Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Multimedia Authoring

The mulsemedia (Multiple Sensorial Media (MulSeMedia)) concept has been explored to provide users with new sensations using other senses beyond sight and hearing. The demand for producing such applications has motivated various studies in the mulsemedia authoring phase. To encourage researchers to explore new solutions for enhancing the mulsemedia authoring, this survey article reviews several mulsemedia authoring tools and proposals for representing sensory effects and their characteristics. The article also outlines a set of desirable features for mulsemedia authoring tools. Additionally, a multimedia background is discussed to support the proposed study in the mulsemedia field. Open challenges and future directions regarding the mulsemedia authoring phase are also discussed.
Dietary strategies to enrich milk with healthy fatty acids – A review

Abstract Feed is the main factor impacting the composition and quality of milk of dairy animals. Therefore, the present review explores the effects of feed and nutrition on milk fat content and levels of healthy fatty acids (FA) in milk consumed by humans. Milk and dairy products are two main sources of healthy and unhealthy FA in human nutrition. The concentrations of FA in milk depend mainly on diets; therefore, milk FA concentrations and ratios can be greatly altered by some feeding strategies. Dietary supplementation of the diets of dairy livestock with vegetable seeds or oils, microalgae and phytogenic feed additives, and feeding of some grasses can enhance the contents of healthy FA, including n-3 FA, α-linolenic acid, conjugated linoleic acid (CLA) and, generally, unsaturated FA in milk and dairy products. Enrichment of milk with healthy FA may make milk a source of anticarcinogens (CLA and polyphenols) for human health. This review, therefore, focusses on the current research findings on enrichment of milk with healthy FA and summarizes some effective supplementation strategies to alter milk FA profile.
Learning means changing: What's changed?

Debriefs are used in both real and simulated work to facilitate team reflection and analysis on how work was done and how it could be done better. However, all debriefs run the risk of blame, despite the best intentions of the facilitator. The inherent risk of inadvertent blame is acknowledged by facilitators of both simulation and adverse incident reviews by the stated intent to create a psychologically safe space and a just culture using a systems approach. But in order to avoid blame, you need to know how it occurs. An analysis of the safety science and simulation literature shows both how to avoid blaming your team for failure and how to set them up for success. In his book ‘The Field Guide to Understanding “Human Error”’, psychologist and professor of Safety Science Sidney Dekker states that blame occurs because of:
Suppression on Particle Movement and Discharge by Nanocomposite Film Coating on DC GIL Electrode Surface

The metal particles’ movement in DC GIL can easily result in gas-gap breakdown or insulator surface flashover, which will seriously reduce GIL’s insulation performance. To suppress the particle movement and discharge, the authors manufactured the colloidal PI/TiO2 nanocomposite film by the in-situ polymerization method and coated the film on the electrode surface. This paper characterized and tested the film’s physicochemical properties and analyzed its influence on the lifting voltage and movement activity of spherical, linear, sheet metal particles. The results show that TiO2 nanoparticles disperse evenly in the polyimide matrix with excellent compatibility between inorganic and organic phases. As nanoparticles’ content increases, the composite film’s mechanical performance and electrical strength decrease, and the corona resistance time increases first and then decreases. Nanocomposite films can significantly increase the lifting voltage of metal particles. The lifting voltage is almost the same, whether it is the bottom-electrode coating or dual-electrode coating. Voltage polarity does not affect the lifting voltage; however, as the film thickness increases, the lifting voltage increases. After coating, the metal particles’ movement decreases and gradually the particles lie on the bottom electrode’s surface. In the meanwhile, the gas-gap discharge process also stops. There is an obvious polarity effect for the movement of metal particles. The bottom-electrode coating method should be adopted for DC positive voltage, while the dual-electrode coating method should be adopted for DC negative voltage.
Leveraging spatio-temporal features for joint deblurring and segmentation of instruments in dental video microscopy

