Reviewerly/exHarmony huggingface dataset:
Digital educational space in the professional training of a musical art teacher

The objective of this study was to determine whether higher education institutions, teachers and students of the specialization in musical art are prepared to create and use the digital educational space in the educational process. The study involved the following methods: literature review and analysis of the content of official documents of higher educational institutions that provide professional training for music teachers; survey; mathematical methods of data processing; Statistica software package. The research has shown that not all higher educational institutions have the necessary resources to implement learning in the digital education space. Besides, not all higher educational institutions teach subjects that help improve students’ digital literacy. There are also teachers who have difficulties using the digital educational space in the learning process due to lack of digital literacy. The study demonstrated awareness of the importance of using the digital educational space in the education of future music teachers. But, it is necessary to pay attention to improving the digital literacy of all participants in the educational process, as well as provide educational institutions with proper modern recourses, software, hardware and equipment, including also computer music technologies and electronic musical instruments.
Explainable Unsupervised Argument Similarity Rating with Abstract Meaning Representation and Conclusion Generation

When assessing the similarity of arguments, researchers typically use approaches that do not provide interpretable evidence or justifications for their ratings.Hence, the features that determine argument similarity remain elusive.We address this issue by introducing novel argument similarity metrics that aim at high performance and explainability.We show that Abstract Meaning Representation (AMR) graphs can be useful for representing arguments, and that novel AMR graph metrics can offer explanations for argument similarity ratings.We start from the hypothesis that similar premises often lead to similar conclusionsand extend an approach for AMR-based argument similarity rating by estimating, in addition, the similarity of conclusions that we automatically infer from the arguments used as premises.We show that AMR similarity metrics make argument similarity judgements more interpretable and may even support argument quality judgements.Our approach provides significant performance improvements over strong baselines in a fully unsupervised setting.Finally, we make first steps to address the problem of reference-less evaluation of argumentative conclusion generations.
Comparing Multiclass AUC ROC Methods

Statistical models are commonly used to predict the outcome of events in a wide variety of fields such as health, finance, and business. Evaluation metrics are used to assess the effectiveness of these predictive models. One classification evaluation metric, called the receiver operating characteristics (ROC) curve has several useful properties, such as being threshold agnostic and can manage class imbalance where the outcomes are not equally represented. Despite the usefulness of the ROC curve, there is not a standard approach to extend to curve to multiclass problems. The purpose of this project was to evaluate multivariate ROC curve implementations with various underlying class proportions and degrees of separation. The methods we evaluated include the Macro, Micro, and Weighted average for one versus rest comparisons as well as the Hand and Till (HT) method. We compared the methods on simulated data with balanced, unbalanced and strongly unbalanced class proportions in combination with no separation, small separation, and large separation between classes. We found the methods were significantly different when class proportions were either unbalanced or severely unbalanced and the distributions were either separated or strongly separated (n=100, p<0.01). Pairwise comparisons found that HT and Macro were significantly different than Micro and Weighted (n=100, p<0.01). This study demonstrates that some of the AUC ROC methods differ depending on the class proportions and underlying distributions. The findings from this project may help practitioners select the most appropriate method according to their goals.Department: Computer Science Faculty Mentor: Dr. Wanhua Su
Network analytics in the big graph database era: A research framework and agenda for business and innovation

Although there have been reviews on social network analysis and big data management, there has not been recent research reviews that acknowledge the growing scope and potential of network analysis enabled by new advances in graph database technology for very large datasets. This article seeks to provide a comprehensive yet concise review of the key research areas where new graph database technology can be applied for the analysis of business and innovation using big data sources. More specifically, a framework that integrates elements from graph databases, machine learning and big data management is proposed. The agenda for future research provides concrete guidance for the design of multi-layer business and innovation studies that integrate multiple big data sources with graph database technology and machine learning methods.
Categorical Belief Updating Under Uncertainty

Author(s): Dewitt, Stephen H; Li, Carmen; Koh, Daniel; Fenton, Norman; Lagnado, David | Abstract: The need to update our estimates of probabilities (e.g., the accuracy of a test) given new information is commonplace. Ideally, a new instance (e.g., a correct report) would just be added to the tally, but we are often uncertain whether a new instance has occurred. We present an experiment where participants receive conflicting reports from two early-warning cancer tests, where one has higher historical accuracy (HA). We present a model showing that while uncertain which test is correct, estimates of the accuracy of both tests should be reduced. However, among our participants, we find two dominant approaches: (1) participants increase the more HA test, reducing the other; (2) participants make no change to either. Based on mixed methods we argue that both approaches represent two sides of a ‘binary’ decision i.e., (1) update as if we have complete certainty which test is correct and (2) update as if we have no information.
Improving the Performance of QUATRE-EAR Using Linear Population Size Reduction

