Reviewerly/exHarmony huggingface dataset:
Missing Features Reconstruction and Its Impact on Classification Accuracy

In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values can be either on the level of individual instances or on the level of entire features. Both situations have a negative impact on the usability of the model on such a dataset. This paper focuses on the scenario where entire features are missing which can be understood as a specific case of transfer learning. Our aim is to experimentally research the influence of various imputation methods on the performance of several classification models. The imputation impact is researched on a combination of traditional methods such as k-NN, linear regression, and MICE compared to modern imputation methods such as multi-layer perceptron (MLP) and gradient boosted trees (XGBT). For linear regression, MLP, and XGBT we also propose two approaches to using them for multiple features imputation. The experiments were performed on both real world and artificial datasets with continuous features where different numbers of features, varying from one feature to $$50\%$$ , were missing. The results show that MICE and linear regression are generally good imputers regardless of the conditions. On the other hand, the performance of MLP and XGBT is strongly dataset dependent. Their performance is the best in some cases, but more often they perform worse than MICE or linear regression.
Expert System for Predicting Children Mental Retardation using Forward Chaining

Mental retardation is also known as intellectual disability.The symptoms of mental retardation can be detected early since children.This research aims to develop an Expert System (ES) to predict mental retardation in children with 4 type of mental retardation, among others: mild, moderate, severe, and very severe.This ES which is contained the expert knowledge is developed using Forward Chaining (FC) algorithm as research method.Besides to detect type of mental retardation, this ES provides the treatment information based on the type of mental retardation that suffered.Black box testing result that showed all of system functionalities run well, the results of the questionnaire on the ease of use of the system with some respondents also showed good results.Besides, based on the experiment with 23 of symtomps, 4 main rules, and 20 additional rules, showed that FC caplable to predict the mental retardation in children well enough.
Estimating Network Flowing over Edges by Recursive Network Embedding

In this paper, we propose a novel semisupervised learning framework to learn the flows of edges over a graph. Given the flow values of the labeled edges, the task of this paper is to learn the unknown flow values of the remaining unlabeled edges. To this end, we introduce a value amount hold by each node and impose that the amount of values flowing from the conjunctive edges of each node to be consistent with the node’s own value. We propose to embed the nodes to a continuous vector space so that the embedding vector of each node can be reconstructed from its neighbors by a recursive neural network model, linear normalized long short-term memory. Moreover, we argue that the value of each node is also embedded in the embedding vectors of its neighbors, thus propose to approximate the node value from the output of the neighborhood recursive network. We build a unified learning framework by formulating a minimization problem. To construct the learning problem, we build three subproblems of minimization: (1) the embedding error of each node from the recursive network, (2) the loss of the construction for the amount of value of each node, and (3) the difference between the value amount of each node and the estimated value from the edge flows. We develop an iterative algorithm to learn the node embeddings, edge flows, and node values jointly. We perform experiments based on the datasets of some network data, including the transportation network and innovation. The experimental results indicate that our algorithm is more effective than the state-of-the-arts.
Uncouple Generative Adversarial Networks for Transferring Stylized Portraits to Realistic Faces

Stylized portraits widely exist in artwork or paintings.It is interesting to restore the original identity of portrait artworks, but consider that the rarity and the style diversity of these artworks, it is difficult to pair and obtain sufficient training data to restore their original identities by existing methods.Therefore, it is challenging to explore a method to restore a single stylized portrait to its original identity.Although CycleGAN can convert paintings into realistic photographs in unpaired datasets, it was not developed specifically for portraits, and photo-realistic faces require more accurate structures, thus the visual results obtained with CycleGAN are not satisfactory.In this paper, we propose Uncouple-Generative Adversarial Networks (UncGANs) for transferring stylized portraits to realistic faces.Our UncGANs framework is inspired by CariGANs to tackle the visual problem in CycleGAN for obtaining realistic faces from stylized portraits.In addition, we introduce three losses, namely, the semantic style consistency loss and the cycle consistency loss to effectively guide the training of generators and discriminators on unpaired datasets, the global and local adversarial loss ensure the consistency of appearance characteristics before and after translation, and the location consistency loss to establish the precise correspondence between the source domain and the target domain as well as assist the discriminators.Extensive experimental results and comparisons with state-of-the-art methods including Style, Deep-Image-Analogy, UNIT, MUNIT, CycleGAN, CP-GAN, and PS2-MAN demonstrate that our framework is better at generating realistic faces from stylized portraits with accurate structures and features.
Comment on “progress testing anytime, anywhere – does a mobile-learning approach enhance the utility of a large-scale formative assessment tool?”

