Reviewerly/exHarmony huggingface dataset:
Construction and Evaluation of a Laboratory Stand for Testing MV Switchgears Using VR Technology in the Power Industry

Abstract The purpose of this study was to build and evaluate a laboratory stand for testing MV switchgears using VR technology in the power industry. In the course of work, an exemplary program of operation of the application created by PRADMA was developed. The developed program was designed to effectively introduce the application user to the basic information about moving the virtual avatar, interacting with the prepared elements, and to provide information on the construction of the MV switchgear and its components. In order to confirm the advantages of VR technology and the quality of the developed VR application program, tests of the VR application were carried out with the help of students of the Warsaw University of Technology. The results of the evaluation questionnaire created for the purposes of the tests were used to develop conclusions regarding the use of VR in the power industry. The evaluation questionnaire questions related to key issues such as the quality of the mapped virtual elements, the level of realism preserved in the virtual environment, the effectiveness of the developed program or the substantive values of the entire exercise. The summary contains the answer to the theses put forward in the paper regarding the profitability and usefulness of using VR applications in the power industry.
Role of Human-Centered Design in Haptic-Enabled Virtual Reality Environments’ Research and Development

With the increasing proliferation of virtual reality technology, there has been a rising interest in improving the sense of immersion in Virtual Reality Environments (VREs). Apart from the auditory and visual modalities, the modality of haptic sensing and touch is critical for the sense of immersion and presence in a VRE. Therefore, designers need to understand the human-centered design (HCD) considerations when developing such haptic sensing and feedback technology for its effective implementation in VREs. This paper critically reviews the role of HCD in haptic-enabled VRE and its applications. The critical review shows that the current research in haptic sensing and feedback is gradually complementing the functionality approach with HCD principles. This shift in the research focus has uncovered distinct directions for research into designing user-centered and practical solutions for immersive Virtual Reality experiences.
VR Game for Powerlifting Training

Abstract Virtual reality applications are becoming more and more popular. In addition to apparent uses like providing entertainment, VR applications are finding use in fields such as education, engineering, and architecture. The market for VR games used in medicine and sports is also thriving. The applications allow monitoring of an athlete’s progress, training advanced movements specific to a given sport, and are difficult to reproduce during traditional training. A significant advantage of this type of solution is the increased safety of the user and, thus, a lower risk of injury. The article presents a VR application designed for the training of a powerlifting triathlon. This sport consists of three exercises. They are performed by both strength athletes and those training in other sports to prepare for the season properly. Due to the fact that they are simple multi-joint exercises, they fall into the collection of exercises often performed by amateur trainers. Despite their significant popularity and the undoubted advantages of performing them, it is often observed that they are performed incorrectly, which significantly increases the risk of injury. The purpose of the application is to enable safe training and learning of correct movement patterns of powerlifting exercises, regardless of the user’s level of proficiency.
Level of Cybersecurity Readiness of Small and Medium Nonprofit Organizations (NPOs) During COVID-19

Considering the unprecedented COVID-19 pandemic crisis, many NPOs, like other types of institutions, have migrated their operations to remote, virtual platforms to maintain at least a minimum level of activity. Consequently, the NPOs are experiencing increased online activity during COVID-19. This study assesses NPOs’ readiness during COVID-19 through the lens of three components of the ISO 22301:2019 - Business Continuity Management Systems (BCMS) – labeled as 3Ps: Policy, People, and Processes. A snowball sampling study was conducted to examine information security readiness in small and medium-sized NPOs during the Covid-19 pandemic in D.C., Maryland, and Virginia (DMV) area. Each item in the three aspects of readiness was measured at high, moderate, or low. The findings of the study demonstrate that the NPOs should emphasize developing and implementing cybersecurity policies during crises to increase awareness and preparedness. The empirical results of this study further shed light on the aspects and factors of cybersecurity readiness that are often less prioritized by NPOs' senior management but should be taken into more consideration when creating cybersecurity policies and procedures.
Improving Computing Education Research through Valuing Design

Exploring alternatives to existing research systems and methods.
Also for k-means: more data does not imply better performance

Abstract Arguably, a desirable feature of a learner is that its performance gets better with an increasing amount of training data, at least in expectation. This issue has received renewed attention in recent years and some curious and surprising findings have been reported on. In essence, these results show that more data does actually not necessarily lead to improved performance—worse even, performance can deteriorate. Clustering, however, has not been subjected to such kind of study up to now. This paper shows that k -means clustering, a ubiquitous technique in machine learning and data mining, suffers from the same lack of so-called monotonicity and can display deterioration in expected performance with increasing training set sizes. Our main, theoretical contributions prove that 1-means clustering is monotonic, while 2-means is not even weakly monotonic, i.e., the occurrence of nonmonotonic behavior persists indefinitely, beyond any training sample size. For larger k , the question remains open.
More Than Just Algorithms