Differential Evolution (DE) was a popular and effective algorithm for numerical optimization, and QUasi-Affine TRansformation Evolution (QUATRE) was a novel variant of DE, in which an enhanced structure was incorporated into crossover operation to further improve the overall optimization performance. In QUATRE, a novel evolution matrix, rather than the crossover rate CR, was introduced to generate trial vectors. Moreover, by incorporating an adaptation strategy of the evolution matrix as well as the adaptation scheme of control parameters, a QUATRE-EAR algorithm was proposed to further improve the QUATRE algorithm. Nevertheless, there were also some other techniques that could be utilized to further enhance the optimization performance of the QUATRE-EAR. Hence, an extended version of the QUATRE-EAR algorithm was proposed by incorporating a novel population size reduction strategy (L-QUATRE-EAR). Moreover, Both the scale factor F and the auto-generated evolution matrix M can be adapted intelligently during the evolutionary process of the L-QUATRE-EAR algorithm. The novel algorithm was verified on CEC2013 benchmarks, it was also contrasted with some other advanced DE variants, and the experiment results showed its competitiveness.
VR-Based Interface Enabling Ad-Hoc Individualization of Information Layer Presentation

Graphical user interfaces created for scientific prototypes are often designed to support only a specific and well-defined use case. They often use two-dimensional overlay buttons and panels in the view of the operator to cover needed functionalities. For potentially unpredictable and more complex tasks, such interfaces often fall short of the ability to scale properly with the larger amount of information that needs to be processed by the user. Simply transferring this approach to more complex use-cases likely introduces visual clutter and leads to an unnecessarily complicated interface navigation that reduces accessibility and potentially overwhelms users. In this paper, we present a possible solution to this problem. In our proposed concept, information layers can be accessed and displayed by placing an augmentation glass in front of the virtual camera. Depending on the placement of the glass, the viewing area can cover only parts of the view or the entire scene. This also makes it possible to use multiple glasses side by side. Furthermore, augmentation glasses can be placed into the virtual environment for collaborative work. With this, our approach is flexible and can be adapted very fast to changing demands.
Path Planning Optimization in SDN Using Machine Learning Techniques

Internet, mobile networks, and mobile devices have contributed to the massive development of telematics applications. Therefore, the underlying communication network that supports the connectivity of these applications must provide an adequate level of QoS. On the other hand, the advent of new networking paradigms such as Software Defined Networks (SDN) has transformed the telco landscape. Consequently, traditional teletraffic engineering techniques cannot comply with the requirements of agile, dynamic, and tailored traffic controls. In this context, a proposal to improve the QoS of communication networks by optimizing the path planning process using the machine learning principles is presented. Thus, path planning is considered a multi-classification problem. Several configurations of three machine learning classifiers have been evaluated to determine the best model. Two class-balanced experimental datasets named Dl and D2 were created for validation purposes. The support vector machine classifier with a linear kernel and cost c = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> was the best model obtaining a mean of an area under the receiver operating characteristics curve of 0.999 using the D1 dataset. The same classifier with a polynomial kernel and cost c = 10 achieved a score of 0.999 using the D2 dataset. These results statistically overcame the remaining classification schemes at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> = 0.05, determining the support vector machine model as the best classifier to find optimal paths between endpoints.
Graph Convolutional Network-Strengthened Topic Modeling for Scientific Papers

Machine learning has been woven into statistics to modernize topic modeling over textual documents written in natural language, and scientific paper search and recommendation can consequently offer higher accuracy instead of counting on traditional keyword-based search. However, topic distribution of a paper resulted from existing topic modeling techniques only relies on the statistics of words contained in the paper itself. We argue that community users’ views of a paper may also provide insights at the time of recommendation. For example, if a paper on fake image detection has been cited heavily by machine learning papers, such a feature should be absorbed in the embedding of this paper, so that it can be recommended for future query on machine learning. In this paper, we present a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) method, which employs GCN technique to refine topic modeling of scientific papers. A citation-oriented knowledge graph is constructed, and topic modeling is mapped to feature embedding of the comprising papers. On top of its own topics carried in its content, each paper learns topics from its neighbors and revise its embedding accordingly. Our empirical studies over real-life scientific literature has proved the necessity and effectiveness of our proposed approach.
Full-reference Image Quality Metric For Computer-rendered Stereoscopic Images