Dear Editor We read the report by Karay et al. (2020) on the application of a progress test using mobile devices. This experience provides new opportunities and perspectives for such use in both un...
Device-Free Activity Recognition: A Survey

With the increasing number of WIFI hotspots and video surveillance equipment deployed, device-free activity recognition based on video and WIFI signals has attracted widespread attention. In order to better understand the current device-free human motion recognition work and the future development trend of device-free perception, this paper provides a detailed review of existing video-based and WIFI-based related work. Meanwhile, the principle of device-free activity recognition is deeply analyzed. We have compared the existing work in different aspects. Finally, by analyzing the shortcomings of the existing methods, the future research direction of device-free motion recognition is proposed.
Characteristics of reversible circuits for error detection

In this work, we consider error detection via simulation for reversible circuit architectures. We rigorously prove that reversibility augments the performance of this simple error detection protocol to a considerable degree. A single randomly generated input is guaranteed to unveil a single reversible error with a probability that only depends on the size of the error, not the size of the circuit itself. Empirical studies confirm that this behavior typically extends to multiple errors as well. In conclusion, reversible circuits offer characteristics that reduce masking effects – a desirable feature that is in stark contrast to irreversible circuit architectures.
Potential Field Path Planning and Potential Field Path Tracking

In robotics studies, problems can be given as mapping, location estimation, exploration and navigation. Navigation problem; It can be subdivided into path planning, path tracking and obstacle avoidance. In the study, in the Gazebo simulation environment, a 4-wheel robot and a laser distance sensor were used. 2D mapping, path planning and path following with respect to the 2D map are implemented on Robot Operating System (ROS). Potential-based path planning was compared with the A* algorithm. Potential-based path tracking was compared with the dynamic window approach (DWA). In tests conducted in the Gazebo simulation environment, it was observed that the potential-based approach yielded successful results in path planning and path tracking.
The Kernel Dynamics of Convolutional Neural Networks in Manifolds

We propose a novel expression from manifolds to define Convolutional neural network (CNN). The layered structure is proceeded by integration in limited space continuously, with weights adjusted including value and direction in neural manifolds. Status transfer functions are proposed to simulate the kernel dynamics as a control matrix. We theoretically analyze the stability and controllability of kernel-based CNNs, and verify our findings by numerical experiments.
Variations of Bi-Rads 5 in Mammography by Age, Ethnicity, and Breast Density: A Retrospective Study in University Malaya Medical Centre

Breast cancer is the most frequently diagnosed cancer among Malaysian women. BI-RADS 5 category in the mammogram report found breast lesion with a highly predictive of malignancy. Age, ethnicity and breast density are the primary risk factor in developing breast cancer. The objective of this study was to identify age, ethnicity, and breast density differences in BI-RADS 5 among mammographic patients at University Malaya Medical Centre (UMMC). This study was conducted retrospectively from 2015 to 2019 in UMMC. The subject criteria selected for this study are women aged 40 years and above which categorized in BI-RADS 5 from the mammogram examination. Patient’s data on age, ethnicity, and breast density were retrieved from the Radiology Information System (RIS) of UMMC. Among 110 cases, 20% were in the age group of 40-49, 28.2% in the age group of 50-59, 35.5% in the age group of 60-69 while the 16.4% were between 70-79 years old. By ethnic distribution, the majority were Chinese with 46.4%, followed by Malays 42.7%, and 10.9% were Indian. 14.5%, 40.9%, 33.6%, 10.9% of patients were classified as having categories A-D breast density, respectively. The study indicated women were in the age group of 60-69 and Chinese ethnic group are most likely to develop breast cancer. Although many studies have shown that risk factor for breast cancer is greater for denser breast tissue, this present study shows that BI-RADS B (scattered fibro glandular breast tissue) is most likely to have breast cancer. It is therefore important to have a regular follow-up of these patients and to raise the awareness of these patients.   Keywords BI-RADS 5, Mammography, Breast cancer
A cost function to determine the optimum filter and parameters for stabilizing gaze data

Prior to delivery of data, eye tracker software may apply filtering to correct for noise. Although filtering produces much better precision of data, it may add to the time it takes for the reporting of gaze data to stabilise after a saccade due to the usage of a sliding window. The effect of various filters and parameter settings on accuracy, precision and filter related latency is examined. A cost function can be used to obtain the optimal parameters (filter, length of window, metric and threshold for removal of samples and removal percentage). It was found that for any of the FIR filters, the standard deviation of samples can be used to remove 95% of samples in the window so than an optimum combination of filter related latency and precision can be obtained. It was also confirmed that for unfiltered data, the shape of noise, signified by RMS/STD, is around √2 as expected for white noise, whereas lower RMS/STD values were observed for all filters.
ADAPTIVE MINIMAX ESTIMATION OF VIDEO-DATA IN AEROSPACE MONITORING APPLICATIONS

