Reviewerly/exHarmony huggingface dataset:
Evaluation of several gaze control methods for a board game with no time pressure

The present study aimed to evaluate six methods of gaze control of pieces in board games without time pressure designed for disabled users who cannot use a keyboard or mouse. Checkers was used as an example game in the experiment, however, the pieces could only be moved one square (i.e. there were no flying kings), but the same control methods with only minor modifications can be used in most board games. By experimenting, we determined that the control method with dwell-time equal to about 600-700ms was the more practical and worked better for users, while the gesture control worked poorly, and the control method involving directly the board was rated higher than the one based on using the buttons around the board.
Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning

Deep learning-based disease diagnosis applications are essential for accurate diagnosis at various disease stages. However, using personal data exposes traditional centralized learning systems to privacy concerns. On the other hand, by positioning processing resources closer to the device and enabling more effective data analyses, a distributed computing paradigm has the potential to revolutionize disease diagnosis. Scalable architectures for data analytics are also crucial in healthcare, where data analytics results must have low latency and high dependability and reliability. This study proposes a microservices-based approach for IoT data analytics systems to satisfy privacy and performance requirements by arranging entities into fine-grained, loosely connected, and reusable collections. Our approach relies on federated learning, which can increase disease diagnosis accuracy while protecting data privacy. Additionally, we employ transfer learning to obtain more efficient models. Using more than 5800 chest X-ray images for pneumonia detection from a publically available dataset, we ran experiments to assess the effectiveness of our approach. Our experiments reveal that our approach performs better in identifying pneumonia than other cutting-edge technologies, demonstrating our approach's promising potential detection performance.
On the role of user segmentation in recommender systems’ performances

Recommender systems are designed to suggest items for purchase or examination based on user preferences. In a variety of industries, such as e-commerce, tourism, social media, etc., these systems are frequently employed. However, they do have certain limits because of the vast amount of data they use. To enhance suggestion quality, artificial learning models have become prevalent in this field, along with data filtering techniques. In this study, as a data filtering technique, a user segmentation similar to well-known Recency-Frequency-Monetary is presented with the goal of examining how the use of user-segmented data affects the precision and caliber of recommendations produced by various algorithms. From the most fundamental popularity-based techniques to the most recent and sophisticated graph neural network approaches, the experiments are performed with eight different approaches. The results of the comparative analysis demonstrate that recommendation performance depends not only on user segmentation but also on other factors of the chosen method or data set. Nevertheless, user segmentation can improve the performance of some algorithms.
VR educational game in public awareness campaign preventing the spread of COVID-19 – a pilot study

Within a few years, virtual reality (VR) has generated a lot of interest. With the development of this technology, many opportunities for its use have arisen. Among the most popular applications, one can also mention education. The typical VR approach in education involves applying games to increase the learner's involvement and motivation. The question is whether engaging in a virtual reality game designed for a public awareness campaign can help the participants enhance their assessment of social distancing measured in meters, potentially aiding in the reduction of COVID-19 transmission. The Sustainable Development Goals have been greatly affected by the COVID-19 pandemic, impacting almost all countries around the world. This has led to changes in political and health priorities as well as research strategies. For this purpose, a pilot study was conducted on 4 people in the game created in the Unity engine. The respondents' reactions were examined with the use of the following EEG indices - Engagement, Arousal, Valence, and Approach-Withdrawal were used. As a result, it was obtained that the created game can improve the assessment of distance by the respondents. Therefore, we can conclude that applying virtual reality games can teach people to follow certain rules and contribute to slowing down the spread of COVID-19.
An unsupervised learning-based methodology for uncovering behavioural patterns for specific types of software defects

Software deffect prediction, a problem of major relevance within the search-based software engineering field, aims to enhance software quality by early and precisely uncovering faulty software modules. Accurate detection of software defects in new software releases might increase the performance of the software development process in terms of cost, time and software quality. Most approaches from the software deffect prediction literature try to develop general solutions that are designed to work with any type of software deffect. From a software engineering perspective, software defects may take various forms and identifying/fixing different types of defects requires different approaches. Starting from the assumption that specific types of software defects have a particular behaviour, we are introducing in this paper, as a proof of concept, an unsupervised learning-based methodology for mining behavioural patterns for specific classes of software defects and identifying features which would be relevant for detecting the uncovered classes. The experiments performed on an open-source software deffect prediction data set collected from all releases of the Apache Ivy software highlight that the results obtained by applying the proposed methodology are highly correlated with the way human domain experts categorise and address software defects. Creating software deffect prediction models that are specifically tailored for different software deffect types may improve the accuracy of the developed models, open the possibility to apply different sets of predictive models based on the domain of the software and may accelerate the adoption of software deffect prediction approaches by the industry.
Preventing Image Data Poisoning Attacks in Federated Machine Learning by an Encrypted Verification Key