In order to evaluate the quality of computer-generated stereoscopic images further accurately, we propose a new quality assessment metric based on the human visual system (HVS) modeling and machine-learning algorithm. First, we consider the existence of binocular rivalry to generate a cyclopean image of the asymmetrically distorted part that can be masked. Furthermore, we can get a pair of cyclopean images, decompose the reference and distorted images with RGB-YUV color channels respectively. We respectively calculate the edge and texture features of the 3-channel color map to obtain 6 pairs of color feature maps, then calculate the color feature difference values of each pair of feature maps. Finally, we use the support vector regression (SVR) for 6 color feature difference values to be calculated, the final quality value can be obtained. It can be seen from the experimental results that our algorithm has better performance and more accurate calculations than existing algorithms.
PPE Circuits for Rational Polynomials

Pairings are a powerful algebraic setting for realizing cryptographic functionalities. One challenge for cryptographers who design pairing systems is that the complexity of many systems in terms of the number of group elements and equations to verify has been steadily increasing over the past decade and is approaching the point of being unwieldy. To combat this challenge, multiple independent works have utilized computers to help with the system design. One common design task that researchers seek to automate is summarized as follows: given a description of a set of trusted elements T (e.g., a public key) and a set of untrusted elements U (e.g., a signature), automatically generate an algorithm that verifies U with respect to T using the pairing and group operations. To date, none of the prior automation works for this task have support for solutions with rational polynomials in the exponents despite many pairing constructions employing them (e.g., Boneh-Boyen signatures, Gentry's IBE, Dodis-Yampolskiy VRF). We demonstrate how to support this essential class of pairing systems for automated exploration. Specifically, we present a solution for automatically generating a verification algorithm with novel support for rational polynomials. The class of verification algorithms we consider in this work is called PPE Circuits (introduced in [HVW20]). Intuitively, a PPE Circuit is a circuit supporting pairing and group operations, which can test whether a set of elements U verifies with respect to a set of elements T. We provide a formalization of the problem, an algorithm for searching for a PPE Circuit supporting rational polynomials, a software implementation, and a detailed performance evaluation. Our implementation was tested on over three dozen schemes, including over ten test cases that our tool can handle, but prior tools could not. For all test cases where a PPE Circuit exists, the tool produced a solution in three minutes or less.
Quantum Resistance for Cryptographic Keys in Classical Cryptosystems: A Study on QKD Protocols

Distribution of keys in classical cryptography is one of the most significant affairs to deal with. The computational hardness is the fundamental basis of the security of these keys. However, in the era of quantum computing, quantum computers can break down these keys with their substantially more computation capability than normal computers. For instance, a quantum computer can easily break down RSA or ECC in polynomial time. In order to make the keys quantum resistant, Quantum Key Distribution (QKD) is developed to enforce security of the classical cryptographic keys from the attack of quantum computers. By using quantum mechanics, QKD can reinforce the durability of the keys of classical cryptography, which were practically unbreakable during the pre-quantum era. Thus, an extensive study is required to understand the importance of QKD to make the classical cryptographic key distributions secure against both classical and quantum computers. Therefore, in this paper, we discuss trends and limitations of key management protocols in classical cryptography, and demonstrates a relative study of different QKD protocols. In addition, we highlight the security implementation aspects of QKD, which lead to the solution of threats occurring in a quantum computing scenario, such that the cryptographic keys can be quantum resistant.
Restoration of Rician Corrupted MR Data Using Improved Hybrid Model

Restoration of MR Images using a hybrid model consisting of two stages is implemented. Application of Fourth Order PDE and the application of relaxed median filter are pre and post stages respectively. The results were calculated using standard parameters like PSNR, SSIM and RMSE. It was found that the hybrid mm gives acceptable results when compared with standard non-linear filters when applied to T1 weighted Thorax Images. For Brain MR Images, the hybrid model filter performs better for a finite range of input noise level ( $$\%N$$ ). The algorithm was implemented on synthetic images of T1 weighted Thorax and Brain MRI using MATLAB 2015a.
Dataset Reduction via Bias-Variance Minimization

