Reviewerly/exHarmony huggingface dataset:
Reconfigurable Information-Measuring System of Thermophysical Properties of Solid Materials with an Intelligent Sensor

An information-measuring system for non-destructive testing of thermophysical properties of solid materials with an intelligent sensor has been developed. Mathematical models for reconfiguring the structure of the information-measuring system and the intelligent sensor were built. Algorithms for changing the configuration of the intelligent sensor and the functioning of the information-measuring system have been developed, allowing us to expand the systems functionality for studying thermophysical properties of solid materials in a wide range of thermal conductivities, as well as to improve the accuracy and efficiency of thermophysical measurements by adapting the system to the class of materials studied.
Research on Personalized Recommendation of Learning Resources Based on Data Mining

The campus learning resource sharing platform provides a platform for students to share and exchange learning resources. Providing personalized recommendations on demand when users log in will provide users with a lot of convenience. In this thesis, the clustering algorithm is first used to classify users. Secondly, the classified users are analyzed according to their grades and faculties, and personalized recommendations are given, which greatly facilitates users and saves them.
Evaluation Maturity Level IT Risk Management of Metatrader Software Using Risk IT Framework With Domain Risk Governance (RG)

Abstract The purpose of this study is to determine the extent to which companies anticipate and reduce information technology risks that will occur, it is necessary to evaluate information technology risk management. The method used in this study was evaluating information technology risk management is the Domain Risk Governance (RG) contained in the IT Risk Framework. The evaluation process using this framework and domain is that it can measure the level of company maturity in carrying out information technology risk management. The results of this study are companies knowing the level of maturity in implementing IT risk management at level 2 (recurring) and level 3 (defined), knowing the GAP between the current level of maturity with the target level of maturity and can increase the current level of maturity to achieve the target level it is desirable to use the recommendations given. So companies can compete with competitors in providing services, ensuring that IT risk management activities are aligned with company objectives, reducing losses in the IT implementation process, integrating IT risk strategies with business strategy risk decisions, and ensuring that decisions are made according to company objectives based on opportunities and the consequences that have been made.
A Distributed Big Data Discretization Algorithm Under Spark

Data discretization is one of the important steps of data preprocessing in data mining, which can improve the data quality and thus improve the accuracy and time performance of the subsequent learning process. In the era of big data, the traditional discretization method is no longer applicable and distributed discretization algorithms need to be designed. Hellinger-entropy as an important distance measurement method in information theory is context-sensitive and feature-sensitive and thus are abundant of useful information. Therefore, in this paper we implement a Hellinger-entropy based distributed discretization algorithm under Apache Spark. We first measure the divergence of discrete intervals using Hellinger-entropy. Then we select top-k boundary points according to the information provided by the divergence value of discrete intervals. Finally, we divide the continuous variable range into k discrete intervals. We verficate the distributed discretization performance in the preprocessing of random forest, Bayes and multilayer perceptron classification on real sensor big data sets. Experimental results show that the time performance and classification accuracy of the distributed discretization algorithm based on Hellinger-entropy proposed in this paper are better than the existing algorithms.
Identity-Based Multi-party Revocable Quantum-Resistant Signature with CSP

Identity-based signature schemes enable any pair of users to communicate securely and to verify each other's identity without exchanging private or public keys, without keeping key directories, and without using the services of a third party. However, when the number of users grows on a large scale, the generation, update, and distribution of keys become difficult problems, which limits its application range. Although some efficient RIBS scheme based on Computational Diffle-Hellam Problem(CDH) assumption has been proposed, few lattice-based RIBS do not achieve this realistic and efficient property. In this paper, we divide the Multi-party users keys update into three parts(Key Generation Center, Cloud Service Provider and user). In our scheme, a user's private key is composed of both an partial private key(by Key Generation Center), time update key(by Cloud Service Provider) and user's partial key(by user). The time update key is periodically updated by CSP and is transmitted over a public channel. our scheme is existential unforgeability against adaptive chosenmessage attacks in the random oracle based on the hardness assumption of Short Integer Solution(SIS) problem. Our scheme has better performance in terms of energy consumption, signature size, signing key size, and the revocation mechanism with public channels compared to other lattice-based signature scheme. As the underlying lattice problem is intractable even for quantum computers, our scheme would work well in the quantum age.
Fusing Shape-from-Silhouette and the Sparsity Driven Detector for Camera-Based 3D Multi-Object Localization with Occlusions