This article is dedicated to the problem of increasing the performance of aerospace monitoring and automatic video-data processing under prior uncertainty. The development of adaptive minimax estimate for the image model parameters is proposed, and the computational algorithm for adaptive estimation of video-data is described.
Teacher’s Readiness to Create Own Online Courses

Rapid development of information educational technologies requires special teacher’s training for possibility to create their own online courses. In particular, it is necessary to understand what do teachers need first of all? How may be solved the problem of formation of the key university teachers competencies?The purpose of this study is to identify the key elements of teachers' readiness to create their online courses and to study the formation of special teachers competences to develop their own online courses.Our study was conducted on the basis of an online course for professional development of university teachers "Pedagogical support of online education". The methods of the study were: online questionnaires, analysis of the creative works of teachers (developed tests and recorded videos) and oral attestation. We analyzed objectives of teacher’s training, the formation of key competencies and relevance of the online course topics. We found out two main reasons to join in our course by the university teachers: recommendations of their leadership (37%) and their own initiative, interest to study subject (29%). 252 people have fully completed all the required tasks and completed their training with a certificate of professional development. 60% of the teachers participating were on such online training the first time. In general, university teachers have been able to demonstrate the necessary theoretical knowledge (64%) and have done the first steps of their application during the preparation for the attestation on our program. They were very good at the writing of the methodical materials, but had not yet mastered recording of the videos. This research confirms the necessity of special training for university teachers for the high-quality creation online courses and allows the online courses authors to find new points of the changing their programs toward the more actual level.
Non-Structural Earthquake Risk Management for Residential Buildings

As buildings become more resilient against structural damage the cost of non-structural damage and their consequences becomes proportionally higher, giving non-structural damage greater importance in earthquake risk reduction. Providing residents with detailed guidance on how to evaluate their risk regarding non-structural earthquake damage, and what mitigation and preparedness options they have, can increase both home and societal earthquake resiliency. Earthquake damage data from destructive earthquakes in south Iceland in 2000 and 2008 were used to develop simple but detailed twelve-step risk-management guidelines for residents. The guidelines are based on a set of disaster-related objectives. A standard loss estimation study was used to develop guidelines for the fixed non-structural elements and photographs from inside homes that had sustained significant non-structural damage were used to develop guidelines for loose items. Virtually every item in the studied homes was considered to understand its importance and its relevance to the function of a home. Information in terms of financial, functional and emotional value were used in the guidelines to help residents decide which mitigation options to take. The photos provided valuable information by placing each item in context with its surroundings, for example, to understand the possibility of motion and consequences to other items. The proposed approach, although based on observations from residential buildings, is useful for facilities that have sensitive operations, such as offices, industrial facilities, hospitals and government services. As societies become more complex and reliant on non-structural elements, systematic and thorough studies such as the one outlined herein become an increasingly critical part of sound earthquake risk management.
Prospects of the 21st Century Teacher's Training to Work with Gifted Children

The article is devoted to the study of the prospects of future teacher's preparation - teachers of the 21st century to work with gifted children during their studies in university, during the vocational pedagogical practice in the lyceum-university system. Future teachers need for pedagogical education in general remains relevant until the end of the university, almost all students associate their lives with educational activities in the subjects of the natural science cycle, in the future become excellent teachers. Education in the pedagogical field includes the practice of students in general education institutions, namely in schools, grammar schools and lyceums, from the first year to the pre-graduate practice in the fourth year. The relevance of the study of this problem is due to the fact that questions remain on the training of teachers, namely, on the conduct of the practice of students in the conditions of identification and accompanying, gifted children to the lyceum-university system. The purpose of the study is to determine the prospect of preparing 21 teachers of the 19th century to work with gifted students. The study, conducted for four years, from 2016 to 2020, involved a group of students - future teachers from the first to the fourth (110 students). For this purpose, a plan has been developed for the interaction of students with gifted children in the educational process during pedagogical practice; The role of teachers-mentors and teachers of the Department of Chemical Education is justified; Analysis and self-analysis of lessons learned and organized; Performance was tested experimentally.
Recognition of Wrong Sports Movements Based on Deep Neural Network

