworthy Semantic-Aware Marketplace for Interoperable Cloud Services

There is a general lack of trust in existing Services’ descriptions and directories, and public repositories and aggregators of such Services are currently missing. Frequent variations in exposed interfaces and lack of proper descriptions hinder interoperability and portability of applications. The present work aims at entrusting the current Cloud Service domain designing a semantic aware-Marketplace relying on the Blockchain to support the secure composition of different Services.
Blockchain-Enabled Secure and Transparent Cross-Regional Model Updating and Sharing Approach in Smart Grid

With the rapid development of smart grid, the lack of a very accurate and consistent grid model has become a bottleneck for its development. However,the traditional maintenance and sharing methods for grid model can not meet the requirements of the current grid environment since it is centralized and once the central server is attacked, huge loss will be caused. To this end, this paper proposes a blockchain-based architecture for province-prefecture power dispatching system to realize secure and transparent cross-regional model parameters updating and sharing, which can ensure the integrity, consistency and traceability of the grid model applied in each local dispatching center. In this paper, the blockchain technology is used to store, update and share model parameters in a secure way, and different clients have different permissions to access the blockchain network. The implementation of the proposed approach shows that it is a feasible and promising solution for smart grid model communication in a secure way.
Inference from Visible Information and Background Knowledge

We provide a wide-ranging study of the scenario where a subset of the relations in a relational vocabulary is visible to a user—that is, their complete contents are known—while the remaining relations are invisible. We also have a background theory—invariants given by logical sentences—that may relate the visible relations to invisible ones, and also may constrain both the visible and invisible relations in isolation. We want to determine whether some other information, given as a positive existential formula, can be inferred using only the visible information and the background theory. This formula whose inference we are concerned with is denoted as the query . We consider whether positive information about the query can be inferred, and also whether negative information—the sentence does not hold—can be inferred. We further consider both the instance-level version of the problem, where both the query and the visible instance are given, and the schema-level version, where we want to know whether truth or falsity of the query can be inferred in some instance of the schema.
CaSIE: Canonicalize and Informative Selection of the OpenIE system

Knowledge extraction has become a hot topic recently with the increasing number of applications needed for large-scale knowledge bases (KBs), such as semantic search and QA systems. The goal of knowledge extraction is to extract relations and their arguments from natural language text. Recent research proposes two kinds of solutions. The first one, called Closed IE, tries to construct KB through predefined features or rules with respect to a specific domain. It requires specifying the interested predicates in advance, which restricts its application to the domains where prior knowledge about the interested predicates must be given. The second one, called Open IE, tries to extract facts by using the parsing structure from the unstructured text. However, they cannot avoid extracting redundant facts. Such extractions can hardly be directly used to populate the existing KB. Moreover, many correct extractions are not relevant to the document, which limits the applications to understand the essential information that the document conveys. In this paper, we propose an end-to-end system which takes a target incomplete KB and documents as input. It first performs joint entity and relation linking to the existing KB based on both contexts of document and background KB information. Then it summarizes the extracted facts by considering the relevance to the document and the diversity between them. Extensive experiments over real datasets demonstrate the effectiveness and efficiency of the proposed methods.
Study on the Evaluation of Scientific Research Ability of University Teachers Based on the Mathematica Software

Under big data era and intelligent technology society, the study on evaluation of scientific research ability of university teachers is important and necessary. It is related to the development and progress of national education, science and technology. Therefore, to explore a more intelligent and objective evaluation method is very necessary. It also affects the development direction and high-quality development of a university's scientific research in the modern society. The evaluation involves many factors such as project, papers published, awards, teaching work load. In this paper, based on the above factors, and take more factors into account, this paper builts a hierarchical structure by use of the AHP method. Followed the principle from qualitative to quantitative, this paper considers all the factors into account, give appropriate weight, and calculate them by Mathematica software, get the corresponding result. In this process, this paper gets a concrete evaluation model of scientific research ability, which can provide reference for high-quality reconstruction of university teachers in modern society, provide the reasonable suggestion for teacher's personal development as well.
Sizing Solar Panels and Storage for Multiple Roofs

Choosing the number of solar panels and the amount of storage needed to meet a certain fraction of the load in a microgrid setting is a difficult problem that needs to balance the competing objectives of efficiency, robustness, and cost. Prior work in this area makes the unrealistic assumption that solar panels are to be installed on a single roof that is capable of supporting all the panels required. In reality, we may need to deploy solar panels on several roof segments, each of limited size, and each with its own tilt, orientation and installation cost. This paper presents an algorithm for sizing solar panels and storage in this context. We evaluate the robustness of our approach using traces derived from the Pecan Street Dataport dataset and demonstrate the value of our approach by using it to size a hypothetical installation on the British Antarctic Survey's research base in Antarctica.
Imbalanced Classification Problems: Systematic Study and Challenges in Healthcare Insurance Fraud Detection

