Reviewerly/exHarmony huggingface dataset:
Expressing Opinions Application: A Case Study at Rajamangala University of Technology, Isan, Khon Kaen Campus

This research study presents an application for expressing opinions by addressing the problems and expressing opinions. To find the solutions to these problems that usually occur quickly, by using the application, the document format makes it convenient to follow up and check. This research has been presented through the application system to listen to the problems and make comments. The application system is developed with Dart, Flutter, and database management, and with MySQL. The test results show that the system can work correctly.
Left and Right Consistent Stereo Image Detection and Classification Based on Deep Learning

Abstract With the development of stereo camera technology, the increasing of the data volume of binocular image makes the target detection and classification of binocular image become one of the research directions in the field of computer vision. Compared with monocular image, binocular image contains the depth information of the image. For using the depth information of the image to improve the target detection and classification accuracy of image, this text puts forward a method of detecting and classifying left and right consistent objects in stereo images based on deep learning, which is based on the Mask R-CNN network and adds the binocular matching algorithm to improve the accuracy of image target detection and classification. The experiment compares the evaluation indexes of monocular and binocular.
Book Review: Coding with XML for Efficiencies in Cataloging and Metadata

Written as a follow-up to an Association for Library Collections &amp; Technical Services (ALCTS) pre-conference held during the 2015 American Library Association (ALA) Annual Conference, Coding with XML for Efficiencies in Cataloging and Metadata is an excellent introduction to the potential of Extensible Markup Language (XML) and related technologies in creating efficiencies in library cataloging and metadata work. As stated in the volume’s introduction, this guide will be most useful to those with some familiarity with XML or Hypertext Markup Language (HTML). However, this is not a requirement as the first chapter introduces XML in a way that will bring most newcomers up to speed. Catalogers will likely benefit most from this volume given that the majority of examples involve MARC 21 bibliographic data. However, metadata managers more broadly will also find value here, particularly in sections on XML Schema Definition Language (XSD), Extensible Stylesheet Language for Transformation (XSLT), XPath, and XQuery. Coding with XML does not purport to be a comprehensive reference for all XML work in libraries, but more of “a tutorial on its subject” (3). This is certainly true—each section provides an approachable and thorough introduction to a particular technology rather than an exhaustive list of features. There is also a useful list of resources at the end of the book for readers who want more information and more in-depth examples.
College Online Learning System Based on NLP Algorithm

This article introduces the results of the second round of teaching application of the artificial intelligence English teaching system application and evaluation project, which is guided by design-based research. In the second round of teaching application, we learned the current situation of oral English learning of high school students, and obtained the principles of improving the system and deepening teaching integration. Finally, we review and reflect on the previous work.
Application of Big Data Technology in National Emergency Management System

China’s emergency management system has been established for a short period of time, and its development is relatively slow. There are many problems, such as lack of information sharing mechanism, large deviation of emergency response and poor decision-making quality In China’s emergency management system. In the era of big data, how to use big data technology to integrate multiple resources to establish an emergency big data sharing platform is an important problem faced by emergency management. Through literature review and field research, this study summarizes the urgent problems in China’s current emergency management development, and puts forward the implementation path of applying big data technology to China’s emergency management system. Such as to set up the big data thinking, as early as possible for the relevant practitioners to carry out systematic big data knowledge training; realize the organic integration of big data technology and emergency management system. In addition, computer network security technology should be further improved, and firewall technology should be used to effectively strengthen the control of user information in network access.
On asymptotic distribution of Koenker-Bassett estimator of the parameter of linear regression model with strongly dependent noise

Asymptotic properties of Koenker - Bassett estimators of linear regression model parameters with discrete observation time and random noise being nonlinear local transformation of Gaussian stationary time series with singular spectrum are studied. The goal of the work lies in obtaining the requirements to regression function and time series that simulates the random noise, under which the Koenker - Bassett estimators of regression model parameters are asymptotically normal. Linear regression model with discrete observation time and bounded open convex parametric set is the object of the studying. Asymptotic normality of unknown parameters Koenker - Bassett estimators are obtained. For getting these results complicated concepts of time series theory and time series statistics have been used, namely: local transformation of Gaussian stationary time series, stationary time series with singular spectral density, spectral measure of regression function, admissibility of singular spectral density of stationary time series in relation to this measure, expansions by Chebyshev - Hermite polynomials of the transformed Gaussian time series values and it‘s covariances, central limit theorem for weighted sums of the values of such a local transformation.
Encoding and classification of permutations bу special conversion with estimates of class power