A discussion with Alfred Spector, Peter Norvig, Chris Wiggins, Jeannette Wing, Ben Fried, and Michael Tingley.
Automatic classification of symmetry of hemithoraces in canine and feline radiographs

Thoracic radiographs are commonly used to evaluate patients with confirmed or suspected thoracic pathology. Proper patient positioning is more challenging in canine and feline radiography than in humans due to less patient cooperation and body shape variation. Improper patient positioning during radiograph acquisition has the potential to lead to a misdiagnosis. Asymmetrical hemithoraces are one of the indications of obliquity for which we propose an automatic classification method.We propose a hemithoraces segmentation method based on convolutional neural networks and active contours. We utilized the U-Net model to segment the ribs and spine and then utilized active contours to find left and right hemithoraces. We then extracted features from the left and right hemithoraces to train an ensemble classifier, which include support vector machine, gradient boosting, and multi-layer perceptron. Five-fold cross-validation was used, thorax segmentation was evaluated by intersection over union (IoU), and symmetry classification was evaluated using precision, recall, area under curve, and F1 score.Classification of symmetry for 900 radiographs reported an F1 score of 82.8%. To test the robustness of the proposed thorax segmentation method to underexposure and overexposure, we synthetically corrupted properly exposed radiographs and evaluated results using IoU. The results showed that the model's IoU for underexposure and overexposure dropped by 2.1% and 1.2%, respectively.Our results indicate that the proposed thorax segmentation method is robust to poor exposure radiographs. The proposed thorax segmentation method can be applied to human radiography with minimal changes.
A DLT-based framework for secure IoT infrastructure in smart communities

In the field of smart communities, significant progress has been made in recent years. The objective of constructing smart communities is to improve the quality of life of their inhabitants. To accomplish this objective, technologies such as Internet of Things (IoT) and Artificial Intelligence (AI) were deployed. The data gathered and processed by IoT devices, particularly those with centralized control, are however susceptible to availability, integrity, and privacy risks. Due to its inherent properties of transparency, immutability, and underlying secure-by-design architecture, Distributed Ledger Technology (DLT) and Smart Contracts enable distributed, decentralized, automated workflows that can be incorporated to automate the management of the next generation of IoT networks. Using a potential use case, a conceptual architecture for securing smart communities with DLT is developed and explained. In this paper, a framework for IoT eco-systems is proposed that provides seamless integration between IoT and DLT to create a decentralized trusted architecture that ensures the trustworthiness of IoT eco-systems at design time and a trust reputation model based on the architecture to protect it at run-time. In addition, the initial implementation steps are described for this framework.
A similarity measure of complex-valued evidence theory for multi-source information fusion

The study of complex-valued evidence theory has become an interesting topic, particularly in the context of information fusion techniques. These studies mainly focus on its geometric interpretation and application. Moreover, little work has been conducted with regard to conflict measure. Therefore, this paper defines a complex similarity measure which quantifies the degree of conflict among complex-valued mass functions by measuring the degree of consistency among complex-valued mass functions. It also satisfies some basic properties, such as non-negativity, symmetry and etc. Complex similarity measure is an extension of traditional similarity measure in complex space, which will collapse to a similarity measure of real space when complex-valued mass functions collapse to mass functions of real space. Furthermore, some numerical examples illustrate the rationality and effectiveness of complex similarity measure. By employing complex similarity measure, this paper proposes a method to combine information and reduce the time cost associated with complex-valued Dempster's rule of combination. Experimental results on datasets indicate that for some data sets (Take iris for example), the proposed method can not only reduce the time cost (75%), but also improve the accuracy of fusion results (96%).
Enhancing LiDAR Point Cloud Segmentation with Synthetic Data

