Reviewerly/exHarmony huggingface dataset:
cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope

We propose a methodology to approximate conditional distributions in the elliptope of correlation matrices based on conditional generative adversarial networks. We illustrate the methodology with an application from quantitative finance: Monte Carlo simulations of correlated returns to compare risk-based portfolio construction methods. Finally, we discuss about current limitations and advocate for further exploration of the elliptope geometry to improve results.
On the information ratio of graphs without high-degree neighbors

We consider the information ratio of graph based secret sharing schemes in a special case of graphs in which vertices of degree at least 3 are not connected by an edge. We prove that – after some trivial reduction of the original graph – the information ratio depends on the maximal value of the difference of the degree and the number of triangles containing a given vertex of the reduced graph. This result can be considered as a common generalization of previous results on the information ratio of special trees and graphs of girth at least 6.
Combining neural networks and symbolic inference in a hybrid cognitive architecture

Recently, there has been a big progress in developing artificial deep-learning neural networks and large-scale knowledge graphs. However, the results in these two research fields have serious drawbacks. The solutions offered by neural networks remain unstable and prone to adversarial attacks: while the percentage of correct answers increases, the incorrect answers often contain glaring errors. Large knowledge graphs contain a lot of facts but little knowledge; they are mostly used for information search and retrieval. The ability to reason conclusions on them is limited, and the majority of modern research turns to approximate methods like neural networks and graph embeddings to draw conclusions and use the accumulated knowledge. In this work, I propose a hybrid cognitive architecture inspired by the observable features of human thinking. Pruning obviously wrong solutions seems to be more natural for human symbolic reasoning than making far-fetched strict logical conclusions, while generating new ideas is often intuitive. So a hybrid cognitive architecture can employ generative neural networks as a sort of "intuition" (generating possible solutions) and symbolic inference as a control contour to verify and filter the generated solutions, weeding out dangerous and wrong ideas. This requires creating knowledge graphs containing negative information: the information of what cannot happen or should not be done and why. The problems of creating negative knowledge graphs are discussed. Hybrid cognitive systems using the proposed architecture will be a lot more trustworthy as they will have a system of human-verifiable rules that ensures avoiding the worst errors which can be used in many fields from decision making to natural-language parsing.
Educational strategy in digital environment

The article analyzes educational transformations as viewed by undergraduate students of the 1 st and the 4 th year as well as aspects of digital transformation based on the conducted survey. Predicated on general trends and collected data, possible development strategy of education in digital environment is introduced. It is assumed that online courses with integrated materials designed on digital platforms will command a larger part in the curricula. At the same time, as the study shows, the traditional education with face-to-face communication is still important to students. Therefore, the educational strategy should emphasize the blended format – face-to-face learning experience on campus complemented by the distance component in the form of synchronous virtual classes, enriched by asynchronous online electronic courses with e-lectures and interactive tasks for students.
Hybrid Method for Contrast Enhancement of Industrial Videoscope Images

Pre-processing is a required step for image enhancement. Videoscope image enhancement is a difficult task as the captured images suffer from noise and poor contrast. Contrast enhancement is very important for videoscope image enhancement as it improves the quality of images. Contrast enhancement techniques include histogram equalization, fuzzy processing, Contrast Limited Adaptive Histogram Equalization (CLAHE), and histogram matching. These techniques are applied to improve the quality of the videoscope images. The hybrid method that contains CLAHE and fuzzy enhancement achieves good results. Simulation results prove a superior performance with higher image quality, higher evaluation metric values, and much more details in images.
Blockchain Technology Empirical Studies on the Demand of Distributed Network

Abstract Blockchain technology has become a new model for encryption, unsustainability and confidence in both consumers and suppliers (SPs) in order to increase the quality of services. A game theory framework is used to simulate a competitive market with the existing platform controlled by a centralizer as a confidant third party to provide an economic overview of such a Blockchain-based platform sector. The portals serve as a mediator in this industry and provide consumers with services delivered by SPs. The key reasons for blockchain based network market performance are I how engagement by SPs represents its service efficiency (QoS) and (ii) how SPs are empowered to contribute their services, such as the virtual environment. A non-cooperative two-stage competitive game is used in our game formulation, whereby the first stage model how to promote SPs on a blockchain basis and the second stage modeleves rivalry between platforms to draw consumers. We therefore have an equilibrium analysis that offers a valuable description of the effect on competition among platforms and SPs’ equilibrium reward policy of the service quality of the blockchain network. Our numerical analysis reveals that the incentive to balance is increasing proportionately to the Fault tolerance of a blockchain-based network when the incentive is unfavourable if the number of participating SPs has a non-increasing QoS.
Development of a low-cost wireless data logging Pyrano-meter with inbuilt temperature and humidity monitoring system