Position estimation of multiple objects in a 3D environment poses a challenging task, even more so in the presence of occlusions due to infrastructure. In this paper we present a method to accurately localize up to 10 moving pedestrians by fusing the output of a Sparsity Driven Detector with volumes generated by a Shape-from-Silhouette approach. We also show how occlusion information from a 3D map of the environment can be integrated into our algorithm to further improve performance. We investigate the influence of different camera heights and image sizes on the optimization problem and demonstrate real-time capability for certain configurations. Additionally, our code is made publicly available under an open source license. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Conceptual foundations for opportunistic behavior of university teachers

Abstract The effective system of secondary and higher education is becoming an essential element ensuring competitiveness of the national economy. At the same time, this system cannot be based only on investments in education. If there are no institutional investments in education, government investments, no matter how large they are, will not help achieve the desired results. Private companies do no invest in education at all. One of the most important conditions for effective educational services market is full-fledged competition in the labor market. Companies as buyers of the final product (specialist) should compete with each other. This is a prerequisite for the development of competition between providers of educational services – Institutes and Universities. Competition between institutions will increase the level of qualification of specialists (quality growth) and correspondence between the professional structure of specialists and the structure of jobs in the labor market. This will happen if the sellers copy the most successful patterns of behavior of those the sellers who have achieved commercial success in the market of educational services. Finally, there will be competition between applicants for the purchase of educational services, which will effectively adjust the policies of sellers – Institutes and Universities. When training specialists, Russian universities focus on requests coming from the institutions (government and quasi-government structures) having high budget revenues rather than on the market needs. This refers to numerous state and quasi-state structures. These customers can significantly reduce efficiency of the higher education system.
Needs Analyses and Initial Design of the Handheld Chinese Reading System for Classic Literature

In this study we aim to design and implement a handheld Chinese reading system to facilitate Chinese classic literature reading. In order to well design the system, before the system design and implementation, we used the classic Chinese literature, Dream of the Red Chamber, as the reading content for our example, to conduct the needs analysis survey on line. We wanted to investigate the target users’ reading behaviors, difficulties in reading Chinese classic literature, reading experiences with the Dream of the Red Chamber, and personal preferences of the proposed system in terms of shapes and colors of the cloud images, etc. Eventually, the results of data analysis would be helpful for our system design and implementation. Thus we present a conceptual framework of the system based on the data and feedback from the participants. It is our hope with the constructive feedback from the potential users, we will be able to build a significant Chinese reading system to meet the user’s actual needs and expectation so as to foster classic Chinese reading enhanced by the emerging information technologies.
Entropy Estimates for the Irish Language

Entropy estimates for natural languages are useful for a number of reasons. For example, they can be used to estimate the length of a translated text or the amount of text required to make a brute-force attack on an encrypted message feasible. This paper briefly reviews the development of techniques for entropy estimation and then applies modern techniques to Irish text. We believe this addresses a gap in the literature giving an entropy estimate for the Irish language. We discuss our results in the context of entropy estimates for equivalent Egalish text.
Cryptanalysis of FRS obfuscation based on the CLT13 multilinear map

The authors present a classical polynomial-time attack against the branching program obfuscator of Fernando–Rasmussen–Sahai (for short FRS, Asiacrypt’17) (with one zerotest parameter), which is robust against all known classical cryptanalyses on obfuscators when instantiated with the CLT13 multilinear map. The first step is to recover a plaintext modulus of the CLT13 multilinear map. To achieve the goal, the Coron and Notarnicola (Asiacrypt’19) algorithm is applied. However, because of parameter issues, the algorithm cannot be used directly. In order to detour the issue, the authors convert an FRS obfuscator into a new programme containing a small message space. Through the conversion, the authors obtain two zerotest parameters and encodings of zero except for two non-zero slots. Then, they are used to mitigate parameter constraints of the message space recovering algorithm. Then, a cryptanalysis of the FRS obfuscation based on the recovered message space is proposed. The authors show that there exist two functionally equivalent programmes such that their obfuscated programmes are computationally distinguishable. Thus, the FRS scheme does not satisfy the desired security without any additional constraints.
Modeling for Analysis of Infeasible Constraints of the Optimal Planning Problem

