Reviewerly/exHarmony huggingface dataset:
Methodology for the Improvement of the Learning Outcomes of the Public Universities of Ecuador Through the Support and Permanent Control Through Academic Tutoring

This paper details an investigation carried out at the Northern Technical University (Ibarra-Ecuador), for the improvement of the results of the teaching-learning processes, through the application of group academic tutorials for students who are repeating a subject (second or third registration). As a case, the methodology was applied in the Industrial Engineering Career of the Technical University of the North, during two academic periods (September 2019-February 2020 and June-October 2020), considering students who are in second or third enrollment, as well subjects, from second to tenth semester. Once applied as a results, an increase of 8.8% was obtained in the approval of subjects for students with second enrollment and increase of 8.3% for students with third enrollment, it should be noted that when planning the semester academic tutorials for students who are taking second or third enrollment subjects, an increase from 81.81% to 93,75% in the execution of academic tutorials.
Digital Image and Video Processing to Motivate Physical Exercise Using a Kinect-Based Module

This work presents the development and results of a prototype to motivate physical exercise in adults through image and video digital processing. The system has a sensor responsible for image and video acquisition (Kinect), a user interface, a dispenser mechanism, and a microprocessor system. By using the Skeletal Tracking of Microsoft Kinect sensor, this prototype identifies the person located in front of the module, recognizes and validates the appropriate execution of predefined physical exercises (squats or lunges), and counts the number of times that the user performs the exercise in a base of time. Once a specific number of exercise repetitions has been properly executed (at least 20 in 120 s), the interface shows the counter result, and the system delivers a water bottle as an incentive. With the use of this module, it is intended to promote physical exercise in an unconventionally and strikingly way.
Implementasi E-Commerce sebagai Media Penjualan Online pada Toko Athena Shop

E-commerce is the act of buying and selling through electronic media related to the internet. Athena Shop is a specialty shop that specializes in baby goods such as feeding, sleeping and dressing items. The last few months have seen a decline in sales in Athens stores. This is because e-commerce is growing in popularity, making it difficult for offline businesses like Athena Shop to compete with them because it provides so many advantages for both buyers and sellers. The implementation of e-commerce with wordpress content management system (CMS) as an online sales platform at the Athena store is one option to overcome this problem. Thanks to e-commerce, Athena Shop can compete in the baby gear market and boost sales. E-commerce has removed the requirement for customers to physically visit stores to make purchases. E-commerce can also make it easier to find out the latest information about the latest products. In addition, it makes it easy for merchants to understand all things related to bestseller reports, price reductions, product shipping, refunds and returns.&#x0D; Keywords: Implementation, internet, E-commerce, online shop, baby shop.
Mediatization of Musical and Theatrical Practice on the Moodle Platform

Degree courses, specifically aimed at the training of professional profiles, have experimented with new e-learning design solutions to make students exercise in experiential practices (internships, practical exercises, laboratories) during the pandemic period. The contribution presents two ad hoc solutions for university professional practice in an e-learning environment (virtual internship at the Giustino Fortunato Telematic University within the Educational Sciences course; online music performance lessons at the Kazan Federal within the Teacher Education course) and the related comparative investigation. Which Moodle resources are the most effective in training performance skills? A cross analysis is done between the averages of students' access to Moodle resource and the grade obtained at the final project works. Results allow the authors to assume video lessons as more effective than webinars, beyond geographical context and specific competence profiles.
Scaling of Streaming Data Using Machine Learning Algorithms

Today, data is generated continuously by millions of data sources, which send in the records simultaneously, in small to large sizes. The rapid growth of data in velocity, volume, value, variety, and veracity has presented big challenges for businesses of all types. This type of data is called streaming data. Streaming data includes a variety of data such as mobile application notifications, e-commerce purchases, sensors in transportation vehicles, information from social applications, IoT sensors. This data is required to be processed sequentially and incrementally on record by record and used for a wide variety of analytics including correlations, filtering, and sampling. Information derived from such analysis gives visibility into many aspects such as customer activity, website clicks, geo-location of devices. There has been a great interest in developing systems for processing continuous data streams. This chapter aims to design a scalable system that can instantly analyze the data using machine learning algorithms.
GAN‐based tone curve learning for colour transfer