Recent studies have uncovered security issues with most of the federated learning models. One common false assumption in the federated learning model is that participants are the attacker and would not use polluted data. This vulnerability enables attackers to train their models using polluted data and then send the polluted updates to the training server for aggregation, potentially poisoning the overall model. In such a setting, it is challenging for an edge server to thoroughly inspect the data used for model training and supervise any edge device. This study evaluates the vulnerabilities present in federated learning and explores various types of attacks that can occur. This paper presents a robust prevention scheme to address these vulnerabilities. The proposed prevention scheme enables federated learning servers to monitor participants actively in real time and identify infected individuals by introducing an encrypted verification scheme. The paper outlines the protocol design of this prevention scheme and presents experimental results that demonstrate its effectiveness.
A Word Sense Disambiguatin Approach For Romanian Language

The automatic word sense disambiguation has been a topic of interest since the 1950s. Meaning disambiguation is not an end in itself, it is an intermediate process, necessary at a certain level to use in natural language processing. It is obviously useful for applications that require language interpretation (message communication, human-machine interaction), but it is also used in fields whose main purpose is not to understand natural language. In this paper we propose an approach for word sense disambiguation for Romanian language, based on a supervised learning, a large number of features, and information gain to reduce the dimensions. The evaluation for the Romanian words subtask is produced by the extensive feature set in conjunction with a Maximum Entropy classifier and outcomes that are better than other similar researches.
Training Learning Weights of Elman Neural Network Using Salp Swarm Optimization Algorithm

Elman Neural Network (ENN) is one of the most common type of recurrent neural network (RNN). It is frequently applied to numerous applications such as classification and predication. However, the ENN model, suffers from serious flaws like network stagnancy and delayed convergence during training. To enhance the effectiveness of the ENN and address the mention problems, numerous studies have been conducted to speed up the learning process of ENN. Many optimization techniques are being used and each algorithm has its own characteristics in terms of efficiency. Some of the algorithms inspire from nature, while others come from the swarm's combined behaviour. Different numbers of optimization methods, including Particle Swarm Optimization (PSO), Bat Algorithm, and Cuckoo Search (CS) are applied to improve the ENN's learning process. As a result, this study suggested using Salp Swarm algorithm (SSA) to address these issues and speed up the ENN algorithm's learning process. The SSA, which is based on the typical behaviour of Salp swarms, is proposed in this research as a novel optimization technique. Due to its effectiveness and Salp intelligent behaviour, a novel Salp algorithm improves the learning process of the artificial neural network (ANN), and ENN models. Therefore this study proposed Salp swarm artificial neural network (SSANN) and Salp swarm Elman neural network (SSElmanNN). Performance of the suggested models is evaluated against ANN, back propagation neural network (BPNN), along with ENN in term of accuracy and Mean Square Error (MSE). Two datasets such as IRIS and Credit Card are used for simulation. The simulation results demonstrate that the suggested models outperform the other algorithms used in this study in terms of MSE and accuracy.
A new concept drift detection method based on the ranking of features in a data stream

The article presents a new concept drift detection method based on analyzing the importance of features of instances in the data stream. The data stream contains information about distribution patterns that reflect different concepts that may be hidden in the data stream. The presented drift detector concept uses information about the fluctuation of the most informative feature inside chunks of the data stream and compares it with the change of the same feature in neighbor chunks. In the case of data streams, the meaning of features can change over time. These changes affect the quality of the classification but can also be a significant indicator of ongoing concept drift. After detecting the drift, the classifier should be trained with the new dataset. But this issue is not addressed in this article. In this work, we propose a new concept drift detector in the data stream for the first time. This goal is achieved by observing the changing importance of features in different parts of the data stream. The proposed approach uses the feature significance measure as a drift detector. The obtained results indicate that the method can be introduced in practice. Because these are only preliminary results, in this paper, we focused on presenting the advantages of our strategy without comparison with other methods.
Customer experiences drive the need of innovation in veterinary practices

