Reviewerly/exHarmony huggingface dataset:
The application exploration of ‘OPERA thinking’ from the perspective of cognitive psychology in the performance of national vocal music in colleges

Cognitive psychology regards music as an important part of the basic research of psychology and biology, and influences the education method of music. With the deepening of the teaching reform and exploration of the national vocal music education in colleges, the vocal music teachers should not only teach students basic vocal music knowledge, but also enrich and expand the teaching mode of national vocal music through the diversified teaching mode, draw lessons from the research results of neuroscience, and explore the application of opera thinking in the performance of national vocal music in colleges.
Another HTML5 Implementation of Web-Com for Embedding Chalk Annotations and Talk Voices into HTML Documents

Web-Com is a tool for teachers to record their chalk annotations and talk voices onto a web document to produce a lively e-learning content. It was originally developed as a Windows application in 2005 and then implemented solely on the basis of HTML5 and JavaScript in 2017 so that it may work on any contemporary web browsers. The only drawback of the previous implementation in 2017 was that the original web document had to be transformed into a single static image file to appear in the resulting e-learning content and thus that the dynamical capability of the original web document is lost. This paper presents a much better implementation that produces the resulting e-learning content as another web document where the original web document, the chalk annotations, and the talk voices are embedded as they are so that the dynamical contents like hyperlinks, buttons, animations and movies in the original web document are preserved in the resulting e-learning content.
The Antikythera Framework: A Novel Design Approach for Accommodating a Large Number of Web Services

The increasing number of services drives small inefficiencies to intolerable levels. In order to cope with this challenge, it is useful to utilize a shared server instance. We propose a shared server instance approach with fair load balancing and minimizing overhead from multiple server instances. Our design is based on a novel approach to accommodate a whole set of services in an OS process on multiple server instances to manage statistical load utilization. The design empowers the dynamic management of computer resources in order to realize fairness among web services. We achieved 64% reduction of server instance costs.
Research Guides: SJU Library Streaming Video Resources: Films On Demand (Infobase)

Overview of the streaming video content providers currently available for SJU campus members through the library.
Non-Local Directional-Guided Filter for Impulse-Gaussian Mixed Noise Image Denoising

We introduce an effective technique to restore the images corrupted by additive Gaussian noise and impulse Salt and Pepper noise. In this Work, a three-step non-local directional-guided filter is seted up. We begin by identifying Salt and Pepper noise, estimate intensity of mixed noise and preliminarily remove and repair it by Maximum Likelihood Estimator. Afterwards, use a set of discrete total variation (TV) models to mine potential directional information and generate a set of directional-guided templates. At last, We build a non-local directional-guided filter to restore lost details. Experimental results verify that the proposed algorithm can obtain the best denoising performance compared With some typical methods. In the case of high intensity noise pollution, our algorithm has more advantages.
Tahul

Tahul is a virtual reality explorative experience created for the SIGGRAPH VR Theater 2020 virtual lobby. This custom, tailored lobby environment serves as an entry point to the traditionally seated program. Currently, Tahul has been adapted to host the trailers and informational videos of the selected works.
Differential D-marking on proper names? A cross-linguistic study

According to recent studies, proper names and common nouns differ cross-linguistically with respect to their morphosyntactic behavior, i.e. their marking for case, definiteness, number and gender, among other grammatical categories.Consequently, it has been argued that proper names possess a special onymic grammar.This study is meant to contribute to the discussion by comparing the marking of definiteness and specificity (D-marking) on proper names and common nouns in a sample of fifty areally, genetically, and typologically unrelated languages.The results show that there is only weak evidence for a differential D-marking of names.
$ \lambda $-DEFINABILITY OF BUILT-IN McCARTHY FUNCTIONS AS FUNCTIONS WITH INDETERMINATE VALUES OF ARGUMENTS

The built-in functions of programming languages are functions with indeterminate values of arguments. The built-in McCarthy functions $ car $, $ cdr $, $ cons $, $ null $, $ atom $, $ if $, $ eq $, $ not $, $ and $, $ or $, are used in all functional programming languages. In this paper we show the $ \lambda $-definability of the built-in McCarthy functions as functions with indeterminate values of arguments. This result is necessary when translating typed functional programming languages into untyped functional programming languages.
Big Data Analytics—Analysis and Comparison of Various Tools