Scientific articles investigating properties and estimates of the number of so-called complete permutations are surveyed and analyzed. The paper introduces a special S-transform on the set of permutations and determines the permutation properties according to this transform. Classification and coding of permutations by equivalence classes according to their properties with respect to S-transformation is proposed. This classification and permutation properties, in particular, generalize known results for complete permutations regarding determining certain cryptographic properties of substitutions that affect the cryptographic transformations security. The exact values of the number of permutations in equivalence classes for certain permutation sizes are calculated and the estimates of the cardinality of classes with various properties are constructed by statistical modeling. The complete list of permutation classes with the exact values of their sizes for permutations of order n = 11 is presented. The interval estimates for the size of classes with various characteristics for permutations of order n = 11, 26, 30, 31, 32, 33, 45, 55 are obtained. Monte Carlo estimates and bounds of confidence intervals used the approximation of the binomial distribution by the normal and Poisson distributions, as well as the Python programming language package Scipy. Statistical tables have been calculated that can be used for further conclusions and estimates. The classification of permutations by their properties with respect to the introduced transform can be used in constructing high-quality cryptographic transformations and transformations with special features. The classes of complete permutations with their properties are selected as the best for rotary cryptosystems applications. The obtained results can be used, in particular, to search for permutations with certain characteristics and properties, to find the probability that the characteristic of the generated permutation belongs to a collection of given characteristics, to estimate the complexity of finding permutations with certain properties. A statistical criterion of consent, which uses the characteristics of permutations by S-transformation to test the generators of random permutations and substitutions is proposed.
Influence of “Corpus Data Driven Learning + Learning Driven Data” Mode on ESP Writing Under the Background of Artificial Intelligence

Objective: at present, in the actual process of foreign language teaching, there are still some deficiencies in the language materials and the knowledge of language analysis. The research aims to better stimulate the academic paper writing of college students of Traditional Chinese Medicine (TCM) by constructing the model of “Corpus Data Driven Learning (C-DDL) + Learning Driven Data (LDD)”. Methods: The junior students (n = 80) in one university of TCM were divided into experimental group (n = 40) and control group (n = 40). After the no difference comparison, the educational experiment was carried out 12 times in 3 months. In the experimental group, the Chinese-English Parallel Corpus of TCM created by Hyconc software and the artificial intelligence network evaluation system were used, while the control group was taught by traditional style. The research tool is the ESP Writing Ability Scale of TCM. Results: in the five dimensions of word accuracy, sentence structure, text structure, writing efficacy and writing motivation, the post test of the experimental group and the control group were significantly better than the pre test (p < 0.05); in the post test, the performance of the experimental group was significantly better than the control group (p < 0.01). In the two dimensions of grammatical accuracy and content innovation, the post test scores of the experimental group and the control group were significantly higher than the pre test (p < 0.05), but in the post test, there was no significant difference between the experimental group and the control group (p > 0.05). Conclusion: through the “Corpus Data Driven Learning (C-DDL) + Learning Driven Data (LDD)” mode, students gradually adapt to the beginning of independent language mining and exploration. In the process of academic language processing, construction, transformation and experiment of TCM English, it can make language knowledge more systematic and academic.
Reliability aware Resource Scheduling based on Fuzzy Cuckoo Search (FCS) Technique for IaaS Cloud

Abstract Resource scheduling assigns the precise and accurate tasks to CPU, network, and storage. The aim behind this is the optimum usage of resources. However, well-organized scheduling is needed for both cloud providers and cloud users. Several resource scheduling algorithms have been discussed in the literature, but there are little emphases to reliability aware resource scheduling. In this research article, an innovative technique is proposed that is known as the Fuzzy Cuckoo Search (FCS) technique based on the fuzzy theory and cuckoo search algorithm to solve real-time optimization problematic issues. The FCS technique is used to address the reliability aware resource scheduling problems in IaaS Cloud. An experiment has been carried out on the CloudSim simulator and results of FCS techniques are compared with the Genetic Algorithm (GA), Honey Bee (HB) and Particle Swarm Optimization (PSO) scheduling algorithms. Finally, computational results demonstrate that the FCS technique is produced 39.21% better optimal solutions than the best solutions obtained by the comparison algorithms in terms of failure rate. It specifies that the FCS technique is more appropriate for reliability aware resource scheduling for IaaS Cloud.
Information Hiding Using Private Content-Based Random Encryption and Data Reduction for RGB Images