The article deals with the analysis of infeasible problems of optimal production planning of a large dimension. The properties of an infeasible task are formulated for a special linear programming problem. For this problem, the complexity of analyzing infeasible constraints increases linearly with increasing dimension. The data on the planning models of a number of refineries and the patterns of infeasible problems that are determined experimentally are given.
Network Challenges for Cyber-Physical Systems in Training Programmes

Joint educational programmes offer opportunities to integrate partner universities in the fields of research, development and education in order to solve such issues as improving the quality of teaching, search for new forms of effective interaction between scientific and educational schools of partners, development of innovative educational technologies. The most sought after now is organisation of educational programmes that implement practice-oriented online courses, short- and long-term skills training, implementation of such technologies that are implemented in educational process at the School of Cyber-Physical and Control Systems. A very important step in the development of interaction between Peter the Great St. Petersburg Polytechnic University and Binh Duong University is the creation of a unified educational environment and laboratory of intelligent control systems and further development of the Synergy Project.
Designing of Recommendation Engine for Recyclable Waste Mobile App

Abstract The objective of this research is to design a recommendation engine for Pilah Matur App. The recommendation engine in this research used a combination of CF (Collaborative Filtering) and LBS (Location Based Service). Collaborative filtering performs data filtering based on the similarity of user characteristics so that it is able to provide new information to other users because CF provides information based on a pattern of one user group that has a similarity. The preference for recycled material used the nearest neighbor similarity method based on GPS coordinates where the recycled material was uploaded by the recyclable waste donor or volunteer then compared to the location of the recyclable waste taker. The recommendations proposed by collaborative filtering methods could be measured for accuracy using Mean Absolute Error (MAE). The results of the MAE calculation in the user-based CF method, the App Maturity dataset has an MAE value of 0.33. While the item-based CF method gets an MAE value of 0.17 using the same dataset. The results of testing the CF method show that in user-based collaborative filtering the prediction errors are more than in item-based collaborative filtering. The results of the CF method recommendations then sorted based on the closest distance by the LBS method.
Instant Replays in the Spanish Basketball League

This chapter addresses the current needs in basketball for the application of a video-assisted refereeing system. It describes the instant replay (IR) system currently used in the ACB, the professional basketball league in Spain, analysing its current application and anticipating possible future improvements. The specific characteristics of basketball make it a suitable sport for referees to receive assistance from audio-visual technology, specifically clarifying controversial moments that can be objectively evaluated during the game. The extensive information acquired by Live Audiovisual, a company with a proven track record in managing and operating the replay system used by the ACB, can serve to examine the use of IRs in professional basketball.
Recurrent algorithm for calculation of value of formal derivative of polynomial over Galois field and its hardware implementation

Abstract Within the scope of this scientific paper the formal derivative of the polynomial over the Galois field GF( p m ) and a formula for calculation of the formal derivative are considered. Calculation of the formal derivative of the polynomial is used in the decoding algorithm of the information frames during application of the Reed-Solomon codes, which are widely used in the data transmission and storage systems with the data redundancy. The definition of the formal derivative of polynomial over the Galois field GF( p m ) and the recurrent algorithm offered by the author for calculation of the value of the formal derivative of the polynomial over the Galois field GF( p m ) with a given argument are also presented. Finally, the hardware implementation of the offered calculation scheme for the case of binary Galois field GF(2 m ) is also given.
Learning Present Continuous Tense Made Easy through “Son, Mummy and the -ing Cat” Module

Learning the grammar, particularly tenses of English language may be difficult or confusing to many second or foreign language learners whose mother tongue does not have a complex grammar system, such as Malay Language.This raises problems in constructing grammatically correct sentences; both in spoken and written work.Hence, there is a need to find means that would be easy and fun to help children learn the grammar of English.This paper presents and discusses a quasi-experimental study that aimed to test the effectiveness of an innovative module developed and named "Son, Mummy and the -ing cat" to aid students in constructing correct present continuous sentences.It integrates elements of storytelling, puppetry, role-play and word cards as an in-class intervention activity.58 Year Six (age 12) pupils experienced using the module for 13 days and their writing performance was assessed in the pre-and post-tests of their sentence-production.Findings of the paired samples t-test show a difference of means between the two tests.The students constructed more and higher instances of correct present continuous tense sentences after the use of the module.This further implies that learning a concept, although complex and confusing as the English grammar, can be enhanced with creative teaching aids.
WaveTract: A hybrid generative model for speech synthesis