A low cost photodiode-based pyrano-meter with an in-built temperature and humidity monitoring system, robust data-logging, long-range wireless interface to an indoor unit capable of measuring solar irradiance, instant temperature, and relative humidity was designed, developed and tested in this study. The work was carried out in Osun State University, Osogbo. The newly developed pyrano-meter comprises two sub-systems: Indoor Unit and Outdoor Unit. The Outdoor Unit is built around BPW21R- planar Silicon PN photodiode. The indoor unit uses PIC18F2550 as the central controller. The Solar radiation, Temperature, and Relative Humidity data received from the Outdoor unit were stored in an SD Card Memory, a reference for calibration and contrast, the SRS 100 pyrano-meter was used. A 73 ± 0.55 Wm −2 calibration constant was achieved. The stability of the systems built was comparable to the SRS 100 on an open day - a sky test that gave the device irradiances of 629 and 648 Wm −2 at 6 p.m. The pyrano-meter was tested with actual metrological standard value and it provides good results. • Industry manufactured pyrano-meter are usually very costly. • Developed system allows measurement of outdoor climatic condition, while stays indoor. • Developed pyrano-meter had similar precision as the industry manufactured one.
Human-Centered Artificial Intelligence Considerations and Implementations: A Case Study from Software Product Development

This paper provides an overview of artificial intelligence (AI) and human-centered artificial intelligence (HCAI). It presents a case study of applying AI and HCAI to software product development. Considerations such as use case development, user involvement and the creation of a smart assistant are reviewed.
A Novel Approach to Recommend Skincare Products Using Text Analysis of Product Reviews

The objective of this research is to develop a system that can categorize products according to their specific features with the help of product-based reviews. This system can be used to recommend suitable products according to their category. This system also avoids recommendation of those products which have harmful side effects as scrutinized with the help of sentimental analysis discerning negative reviews. Every user who searches for skincare products typically targets his requirement, and a recommendation system should be able to cater to such users. In this paper, we have proposed a model that integrates K-means clustering and topic modeling (LDA) to identify the specific topics included in the review text of a particular product. To further classify products concerning the topic, we have also used coherence scores and similarity scores to evaluate the model based on latent Dirichlet allocation (LDA). The coherence score would be a measure of semantic similarity between the words of topics which will be useful for assessing the quality of topics as predicted by the algorithm. The results turn out promising as products can be filtered by sentiment analysis and categorized according to their topics. The system can be used to not only approve suitable products but can also discard those products which can have harmful side effects, thereby eliminating the chance of dangerous side effects to a considerable extent.
On the distributed software architecture of a data analysis workflow: A case study

Abstract Hybrid distributed computing software architectures gain great importance in data analysis workflows as the number of available underlying machine learning libraries and data storage systems increase. We argue that there is a need for novel approaches for software architecture designs that can enable machine learning data analysis workflows to run on top of different subsystem libraries. To address this need, we propose a hybrid distributed software architecture in this manuscript. The proposed architecture manages machine learning models for both supervised and unsupervised machine learning data analysis workflows. To show the usability of the proposed architecture, we implement a prototype for the banking sector as a case study. The prototype application includes two data analysis workflows: a workflow for predicting the loan usage tendency of customers, and a workflow for clustering the customers based on the usage patterns of banking loans. The prototype is tested on a large scale banking dataset. Performance tests were carried out to investigate the performance in terms of both responsiveness and scalability of the system. The results obtained reveal the usability of the proposed architecture.
Doing more with less: Growth, improvements, and management of NMSU’s computing capabilities

Deployed in 2015, Discovery is New Mexico State University’s commonly-available High-Performance Computing (HPC) cluster. The deployment of Discovery was initiated by Information and Communication Technologies (ICT) employees from the Systems Administration group who wanted to help researchers run their computations on a more powerful system than one they had sitting in their offices. Over the years, the cluster has grown 6 times, and as of March 2021 has 52 compute nodes, 1480 CPU cores, 17 Terabytes of RAM, 30 GPUs, and 1.8 Petabytes of usable storage. Discovery’s hardware is acquired using a combination of university funds, condo-model based funds, and grant funds, causing Discovery to be a heterogeneous system containing several CPU generations.
JPEG Meets PDE-based Image Compression

