Reviewerly/exHarmony huggingface dataset:
THE EFFECTIVENESS OF IMMERSIVE TECHNOLOGIES FOR FUTURE PROFESSIONAL EDUCATION

The research on the application of immersive technologies in education began relatively recently and the issue remains open. In order to modernize the learning process, educational simulators, simulators, and textbooks are being developed. Immersive technologies help to absolutely immerse in the learning process, interact with objects inaccessible in the usual reality, get used to acting in acute situations, and as a result - to improve the effectiveness of learning. The aim of the work is an empirical analysis of the methodological basis of the experience of using one of the artifacts and mercurial technologies - virtual reality. Despite the fact that virtual reality has recently been referred to ultra-modern technology, today virtual reality headsets are not a novelty of the technology, but mediators of knowledge. The use of virtual reality devices for immersive learning is becoming relevant and accessible. Methods - empirical research using systematic search, and comparative analysis of two pedagogical experiments involving artifacts. Results - it turned out that virtual reality seems to be as effective as previous technological tools used in education, and in certain specialties can contribute to better performance in learning. Practical significance - the results of the work may establish specific learning contexts in which virtual reality is more effective than others and may allow new educational opportunities, especially in shaky geopolitical situations. Virtual reality is a modular technology that adapts to all situations and allows the creation of extended learning materials. Nevertheless, it has proven to be more effective for certain specialties, such as the medical field.
Improvement of super resolution reconstruction method for real text images

Super resolution refers to the process of restoring a low resolution image to a high resolution image. In recent years, researchers in the field of super-resolution are not satisfied with restoring artificially defined low-resolution images, and try to restore low-resolution images in natural scenes. For this situation, a real scene text super-resolution dataset TextZoom is proposed. It contains one-to-one mapping of low-resolution and high-resolution images captured by the camera, which is more realistic and challenging than manufactured data. A super-resolution network for TextZoom, which is called TSRN is also proposed. By adding channel attention and increasing the proportion of gradient loss function, the overall network pays more attention to the restoration of text and enhances the lines, and finally improves the recognition rate of medium and difficult graded text images in the TextZoom dataset after super-resolution preprocessing.
Critical Feature Selection and Machine Learning-based Models for Monofacial and Bifacial Photovoltaics

Prediction accuracy has paramount importance in reliable PV solar plant performance. This helps with optimal plant design, economic assessment, smooth grid integration, and plant operations. Machine learning (ML) models help with faster, reliable, and accurate prediction of annual energy yield that is valid over a wide range of climatic conditions, module specifications, and site conditions. In this study, an ensemble-learning algorithm with regression trees is used to predict the performance of both monofacial and bifacial modules. Training data is prepared from parametric simulation results obtained using System Advisor Model (SAM) with an energy yield range of 120–584 kWh/m2 for monofacial modules and 134–706 kWh/m2 for bifacial modules. The results showed that ensemble-learning based ML algorithm can predict the energy yield of monofacial and bifacial modules with RMSE of 2.89 kWh/m2 and 4.65kWh/m2.
Comparative Study Of The Laskar Pelangi Novel With The Dua Belas Pasang Mata Novel Reviewed From Structural Approach

The goal of this research is to compare the Laskar Pelangi novel to the Dua Belas Pasang Mata novel in order to establish the narrative's building elements. This study employed the descriptive research method. The author describes the data obtained in a systematic, factual, and correct manner in order to discover facts and causative processes. The data is in the form of recorded documents that describe the structure of the novel using literary analysis. As a result of this inquiry, Andrea Hirata's novel Laskar Pelangi contains 48 chapters and diverse themes that will be transferred. The key plot elements of this work are as follows: The story begins with the registration of students at a Muhammadiyah elementary school in Gantong village, Gantong sub-district, East Belitong, South Sumatra. To better comprehend the arrangement, there will be three steps: establishing the area, the period, and the community from the start to the last chapter, the action of this novel takes place in Belitong. This novel is set in several locales across Belitong. This novel uses the viewpoint as the primary character based on the role. Furthermore, the central theme of the novel Dua Belas Pasang Mata is the main character's sympathy and affection. Starting with a story about Miss Oishi's first visit to Tanjung village as a substitute teacher at a branch school and the reaction of the local people to her wearing western attire at the time. Thus, it is known that both novels have extraordinary social crisis stories.
Power Supply Cloudsim: An Extension of CloudSim for Modeling and Simulation of Power Supply Devices in Cloud Data Centers