Animal healthcare practices are continuously evolving, and new technologies and innovations have brought significant changes to the veterinary industry. This article aims to explore the importance of innovation in animal healthcare facilities and propose some potential innovations that can revolutionize veterinary practices. The article draws on existing literature on innovation in healthcare and veterinary practices and identifies key challenges and opportunities in animal healthcare. In the article the Author attempted to answer three research questions: What innovative activities should be implemented in veterinary clinics? Are customers of veterinary units satisfied with the services? What kind of organizational problems they experience when using veterinary services? The aim of the conducted research was to analyze the level of satisfaction of pet owners when using veterinary services and propose some innovative activities that can be implemented in animal healthcare facilities to improve their functionality. For this purpose, the Author conducted relevant literature reviews, inductive and deducting reasoning and 102 in-depth interviews (IDI). The results of the study may be useful to other researchers as well as executives in the animal healthcare facilities.
Secure KNN Computation on Cloud

Cloud computing has emerged as a trend of outsourcing database and query services to a powerful cloud to ease local storage and computing pressure. However, private data storage and computation in the cloud come with a risk of losing the privacy and confidentiality of the data. Thus, sensitive applications running on the cloud require to be encrypted before storing it in the cloud. Additionally, to run some data mining algorithms, viz., k-NN requires the data to be in the encrypted domain computation-friendly ciphertext form. Thus, data are encrypted using a searchable encryption scheme before outsourcing it into the cloud. Asymmetric scalar-product-preserving encryption (ASPE) scheme provides secure k-nearest neighbors computation that is designed to provide both Data Privacy and Query Privacy. However, this scheme assumed that the query users were trusted entities. Enhancements to this work further showed that trusted query user assumption is no longer necessary if we use the Paillier cryptosystem. In this work, we have shown that even if we do not use the Paillier cryptosystem, query privacy in the cryptosystem can be achieved using the ASPE technique alone. Using ASPE for query encryption reduces the query encryption time, further improving the practicality of the encryption scheme.
meta-GRS: A Graph Neural Network for Cross-Domain Recommender System via Meta-Learning

Despite the fact that Recommender Systems have been researched and developed for quite a while, they still hold some certain challenges, two of which are Cold Start and Data Sparsity. Being introduced as a savior, the Cross-domain Recommender system (CDR) owns the capacity to transfer knowledge across domains which makes it become the practical solution for the mentioned issues. In CDR approaches, the family of graph-based solutions is very effective, which builds graphs to illustrate the relationship between users, items, and other factors and learn their representations through graph representation learning. However, most graph-based approaches focus on extracting either domain-specific or domain-shared features and do not have the mechanism to prevent the transfer of private features, which can degrade the quality of vector representations. Moreover, the knowledge transfer process built on overlapping users makes the model biased toward these users, thus downgrading the performance on cold-start users. This paper proposes a meta-GRS framework that uses Graph Neural Network with Meta-Learning and Adversarial Learning for cross-domain recommendation. In meta-GRS, the representative user and item embeddings are improved thanks to the features extracted by the private and cross-domain graphs. Domain-shared features are learned under adversarial learning such that the domain discriminator is unable to determine whether the domain they came from can ensure the positive transfer. To optimize the model performance effectively, we use a Dynamic Weight Averaging algorithm to learn loss weights automatically. The model's parameters are optimized under the optimization-based meta-learning method provides our model the capability of generalization to the new users. Experiments on practical datasets illustrate that the meta-GRS leads the chart in the comparison of other state-of-the-art baselines in recommendation accuracy.
Strategy-Following Multi-Agent Deep Reinforcement Learning through External High-Level Instruction

In this paper, we propose the strategy-following distributed attentional actor architecture after conditional attention (sfDA6-X) for multi-agent deep reinforcement learning (MADRL). The architecture is designed to provide controllability of coordinated behaviors in MADRL through the use of a saliency vector that captures conditions from the environment and rough, high-level instructions given by external experts such as the system designers or users. We introduce destination channels, which enable agents to be instructed to change their behaviors only by specifying the regions where individual agents should work. To validate the effectiveness of sfDA6-X, we conducted experiments in the object collection game and analyzed how agents change their coordinated and cooperative behaviors based on the given instructions. Our findings suggest that our approach provides a preliminary but promising solution for controlling coordinated behaviors in MADRL via arbitrary instructions represented in destination channels.
Challenges of Automatic Speech Recognition for medical interviews - research for Polish language