A new approach for reflecting the colour tone of a reference image on the input image is proposed. Depending on the source and reference image pairs, conventional statistical colour transfer methods often lead to undesirable colour transfer. Conversely, deep learning methods depend on prior learning, which results in unnatural output images when inappropriate images are learned; moreover, in such situations, analysing what kind of colour transformation has actually been performed is difficult. This state of the art motivates the proposal of a new colour transfer method that estimates tone curves based on generative adversarial nets. This method does not require any data set other than input and reference images, thus enabling a more appropriate colour transfer. The superior output of the proposed method compared with some baseline approaches is demonstrated through experiments.
Morphological Adjunctions Represented by Matrices in Max-Plus Algebra for Signal and Image Processing

In discrete signal and image processing, many dilations and erosions can be written as the max-plus and min-plus product of a matrix on a vector. Previous studies considered operators on symmetrical, unbounded complete lattices, such as Cartesian powers of the completed real line. This paper focuses on adjunctions on closed hypercubes, which are the complete lattices used in practice to represent digital signals and images. We show that this constrains the representing matrices to be doubly-0-astic and we characterise the adjunctions that can be represented by them. A graph interpretation of the defined operators naturally arises from the adjacency relationship encoded by the matrices, as well as a max-plus spectral interpretation.
Structure‐aware dehazing of sewer inspection images based on monocular depth cues

Abstract In sewer pipes, haze caused by the humid environment seriously impairs the quality of closed‐circuit television (CCTV) images, which leads to poor performance of subsequent pipe defects detection. Meanwhile, the complexity of sewer images, such as steep depth change and extensive textureless regions, brings great challenges to the performance or application of general dehazing algorithms. Therefore, this study estimates sewer depth maps first with the help of the water–pipewall borderlines to produce the paired dehazing dataset. Then a structure‐aware nonlocal network (SANL‐Net) is proposed with the detected borderlines and the dehazing result as two supervisory signals. SANL‐Net shows its superiority over other state‐of‐the‐art approaches with 147 in mean square error (MSE), 27.28 in peak signal to noise ratio (PSNR), 0.8963 in structural similarity index measure (SSIM), and 15.47M in parameters. Also, the outstanding performance in real image dehazing implies the accuracy of depth estimation. Experimental results indicate that SANL‐Net significantly improves the performance of defects detection tasks, such as an increase of 23.16% in mean intersection over union (mIoU) for semantic segmentation.
From unified phrase representation to bilingual phrase alignment in an unsupervised manner

Abstract Significant advances have been achieved in bilingual word-level alignment, yet the challenge remains for phrase-level alignment. Moreover, the need for parallel data is a critical drawback for the alignment task. This work proposes a system that alleviates these two problems: a unified phrase representation model using cross-lingual word embeddings as input and an unsupervised training algorithm inspired by recent works on neural machine translation. The system consists of a sequence-to-sequence architecture where a short sequence encoder constructs cross-lingual representations of phrases of any length, then an LSTM network decodes them w.r.t their contexts. After training with comparable corpora and existing key phrase extraction, our encoder provides cross-lingual phrase representations that can be compared without further transformation. Experiments on five data sets show that our method obtains state-of-the-art results on the bilingual phrase alignment task and improves the results of different length phrase alignment by a mean of 8.8 points in MAP.
CSF: Closed-mask-guided semantic fusion method for semantic perception of unknown scenes

Though many high-precision semantic segmentation models have been proposed, how to improve the generalization ability of these models is still an urgent problem. Recently, a great number of unsupervised domain adaptation (UDA) algorithms around domain adaptation problems have been studied in semantic segmentation. These methods require labeled source domain data and unlabeled target domain data. In this paper, we propose a closed-mask-guided semantic fusion method (CSF) to improve the semantic segmentation results of unknown scenes, where the target domain data is not obtained in advance. First, a Closed Mask Generation (CMG) module is designed to convert the edge detection result into a mask that can segment the image into several image blocks. Then, a Semantic Confidence Fusion (SCF) module based on information entropy and voting method is introduced, which can select reliable semantic segmentation results for each image block by comparing the confidence of several semantic segmentation networks. In addition, the experimental results on both KITTI and COCO Stuff datasets validate the effectiveness of this method. The code is publicly available at https://github.com/tryhere/CSF.
A Comparative Analysis of Mathematics Achievement for Face-to-Face Classes pre COVID-19 and Online Classes post COVID-19 : For First-year Engineering Students