During physical education (PE), the teaching quality is severely affected by problems like nonstandard technical movements or wrong demonstrative movements. High-speed photography can capture instantaneous movements that cannot be recognized with naked eyes. Therefore, this technology has been widely used to judge the sprint movements in track and field competitions, and assess the quality of artistic gymnastics. Inspired by three-dimensional (3D) image analysis, this paper proposes a method to recognize the standard and wrong demonstrative sports movements, based on 3D convolutional neural network (CNN) and graph theory. Firstly, a 3D posture perception strategy for demonstrative sports movements was constructed based on video sequence. Next, the authors provided the framework of the recognition system for standard and wrong demonstrative sports movements. After that, a 3D CNN was stablished to distinguish between standard and wrong demonstrative sports movements. The proposed method was proved effective and superior through experiments. The research results provide a good reference for the application of 3D image analysis in the recognition of other body behaviors and movements.
Acceleration of Neural Network Training on Hardware via HLS for an Edge-AI Device

The neural network has two computational steps: inference and training. In recent years, high-performance inference processing is accelerated by AI-specific hardware accelerators. However, training phase, which is the most important part of the computational load of a neural network, remains a difficult problem to solve in low-power edge devices. From a design productivity perspective, designing efficient hardware accelerator for training phase at RTL level is a time-consuming process due to cumbersome of the low-level hardware description languages. Therefore, we propose a high-level description (C) of the training algorithm: gradient descent algorithm using high-level synthesis-Vivado HLS, to generate a computationally efficient accelerator. Empirical results show that the resource constrained edge devices can also perform the training part while they are also inferring. In this study, we propose a system-on-chip solution to accelerate neural network training workloads for an edge-AI device.
Applying spectral analysis for identification of alteration zones in north Saveh area, Iran

An extensive series of volcanic rocks are exposed in the north of Saveh city, Iran, which consist of phyllic, argillic and propylitic hydrothermal alteration types. For the purpose of the investigation, a FieldSpec3® spectroradiometer was used to measure the spectral response of the mineral content of these rocks. The spectral analyses of reflectance curve by The Spectral Geologist (TSG) software could discriminate kaolinite and montmorillonite (argillic), illite, muscovite, phengite and paragonite (phyllic), hornblende and chlorite, siderite (propylitic), hematite and goethite from the gossans. It also detected gypsum of hydrothermal alteration zones. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image, which was used for mapping the hydrothermal alteration minerals, contains the Visible and Near Infrared (VNIR) wavelengths between 0.52 µm and 0.86 µm, Short Wave Infrared (SWIR) wavelengths between 1.6 µm and 2.43 µm and Thermal Infrared (TIR) wavelengths between 8.125 µm and 11.65 µm with 15, 30 and 90 m spatial resolutions, respectively. For calibration of the ASTER images, the extracted spectra of different rocks and minerals were used for atmospheric and radiometric corrections. Mixture tuned matched filtering (MTMF) and Spectral Angle Mapper (SAM) were applied on ASTER data to map the hydrothermal alteration of minerals. The use of the spectroradiometry techniques in conjunction with other data exhibits the ability of these new methods for non-destructive and rapid identification of mineral types for more detailed investigation. The results show that the area has undergone different levels of hydrothermal alteration, so much so that phyllic, argillic and propylitic types of hydrothermal alteration are present in the study area. This may point to high potential and promising zones for the exploration of porphyry mineralisation.
Implementasi Algoritma Support Vector Machine dan Chi Square untuk Analisis Sentimen User Feedback Aplikasi

In order to adapt with evolving requirements and perform continuous software maintenance, it is essential for the software developers to understand the content of user feedback. User feedback such as bug report could provide so much information regarding the product from user’s point of view, especially parts that need improvements. However, it is often difficult to read all the feedback for products with enormous number of users as manually reading and analyzing each feedback could take too much time and effort. This research aims to develop a model for automatic feedback classification by implementing Support Vector Machine for the classifier’s algorithm and Chi-square method for feature selection. The model is developed using Python programming language and is then evaluated under different scenarios in order to measure its performance. Using a ratio of training and testing set of 80:20, our model achieved 77% accuracy, 50% precision, 55% recall, and 73% F1-score with 6.63 critical value and C=100 and gamma 0.001 as the SVM hyperparameters.
DC Motor Control using Hand Gestures

A gesture is a form of non-verbal communication in which body actions communicate some particular messages. Due to the high processing capacity of today's computers, hand gesture recognition systems can be used to simplify interactions with electronic devices. In this paper, a hand posture recognition system was implemented, which is able to recognize five posture of a hand. The five postures consist of how many fingers are shown, and then, this information is used to control the position of a DC motor. Hand gestures are recognized by an image processing system that uses a neural network implemented on a microcontroller.