Reviewerly/exHarmony huggingface dataset:
VIS-HAPT: A Methodology Proposal to Develop Visuo-Haptic Environments in Education 4.0

Education 4.0 demands a flexible combination of digital literacy, critical thinking, and problem-solving in educational settings linked to real-world scenarios. Haptic technology incorporates the sense of touch into a visual simulator to enrich the user’s sensory experience, thus supporting a meaningful learning process. After developing several visuo-haptic simulators, our team identified serious difficulties and important challenges to achieve successful learning environments within the framework of Education 4.0. This paper presents the VIS-HAPT methodology for developing realistic visuo-haptic scenarios to promote the learning of science and physics concepts for engineering students. This methodology consists of four stages that integrate different aspects and processes leading to meaningful learning experiences for students. The different processes that must be carried out through the different stages, the difficulties to overcome and recommendations on how to face them are all described herein. The results are encouraging since a significant decrease (of approximately 40%) in the development and implementation times was obtained as compared with previous efforts. The quality of the visuo-haptic environments was also enhanced. Student perceptions of the benefits of using visuo-haptic simulators to enhance their understanding of physics concepts also improved after using the proposed methodology. The incorporation of haptic technologies in higher education settings will certainly foster better student performance in subsequent real environments related to Industry 4.0.
Current trends in training in continuing education: theoretical principles

The article supports the theoretical-methodological provisions that allow a systematic and comprehensive study of the postgraduate pedagogical training of teachers. The essence and characteristics of postgraduate pedagogical training for teachers from various training institutions were defined, providing professional and pedagogical training for this category of pedagogical staff; regularities, principles and conditions were developed for the implementation of promising directions for the development of the postgraduate pedagogical training system for teachers of different profiles; ifdeveloped technological support for postgraduate education for teachers from different backgrounds; promising paths for the development of postgraduate pedagogical training for teachers of multidisciplinary training in professional education were identified and based on the modernization of society.
Information Fusion and Decision Support for Autonomous Systems

In this talk we present our works on decision support analytic for autonomous systems. Decision support analytic process multiple sensory information collected by an autonomous system such as lidar, camera, RGBD, acoustic to perform signal detection, target tracking, object recognition. As multiple sensors are involved, our system uses sensor registration, data association and fusion to combine sensory information. The next layer of the proposed decision support system orients the processed sensory information at feature and classification levels to perform situation assessment and treat evaluation. Based on the assessment, the decision support system will recommend decision. If the uncertainty is high, actions including resource allocation, planning will be used to extract or reassess the sensory information to get a recommended decision with lower uncertainty. This talk will also presents the applications of the proposed decision support analytic in four industrial projects including 1) goal-driven net-enabled distributed sensing for maritime surveillance, 2) autonomous navigation and perception of humanoid service robots, 3) distance learning for oil and gas drilling and 4) cognitive vehicles.
Simulation Model of Sports Entrepreneurship Performance Based on Wireless Sensor Network

Entrepreneurship has become the choice of many young people today, and many sports entrepreneurs have joined it. However, the entrepreneurial performance of sports entrepreneurs cannot be effectively guaranteed. Therefore, the design of the simulation model of the early warning management system of entrepreneurial performance is extremely urgent. It is based on the wireless sensor network that provides an important guarantee for the model design. Therefore, it is necessary to study the simulation model of sports entrepreneurship performance early warning management system based on wireless sensor network. The purpose of this paper is to start with the research on the simulation model of sports entrepreneurship performance early warning management system based on wireless sensor networks, select entrepreneurs from a sports university in our city as the analysis object, establish a wireless sensor sports entrepreneurship performance early warning management system simulation model, and then conduct multidimensional comparison. The research results show that the entrepreneurial performance of entrepreneurial projects continues to rise quarter by quarter. By the fourth quarter, product growth rates, labor productivity, and return on net assets had all increased by about 20%, while the turnover rate remained within the range of 20%-25%. It can be seen that the research on the simulation model of sports entrepreneurship performance early warning management system based on wireless sensor network can effectively improve the competitiveness of entrepreneurial projects.
Window Median Filter (WMF) to Eliminate Salt and Pepper Noise from Digital Color Image﻿