Inpainting-based image compression is emerging as a promising competitor to transform-based compression techniques. Its key idea is to reconstruct image information from only few known regions through inpainting. Specific partial differential equations (PDEs) such as edge-enhancing diffusion (EED) give high quality reconstructions of image structures with low or medium texture. Even though the strengths of PDE-and transform-based compression are complementary, they have rarely been combined within a hybrid codec. We propose to sparsify blocks of a JPEG compressed image and reconstruct them with EED inpainting. Our codec consistently outperforms JPEG and gives useful indications for successfully developing hybrid codecs further. Furthermore, our method is the first to choose regions rather than pixels as known data for PDE-based compression. It also gives novel insights into the importance of corner regions for EED-based codecs.
Recognition of Kannada Handwritten Words from Answer Scripts Using Machine Learning Approaches

In this paper, handwritten word recognition system for answer scripts written in Kannada language is proposed, which comprises a new concept for handwriting word recognition architecture based on CNN model and SVM classifier. Paper provides a detailed concept and implementation of Kannada word recognition. Real-time dataset is used for training the model. The proposed system has achieved the accuracy of 90% for Kannada handwritten words.
Interdisciplinary Connections in the General Course of Higher Mathematics

The article discusses the methods of teaching higher mathematics, arising at the modern level of education in our country. The author relies on his experience working at the Faculty of Information Technologies of the Moscow State Psychological and Pedagogical University.
A Practical Approach for Rate-Distortion-Perception Analysis in Learned Image Compression

Rate-distortion optimization (RDO) of codecs, where distortion is quantified by the mean-square error, has been a standard practice in image/video compression over the years. RDO serves well for optimization of codec performance for evaluation of the results in terms of PSNR. However, it is well known that the PSNR does not correlate well with perceptual evaluation of images; hence, RDO is not well suited for perceptual optimization of codecs. Recently, rate-distortion-perception trade-off has been formalized by taking the Kullback-Leibler (KL) divergence between the distributions of the original and reconstructed images as a perception measure. Learned image compression methods that simultaneously optimize rate, mean-square loss, VGG loss, and an adversarial loss were proposed. Yet, there exists no easy approach to fix the rate, distortion or perception at a desired level in a practical learned image compression solution to perform an analysis of the trade-off between rate, distortion and perception measures. In this paper, we propose a practical approach to fix the rate to carry out perception-distortion analysis at a fixed rate in order to perform perceptual evaluation of image compression results in a principled manner. Experimental results provide several insights for practical rate-distortion-perception analysis in learned image compression.
STC: Stacked Two-stage Convolution for Aspect Term Extraction

Aspect term extraction (ATE) aims to extract aspect terms from reviews as opinion targets for sentiment analysis. Although some of the previous works prove that dependency relationship between aspect terms and context is useful for ATE, they have barely tried to use graph neural networks to capture valuable information in dependency patterns automatically. In this paper, we propose a novel sequence labeling method for ATE, which exploits convolutional neural network (CNN) to capture local information of a sentence, and further aggregate k-order neighbor nodes’ information via graph convolutional network (GCN) over dependency tree. Differently from approaches based on sequential networks like recurrent neural network (RNN), our convolution model can be calculated in parallel, which improves the training and inference speed. Experimental results show that our approach outperforms other baseline methods, which don't rely on pre-trained transformer model.
Pressure Analysis in Dynamic Handwriting for Forgery Detection

Handwritten signatures are extensively used over many fields for processing transactions and contracts. Online (dynamic) handwritten signatures have been used as a way of identification and there has also been intensive research done on distinguishing genuine signatures from forgeries for over past 40 years. However, dealing with high-skilled forgeries still remains a challenge. Therefore, handwritten signatures have not been used for authentication in electronic systems. Unlike static signatures, online handwritten signatures contain dynamic features and are able to identify individuals with more precision. The pressure value recorded during the online signature process has been a crucial information for automatic signature verification systems. In addition, the pressure an individual applies to mouse is also shown to enable continuous authentication. This paper focuses on the analysis of the pressure patterns during the process of dynamic signature. Comparing the pressure values for genuine signatures and skilled forgeries allows us to detect forgery while having the minimal information about the genuine signature. This fact significantly decreases the need of storing very sensitive biometric information with the potential to make the process writer independent.
A Novel Massive Big Data Analysis of Educational Examination Research Using a Linear Mixed-Effects Model

