Reviewerly/exHarmony huggingface dataset:
Research on Two Dimensional Code Encryption Method Based on Embedded Device

As the carrier of information storage, the generation standard and identification method of two-dimensional code are shared. A set of unified standards used, but without corresponding encryption measures, it is difficult to ensure that the two-dimensional code information will not be attacked and tampered by criminals in the process of transmission, resulting in information security risks. With the intellectualization of embedded devices, two-dimensional code has found an increasingly wide application, but its safety issue is becoming more and more prominent. This paper proposes a two-dimensional code encryption method based on embedded devices. In this paper, AES symmetric encryption algorithm which ensures both encryption and decryption speed and security is selected to encrypt the two-dimensional code. Key expansion with the traditional AES encryption algorithm has some flaws in that once a key in one of the rounds is intercepted, the previous and the following subkeys will be calculated through fixed algorithm. Random function is used to improve the generating algorithm of expanded keys, hence enhancing the anti-attack ability of the encryption algorithm. By putting random function and g function into Cloud for operation, the speed of encryption and decoding QR code of the embedded device is increased. The test results show that the system designed in this paper can effectively hide the information contained in the QR code picture, which shows that this method ensures high security.
Signal processing for extracting velocity information from spatial filtering signals

Obtaining transient central frequency from non-stationary spatial filtering signals is essential for measuring velocity with spatial filtering technique. According to the property of spatial filtering signal, a method for processing the unsteady spatial filtering signals based on complex-wavelet transform (CWT) was investigated to extract the velocity information, where two Gaussian derivatives with adjacent orders are taken as real and imaginary parts to construct a complex basic-wavelet, and then are employed to implement the CWT. The central frequencies of the spatial filtering signals are derived from the CWTs, enabling to measure the velocities of the arbitrarily irregular materials with some random distributions. The spatial filtering signals of a rotational velocity measurement were utilized to evaluate the accuracy and instantaneity of the signal processing method. The evaluation results indicate that the proposed method is generally prior to the traditional temporal-period method and Fourier-transform-based method, and that it is fit for acquiring the transient central frequencies of unsteady spatial filtering signals.
Hesitant Fuzzy MP Filters of R0-Algebras

Abstract In this paper, we introduce the notion of hesitant fuzzy( correlation) MP filters, some properties and equivalent conditions are investigated. The relationships between hesitant fuzzy MP filter and hesitant fuzzy correlation MP filter is disscussed. Finally, we prove the invariance between the image and the original image of hesitant fuzzy correlation filter is studied under the premise of homomorphism.
Open Entity Semantic Relation Extraction in Big Data Environment

Aiming at the problem that traditional entity relationship extraction needs to define a comprehensive entity relationship type system, poor portability, and the inability to process massive Internet texts, an open Chinese entity relationship extraction method based on CRFs(Conditional random fields) is proposed. At the same time, due to the limited computing resources of a single machine, an open entity relationship extraction method based on the Hadoop distributed computing framework is proposed. Experiments show that the CRFs model with syntactic parse tree has improved accuracy and recall in the entity relationship extraction task; this paper also designed an experiment to compare the entity relationship extraction based on the Hadoop distributed computing framework with the stand-alone version. The experiment shows that the former extraction effect is better.
Refining Fortran Failed Images

The Fortran 2018 standard introduced syntax and semantics that allow a parallel application to recover from failed images (fail-stop processes) during execution. Teams are a key new language feature that facilitates this capability for applications that use collective subroutines: when a team of images is partitioned into one or more sets of new teams, only active images comprise the new teams; failed images are excluded. This paper summarizes the language facilities for handling failed images specified in the Fortran 2018 standard and subsequent interpretations by the US Fortran Programming Language Standards Technical Committee. We propose standardizing some semantics that have been left processor (implementation) dependent to enable the development of portable fault-tolerant parallel Fortran applications. Finally, we present a prototype implementation of a substantial subset of the Fortran 2018 failed images functionality, including semantic changes proposed herein. This prototype comprises OpenCoarrays, with failed-images enhancements constructed using Open MPI ULFM routines, and a GFortran compiler customized to support additional syntax needed to enable fault tolerant execution of image control statements.
Tools and Activities for Developing Higher Order and Professional Skills with a Case Study on the Subject of Computer Architecture and Organization