The recent developments made in the data mining technologies have greatly influenced the data classification process. The growth of applications has increased the volume of the data and thus, the classification task becomes quite complex. Due to the uncertainties and unbounded nature of the data, class imbalance is one of the significant issues which determine the performance of the classifiers. In this paper, we present the challenges of the imbalanced classifications in the healthcare insurance claiming frauds. Most classification algorithms make use of majority class by ignoring the minority class. There are different approaches available to deal with the imbalance datasets which is reviewed in this study. A systematic study is done on each approach which presents the challenges pertaining in the class imbalance issues.
Project-based Learning of Web Systems Architecture

Effective technical communication, collaboration, and leadership are among essential skills required from computer scientists. Project-based learning using open-ended projects is praised for fostering these skills in the context of software development. Our observations, in the context of a systems course, suggest that while learning outcomes are achieved, open-ended projects are stressful for students. We investigate the appropriate methods, levels of specification, and scaffolding to help students learn the concepts while implementing real-world web projects in student-formed groups.
Relationship between photosynthetically active radiation and global radiation in Petrolina and Brasília, Brazil

ABSTRACT Photosynthetically active radiation (PAR) comprises the spectral range of global solar radiation (Rs) that is highly related to vegetation productivity. The study aimed to evaluate the relationship between PAR and Rs in Petrolina, PE, and Brasília, DF, Brazil, with data measured in 2011 and 2013 at two stations of the Sistema Nacional de Organização de Dados Ambientais located in Petrolina, PE and Brasília, DF, Brazil, and the obtained models were evaluated using the measurements of 2014. It was verified that the PAR, in instantaneous values (μmol m-2 s-1), can be estimated at 2.31 times the Rs (W m-2) measured in Petrolina, while for daily values of PAR (MJ m-2) is equal to 50% of Rs (MJ m-2). In Brasília, PAR (μmol m-2 s-1) is 2.05 times the Rs (W m-2) and, in daily values, equal to 44% of Rs (MJ m-2). The variability of the PAR/Rs ratio followed the local variations of clearness index (Kt) and Rs. The models presented an adequate performance based on statistical indices mean absolute error, mean relative error, and root mean square error and can be used to estimate PAR.
Data Disclosure Mechanism Design with Non-zero Leakage

We study an information-theoretic privacy problem, where an agent observes useful data Y and wants to reveal the information to a user. Since the useful data is correlated with sensitive data X, the agent employs a privacy mechanism to produce data U that can be disclosed. Thus, we study the privacy mechanism design that maximizes the revealed information about Y while satisfying an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -privacy criterion under the Markov chain X-Y -U. When a sufficiently small leakage is allowed, we show that the optimizer of the design problem has a specific structure which allows us to use a local approximation of mutual information. More specifically, we show that the optimizer vectors are perturbations of fixed distributions. By using this approximation the original optimization problem can be reduced to a linear programming problem and an approximate solution for privacy mechanism design can be obtained.
A Spatio-Angular Filter for High Quality Sparse Light Field Refocusing

The ability to render synthetic depth-of-field effects post capture is a flagship application of light field imaging. However, it is known that many existing light field refocusing methods suffer from severe artefacts when applied to sparse light fields, known as angular aliasing. We propose in this paper a method for high quality sparse light field refocusing based on insights from depth-based bokeh rendering techniques. We first provide an in-depth analysis of the geometry of the defocus blur in light field refocusing by analogy with the defocus geometry in a traditional camera using the thin lens model. Based on this analysis, we propose a filter for removing angular aliasing artefacts in light field refocusing, which consists in modifying the well known shift-and-sum algorithm to apply a depth-dependent blur to the light field in between the shift and the sum operations. We show that our method can achieve significant quality improvements compared to existing approaches for a reasonable computational cost.
Specifics of developing and implementing remote classes in chemistry with hearing impaired students at a technical university of a general type

This article discusses the experience of remote teaching chemistry of hearing impaired students at Bauman Moscow State Technical University during pandemic. The key challenge of such a students is the limited content accessibility of educational resources in STEM disciplines, including “Chemistry”. In ordinary learning conditions, this problem is being solved by creating an accessible environment and implementing an additional cognitive-technology course in chemistry. In situation of pandemic, teachers have to transfer classroom “Chemistry” course to a distance format with creating conditions for effective remote students' learning. This problem is being solved considering their individual cognitive peculiarities of information perception. All remote chemistry classes (lectures, laboratory works and seminars) are being taught through webinars. Principles of designing accessible webinars include creating multimodal student-friendly environment; accessibility of displayed education materials; thoughtful sequence of switching information sources; effective visual interaction with students during classes; establishing stable feedback between students and the teacher. Practical tasks are being solved applying technology of algorithm development based on interdisciplinary approach. In webinars, various cognitive problems of these students are being identified and solved successfully. The teacher plays a multi-functional role, being a teacher, a moderator, a tutor, and mainly a “team captain”.
Dynamic Programming in Topological Spaces