LiDAR point-cloud segmentation is a crucial issue for autonomous cars applications. The standard method for segmenting large-scale point clouds is to project 3D point cloud onto a 2D LiDAR image and apply convolutions to it. In this paper, we also follow this method and we want to detect and classify occurrences of road-objects, namely cars, cyclists, and pedestrians. To achieve this goal, we adapted the SqueezeSeg deep neural network. To address the challenge of obtaining labeled data for training autonomous driving systems, we used the CARLA autonomous driving simulator to generate a synthetic dataset in a simulation environment. The proposed network is initially trained on real-world LiDAR point-cloud data acquired from the KITTI dataset. Then, we created a synthetic dataset using the CARLA autonomous driving simulator in order to obtain more data and determine its impact on the validation accuracy of real-world data. To compare our current work to earlier work, we employ the same method. Our synthetic dataset has additional classes, such as cyclists and pedestrians, and when combined with real-world data, it significantly improves validation accuracy for each class, surpassing previous work. This demonstrates the effectiveness of our approach in detecting and classifying road-objects using LiDAR point-clouds, which is essential for the safe operation of autonomous vehicles.
An Experimental Analysis of Deep Learning Models for Human Activity Recognition with Synthetic Data

In this paper, an experimental study of state-of-the-art techniques in Human Activity Recognition (HAR) is presented. Different Deep Learning algorithms, including CNNs and RNNs, are examined and compared. The experimental part is done using two real-life datasets Kinetics-400 and UCF-101 and one synthetic - SURREACT. All of them are used both for training and testing. The models - SlowFast, X3D and MViT are evaluated using accuracy top-1, and the results are identifying the best-performing combinations of dataset and model. One important question this study is trying to answer is whether a synthetic dataset can replace a real-world one. Finally, limitations and future directions are discussed, along with potential real-world applications.
Comparative Analysis of Local Adaptive LMS Image Filtration

In this paper the 2D part of the developed method for local adaptive 3D filtration of multidimensional grayscale images is compared with some advanced methods for image filtrations - BM3D, Average, Gaussian and etc. The adaptation of the presented algorithm is based on the information from a small local area of the neighbouring pixels of the processed images using the developed 2D Adaptive LMS filter. A comparative analysis of the quality of the processed images was made in terms of both peak signal-to-noise ratio calculations and subjective visual quality. The experimental results were carried out with a set of test halftone images that were noised with Additive White Gaussian Noise (AWGN) with different variance levels.
An Encryption Algorithm Based on Recurrent Appell's Hypergeometric Functions of Two Variables

This paper introduces a novel method based on symmetric cryptography relying on Appell's recurrent hypergeometric functions of two variables. The implementation was done in C# and our method compared to standard methods when it comes to encryption and decryption processing time: DES, 3DES, Caesar cipher and RSA. The achieved results seem promising as it outperforms DES algorithm which represented de facto standard in cryptography for long period.
Modifying Neural Networks in Adversarial Agents of Multi-agent Reinforcement Learning Systems

This paper proposes a method to reduce the malicious agent’s negative effects on a multi-agent reinforcement learning (MARL) system, including actor-critic architecture. The method achieves the overall goal of the MARL system, which is to increase the cumulative reward of all individual agents and reduce the malicious agents’ harmful effects on the entire MARL system. Assuming that the adverse agent is detectable, we propose to change the malicious agent’s neural network (NN) structure. By leveraging a comparative methodology, we have demonstrated that a specific NN architecture using a linear activation function surpasses another utilizing a sigmoid activation function in minimizing loss. Our analysis indicates that this performance differential is attributable to the utilization of distinct activation functions within the models. This approach involves calculating the gradient of the loss function with respect to the activation function. The claims have been proven theoretically, and the simulation confirms theoretical findings.
A New Multi-objective Ali Baba and the Forty Thieves Optimization Algorithm

In this paper a multi-objective version of Ali Baba and the forty thieves optimization algorithm is proposed. The algorithm models by the events of the thieves trying to catch Ali Baba, who learned the location of their treasure stores, and Ali Baba's assistant Marjaneh trying to mislead the thieves. As a result of the comparisons made under different conditions, it was seen that the multi-objective Ali Baba and the forty thieves algorithm gives the most successful results compared to the some other multi-objective intelligent optimization algorithms.
Implementation of the Serbian Language POS Taggers Using the NLTK Library

This paper describes research on different methods for implementing part-of-speech taggers for the Serbian language. Experiments were conducted on the SrWaC corpus, which consists of 10 million tokens collected from. rs web domain. Algorithms from NLTK library were used for creating tagging models. The tagset was modified and adapted for semi-deep tagging. The experiments involved preprocessing of the corpus to correct tagging mistakes and reversing the word order in sentences to obtain a reversed corpus, which is used for creating backwards tagging model. To achieve greater accuracy, a tagger that combines n-gram taggers was implemented using the NLTK library. However, the best result achieved during tagging was with the Perceptron tagger, which reached 95% accuracy. The goal of this work is, among other things, to ensure that tagging is fast, as the intention is to use the best models for a syntactic analysis tool for the Serbian language.
A Population Size Analysis of Adaptive Memetic Binary Optimization Algorithm for Feature Selection