This study examines mathematics achievement for freshmen in engineering colleges pre and post COVID-19 pandemic. For this purpose, we have compared various aspects after converting these 3 results into 100 points which were the diagnostic assessment results that conducted immediately after the admission, the mid-term and final exam results from the 1st semester at the provincial university in 2019 and 2021.&#x0D; The findings are as follow; first of all, we were able to confirm the decline in scholastic ability in mathematics in 2021 as a result of the comparison in the diagnostic assessment before and after COVID-19. However, there was no statistically significant score gap on the midterm exam. On the other hand, a result of the final exam confirmed to have statistically significant higher points of post COVID-19 than pre. Secondly, according to the comparison outcome of the mathematics achievement that classified into the upper and lower-level groups using the diagnostic assessment, the final exam results declined at a statistically significant rate more than mid-term exam results for both upper-level group pre and post COVID-19. For the lower group, the final exam results had statistically significant growth compared to the mid-term in 2019, while there was no statistically significant difference in 2021. Finally, the upper groups’ aggregate results of mid &amp; final exam were also compared, then the mathematics achievement was higher in post COVID-19 than before. And the lower groups comparison was not statistically significant gap. In conclusion, we found that online classes aggravated the gap between the upper and lower level groups in mathematics achievement. Furthermore, this indicate that the teaching methods for mathematics achievement is respectively, the online classes for upper groups, the face-to-face classes for lower groups.
Teaching Spatial Geometry Through 3D Visualization and Augmented Reality

Geometry is a branch of mathematics that helps us to work with distances, volumes, and areas of the real world. But spatial geometry presents an unusual obstacle: how to understand the elements of a three-dimensional object when they are normally presented in a two-dimensional form. The study was conducted with two classes in the 2nd year of high school in a public school. Class 1 used traditional resources: textbook, blackboard, and chalk. Class 2 used augmented reality (FLARAS) and 3D visualization (Sketchup) and textbook. It was expected that, with the help of augmented reality, the students of Class 2 would perform better than Class 1. A better understanding of the content was achieved, as they would have better visualization of spatial objects. Consequently, better performance was achieved in the exercises and problem questions presented.
Adaptation to Pandemic Through Universal Access to Innovative Technologies

The implementation of information technology into the healthcare sector is inevitable to prevent future pandemics, as COVID-19 had a huge impact on healthcare services and humanity. Therefore, universal access to technologies in managing unforeseen pandemics is necessary. The objective of this study is to examine how healthcare institutions use innovative technologies to address future pandemics. The study reflects on one of the targets of Sustainable Development Goal (SDG) 9, which is to significantly increase access to IT and strive to provide universal and affordable technology access to global citizens by 2030. This is to obtain the vision to work towards building an open, inclusive, and digital network for a secure future. This study used discourse analysis to critically analyze the use of innovative technologies like AI systems, machine learning, the internet, mobile phones, mobile computing, and other technologies adopted to manage the global pandemic. This study recommends to policymakers the importance of universal access to innovative technology to address pandemic issues.
Improving Counterfactual Regret Minimization Agents Training in Card Game Cheat Using Ordered Abstraction

Counterfactual Regret Minimization (CFR) has been one of the most famous iterative algorithms to learn decent strategies of imperfect information games. Vanilla CFR requires traversing the whole game tree on every iteration, which is infeasible for many games, especially as a number of them require infinite steps due to repeated game states. In this paper, we introduce an abstraction technique called Ordered Abstraction to enable us to train using a much smaller and simpler version of the game by limiting the depth of the game tree. Our experiments were conducted in an imperfect information card game called Cheat, and we introduce the notion of “Health Points” a player has in each game to make the game length finite and thus easier to handle. We compare four variants of CFR agents, evaluate how the results from smaller games can improve the training in larger ones, and show how Ordered Abstraction can help us increase the learning efficiency of specific agents.
Self-supervised Learning for Medical Image Classification Using Imbalanced Training Data

Medical image classification is challenging for the lack of labeling examples due to time-consuming and expensive annotations, and the imbalance of classes caused by the scarcity of positive diseased individuals. Self-supervised pre-training with supervised fine-tuning is of great significance in image recognition, but it has received limited attention in medical image classification. In this paper, we propose a novel mechanism for medical image classification as an imbalanced learning strategy based on the popular self-supervised frameworks. In short, this mechanism gives up label information and conducts self-supervised pre-training (SSP) in the first stage of long-tail learning. After this stage, we use the Balanced-MixUp to train the final model. We experiment with long-tail datasets of skin cancer and retinal fundus. Experimental results demonstrate that this mechanism outperforms conventional imbalanced learning techniques and loss functions dealing with data imbalance.
A Generic Approach for Player Modeling Using Event-Trait Mapping Supported by PCA