To further solve the problems of storage bottlenecks and excessive calculation time when calculating estimators under two different formats of massive longitudinal data, an examination data analysis and evaluation method based on an improved linear mixed-effects model is proposed in this paper. First, a three-step estimation method is proposed to improve the parameters of the linear-effects model, avoiding the complicated iterative steps of maximum likelihood estimation. Second, we perform spectral clustering based on test data on the basis of defining data attributes and basic evaluation rules. Finally, based on cloud technology, a cross-regional, multiuser educational examination big data analysis and evaluation service platform is developed for evaluating the proposed method. Experimental results have shown that the proposed model can not only effectively improve the efficiency of test data acquisition and storage but also reduce the computational burden and the memory usage, solve the problem of insufficient memory, and increase the calculation speed.
Rock Classification with Machine Learning: a Case Study from the Zinkgruvan Zn-Pb-Ag Deposit, Bergslagen, Sweden

In this paper we assess two traditional machine learning (ML) methods which can be used for automatic rock type classification: (1) the Self-Organising Map (SOM) with k-means clustering, and (2) Classification and Regression Trees (CART). The dataset used for this paper were chemical compositional data of rocks acquired through X-Ray Fluorescence (XRF) analysis. The ground truth of the dataset was generated by human experts in the field of geology. The complexity of the chosen dataset influenced the evaluation performance of the two ML models. We achieve an overall accuracy of 68.02 % and 62.79 % respectively when using SOM with k-means and CART.
The system of professional development and assessment of the competencies of university teaching staff to ensure quality education in the interests of sustainable development of society: a case study of Russia

This article substantiates the need to form a comprehensive system of professional development and assessment of teachers’ competencies in order to ensure quality education in the interests of sustainable development of society. The main goals and objectives are formulated and the methodological tools developed by the authors for the formation of this system are described. The features of the implementation of the system in the practice of conducting the educational process at Peter the Great St. Petersburg Polytechnic University (St. Petersburg, Russia) are revealed. The risks are identified and recommendations for their minimization are given for other educational organizations of higher education that implement a comprehensive system of professional development and assessment of the competencies of teachers using digital services.
A Wavelet-Based Segmentation Technique for Medical Images

In order to extract any abnormality in the given image, the first step is to segment it so that detailed information can be identified clearly. This paper describes the wavelet-based segmentation method for segmentation of medical images, particularly X-ray images, to locate any fracture in the bones. Of the images considered in this study, the third level of decomposition of Haar wavelet is found most suitable in terms of statistical image identifier such as signal-to-noise ratio (SNR), entropy etc.
FPGA Implementation of Support Vector Machine Using Ising Model for AI on Things

In this study, we classified three datasets by using a support vector machine (SVM) implemented in an Ising model. In the previous research, the Ising SVM system succeeded in linearly classifying plotted points on a secondary plane into two classes in a simulation. For this study, we improved the system in two ways: we applied a nonlinear transformation by a Gaussian, and we enabled the system to handle feature vectors of three dimensions or more. The performance of the improved Ising-SVM system was evaluated by simulation with MATLAB and actual machine verification with a field-programmable gate array (FPGA). The performance was evaluated on three datasets assuming real-world applications. In the MATLAB simulation, the identification rates of the Ising-SVM system were almost the same as those of a conventional SVM. Furthermore, the calculation time of the proposed system was reduced to an average of 18.0% of that of the standard SVM. Finally, almost the same identification rates were obtained in the actual FPGA verification as in the MATLAB simulation.
The Mobile Phone in Governance for Environmental Sustainability in Sub-Saharan Africa

In this study, we assess how the mobile phone can be leveraged upon to improve the role of governance in environmental sustainability in 44 Sub-Saharan African countries. The Generalised Method of Moments is used to establish policy thresholds. A threshold is a critical mass or level of mobile phone penetration at which the net effect of governance on Carbon dioxide (CO2) emissions changes from positive to negative. Mobile phone penetration thresholds associated with negative conditional effects are: 36 (per 100 people) for political stability/no violence; 130 (per 100 people) for regulation quality; 146.66 (per 100 people) for government effectiveness; 65 (per 100 people) for corruption-control and 130 (per 100 people) for the rule of law. Practical and theoretical implications are discussed. The study provides thresholds of mobile phone penetration that are critical in complementing governance dynamics to reduce CO2 emissions.