Feature selection is essential for identifying beneficial features in data and reducing the curse of dimensionality. In this paper, we analyze the effect of population size on the performance of the adaptive memetic binary optimization algorithm for feature selection. The adaptive memetic binary optimization algorithm is an optimization algorithm that works exclusively with binary values in a discrete search area. The choice of the population size may notably impact the quality of the solution. We conducted experiments on three datasets and used population sizes of 10, 20, 40, and 80 to evaluate the performance of the adaptive memetic binary optimization. Our experimental results show that when the population size is set to 20, the adaptive memetic binary optimization produces the best solutions regarding the fitness function, average selected features, and classification error rate. The convergence graphs analysis results suggest that the adaptive memetic binary optimization algorithm performs better on datasets with larger population sizes, leading to faster convergence and a better solution. These results provide important insights into the impact of population size on the performance of adaptive memetic binary optimization for feature selection.
An Approach to User-Friendly GUI Model Using HCI Principles on University Websites

Universities need websites to communicate successfully with the academic communities of today. This article suggests an HCI and ergonomics-based strategy for university websites. This research investigates student website concerns. User-interaction issues were studied on five university websites. Based on the flaws, a survey was conducted to see how users felt. Based on reviews, a new model and sample website were created. After developing the website, the second survey of five university students was conducted. The new model was well-received in comparison to its predecessors.
Unsupervised Anomaly Detection for Multivariate Incomplete Data using GAN-based Data Imputation: A Comparative Study

With the increasing interconnectivity of cyber-physical systems (CPSs) in various fields, such as manufacturing plants, power plants, and smart networked systems, large amounts of multivariate data are generated through sensors and actuators, also other data sources such as measurements and images. This paper focuses on the anomaly detection (AD) problem, also known as fault detection or outlier detection, depending on the type of dataset, which involves identifying anomalous values in the dataset using analytical methods. However, datasets often contain missing values, which can lead to incorrect outcomes and affect the availability of anomalous samples that are fewer in amount, making incomplete datasets. Therefore, a generalized AD method is proposed for incomplete datasets, which involves two steps: data imputation (DI) to obtain complete datasets using GAN and later AD for the complete datasets. While statistical-based imputation methods are commonly used, they do not consider data distribution for datasets with anomalous samples. The capabilities of GANbased DI are tested under different hyperparameter settings and percentages of missing values. The AD problem is then addressed using seven unsupervised anomaly detection methods on six different datasets, including a real dataset from a steel manufacturing plant in Italy. Each dataset is analyzed to determine which DI and AD method combination performs the best. The results show that GAN-imputed data provides the best DI performance, while the reweighted minimum covariance determinant (RMCD) method offers the overall best AD results combined with GAN.
A Serbian Question Answering Dataset Created by Using the Web Scraping Technique

Every artificial intelligence task requires a particular dataset to train the model and test it. As the expansion of the field of AI accelerates, data is becoming a critical resource. Natural language processing is a specific field in artificial intelligence that requires separate datasets for each task and each processed language. This paper describes the process of collecting a dataset for a question answering system in the Serbian language. Data collection was achieved using the Web scraping method. The Web scraper was implemented in the Python programming language. The resulting dataset contains 16374 questions and answers in 6 different fields: history, biology, geography, physics, chemistry, and mathematics.
Optimization-based Task and Trajectory Planning for Robot Manipulators

We introduce optimization-based algorithms that address the problem of robot task scheduling and trajectory planning. Following a two-layer hierarchical control structure, we first decouple the task scheduling from the trajectory planning by introducing two separate optimization problems, a discrete one for task scheduling and a continuous optimization problem for trajectory planning. In a further step, we integrate both planning layers into a monolithic layer in the form of a mixed-integer recursive optimization problem. The algorithms are implemented and validated using Robot Operating System (ROS) on an experimental setup with a robot manipulator performing pick-and-place tasks.
Cooperation Strategy for Optimal Motion of Aerial and Ground Vehicles

In this paper, a route selection algorithm is proposed for aerial and ground vehicle cooperative control. The main goal is to determine a feasible trajectory for a drone, which satisfies several limitations. Moreover, during the route selection, the flight time is also minimized to increase the efficiency of the entire system. The route selection is performed by a graph-based method, which is evaluated for different initial conditions. Then, the proposed algorithm determines the trajectories for the drone, and the predefined limitations are also considered. The method is validated in a MATLAB-based simulation environment, in which the whole algorithm is implemented.