As the number of internet data centers (IDC) increases, the energy consumption of modern data centers is growing rapidly, which takes an increasing part of global electricity consumption. The data center load, as a flexible electricity load which can be scheduled spatially and temporally is increasingly drawing attentions in the research of demand response and distributed network planning. However, there are hardly software which is dedicated to simulating the electrical characteristics of data centers, and most of the existing data center simulation software ignores the characteristics of data center power supply devices, which is not fit for verifying the demand response method proposed by the researches in electrical field. In view of this, we modified the most popular data center simulation software CloudSim by adding modules which can model the data center power supply system. A simulation of a typical day is implemented on the modified platform, which has verify the effectiveness of the modified software.
Phonetic Convergence in a Prototype Dialogue System

This chapter describes the systematic development of a prototype dialogue system for educational purposes. The idea appeared while designing a dialogue system to assess models of phonetic convergence between the human user and a computer dialogue system. The corpus for analysing the phonetic convergence was created within the Harmonia project which aimed at: (1) extracting phonetic features mappable on a synthetic signal, (2) creating dialogue models applicable in a human–machine interaction and (3) practically evaluating the convergence. The analysis of phonetic convergence was carried out on the Harmonia dialogue corpus containing over 27 h of speech communication between native and non-native speakers of Polish and German. The recording scenarios consisted of controlled, neutral and expressive tasks which helped to develop the convergence models evaluated using perception tests. The assessment showed that the high pitch and tempo are strong factors in human–computer communication. Finally, the paper describes the creation of a prototype dialogue system in Python.
Refacing Defaced MRI with PixelCNN

Privacy protection is one of the most crucial factors when sharing MR images between researchers. There are many defacing software programs that can blur or remove the face of MR images. However, there are reasons to believe that the brain and other remaining features are not only identifiable but also can be used for facial reconstruction to fill the face of the subject back into the image, and it is possible to rebuild the facial part of the images using recently developed machine learning models. A demonstration of this is of practical significance as it could convince the community to adopt stricter data-sharing standards. Additionally, even if the reconstructed faces are not identifiable, smooth completion of the “damaged” MRI images may improve methods that depend on the head modeling, such as source localization approaches. Recent work has been focusing on using the generative adversarial networks (GAN) for this task, which is generally believed to be the best method. Because we hope the model can generate the face entirely depending on the information from the brain, we show here an alternative approach, pixel constrained CNN, which is a purely supervised facial reconstruction. We simulated the rebuild process and showed convincing results.
Noisy Label Rectification Using Weighted Fusion for Deep Unsupervised Salient Object Detection

Salient Object Detection (SOD) based on deep neural networks (DNN) depends on pixel-level human annotations for training, which consumes much labor. Alternatively, we use pseudo-ground-truth labels generated from multiple handcrafted saliency methods to substitute for expensive ground-truth human annotation for deep salient object detection training. To remedy the noise of pseudo labels, we use a weighted fusion strategy to integrate several hand-crafted methods at the initial stage and rectify pseudo-ground-truth at the label update stage.
Towards Making the Most of Pre-trained Translation Model for Quality Estimation

Machine translation quality estimation (QE) aims to evaluate the quality of machine translation automatically without relying on any reference. One common practice is applying the translation model as a feature extractor. However, there exist several discrepancies between the translation model and the QE model. The translation model is trained in an autoregressive manner, while the QE model is performed in a non-autoregressive manner. Besides, the translation model only learns to model human-crafted parallel data, while the QE model needs to model machine-translated noisy data. In order to bridge these discrepancies, we propose two strategies to post-train the translation model, namely Conditional Masked Language Modeling (CMLM) and Denoising Restoration (DR). Specifically, CMLM learns to predict masked tokens at the target side conditioned on the source sentence. DR firstly introduces noise to the target side of parallel data, and the model is trained to detect and recover the introduced noise. Both strategies can adapt the pre-trained translation model to the QE-style prediction task. Experimental results show that our model achieves impressive results, significantly outperforming the baseline model, verifying the effectiveness of our proposed methods.
The Intelligent Human Sensory Interaction of a Wheelchair for the Elderly

In this paper the human-computer interaction system of wheelchair for the elderly and the disabled is studied. The system is divided into three modes: follow mode, gesture control mode and manual control mode. The system uses Kinect to model the human skeleton, obtain the distance between the following object and the camera, and track the human motion; Use leap motion to obtain hand point cloud data, recognize user gestures, obtain hand displacement information in space, and control wheelchair movement; The pressure sensors on both sides of the wheelchair armrest are used to wirelessly transmit the push of medical staff to the wheelchair through the HC-12 module, which can better help medical staff reduce physical consumption, especially when going uphill.
Adversarial Learning of Hard Positives for Place Recognition