In this research work, we have successfully reduced the content present in red-green-blue (RGB) image for secure transmission, for this purpose, we have used a private content-based image encryption (PCBIE), model. The PCBIE model works on three basic steps image encryption followed by first-order statistical parameter calculation for the encrypted image and original image and finally a comparison of the obtained parameters with the original image parameters. Through this methodology, one can easily prevent the original identity of the image. Here, we have taken four different categories of images and performed encryption on them, in the result, we have obtained that identity of the image is totally hidden and one can identify the original image only on the basis of the parameter value. The first-order statistical parameter includes the mean, variance, and standard deviation.
Prominent Attribute Modification using Attribute Dependent Generative Adversarial Network

Modifying the facial images with desired attributes is important, though challenging tasks in computer vision, where it aims to modify single or multiple attributes of the face image. Some of the existing methods are either based on attribute independent approaches where the modification is done in the latent representation or attribute dependent approaches. The attribute independent methods are limited in performance as they require the desired paired data for changing the desired attributes. Secondly, the attribute independent constraint may result in the loss of information and, hence, fail in generating the required attributes in the face image. In contrast, the attribute dependent approaches are effective as these approaches are capable of modifying the required features along with preserving the information in the given image. However, attribute dependent approaches are sensitive and require a careful model design in generating high-quality results. To address this problem, we propose an attribute dependent face modification approach. The proposed approach is based on two generators and two discriminators that utilize the binary as well as the real representation of the attributes and, in return, generate high-quality attribute modification results. Experiments on the CelebA dataset show that our method effectively performs the multiple attribute editing with preserving other facial details intactly.
VisQueue

Monitoring a supercomputer’s performance is vital for high performance computing (HPC) support and development, often its users, and even appreciated by the general public. Identifying the effectiveness of a system is key for its host institution’s confidence and credibility, and accounting for necessary operational funding. Tracking the research topics and sciences which dominate a system’s time and CPU can help guide decisions regarding design and support for both current and future system resources. This information is also useful for those whose tasks are to inform users, collaborators, and the general public about a system’s utilization.
REQUIREMENTS FOR AXIS PROTECTION MECHANISMS WITHIN THE CLASS OF USE

One of the components of modern information systems is general-purpose computer equipment on which specialized software is installed or on which specialized software is working. This computer equipment requires a general-purpose operating system. To protect the information system it is necessary to protect all its links, including the operating system. In particular, in case of client-server technologies it is necessary to pay attention both to protection of server component and to protection of a client component. Protection of an operating system demands understanding, within what limits of what information system the OS will work, what working conditions and restrictions are imposed on the protected environment, what threats to the protected environment is required, what mechanisms is it possible to provide with protection of the environment necessary to us and what "price" of their application or nonapplication and other will be. Therefore, software safety should rely on a policy of safety of an operating system, expanding and specifying it and as much as possible. It is necessary to rely on safety mechanisms which are provided by an operating system or information system at software working out. This is necessary for unification and simplification of system of safety, simplification of its service, at the expense of reduction of quantity of the mechanisms created for the decision of the same problem. It is also necessary to apply typical and well-known components and standards, to avoid components with the closed source code as much as possible, or components which do not support, or incorrectly support typical standards. Obviously, incorrect protection of the operating system can lead to the failure of the security system as a whole, because the operation of specialized software and work with peripherals is under the control of the operating system. In article in detail offers concerning requirements to mechanisms of protection of operating systems are considered. These requirements are focused not on universal requirements, and consider various variants of use of computer devices
CHROMITE DEPOSITS OF PAKISTAN: A SHORT REVIEW