Big data is the latest terminology in the computer world. The data collection is increasing day by day, and many technological changes can bring some new methods for decision-making process in many areas such as health and finance. As the complexities are increasing due to volume, veracity, variety and velocity, our focus is on the methods to calculate the value of data using various big data analytics technologies. The analytics process used with respect to big data can be improvised by using new algorithms, which enhance the analytical aspects and can be used to extract the hidden knowledge very efficiently and effectively.
Octave Residual Network for Image Motion Deblurring

Abstract Image motion deblurring technology is an essential topic. Traditional methods with a high algorithm complexity when estimating the blur kernel require a lot of calculations. To solve this problem, we proposed Octave convolution residual block, and proposed Octave Residual Network (ORN) based on this block. ORN is a Generative Adversarial Networks, where the generator is a multi-scale recurrent network, and the discriminator is a deep neural network. ORN restore sharp images in an end-to-end manner where blur is caused by various sources. We present multi-scale loss function that mimics conventional coarse-to-fine approaches. To verify the validity of ORN, we trained our model on GOPRO dataset, the peak signal-to-noise ratio (PSNR) of the model reached 30.04, and the operation time of each step is reduced by 0.04 seconds. Experimental results show that our method achieves the state-of-the-art performance of image motion deblurring not only in performance but also in training speed.
Global validation of satellite-based and reanalysis surface solar radiation data sets

&amp;lt;p&amp;gt;The solar radiation reaching the Earth&amp;amp;#8217;s surface determines our climate and is therefore important to be monitored as consistent and complete as possible. Even though surface reference measurements of surface solar radiation are available (e.g. from the Baseline Surface Radiation Network (BSRN)), their density remains low and large areas, like the oceans, remain poorly covered. To fill the gaps in space and time, satellite-based data records (like CLARA-A2 and SARAH-2.1 from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF)) or model-based reanalysis data records (like ERA-5) are used. They provide surface solar radiation data with regional and global coverage, which are needed to understand its distribution and variability from the regional to the global scale.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here we present a validation and analysis of monthly mean surface solar irradiance from multiple satellite-based and reanalysis data sets on the regional and global scale with reference to a data base of hundreds of surface measurements over land and ocean, collected from different sources (incl. BSRN, GEBA, WRDC, and buoy networks). This study provides new insights about the quality and uncertainty of available state-of-the-art satellite-based and reanalysis data records for climate studies. Regions of agreement as well as areas where the gridded data records exhibit larger differences are identified, providing important information on our current knowledge of the surface solar radiation climatology and possible improvements for future developments.&amp;lt;/p&amp;gt;
Loading, searching and retrieving data from local data nodes on HDFS

The loading and searching the data from data with in local data nodes by using the Hadoop environment. In general the loading and searching data by using a query is more complex, because the capacity of the dataset may large. We propose a technique to handle the data in local nodes without overlapping and data retrieved by script. The main task of the query is to store the information on distributed environment and searching the without any delay. Here we define the script to avoid the redundancy of the duplicate while searching and loading the data in dynamic mechanism. And also provide the Hadoop file system in distributed environment. The apache script is used to loading and searching the information instead of the SQL mechanism. We improve the performance of query execution and graph theory. The query can split into three parts to search the data individually and combined the results in execution. Here we used the replica concept to store the data at time of executing query in Hadoop file system. The script is executed on the locating environment of Hadoop file system.
A-Frame as a Tool to Create Artistic Collective Installations in Virtual Reality

Virtual Reality, due to its complexity and technological requirements, has a set of frictions that hinder its dissemination. The main ones can be summarized in the requirement to learn complex developing environments, like game engines, then we need to install applications, specific to each operating system and according to the means through which they can be accessed.
Fast recognition and classification of static and dynamic signs for Persian sign language

Sign Language (SL) is the most effective way for communication between deaf and hearing-impaired people. Since most non-deaf people are not familiar with SL, a vision-based translator/interpreter can be a very useful tool to enhance their communication. This paper presents a recognition system for Persian static and dynamic signs. The system is designed based on proposed modified non-linear kernel-based fast feature extraction methods, consisting of hybrid kernel principal component analysis and hybrid kernel discriminant analysis. For recognition of dynamic signs, the proposed feature extraction method is employed in association with spatio-temporal approach. The proposed methods are examined and compared with several existing feature extraction methods, including linear and non-linear kernel-based methods. The experiments indicate that our feature extraction methods significantly outperform other methods and reduce computational time while they achieve high recognition rates. Our simulations achieved a promising classification accuracy rate of 96.78% on static and 96.99% on dynamic signs, respectively.
The Effects of Foreign Direct Investment Uncertainty on Financial Development in Nigeria: An Asymmetric Approach