Higher-order thinking, commonly known as higher order thinking skills (HOTS), is a concept of education reform based on the learning taxonomies such as Bloom's taxonomy.For various Bloom's Taxonomy levels as Remembering, Understanding, Applying, Analysing, Evaluating and Creating, digital tools are available for students to develop those levels of skills.However, it requires that for each subject area in the engineering education, the faculty members have to identify the specific thinking skills as applicable for the subject and need of the student.Then they have to design the activities and projects with the necessary specific tools.In view of the above, a group of faculty members in our institution took up a case study on Computer Architecture and Organization and identified the thinking skills to be developed.Based on this, they designed a set of activities and projects with the necessary tools as specific to the subject.It is required to use the generic digital tools.
Modelling Energy Performance of Non-domestic Buildings

This chapter presents an energy performance prediction model for the UK non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic building energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using advanced evolutionary algorithms provides a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space.
Data-Driven Auxiliary Decision-Making Method for Power System Stability

Critical clearing time (CCT) is one of the most important indexes for large-disturbance rotor angle stability margin evaluation. In actual operation, model-driven methods, e.g., integrated extended equal area criterion (IEEAC) methods, are proven to be good methods to calculate CCT. Nevertheless, classical generator models are always used to reduce computation burden, where differences between classical models and detailed models could make CCT predictions useless. To correct error caused by model differences, data-driven method is considered and integrated with classical generator model based IEEAC, making an integrated method. In this paper, extreme learning machine (ELM) is used as the data-driven correction part. Further, cost-sensitive ELM model is constructed by substituting common error summation part with weighted error rate summation part in training objective. Prediction error for cases with small actual value is expected to be lower than that with large actual value by this transformation. Finally, validations on WSCC 9 and New England 39 bus system show effectiveness of proposed integrated method in correcting error caused by model difference and changing error distribution.
Unsupervised Feature Learning for Output Control of Generative Models

Deep generative models are being actively studied, particularly variational autoencoders (VAEs) because they can generate high-quality images. The M2 model supports semi-supervised learning from both labeled and unlabeled data, which enables the generated images to be easily controlled by changing the class label values. However, generative models must be learned from only unlabeled data when class labels are not available. A model is presented that incorporates a deep clustering method into the M2 model, which enables clusters to be identified among unlabeled data so that each data point can be assigned to one of the clusters. The generated images in unlabeled datasets can easily be controlled by changing the cluster assignment of each data point.
A Study on the Attitude and Practice of Chinese Teachers and Students Towards VR Teaching for Foreign Language Learning

In the field of foreign language education in China, virtual teaching is still in its infancy. The unique modal reconstruction function of virtual reality technology can provide new ideas for foreign language teaching. Virtual reality technology can create scene experience for learners, adjust modal information, and give them a new identity. VR Teaching for Foreign Language Learning is helpful for learners to participate in multi-sensory collaborative learning, deepen the effect of perception and experience, and improve the learning effect and language application ability. Based on the practice of foreign language teaching in China, this paper analyzes the efforts of Chinese foreign language education in the field of VR, discusses the difficulties in the process of reconstructing teaching mode assisted by virtual reality technology, and puts forward feasible suggestions for realizing foreign language learning objectives through VR teaching.
Information and Communication Technologies in Project Training for University Students

Under conditions of digitalization of economy the issues of information and communication technology use are of great importance in teaching university students. Allowing combining the diverse areas of human activity reflected in the disciplines of the educational process, method of project-based learning is gaining wide recognition. This helps us consider the problem of integration in educational process through the toolkit of project-based learning, knowledge and skills in the sphere of information and communication technologies and the subject area of students' training. The authors of the paper actualize the problems of conformity of the university software products used in the process of university management, learning process to the requirements of fast-paced processes of economy digitalization. There have been developed some recommendations on the use of software products with the help of project-based learning method.
Exploring TESL Trainee Teachers' Reflective Practice Experiences through the Use of Facebook Group Discussion

Teaching reflection has been practiced through a variety of mediums, such as the use of teaching logs and diaries, blog sites, online forums and chat sites, and more recently social networking sites, like Facebook.However, the use of social networking websites, such as Facebook, has received little attention, even though a handful of studies have also reported on its many benefits for reflection of teaching practice.Therefore, this study explored the experiences of three trainee teachers from a teacher education institution in Selangor who were undertaking Diploma in Teaching (Teaching of English as a Second Language) on their use of Facebook group discussion platform to reflect on their teaching practices.This study adapted a qualitative approach which includes semi structured interview and document analysis of their Facebook group discussion platform in reflecting their teaching practicum experiences.Based on the findings, it was found that trainee teachers showed high interest in using Facebook in an academic setting.Due to certain features in Facebook that provided a systematic and structured organization of notifications, Facebook caters as a supportive virtual platform in promoting reflective practice as it provides moral support during teaching practicum session and it was able to create solidarity among them.
Signal and Video Processing: Developing the Noise Music Trend in Digital Edugaming