In this paper we propose a hybrid speech synthesizer, which combines a neural network based synthesizer with an acoustical vocal tract model. Low frequency audio of the speech is generated with a WaveGlow. This audio is then upsampled by a simulated vocal tract. The proposed hybrid model can be trained as any supervised neural network. Since the WaveGlow used in this proposed model is smaller and the vocal tract model can be implemented efficiently, it produces audio at a much higher speed and delivers a decent audio quality. The proposed model also makes it possible to divide the inference into two parts. The upsampling process can be done on a low performance CPU at the client side, for example mobile phones or home assistant devices. This way the required performance at server side will also be lower.
Research and Implementation of Geography Information Query System Based on HBase

Abstract With the rise of cloud computing research, the advantages of cloud computing and storage resources are discovered continuously: distributed computing, massive, dynamic and so on. Consequently, more and more application systems begin to migrate to the cloud platform. However, the difference between cloud platform and traditional single or multiple sever model bring certain challenges to the system development. In this thesis, a Geography information query system based on HBase was studied and implemented rellied on National Geography Public Welfare Project. The query system can make users to retrieve the information of sea wind and satellite images by the graphical interface. Through the effective application of query technology, the system can efficiently extract the concerned Geography data information for the fishery production and Geography disaster prevention. This thesis firstly displayed the Geography information query system with the running examples, and secondly tested and analized the HBase query optimization technology to vertify its great expandability and usability. Finally, the performance test of Geography information query system indicated that the system had high scalability and high reliability in the cloud environment.
Enhanced Slicing+: A New Privacy Preserving Micro-data Publishing Technique

According to studies, frequent and easily availability of data has made privacy preserving micro-data publishing a major issue. Any record in its native form is considered sensitive. These records must be kept secure from the threat as if the records are made freely available there are chances of privacy breach. The term micro-data is defined as data which contains complete information of an entity such as name, gender, salary, address etc. In this paper, a new technique is proposed i.e. Enhanced Slicing+ which ensures the privacy of data of an individual and reduces the risk of identification. In this paper concept of l-diversity and k-anonymity are used in order to achieve this. A privacy preserving technique is considered essential because it maintains the trade-off between utility and as well as privacy of individual’s records. The proposed technique is found to be less reliable to attack and preserves the privacy of an individual and it also contribute towards preserving privacy of records/data having various sensitive attributes, lesser information loss and better utility of data.
Multi-scale Residual Dense Block for Video Super-Resolution

Recent studies on video super-resolution (SR) has shown that convolutional neural network (CNN) combined with motion compensation (MC) is able to merge information from multiple low-resolution (LR) frames to generate high quality images. However, Most SR and MC modules based on deep CNN simply increase the depth of the network, which cannot make full use of hierarchical and multi-scale features, thereby achieving relatively low performance. To address the above problem, a novel multi-scale residual dense Network (MSRDN) is proposed in this paper. A multi-scale residual density block (MSRDB) is first designed to extract abundant local features through dense convolution layer, which helps to adaptively detect image features of different scales with convolution kernels of different scales. Then, we redesign SR module and MC module with MSRDB, which adaptively learns more effective features from local features and uses global feature fusion to jointly and adaptively learn global hierarchical features. Comparative results on Vid4 dataset demonstrate that MSRDB can make more full use of feature information, which helps to effectively improve the reconstruction performance of video SR.
ISDNet: Importance Guided Semi-supervised Adversarial Learning for Medical Image Segmentation

Recent deep neural networks have achieved great success in medical image segmentation. However, massive labeled training data should be provided during network training, which is time consuming with intensive labor work and even requires expertise knowledge. To address such challenge, inspired by typical GANs, we propose a novel end-to-end semi-supervised adversarial learning framework for medical image segmentation, called “Importance guided Semi-supervised Deep Networks” (ISDNet). While most existing works based on GANs use a classifier discriminator to achieve adversarial learning, we combine a fully convolutional discriminator and a classifier discriminator to fulfill better adversarial learning and self-taught learning. Specifically, we propose an importance weight network combined with our FCN-based confidence network, which can assist segmentation network to learn better local and global information. Extensive experiments are conducted on the LASC 2013 and the LiTS 2017 datasets to demonstrate the effectiveness of our approach.