A color digital image is a very important type of digital data, and this image may be exposed to the effect of negative noise, which distorts it and changes some of its characteristics. Therefore, it is necessary to search for a way to get rid of this noise. In this research paper, we an easy and flexible way to deal with salt and pepper noise will be presented tested and implemented, this method will base on selecting a window to deal with the noisy pixels based on median filter, image padding will be used to select the window with a suitable size. The practical results of the proposed method will be matched and compared with other methods used to remove noise to prove the efficiency of the proposed method for removing noise from the digital color image.
Anomaly Detection of Hydropower Bearing Based on Convolutional Neural Network Autoencoder

Hydropower has become an important energy supply method due to its advantages of pollution-free, renewable, and abundant resources. With the development of detection technology, methods that can detect anomaly without stopping the hydropower unit have gradually gained attention. An autoencoder based on one-dimensional convolutional neural network is proposed for anomaly detection of bearing condition monitoring system of the hydropower unit. The input sensor signals contain 7 features, and the anomaly is judged by the residual error between the reconstructed data and the original data. The proposed method is compared with one-class support vector machine method and autoencoder method. The results show that the accuracy, precision and recall of the proposed method are significantly better than other methods. F1 score, AUC and confusion matrix also show that the proposed method is optimal. The proposed method achieves a detection effect superior to the existing methods with the combination of autoencoder and convolutional neural network.
Comparative Multinomial Text Classification Analysis of Naïve Bayes and XGBoost with SMOTE on Imbalanced Dataset

In supervised machine learning, with an imbalanced dataset, achieving better classification in minority classes is a major challenge. In such situation, machine learning model shows biasness toward majority classes, which result into poor performance in another set of classes. This paper examined how Synthetic Minority Over-Sampling Technique (SMOTE) techniques help in multinomial text classification on the imbalanced dataset. The performance of SMOTE was examined with Naive Bayes (NB) and Extreme Gradient Boosting (XGBoost) algorithms. 701 questions were collected from college students residing in Mumbai, related to their lifestyle problems. Results showed that XGBoost with SMOTE (XGBoost + SMOTE) technique worked better on an imbalanced dataset in comparison with NB with SMOTE (NB + SMOTE), NB without SMOTE (NB-SMOTE), and XGBoost without SMOTE (XGBoost-SMOTE) techniques. The average classification accuracy for Naive Bayes (with and without SMOTE) was 68.0% while the average accuracy for XGBoost was 71.0%. In the selection of XGBoost and NB, researcher can opt for XGBoost with SMOTE technique to work on the multinomial imbalanced dataset.
Mitigating Black-Box Adversarial Attacks via Output Noise Perturbation

In black-box adversarial attacks, attackers query the deep neural network (DNN) and use the query results to optimize the adversarial samples iteratively. In this paper, we study the method of adding white noise to the DNN output to mitigate such attacks. One of our unique contributions is a theoretical analysis of gradient signal-to-noise ratio (SNR), which shows the trade-off between the defense noise level and the attack query cost. The attacker&#x2019;s query count (QC) is derived mathematically as a function of noise standard deviation. This will guide the defender to find the appropriate noise level for mitigating attacks to the desired security level specified by QC and DNN performance loss. Our analysis shows that the added noise is drastically magnified by the small variation of DNN outputs, which makes the reconstructed gradient have an extremely low SNR. Adding slight white noise with a very small standard deviation, e.g., less than 0.01, is enough to increase QC by many orders of magnitude yet without introducing any noticeable classification accuracy reduction. Our experiments demonstrate that this method can effectively mitigate both soft-label and hard-label black-box attacks under realistic QC constraints. We also prove that this method outperforms many other defense methods and is robust to the attacker&#x2019;s countermeasures.
Predicting with Confidence on Unseen Distributions

Recent work has shown that the performance of machine learning models can vary substantially when models are evaluated on data drawn from a distribution that is close to but different from the training distribution. As a result, predicting model performance on unseen distributions is an important challenge. Our work connects techniques from domain adaptation and predictive uncertainty literature, and allows us to predict model accuracy on challenging unseen distributions without access to labeled data. In the context of distribution shift, distributional distances are often used to adapt models and improve their performance on new domains, however accuracy estimation, or other forms of predictive uncertainty, are often neglected in these investigations. Through investigating a wide range of established distributional distances, such as Frechet distance or Maximum Mean Discrepancy, we determine that they fail to induce reliable estimates of performance under distribution shift. On the other hand, we find that the difference of confidences (DoC) of a classifier's predictions successfully estimates the classifier's performance change over a variety of shifts. We specifically investigate the distinction between synthetic and natural distribution shifts and observe that despite its simplicity DoC consistently outperforms other quantifications of distributional difference. $DoC$ reduces predictive error by almost half ($46\%$) on several realistic and challenging distribution shifts, e.g., on the ImageNet-Vid-Robust and ImageNet-Rendition datasets.
Arabic Poems Generation using LSTM, Markov-LSTM and Pre-Trained GPT-2 Models