In dentistry, microscopes have become indispensable optical devices for high-quality treatment and micro-invasive surgery, especially in the field of endodontics. Recent machine vision advances enable more advanced, real-time applications including but not limited to dental video deblurring and workflow analysis through relevant metadata obtained by instrument motion trajectories. To this end, the proposed work addresses dental video deblurring and instrument segmentation in a Multi-task Learning fashion, leveraging spatio-temporal adaptive kernels via a recurrent design. The task-specific branches of our architecture employ the responses of those kernels to recover sharper video frames and yield the dental instrument segmentation mask. We demonstrate that the proposed method improves deblurring while retaining segmentation performance under a low computational footprint.
Impact of Pilot's Expertise on Selection, Use, Trust, and Acceptance of Automation

Automation regroups a variety of advanced tools meant to improve performance and decrease human workload. This article was designed to investigate how different automation solutions, engaging different human–machine cooperation modes, interact with expertise. Aircraft pilots (i.e., experts) and nonpilots (i.e., novices) were presented with a set of simplified flight piloting tasks monitored simultaneously using the Open MATB (Open Multiattribute Task Battery) in four different automation conditions (manual, assisted, cooperative, and supervisory control). Experts’ performances at the Open MATB were higher than those of novices. Experts also exhibited a lower level of workload. Apart from function delegation, where no human performance is required, automation solutions benefit experts and novices similarly. Participants were then asked to choose repeatedly between the four automation conditions. Experts and novices exhibited different strategies. Novices spread their choices over the different automation conditions, whereas experts clearly favor cooperative control, the automation solution that enhanced their expertise.
Machine-Learning-Based Prediction Models of Coronary Heart Disease Using Naïve Bayes and Random Forest Algorithms

Coronary heart disease (CHD), alternatively known as cardiovascular disease (CVD) is the number one cause of death in the world. Accordingly, a plethora of research have been conducted to predict the early diagnosis of the heart disease and determine the most important risk factors associated with the disease. Despite these considerable efforts, the accuracy of the prediction has remained inadequate and the most important risk factors have remained elusive. This research paper discusses many risk factors associated with the disease and presents the prediction models of coronary heart disease using supervised machine learning algorithms, namely Gaussian Naïve Bayes, Bernoulli Naïve Bayes and Random Forest algorithms. It uses the public dataset from the Cleveland database of UCI repository of coronary heart disease patients. The results show that the Gaussian Naïve Bayes, Bernoulli Naïve Bayes and Random Forest algorithms have accuracies of 85.00%, 85.00% and 75.00%, respectively. Moreover, the precision, F-measure and recall of the Gaussian and Bernoulli Naïve Bayes are higher than those of Random Forest algorithm, signifying its importance in predicting the early diagnosis of the disease.
The New Promising Network-on-Chip Topologies Development Using Product Operation

This article analyzes new promising topological solutions for the on-chip communication subsystem for network-on-chip (NoCs). A study of Paley graphs as a subclass of circulant graphs is given. The main parameters (diameter, average path length, graph density, number of edges and degrees of vertices) of modifications of Paley graphs are studied using the direct product of Paley graphs with Paley graphs and ring graph. Other types of products of graphs are considered, namely strong, tensor and lexicographic. Due to the more preferable characteristics, the direct product of the graphs was chosen as the most suitable among the considered ones. A comparative analysis of obtained graphs is provided.
A Review on Distance Measure Formula for Enhancing Match Detection Process of Generic Code Clone Detection Model in Java Application