Modeling players based on their in-game events is essential for predicting their future behaviors. Player modeling studies mostly target a specific game or genre. This makes it difficult to transfer existing methods from one game to another. In this study, we propose a generic event-trait mapping and unsupervised learning approach for player modeling that extends our earlier modeling method with Principal Component Analysis (PCA). We present a case study of this new approach on a dataset of ten thousand players of World of Warcraft (WoW), a massive multiplayer online role-playing game (MMORPG). The base and the extended approaches are compared with an AutoEncoder (AE) based approach on this dataset. The methods generate clusters (persona) as mixtures of different character traits. The best results are obtained with the extended event-trait mapping approach for player modeling.
Web Data Mining Algorithm in Cloud Service Architecture Based on New Popular Learning Algorithm and Adaptive Adjustment Mechanism

Due to the complex distribution of web data and frequent updates under the cloud service architecture, it is easy to drive data disasters and data accidents. This leads to many problems in web data mining, such as how to choose the best algorithm to process the corresponding type of data, and how to effectively reduce the dimensionality of the web data in the cloud service architecture and process multiple target information and multiple customer needs at the same time. In order to overcome these problems, we transform the multi-information goal and multi-user demand (constraint conditions) in web data mining into a constrained multi-objective optimization model, and solve it by a constrained particle swarm multi-objective optimization algorithm. In order to make it easier for the constrained multi-objective particle swarm algorithm to solve different types of problems to find an effective solution set closer to the real Pareto front, a new manifold learning algorithm based on the constrained multi-objective particle swarm algorithm is built and used to solve this problem. Experiments results demonstrate that this can improve the service efficiency of cloud computing.
BoxOff is NP-Complete

BoxOff is a one-player game invented by Steven Meyers: a rectangular board is covered with colored stones; a legal move is to remove two same-colored stones from opposite corners of an otherwise empty rectangle. Removing all stones wins the game. We show that it can be hard to determine whether a BoxOff puzzle is winnable: by reducing from Boolean Satisfiability, we show that BoxOff is NP-complete, even when only four colors are used.
Quantifying the Space of Hearts Variants

Hearts is a card game with a rich history and many interesting variants. Why has it remained popular while undergoing significant changes? We use computational simulations of Hearts to understand the experience of players through the application of four heuristics which quantify the drama and security felt by the winning player, the ability of players to win in chaotic imperfect-information situations, and the player’s ultimate interest in their decisions. We find that there is a direct relationship between the historical evolution of Hearts through ludemic change and subsequent heuristic improvements to game play.
Secure e-Prescription Management System: Mitigating Blended Threat in IoBE

New information and communication technologies (ICT) are being applied in various industries to upgrade the value of the major service items.Moreover, data collection, storage, processing, and security applications have led to the creation of an interrelated ICT environment in which one industry can directly influence the other.This is called the "internet of blended environments" (IoBE), as it is an interrelated data environment based on internet-ofthings collection activities.In this environment, security incidents may increase as size and interconnectivity of attackable operations grow.Consequently, preemptive responses to combined security threats are needed to securely utilize IoBE across industries.For example, the medical industry has more stringent information protection measures than other industries.Consequently, it has become a major target of attackers, as more clinician-patient interactions occur over the internet owing to COVID-19.Therefore, this study aims to acquire security for IoBE while focusing on the medical industry.Among the various types of medical ICT services, this study analyzes data flow and potential security threats from the e-prescription lifecycle perspective, which is highly utilized, strongly data-centric, and has numerous security issues.Based on our analysis, we propose a secure authentication and data-sharing scheme.
Gamma Correction for Brightness Preservation in Natural Images

Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness.The simplest way to improve the preservation is the implementation of histogram equalization.Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image.This paper proposes a multi-scale decomposition for brightness preservation using gamma correction.After transformation to hue, saturation and intensity (HSI) channel, the 2D-discrete wavelet transform decomposed the intensity component into low and high-pass coefficients.At the next phase, gamma correction is used by auto-tuning the scale value.The scale is the modified constant value used in the logarithmic function.Further, the scale value is optimized to obtain better visual quality in the image.The optimized value is the weighted distribution of standard deviation-mean of low pass coefficients.Finally, the experimental result is estimated in terms of quality assessment measures used as absolute mean brightness error, the measure of information detail, signal to noise ratio and patch-based contrast quality in the image.By comparison, the proposed method proved to be suitably remarkable in retaining the mean brightness and better visual quality of the image.
<i>Kleinian KenKen</i>

SummaryWe consider the collection of all KenKen puzzles over the Klein four-group with certain symmetry conditions and classify all such puzzles by their number of solutions.