This article investigates the dynamic effects of foreign direct investment uncertainty on financial development in Nigeria and the interacting role of financial inclusion and economic growth. We used the annual time series data of Nigeria covering the period 1970-2018. Through advanced econometric techniques, we first substantiated stationarity level and co-integration among the scrutinized variables, which is genuinely done for reliable findings. Following that, we applied Gregory and Hansen (1996) co-integration test, Non-linear ARDL as the elasticity estimator, and Diks and Panchenko (2006) causality test for the analysis. The Empirical evidence postulates the asymmetric nature of foreign direct investment uncertainty to financial development. We also found a non-linear uni-directional causality running from economic growth to financial development, foreign direct investment uncertainty to financial development, and financial inclusion to financial development. In the end, the authors proposed the needed policy recommendations to strengthen the Nigerian financial sector.
Augmented Fine-Grained Defect Prediction for Code Review

Code review is a widely used technique to support software quality. It is a manual activity, often subject to repetitive and tedious tasks that increase the mental load of reviewers and compromise their effectiveness. The developer-centered nature of code review can represent a bottleneck that does not scale in large systems with the consequence of com- promising firms’ profits. This challenge has led to an entire line of research on code review improvement. In this thesis, we present our results and remarks on the effectiveness of using fine- grained defect prediction in code review while investigating what are the information needs that lead a proper code review. We started reimplementing the state of the art of defect prediction to understand its replicability; then, we evaluated this model in a more realistic scenario that is typically considered. To improve defect prediction techniques, we come up with a fine-grained just-in-time defect prediction model that anticipates the prediction at commit time and reduces the granularity at the file level. After that, we explored how to improve further prediction performance by using alternative sources of information. We conducted a comprehensive investigation of code comments written by both open and closed source developers. Finally, to understand how to improve code review further, we explored from a reviewers’ perspective what is the information that reviewers need to lead a proper code review. Our findings show that the state of the art of defect prediction, when evaluated in a realistic scenario, cannot be directly used to support code review. Furthermore, we assessed that alternative sets of metrics, anticipated feedback, and fine-grained suggestions represent independent directions to improve prediction performance. Finally, we discovered that research must create intelligent tools that other than predict defects must satisfy actual reviewers’ needs, such as expert selection, splittable changes, realtime communication, and self summarization of changes.
Approaches To Monitoring And Diagnostics Of Education System

Monitoring in the education system comes to the fore in connection with the need to assess the effectiveness of educational organizations. The monitoring system should ensure the observability of a whole vector of state parameters of the education management system. The article substantiates the minimum set of parameters guaranteeing complete observability of the state of the education management system. In turn, the observability of the parameters of the state of the education management system and the analysis of the spectrum of these parameters will improve the monitoring of the education system. The following parameters are distinguished: parameters related to educational activity, determined by state educational standards and qualification requirements, and state parameters characterizing the quality of training of students, products and services. The latter include the parameters of the current and final state of education of the student, staffing and the functional state of the governing bodies, the methodological content of the elements of the educational material base, the functional state of the personnel of the governing bodies. The effectiveness of the control system in any technological circuit, which can manifest itself only during the functioning of the system and in the direction of creating the following effects: manifestations of additional quality and quantity, cost savings, productivity, invariance to disturbances.
Cross-Multidimensional Spaces And Environments As A New Reality Of Education

The article presents the concept of cross-multidimensional spaces and environments as a new reality of innovative education, a factor in the organization of educational activities in a comprehensive school in the modern conditions of its functioning, formation and development. The potential for innovative renewal and improving efficiency of educational activity by building effective dialogue models of its interaction with digital, innovative, technological, ethnocultural environments, axiological, semantic, event spaces. Particular attention is focused on the features of teacher pedagogical activities, educational and research activities of schoolchildren as subjects of cross-multidimensional relations. All this necessitates the staging and resolution of such an important scientific problem as the problem of innovative renewal of educational activities in the context of cross-multidimensional spaces and environments. The main idea can be formulated as the study of new phenomena of pedagogical reality, arising from the interaction and intersection of the educational environment with different spaces and environments. Taking into account the definition of "cross-multidimensional spaces and environments" the work considers various pedagogical situations, when a number of spaces and environments are at intersection, permanently affecting the educational environment. At the same time, the educational space itself also actively influences other spaces and environments (semantic, historical and cultural, axiological, network, architectural, geographical, etc.). Understanding the design mechanisms of the innovative educational process in a cross-multidimensional educational space determines the place and role of students and educators in this process.
Application of Adaptive Virtual Environments Through Biofeedback for the Treatment of Phobias