Image retrieval methods for place recognition learn global image descriptors that are used for fetching geo-tagged images at inference time. Recent works have suggested employing weak and self-supervision for mining hard positives and hard negatives in order to improve localization accuracy and robustness to visibility changes (e.g. in illumination or view point). However, generating hard positives, which is essential for obtaining robustness, is still limited to hard-coded or global augmentations. In this work we propose an adversarial method to guide the creation of hard positives for training image retrieval networks. Our method learns local and global augmentation policies which will increase the training loss, while the image retrieval network is forced to learn more powerful features for discriminating increasingly difficult examples. This approach allows the image retrieval network to generalize beyond the hard examples presented in the data and learn features that are robust to a wide range of variations. Our method achieves state-of-the-art recalls on the Pitts250 and Tokyo 24/7 benchmarks and outperforms recent image retrieval methods on the rOxford and rParis datasets by a noticeable margin.
Design of Intelligent Test-composition System for English Test based on RBF Algorithm

With the progress of society and the development of human beings, examination as a means of evaluation and selection, its fairness and fairness directly affect the final evaluation results, and the requirements for examinations in various fields of society are gradually increasing. Today, due to the development of information and computer science and technology, the technology of intelligent test paper has also emerged, and it has a very broad application prospect in the examination. This paper focuses on improving the selection operation in the algorithm on the basis of RBF, aiming at the problem of intelligent test set in distance network teaching. The execution effect of the algorithm is further improved. At the same time, according to the actual characteristics of intelligent volume, a perfect adjustment is made to make it have corresponding orientation.
Transfer Learning based Intra-Modulation of Pulse Classification using the Continuous Paul-Wavelet Transform

This paper presents the evaluation of an approach for automatic modulation classification (AMC) using continuous wavelet transform (CWT) with the Paul-wavelet as the signal input domain for a pre-trained convolutional neural network (CNN). AMC plays an elementary role in the field of electronic support measures (ESM) within the context of electronic warfare (EW) for the correct emitter and threat detection as well as classification respectively. Similarly, in the area of electronic countermeasures (ECM) for consistent jamming performance, accurate knowledge of the modulation used is necessary. The focus of the presented work is on transfer learning, an approach where a pre-trained convolutional network is applied to modulation classification by re-training the last learnable and the final classification layer. The CWT using the Paul-wavelet offers feature-visibility of transients, especially interesting for phase-modulated signals and therefore offers the ability as feature input domain to the CNN. Accordingly, the classification approach was performed and evaluated with selected modulation and pulse parameter configurations under different signal-to-noise ratios (SNR). The approach presented here shows an overall average classification rate of over 90 % for SNR values greater 5 dB.
A Meta-Analysis on the Effects of Learning with Robots in Early Childhood Education in Korea

This meta-analysis examines the effects of learning with robots (r-learning) on young children and, on this basis, gives suggestions for using robotics in education for young children. A test of homogeneity was performed for 27 Korean studies done between 2008 and 2016 and a random effect model introduced to reveal the effect sizes. The overall effect size was medium to large at 0.72. After analyzing gaps in effect sizes with different categorical moderator variables, the authors found significant differences depending on platform type, activity type, dependent variables for r-learning effect, and age. The study also investigates relationships between effect size and continuous moderator variables such as treatment period and year of publication. The meta-regression model showed a significantly negative relationship between effect size and year of publication. Thus, effects of r-learning on a young child are generally beneficial, and r-learning also improves variables like a child's social nature, though conversely the effect on language development appears below average.
Feature extraction with mixture gaussian for stroke classification

The stroke remains the second leading cause of death in the world. The stroke diagnosis is usually obtained by neuroimaging analysis, and among the main techniques, Computed Tomography (CT) is the most used. A quick diagnosis of stroke can generally contribute positively to the patient’s recovery. CAD systems that analyze CT scan images become extremely important when diagnosis speed is a relevant factor for patient recovery. This work presents a new feature extractor using gaussian mixtures, called Mixture Gaussian Analysis of Brain Tissue Density (MGABTD). The MGABTD achieved accuracy and f1-score of 99.9%. The results demonstrated the effectiveness of the method in extracting features used to determine whether a CT scan is normal or shows an ischemic or hemorrhagic stroke.
A Multi-label Classification Framework Using the Covering Based Decision Table