In digital educational gaming, there are presently no games devoted to the trends of contemporary music. Also, there are no studies of noise-related sound in contemporary gaming discourse, yet noise is presently one of the most current trends in the arthouse world. The author of this study is carrying out a post-doctoral research into contemporary audio-visual art and digital game theory. Next to the other trends of modern gaming, the study incorporates analysis of noise-related artefacts that are of particular interest to the author taking into account her musicological education. The author intends to transfer knowledge gathered in the research process to the general public with an aim to facilitate the comprehension of noise music. Noise-related sound does not belong to the traditional system of musical expression thereof it requires an explanation and justification in order to be well apprehended. It has been explained in the literature of musicology, but serious gaming would help to disseminate these results and thus support intellectual education. The new experimental game Art Space explores the noise effect in order to deepen the understanding of this fuzzy area of contemporary culture. The game incorporates the historical background of noise music and its contemporary examples in the academic and alternative genres. The methods used in this study are literature analysis (theory of gaming and musicology), analyses of game sound scores and music examples. The mission of the research project and the innovative game Art Space is to pave the way to a new type of edugame that supports the documentation and analysis of aesthetical trends today.
University Research Graph Database For Efficient Multi-Perspective Data Analysis Using Neo4j

In general, research-related data are modeled using a relational database optimized for transaction processing. In many cases, this solution is effective and efficient enough to answer basic queries and simple reporting requirements. However, when users request a more-in-depth, more expansive, multi-perspective, and sometimes more abstract analysis, the relational database struggles to provide answers. This study proposes a research graph database implemented using neo4j as an effort to answer the problems. The database consists of a core model and an extension model. The core model represents scientific articles-related data loaded with real data scraped from Google Scholar. The extension model indicates research and community engagement activities done by researchers loaded manually. The database enables the university to analyze researchers' individual and collaborative performances with fellow researchers inside and outside universities. The study concludes that the research graph database implementation is more efficient in answering similar questions than the relational database implementation.
TO THE QUESTION OF THE ROLE OF STRATEGIC PLANNING IN THE SOCIO-ECONOMIC DEVELOPMENT OF MUNICIPAL EDUCATION

The article is devoted to the actual problem of strategic planning of socio-economic development of municipalities. It is shows that the strategy, development of strategy, although not an obligatory element of municipal govermance, is to a largely an effective instrument of govermance management tool in general. Strategies perform a coordinating and guiding role, representing a certain vector of socio-economic development of municipalities. The goals should be potentially achievable and set on the basis of the analysis of the current situation, take into account unique features of the development of this or that territory and at the same time be coordinated with the general goals of the region’s development. The authors, within the framework of the strategic project of the flagship university, developed a strategy for the municipal district. The article outline the experience of such work and gives its brief description. And also the experience of similar work in the Russian Federation and in foreign countries is considered. Conclusions are made about the importance of the strategic planning process for municipalities and the impact of the quality of this process as a whole on the socio-economic development of the territory.
Data Mining and Fuzzy Data Mining Using MapReduce Algorithms

Data mining is knowledge discovery process. It has to deal with exact information and inexact information. Statistical methods deal with inexact information but it is based on likelihood. Zadeh fuzzy logic deals with inexact information but it is based on belief and it is simple to use. Fuzzy logic is used to deal with inexact information. Data mining consist methods and classifications. These methods and classifications are discussed for both exact and inexact information. Retrieval of information is important in data mining. The time and space complexity is high in big data. These are to be reduced. The time complexity is reduced through the consecutive retrieval (C-R) property and space complexity is reduced with blackboard systems. Data mining for web data based is discussed. In web data mining, the original data have to be disclosed. Fuzzy web data mining is discussed for security of data. Fuzzy web programming is discussed. Data mining, fuzzy data mining, and web data mining are discussed through MapReduce algorithms.
An Entropy Metric for Regular Grammar Classification and Learning with Recurrent Neural Networks