This study proposes solutions to help people with phobias through the use of virtual environments that allow a contact between the subjects and these phobias. Using neurofeedback, the systems, depending on the emotional state of the user, adapt the scenarios allowing more or less intensity. The phobias these systems treat are social phobia, entomophobia and claustrophobia. The solutions have been developed using Unity, Muse 2 and Vive HTC.
Analysis of Tukul Arwana’s Speech Acts in a Bukan Empat Mata Programe in Trans 7: A Pragmatic Study

The purpose of this study is to describe the use of localized speech acts, illocutionary (assertive, directive, commissive, expressive, and declarative), and perlocution used by TukulArwana in the Non-Four Eyes event in Trans7.This research is a qualitative study using descriptive methods.Data collection techniques used in this research are the recording, listening and note taking techniques.Furthermore, the data are analyzed by translating speech into written form, classifying based on the type of speech act into tabular form, describing speech that has been classified, making discussion, and concluding.The results showed that out of 576 TukulArwana's speeches in several episodes of the Non-Four-Eyes program that were observed, there were 147 speeches which included locus speech acts, and illocutionary speech acts which consisted of assertive, directive, expressive, and commissive speech acts.The most widely used form of speech acts is directive and expressive illocutionary speech acts.The least speech acts are commissive speech acts, and those that are not found are declarations and speech acts.
Skew decision process based on machine learning content analysis and clustering

This paper presents a robust and fast skew detection approach based on content analysis and projection profile clustering. The proposed approach is quite generic and therefore, could detect skew angle for various types of documents such as graphics, charts, postal labels, handwritten text, forms, drawings and their possible combination. The proposed approach is robust that could deal up to ±89 degree skew angle even scanned at least resolution of 50 dots per inch. Additionally, it is language independent. The proposed technique consists of mainly two steps. In the first step, corners are located except top corner. The skew angle is estimated using the cross correlation of located corners with minimum computational complexity. However, to verify the alignment, horizontal projection profile is analysed. The proposed approach is examined using open access benchmark database of scanned document images. Success rate approaching 100% within a confidence range of 0.3 degrees is reported.
Convolutional Neural Network Visualization in Adversarial Example Attack

In deep learning, repeated convolution and pooling processes help to learn image features, but complex nonlinear operations make deep learning models difficult for users to understand. Adversarial example attack is a unique form of attack in deep learning. The attacker attacks the model by applying invisible changes to the picture, affecting the results of the model judgment. In this paper, a research is implemented on the adversarial example attack and neural network interpretability. The neural network interpretability research is believed to have considerable potential in resisting adversarial examples. It helped understand how the adversarial examples induce the neural network to make a wrong judgment and identify adversarial examples in the test set. The corresponding algorithm was designed and the image recognition model was built based on the ImageNet training set. And then the adversarial-example generation algorithm and the neural network visualization algorithm were designed to determine the model learning heat map of the original example and the adversarial-example. The results show that it develops the application of neural network interpretability in the field of resisting adversarial-example attacks.
Nondeterministic Strategies and their Refinement in Strategy Logic

Nondeterministic strategies are strategies (or protocols, or plans) that, given a history in a game, assign a set of possible actions, all of which are winning. An important problem is that of refining such strategies. For instance, given a nondeterministic strategy that allows only safe executions, refine it to, additionally, eventually reach a desired state of affairs. We show that strategic problems involving strategy refinement can be solved elegantly in the framework of Strategy Logic (SL), a very expressive logic to reason about strategic abilities. Specifically, we introduce an extension of SL with nondeterministic strategies and an operator expressing strategy refinement. We show that model checking this logic can be done at no additional computational cost with respect to standard SL, and can be used to solve a variety of problems such as synthesis of maximally permissive strategies or refinement of Nash equilibria.
Application of Web-Based Competency Test (UKSI) with Framework Code Igniter (CI)

Information Systems, Application programs, web based, Competency Test, Competency Certification, Framework Code Igniter (Ci)