The amount of generated, collected and labelled data rapidly increases nowadays, which raises the question of creating methods for extracting dataset subsets, learning on which a model can achieve the same generalization level as when learning on the whole available set of data. Methods of the optimal design family solve this problem, but they are poorly applicable to modern multidimensional data and complex models. In this paper, we construct a method based on an intuitive way to measure the quality of the dataset subset, propose ways to approximate this functional and develop an efficient optimization method to reduce the dataset size. We demonstrate the capabilities of our approach on the MNIST dataset in application to the logistic regression.
On Actively-Secure Elementary MPC Reductions

We introduce the notion of elementary MPC reductions that allow us to securely compute a functionality f by making a single call to a constant-degree "non-cryptographic" functionality g without requiring any additional interaction. Roughly speaking, "non-cryptographic" means that g does not make use of cryptographic primitives, though the parties can locally call such primitives.
How do machine translators measure up to human literary translators in stylometric tests?

Abstract This study aims to test the classification of literary translations by human and machine translators in a bid to explore stylistic distinctions between the two groups and among machine translators and their evolution over 1 year between 2019 and 2020. For this, the study takes a stylometric approach by employing two statistical methods popularly used in authorship attribution—support vector machine (SVM) and principal component analysis (PCA), which are applied to analyzing three types of features—1-grams, 2-grams, and 3 grams. The results show that over the researched period, the three machine translators investigated (Google, Bing, and Papago) moved in the direction of converging in style while advancing toward human translators. The distance gained by them on human translators, however, was not significant enough to challenge the clear-cut divide between the two groups. The PCA tests additionally revealed some characteristics of the machine translator that might be responsible for the stylistic distinction between them and their human counterparts including their tendency of overusing basic standard language.
Computer-Aided Malaria Detection Based on Computer Vision and Deep Learning Approach

This work describes an automatic system for malaria detection. Red blood corpuscles infected with malaria parasites of Giemsa stained segmented cells of thin-blood smeared slides are taken as input images. Initially, image processing techniques such as image resizing and bilateral filtering technique for noise removal, are applied. Further, deep learning-based convolution neural layer network models are proposed for malaria detection. Additionally, alongside comparison with other approaches and methodologies, comparison of various traditional machine learning algorithms is also done. Results show that the proposed model demonstrated in this work performs the best on the given input images with the highest accuracy of 95%, specificity score of 93.2% and sensitivity score of 96.8%.
Corrections to “Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , we had mentioned the SSMA in many result tables, but unfortunately, we had made a mistake in the citation, which would have made the readers unable to link to the original material of the method. The faults in the above article have been corrected in this article. The self-supervised model adaptation (SSMA) <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> is a fusion module combining two feature maps. This module was developed for multi-modal semantic segmentation and achieved the best efficiency in many conditions.
Edge-Cloud Intelligence in Self-Diagnostic of Land Mobile Radio Systems

IIoT sensors are usually deployed on a massive scale with stringent scalability, modularity, and interoperability requirements. It is indisputable that they produce a large amount of high-speed and heterogeneous data streams that pose many challenges to perform management, processing, and analytical tasks. This paper proposes an integrated edge-cloud continuum platform that can harvest IIoT data streams from a variety of sensors deployed at a remote RF site; and can harmonize different machine learning models for diagnosing problems that enhance infrastructure monitoring and long-term structural resilience. A real-world experiment was carried out to evaluate the proposed platform for supporting a self-diagnostic process for intelligent maintenance of Land Mobile Radio (LMR) infrastructures.
Traffic congestion recognition based on information entropy

Urban road traffic key nodes have a great influence on the urban road traffic, traffic state key nodes will also affect the adjacent nodes, thus a greater influence on road network, the information entropy can characterize degree of chaos system, this article through to acquisition of various lane traffic tunnel section, the information entropy to calculate the key nodes, Then, node information entropy is used to represent the degree of traffic congestion in urban tunnel sections. Finally, statistical analysis is conducted to find the change law of information entropy at the intersection entry point and the entrance information entropy at the tunnel exit.
Design and Implementation of Artificial Intelligence Image Detection System Based on Plane Features

Artificial intelligence has entered modern people's life, and image detection is the cornerstone in many application fields, especially in the field of Internet of Things. Image is a special data format. If we want to obtain different connotation information according to different images, we need some image detection technology, and also need some image feature algorithms. In this paper, we propose to realize registration by using plane features, which are obtained by segmenting depth images. At the same time, a new geometric feature invariant-feature ratio is proposed, and a new affine invariant image feature descriptor is constructed based on it. By constructing a series of straight lines intersecting the target image, the descriptor represents the image with the positional relationship of a series of collinear points. Experimental results show the accuracy and robustness of this registration method.