Automatic Speech Recognition (ASR) systems are quickly becoming a crucial element in supporting healthcare providers, improving the flow of information among medical teams, and enhancing the patient's experience. However, to be fully supportive, these ASR systems must meet certain requirements dictated by market realities: high accuracy of speech recognition and low rate of errors, the possibility of additional training the model, and the possibility of on-premise system installation. Therefore, the aim of this paper is to perform a comparative analysis of leading ASR systems available on the Polish market for the needs of conducting medical interviews. We selected three systems, Google ASR, Microsoft ASR, and Techmo ASR, and we compared their performance on a prepared data set of medical-related expressions spoken in Polish. The results of our analysis indicated that there are minor discrepancies in the accuracy of speech recognition between all three evaluated ASR systems, whereas only two ASR systems met the raised requirements, in both cases partially. Still, they all exhibited specific problems in recognising word endings or word boundaries. We were able to categorise such problems into: Misrecognitions, Quality Problems, and Word Boundaries, varying in their level of influence on the further speech recognition process. Our research findings are expected to provide valuable insights to a wide range of stakeholders facilitating the development of tailored speech recognition solutions that meet the specific needs of medical sector.
Personalization of Distance Learning: Using PLS-SEM for Analyzing the Possibility of Differentiating Students with the Felder-Silverman Learning Style Model

Personalization of learning, that is, taking into account various characteristics and needs of students, is a critical issue in organizing education and determines the relevance of finding ways to differentiate students for predicting the vector of their integration into the learning process, including in a distance learning format. This article discusses the possibility of using the Felder-Silverman model for personalizing distance learning. The purpose of the study is to explore the relationship between individual learning styles and factors causing anxiety in distance learning and shaping students' attitudes towards distance learning. The PLS-SEM method was used as a research tool. It is shown that the stylistic features of students determine the direction of their attitudes towards the negative and positive aspects of distance learning. Students' attitudes towards such social phenomena in distance learning as difficulties in communicating with other students, lack of student activity, social isolation, absence of competition between students, and difficulties in communicating with pedagogues depended on the stylistic features of the students. At the same time, the stronger these social phenomena caused anxiety in students, the higher their negative attitude towards the positive aspects of distance learning and the possibility of a full transition to distance learning or a full replacement of in-person learning with distance learning. Moreover, the higher the importance of classroom lessons and the presence of printed textbooks for students, the more anxiety the mentioned social phenomena caused in students. It is shown that such constructs of the PLS-SEM structural model as Social factors of distance learning anxiety and Importance of material factors in learning have a negative relationship with Attitude towards distance learning.
TCR-M: A Topic Change Recognition-based Method for Data Stream Learning

Data stream learning has received more and more attention in recent years, change tracking and real-time prediction of data streams under uncertainty have been highly focused. With the development of the information age, more and more different types of data streams have been generated, bringing challenges to the research in this field. Among them, text data streams, as one of the categories, also need to be mined and predicted in real-time. This paper addresses this problem by proposing a topic change recognition-based method (TCR-M) for data stream learning, thus helping support text data stream learning. We first propose a topic change recognition process that extracts the topics of the text data stream at each time point, tracks and determines the severity of the topic change, and locates the time points when significant changes occur. Next, an ensemble learning model is constructed and a separate base learner is simultaneously trained to correct the prediction results of the ensemble learning model, which is updated based on the topic change recognition results. To verify the effectiveness of the method, a number of text data streams are collected for evaluation, then outputting the topic change recognition results and prediction results. By comparing it with benchmark methods, the proposed method shows its efficiency. In future research, further improvements are needed for learning and application.
Creative teaching using hybrid e-learning and virtual reality

This paper describes three creative teaching activities that motivate and engage students into active learning. The teaching methods used include hybrid e-learning and virtual reality. The hybrid e-learning style is a combination of e-learning and a hands-on activity in a laboratory setting. For one hybrid e-learning project, students are asked to design, build, and test seawalls to help protect Japan from future tsunamis. In the other hybrid e-learning project, student teams are challenged to invent the best tasting nutritious fruit juice by using various fruits, blenders, water, plasticware, and other items. For the third project, participants use virtual reality (VR) headsets to learn about rollercoasters and to experience the thrill of riding on one.