Nowadays, artificial intelligence applications are increasingly integrated into every aspect of our lives. One of the newest applications in artificial intelligence and natural language is text generation, which has received considerable attention in recent years due to the advancements in deep learning and language modeling techniques. Text generation has been investigated in different domains to generate essays and books. Writing poetry is a highly complex intellectual process for humans that requires creativity and high linguistic capability. Several researchers have examined automatic poem generation using deep learning techniques, but only a few attempts have looked into Arabic poetry. Attempts to evaluate the generated pomes coherence in terms of meaning and themes still require further investigation. In this paper, we examined character-based LSTM, Markov-LSTM, and pre-trained GPT-2 models in generating Arabic praise poems. The results of all models were evaluated using BLEU scores and human evaluation. The results of both BLEU scores and human evaluation show that the Markov-LSTM has outperformed both LSTM and GPT-2, where the character-based LSTM model gave the lowest yields in terms of meaning due to its tendency to create unknown words.
Pedestrian Re-identification Method Based on Bilateral Feature Extraction Network and Re-ranking

In recent year, convolution neural network(CNN) has been widely used in the field of pedestrian re-identification. However, most models based on CNNs need complex calculation and the features extracted by these models are easy to ignore the shallow detail information, which leads to insufficient recognition for pedestrians with small differences. Aim at the above problems, we propose a bilateral feature extraction network(BiFeNet). The network includes two parts: detail branch and semantic branch. Detail branch consists of simple convolution operations and down-sampling to obtain high-resolution feature maps with rich detail information. Semantic branch uses a relatively deep network to obtain strong semantic information. For loss function, we use Circle loss that unifies the two basic learning paradigms. Circle loss can offer a more flexible optimization towards a more definite convergence target. Finally, the K nearest neighbor method is used to reorder the initial list obtained by using Euclidean distance to make more similar matches ranked higher, thereby improving the accuracy of re-identification. On Market-1501, DukeMTMC-ReID and CUHK03 datasets, the mAP of our proposed method reached 89.53%, 77.67% and 80.54%, respectively. Experimental results show that our proposed method can extract more recognizable features and improve the accuracy of pedestrian re-identification.
Development of an Android-Based Application as an Information System for Sports Venues and Sport Community

The application developed is android-based AYOOLAHRAGA 1 , which is an information system for sports venues and sports communities for smartphone users.This development of such application has the main purpose to help users to find out some information readily and quickly about physical activities fields available in Yogyakarta and the nearest places.The information developed in the AYOOLAHRAGA application can be accessed by anyone having a smartphone especially those residing in Yogyakarta city (DIY).This research is a product development study, popularly known as Research and development (R&D) research.Whilst collecting data, three types of instruments were employed, namely a questionnaire for expert media validators, along with two different tests administered in two terms based on the sample size involved.The small-scale test used a sample of 50 respondents whereas the large-scale sample consists of 100 respondents.The data were analysed using quantitative and qualitative approaches.After addressing all data, it was revealed that the results of the material expert test obtained a percentage of 98.82%, felt in strongly agree category of application acceptance while the media expert test obtained a percentage of 97.21% likewise.The results of the small 1 Downloading information AYOOLAHRAGA is available in this link below: https://play.google.com/store/apps/details?id=com.digitcreativestudio.ay oolahragaid and large-scale testing showed that most respondents stated that the development of the present application for sports lovers' benefits is good and needed so far.Therefore, it can be concluded that an android-based sports venues and sports community information system is useful and feasible to use.
Detecting Invisible People