Chromite is an important source for the manufacturing of stainless steel, nichrome, paint, and chemicals industries and has wide industrial utilization. In Pakistan, the chromite deposits are mostly associated with the ophiolitic sequence resulting as an outpouring of molten material in the fractured zones of the Indian plate and the Neo-Tethys Ocean through the Cretaceous era. The discovery and documentation of chromite in Pakistan is traveling back to 1960s, but unfortunately, most of the reports are lacking authentication in reserve estimation and grading. If the modern and proper geophysical and geochemical analysis is performed to the reported area, then there are more bright chances of acquiring the more authenticate reservoir estimation, its distribution in the prospect zone and chemical composition per metric ton. The current article is an effort towards making a composite record of all the published/unpublished reports and articles regarding the chromite discovery, estimation, and extraction in Pakistan.
Case Study of Analysis on Requirements for Online Platform for Teaching Practicum: Focused on Practicum Guidance Teachers’ Opinions

This study is a basic research for designing teaching practicum support online system for teacher training. The primary purpose of this study is to identify the meaning that the present instructors who will use online platform gives to the teaching practicum support online system and what they demand for the teaching practicum platform. According to the purpose and intent of this study, one-on-one interview with the researcher was conducted and the subjects who participated included 2 infant teachers, 2 elementary teachers, and 3 middle school teachers: 7 in all. The interview was conducted from Nov. 2019 till Mar. 2020, in one-on-one interview method, and once for each. As a result of the study, first, relating to the meaning of the online platform supporting educational practice, the teachers who participated in this study recognized the online platform as a support system to reduce the work of on-site teaching practicum, and as a support system to help the pre-service teachers connect well with the educational theory and educational practice. Second, the teacher s demand for the platform system has been largely twofold: one is that the online platform should be useful in the process of writing and reviewing various documents including teaching practicum journal training practice books and teaching guides; the other is that the online platform should serve as a medium for cultivating attitudes and character as teachers by providing advance preparation and guidance for understanding teaching culture.
Online parameter tuning of the flow classification method in the energy-efficient data center network “HOLST”

An online parameter-tuning method for the energy-efficient data center network (DCN) named HOLST (high-speed optical layer 1 switch system for time-slot-switching-based optical data center networks) is proposed. HOLST comprises an optical circuit switching network, optical slot switching network, and electrical packet switching network for the spine layer. It requires flows to be assigned to optimal switches to achieve high power savings. In this study, first we experimentally confirm that the change in the DCN characteristics in the short term of actual data center traffic downgrades the accuracy of flow classification. Subsequently, we propose a procedure for reconfiguring a flow classification function and a method for online parameter tuning for this function. Finally, the accuracy of the flow classification method using the proposed tuning and estimated switching energy consumption in the spine layer are evaluated. The proposed online parameter-tuning function increases the accuracy of flow classification and reduces switching energy consumption relative to the conventional flow classification function.
Improving Active Shape Models Robustness Towards Locating Facial Landmarks In Profile Contour

Active Shape Models (ASM) are widely explored for facial landmarks detection. The task is commonly approached in frontal perspective where target features such as eyes, mouth, and nose lie within the face's contour. Conversely, few application proposals are aimed to find facial landmarks in a profile view. This paper addresses an issue with that technique applied to locate facial landmarks lying on the profile contour. Those boundary points present an extra challenge for ASM's searching method when applied in images with background lighting variations. An ASM trained with profile images captured with different brightness backgrounds may lead the method to model texture profiles in segregated clusters. In those cases, it is inaccurate to assume that sampled texture profiles follow a Gaussian distribution, which is essential to estimate new landmarks locations with the Mahalanobis distance. We propose an alternative to improve ASM's robustness in such a scenario by utilizing K-Means to identify those clusters and create specific texture models for each one. Then, during the searching stage, new estimates are compared with each model individually, assuming the texture profiles follow a normal distribution inside their corresponding clusters. We trained and tested our improved ASM with a private database of 670 face images in profile view captured in a controlled environment. We report an average error reduction of 46% for landmarks location compared to the classical method. We also demonstrate that, regarding this issue, our proposal drives ASM's searching method to the point of convergence, whereas the original method does not.
Community garden

We present Community Garden, an interactive museum exhibit designed to promote connectedness both at the museum and online. To interact with the exhibit, participants draw and share flower petals which are displayed on a communal field, blooming with flowers of other participants. The exhibit's drawing interface is accessed online, allowing for live participation and inclusion of people who cannot physically visit the museum. This paper describes some of the design principles that shaped the development of the exhibit prototype, as well as the technology that allows the exhibit to function, requirements for demonstrating, and future plans to evaluate the exhibit.