Dynamic programming is a mathematical optimization method and a computer programming method as well. In this paper, the notion of sheaf programming in topological spaces is introduced and it is demonstrated that it relates very well to the concept of dynamic programming.
Motion Projection Consistency-Based 3-D Human Pose Estimation With Virtual Bones From Monocular Videos

Real-time 3-D human pose estimation is crucial for human–computer interaction. It is cheap and practical to estimate 3-D human pose only from monocular video. However, the recent bone-splicing-based 3-D human pose estimation method brings about the problem of cumulative error. In this article, the concept of virtual bones is proposed to solve such a challenge. The virtual bones are imaginary bones between nonadjacent joints. They do not exist in reality, but they bring new loop constraints for the estimation of 3-D human joints. The proposed network in this article predicts real bones and virtual bones, simultaneously. The final length of real bones is constrained and learned by the loop constructed by the predicted real bones and virtual bones. Besides, the motion constraints of joints in consecutive frames are considered. The consistency between the 2-D projected position displacement predicted by the network and the captured real 2-D displacement by the camera is proposed as a new projection consistency loss for the learning of the 3-D human pose. The experiments on the Human3.6M data set demonstrate the good performance of the proposed method. Ablation studies demonstrate the effectiveness of the proposed interframe projection consistency constraints and intraframe loop constraints.
Prv<sup>2</sup>chain: Storage of Tree-structured Provenance Records in Blockchain with Linkage Privacy

Storing provenance records on blockchain has the benefit of guaranteed integrity, however keeping the records private against the peers maintaining the blockchain is challenging especially when the records have linkages among them. This paper studies the problem of storing tree-structured provenance records with linkages in blockchain while keeping the linkages private. We present Prv <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> chain, a framework that allows users to privately store their linked provenance records while protecting the records and their linkages. Users are also allowed to perform on-chain tree traversal efficiently. These are enabled by our novel record ID generation algorithms. Our experiments indicate the extra overhead incurred by Prv <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> chain is little enough for practical deployment.
Quantitative Assessment of Service Pattern: Framework, Language, and Metrics

For modern service industry (MSI), service pattern is a service provision approach to support the realisation of business model that involves participants from various domains and organizations. A comprehensive description and quantitative assessment of service patterns is of great significance for optimizing the organizational cooperation process in MSI and improving the competitiveness of enterprises. However, most relevant studies on service patterns stay at the level of business processes and qualitative analysis, lacking a comprehensive description of data, resources, and value exchanges among participants. Studies related to pattern assessment focus more on QoS (Quality of Service) rather than consideration of the utility of multi-participant collaboration. Hence, two issues need to be tackled for future development of MSI: a) How to systematically describe and distinguish service patterns with the same business processes. b) How to assess and compare service patterns quantitively and comprehensively. In this article, we propose a service pattern assessment framework which consists of two parts. As part one, we complement the service pattern description language (SPDL) with extended elements and observable attributes to empower it with quantitative analysis, namely Quantitative SPDL (SPDL-Q). In part two, a set of service pattern assessment metrics are designed to assess not only the quality of the services but also the cooperation efficiency of the participants and the orchestration effect of the service patterns elements. The proposed framework was then further validated by a case study, of which four E-commerce service patterns were studied to reveal their evolvement processes. Correlation experiments were also performed to identify the pattern features that have the greatest impact on each metric, so to provide guidance and suggestions for pattern design. Finally, the innovation and significance of the work are outlined and discussed.
Prediction and Early Warning of Regional Coordinated Development Based on Convolution Neural Network Algorithm

In order to respond to the regional coordinated development of the country, it is necessary to put forward a method that can predict and analyze the development trend according to the current development situation. In view of this, the research will carry on the present situation and forecast analysis to the coordinated development of urban agglomeration in Western China. Firstly, the 3E system is used to establish the regional coordination degree evaluation model, and on this basis, the ellipsoid model is introduced for better coordination degree evaluation. In addition, in order to improve the prediction ability of the model, the convolution neural network is used to realize the big data analysis of the model. The results show that the overall coordination degree of the western urban agglomeration is in a weak coordination state in 2015, but the coordination degree of the region will reach 147.35 in 2020. The results show that the overall coordination degree of western urban agglomeration will gradually show a good trend, but the change speed is slow. The above results show that the prediction model in the study has strong practicability, the calculation results can fit the current situation, and the good prediction ability can provide decision-making suggestions for many governments.
Auto-Encoded Reservoir Computing for Turbulence Learning

We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn the dynamics of a 2D turbulent flow. The AE-RC consists of an Autoencoder, which discovers an efficient manifold representation of the flow state, and an Echo State Network, which learns the time evolution of the flow in the manifold. The AE-RC is able to both learn the time-accurate dynamics of the flow and predict its first-order statistical moments. The AE-RC approach opens up new possibilities for the spatio-temporal prediction of turbulence with machine learning.