Monocular object detection and tracking have improved drastically in recent years, but rely on a key assumption: that objects are visible to the camera. Many offline tracking approaches reason about occluded objects post-hoc, by linking together tracklets after the object re-appears, making use of reidentification (ReID). However, online tracking in embodied robotic agents (such as a self-driving vehicle) fundamentally requires object permanence, which is the ability to reason about occluded objects before they re-appear. In this work, we re-purpose tracking benchmarks and propose new metrics for the task of detecting invisible objects, focusing on the illustrative case of people. We demonstrate that current detection and tracking systems perform dramatically worse on this task. We introduce two key innovations to recover much of this performance drop. We treat occluded object detection in temporal sequences as a short-term forecasting challenge, bringing to bear tools from dynamic sequence prediction. Second, we build dynamic models that explicitly reason in 3D, making use of observations produced by state-of-the-art monocular depth estimation networks. To our knowledge, ours is the first work to demonstrate the effectiveness of monocular depth estimation for the task of tracking and detecting occluded objects. Our approach strongly improves by 11.4% over the baseline in ablations and by 5.0% over the state-of-the-art in F1 score.
Classification of Rigid and Non-Rigid Objects Using CNN

Classifying moving objects in video surveillance can be difficult, and it is challenging to classify hard and soft objects with high Accuracy. Here rigid and non-rigid objects are limited to vehicles and people. CNN is used for the binary classification of rigid and non-rigid objects. A deep-learning system using convolutional neural networks was trained using python and categorized according to their appearance. The classification is supplemented by the use of a data set, which contains two classes of images that are both rigid and not rigid that differ by illuminations.
Multimodal Smart Wheelchair Integrated with Safety Alert System

Elderly and people with disabilities often rely on others for their locomotion. With this emerging world of automation and technologically advanced society we live in a smart wheelchair with appropriate automation can be a life changing innovation for them. One might wonder what is the need for a wheelchair to be smart or for that matter why anything needs to be smart. The answer is simple, to overcome the limitations of the existing technology. We aim on integrating a simple Manually operated wheelchair with features like Obstacle Detection with appropriate technologies such as voice control or gesture control for people who are not able to locomote like a normal person can. With this project, not only do we help a person using a normal wheelchair more easily but also make life easier for those who have other disabilities which are standing as an obstacle making it difficult for them to walk. For instance, a blind person can use a smart wheelchair that allows voicecontrolled movement or even gesture-controlled movement. Another might be of a person who can’t speak, will now be able to control everything with his bare hands. Wheelchair coupled with the appropriate sensors that automatically detects the obstacles/objects in the proximity and takes appropriate action consequently and In addition to that it may also be controlled by another person taking care of the disabled by giving commands such as forward, backward, upright etc. This not only reduces the user's efforts but also helps people to take care of their elderly. Voice/gesture control system makes everything simple. Just visualize the application of it in a hospital where the nurse has to manually handle the patient's wheelchair for even the slightest of movement. This system on the other hand needs only text or voice input command, and based upon the predefined command received from the user, the system will execute the task. This project even enforces a GSM module which uses the sim card, which can help in tracking the wheelchair when required, like in case where a user is in difficulty and needs emergency help, a message asking for help can be sent to the intended person.
Keyword Extraction from Biomedical Documents Using Deep Contextualized Embeddings

Due to the rapidly increasing amount of biomedical publications, it has become challenging to follow scientific articles and new developments. Keywords in scientific articles provide a quick understanding and summarize the important points of the context. When keywords are not used in some biomedical articles or are not sufficient to express the content of the text, automatic keyword extraction systems are needed. This paper addresses the keyword extraction problem as a sequence labeling task where words are represented as deep contextual embeddings. We predict the keyword tags identified in sequence labeling by fine-tuning XLNET and BERT-based models such as BERT, BioBERT, SCIBERT, and RoBERTa. Our proposed method does not need extra dictionaries required by rule-based methods and feature extraction as in traditional machine learning methods. Performance evaluation on the benchmark dataset for biomedical keyword extraction shows that domain-specific contextualized embeddings (BioBERT, SciBERT) achieve state-of-the-art results compared to the general domain embeddings (BERT, RoBERTa, XLNET) and unsupervised methods.
Probabilistic causal model for the detection of obsolete personal information to prevent falls in the elderly

The information describing a person is constantly evolving and may become obsolete and contradict other information. A personal database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. This study focuses on proposing a novel approach for dealing with the information obsolescence problem in the elderly-fall prevention context. Our approach aims to continuously monitor elderly information in order to detect the change in the behavior of an elderly person and to prevent him from falls. It consists of detecting contradictions between newly acquired information about a single elderly and what we already know about that person, then identifying among his observations those that have become obsolete and need to be updated. We propose a new approximate concept,£-Contradiction, which represents the confidence level of having a contradiction in a set of observations when a causal Bayesian network is our representation model. We propose a polynomial-time algorithm for detecting obsolete information and show that the resulting obsolete observations are given as an explanation AND-OR tree.