Recently, there has been a resurgence of formal language theory in deep learning research. However, most research focused on the more practical problems of attempting to represent symbolic knowledge by machine learning. In contrast, there has been limited research on exploring the fundamental connection between them. To obtain a better understanding of the internal structures of regular grammars and their corresponding complexity, we focus on categorizing regular grammars by using both theoretical analysis and empirical evidence. Specifically, motivated by the concentric ring representation, we relaxed the original order information and introduced an entropy metric for describing the complexity of different regular grammars. Based on the entropy metric, we categorized regular grammars into three disjoint subclasses: the polynomial, exponential and proportional classes. In addition, several classification theorems are provided for different representations of regular grammars. Our analysis was validated by examining the process of learning grammars with multiple recurrent neural networks. Our results show that as expected more complex grammars are generally more difficult to learn.
Augmentation of Local Government FAQs using Community-based Question-answering Data

To reduce the cost of administrative services, many local governments provide a frequently asked questions (FAQ) page on their websites that lists the questions received from local inhabitants with their official responses. The number of Q&A items posted on the FAQ page, however, will vary depending on the local government. To address this issue, we propose a method for augmenting local government FAQs by using a community-based Q&A (cQA) service. We also propose a new FAQ augmentation task to identify the regional dependence of Q&A to achieve the goal mentioned above. In our experiments, we fine-tuned the bidirectional encoder representations from transformers (BERT) model for this task, using a labeled local-government FAQ dataset. We found that the regional dependence of Q&As can be identified with high accuracy by using both the question and the answer as clues and with fine tuning for the deeper layers in BERT.
Multi-core Implementation of Chaotic RGB-LSB Steganography Technique

The paper presents shared memory implementation of chaotic RGB LSB steganography technique, The proposed technique involves hiding the secret information into RGB components of the cover image. Chaotic logistic map has been used to generate highly random numbers for enhancing the security of embedded information. Encryption and decryption process is parallelized using OpenMP API in multicore environment, and results show significant speed up and highly scalable results even with large amount of data.
Analysis on Adversarial Robustness of Deep Learning Model LeNet-5 Based on Data Perturbation

At present, deep learning technology is widely used in daily life. From recommendation algorithms to autonomous driving, deep learning models play an important role. However, once these models face perturbation, especially in case of adversarial attack perturbation, depending on the situation, the wrong output of the model may cause adverse consequences, such as property damage or personal safety accidents. Therefore, the ability of the model to resist the perturbation of adversarial attacks, that is, adversarial robustness, remains as a problem worthy of attention. In the present study, a deep learning model based on the convolutional neural network LeNet-5 was used as the experimental object, and adversarial examples are formed by adversarial attacks on the input data of the model, in order to observe the changing law of the adversarial robustness of the deep learning model.
Possible application of elementary geochemical landscapes map for mapping heavy metals and metalloids content in urban soils

Mapping heavy metals and metalloids (HMM) content in urban soils is necessary for the public health risk assessment. The sampling step is usually large in comparison with typical variability intervals due to the complexity and expensiveness of analysis. This paper considers an attempt to map HMM concentration coefficients based on landscape-geochemical positions (LGP). The case study area is Darkhan, Mongolia, a large industrial and transport hub. 126 soil samples were taken for analysis of contaminants As, Cd, Cr, Cu, Pb, Sb, W; the distance between sampling points was 500–700 m. For each point, the concentration coefficient (DF) of each pollutant was calculated. The LGP map was derived from SRTM digital elevation model, with supplement of hydrographic network data. The final maps of the concentration coefficients were created using areal interpolation technique with the Voronoi diagram of sampling points as an input data and the LGP polygons as a target dataset. The relatively low sampling points density, as well as the relatively large DEM cell size limit the possibility to harmonize datasets. This leads to the noticeable difference between the parameter distribution obtained from areal interpolation and the distribution obtained from deterministic method. Besides, some resulting features should be considered as interpolation artifacts. Nevertheless, the potential suitability of LGP maps as a basis for mapping pollution of urban areas is shown.
Data repositories: theory and practice

The need to create and use data repositories for sharing and reuse of data by researchers is considered, issues of reproducibility of research, increasing the likelihood of citation are discussed. Criteria the repository must meet are provided. The aspects that hinder the dissemination of data are analyzed, among them data mistrust and misuse of data by other researchers.