Multi-label classification (MLC) has recently drawn much attention thanks to its usefulness and omnipresence in real-world applications, in which objects may be characterized by more than one labels. One of the challenges in MLC is to determine the relationship between the labels due to the fact that there is not any assumptions of the independence between labels, and there is not any information and knowledge about these relationships in a training dataset. Recently, many researches have focused on exploiting these label relationships to enhance the performance of the classification, however there have not many of them using the covering rough set. This paper propose a multi-label classification algorithm named CDTML, based on ML-KNN algorithm, using covering based decision table which exploits the relationship between labels to enhance the performance of the multi-label classifier. The experimental results on serveral dataset of Enron, Medical and a Vietnamese dataset of hotel reviews shown the effectiveness of the proposed algorithm.
Exploring the Impact of Adding Adversarial Perturbation onto Different Image Regions

Adversarial attack has been a hot topic for a long time in machine learning and deep learning. Studying adversarial attack has vital significance to artificial intelligence security. Existing methods mainly pursue a higher attack success rate. Few researches pay attention to the region where adversarial perturbations are added. Actually, different pixels in an image usually have different contributions in results, which motivates us to apply region constraint in the image for adversarial perturbations generation. In this paper, we present an easy-to-implement way to decrease the unnecessary adversarial perturbations while preserving a relatively high attack success rate. Specifically, we do not use the same constraint of perturbations in the input image but set specific constraint for specific region. Furthermore, we point that adversarial examples work in a different way to normal images. Directly using the activated region in normal images is not optimal. Then, to get the crucial area in adversarial attacks, we propose six transformation schemes to revise the activated region which is generated by the normal image. We launch extensive experiments on ImageNet dataset and the results show that our methods can get better attack strength under the same perturbation level when compared to the baseline methods.
A proposal for an FPGA-based graphical pipeline for virtual depth image generation

Currently, industrial robots can follow well-defined, repetitive tasks, which can substantially increase the productivity of the manufacturing processes. But the reaction to a dynamically changing environment is a frequently studied part of robotics. In our paper, we propose an FPGA-based virtual depth image generation process, which implements a modified graphical pipeline on the FPGA. Using the manipulator's current configuration and camera parameters, the robot's 3D model is transformed and projected into a 2D plane. During the rasterization stage, a virtual depth image is generated by determining the depth values for all the pixel coordinates. Although the FPGAs have limited capability for implementing a generic graphical pipeline, the described task-specific optimizations in the paper can provide a fast and energy-efficient implementation.
DAAM: Drop Activity Alert Manager For Elderly

Falls are a leading and serious problem in elderly individuals due to their aging, cognitive, medications, balance problems, vision and other environmental hazards. These falls can cause broken bones, head injuries and even death if left untreated. A fall can result in decreased quality of life and functional independence. Immediate assistance and medical help should be provided to avoid any complications. The system proposed is a fall detection model based on real-time surveillance. The system applies human pose estimation, object detection algorithm and fall detection conditions on the surveillance video. OpenPose is used for human key point detection combined with YOLO for object detection and various fall detection methods. If a fall occurs, an alert notification is sent to their emergency contacts.
Multi-fairness Under Class-Imbalance

Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced. The class-imbalance problem is more severe for the protected group in the critical minority class (e.g., female +, non-white +, etc.). Still, existing methods focus only on the overall error-discrimination trade-off, ignoring the imbalance problem, and thus they amplify the prevalent bias in the minority classes. To solve the combined problem of multi-discrimination and class-imbalance we introduce a new fairness measure, Multi-Max Mistreatment (MMM), which considers both (multi-attribute) protected group and class membership of instances to measure discrimination. To solve the combined problem, we propose Multi-Fair Boosting Post Pareto (MFBPP) a boosting approach that incorporates MMM-costs in the distribution update and post-training, selects the optimal trade-off among accurate, class-balanced, and fair solutions. The experimental results show the superiority of our approach against state-of-the-art methods in producing the best balanced performance across groups and classes and the best accuracy for the protected groups in the minority class.
Time Series Forecasting Using Online Performance-based Ensemble Deep Random Vector Functional Link Neural Network

Time series forecasting remains a challenging task in data science while it is of great relevance to decision-making in various industries such as transportation, finance, electricity resource management, meteorology. Traditional forecasting models based on statistics fail in challenging tasks with high non-linearity and complicated characteristics. Due to its architecture bias, deep learning-based models overfit randomness and noise. This paper proposes a novel online performance-based ensemble deep random vector functional link neural network model for the time series forecasting tasks. The proposed model supports the non-iterative online learning and dynamic ensemble method, which keeps adjusting the parameters and the weights of each output layer based on the dynamic evaluation of the latest prediction performance. Extensive experiments show that our proposed method outperforms the state-of-the-art statistical, machine learning-based, and deep learning-based models.