Two code fragments that are identical to each other and repeatedly being implemented in a software program are called code clones. Code clones are classified into four different type which are clone Type-1, clone Type-2, clone Type-3 and clone Type-4. In the past decades, variegated code clone approaches as well as tools have been utilized for detecting code clones. However, the minimum comprehensiveness or lacking of generality in detecting all four types of code clone, has prompted other researchers to develop a code clone detection model. Generic Code Clone Detection (GCCD) Model is the model that implemented a clone detection for the four types of code clone. GCCD incorporates with process namely Pre-processing, Transformation, Parameterization, Categorization and Match Detection process. This research study intends to ameliorate the code clone detection of GCCD Model through Match Detection process of the respective model. A literature review is done to center a few other models and techniques and distance measure formula that available for calculating the code clones in Match Detection process. Based on the analysis, there are ten distance measure formula will be used to decide on the best method for calculating the code clone in Match Detection process. This study hopes to be used as reference for other researchers in aiding their research regarding code clone detection model.
Dynamic Performance Evaluation of Inverter Feeding a Weak Grid Considering Variable System Parameters

Interfacing a weak grid imposes challenges on distributed generators (DGs). These challenges include transient frequency and voltage dynamics that can destabilize the system. Accordingly, this paper investigates the grid stiffness based on different scenarios and the dynamics of a grid feeding DG connected to a weak grid. Moreover, the dynamic effects of the physical and control parameters on the system’s stability are deeply analyzed and evaluated. Specifically, complete mathematical models and graphical representation are carried out to precisely examine the impact of the system parameters on the stability and performance of the DG. Therefore, stable deployment of renewable energy resources into power networks can be achieved as well as an efficient and robust performance of DGs can be ensured when connected to weak grids. The obtained results show the importance of the performed study in optimal sizing and designing the output filter of the inverter and the impact of tuning the control parameters on the system dynamics. As a result, a proper design of system physical and control parameters can be accurately achieved considering all factors affect the system performance. The paper also conducts detailed and elaborated analyses and simulations to evaluate the performance of a PI-controlled RC damped inverter connected to a weak grid. The proposed filter design of the interfacing inverter can significantly extend the integration of DGs into microgrids without requiring complex control schemes or oversized components.
Multi-Criteria Group Assessment of E-Commerce Websites Based on the New PROSA GDSS Method–the Case of Poland

Due to the COVID-19 pandemic and lockdowns, the popularity and number of users of e-commerce websites has increased significantly recently. In order to retain new users in the future, e-commerce websites need to develop their quality and constantly monitor it. The problem of assessing the quality of websites is a multi-criteria problem and multi-criteria decision making methods are used in it. Taking into account the need for a consensus of experts or users evaluating the website, the article developed a new multi-criteria group decision making method, called PROSA GDSS (PROmethee for Sustainability Assessment– Group Decision Support System), which allows to reward the consensus of decision makers’ preferences and penalize conflicts of preferences. This method was used in the evaluation of e-commerce websites operating on the Polish market that offer the widest range of products. In the course of research works, a wide applicability of the developed method in the problems of group assessment and its broad analytical possibilities was demonstrated. As a result of the research, it has been found that local parties have an established position on the Polish e-commerce market, and parties operating from abroad, and those entering the Polish market, must catch up with the distance that separates them from local tycoons.
DAUNet: Deep Augmented Neural Network for Pavement Crack Segmentation

Crack detection and measurement are essential tasks for maintaining and ensuring safety. Accurate crack detection is very challenging because of non-uniform intensity, poor continuity, and irregular patterns of cracks. The complexity of the background and variability in the data acquisition process also complicate the problem. Many approaches to crack detection have been proposed, but the accuracy of the detection leaves much to be desired. The aim of this study is to develop a practical crack detection method for real-time maintenance. We focus on a deep end-to-end and pixel-wise crack segmentation. We propose a lightweight U-Net-based network architecture with emphasis on the learning process. In order to verify the effectiveness of the proposed method, we conduct tests on publicly available pavement crack datasets and compare our model with state-of-the-art crack detection methods. Extensive experiments show that the proposed method effectively detects cracks in a complex environment, and achieves superior performance. The code and proposed model can be found in https://github.com/dvalex/daunet
Attention Multisource Fusion-Based Deep Few-Shot Learning for Hyperspectral Image Classification

Recently, deep learning-based methods outperform others in hyperspectral image (HSI) classification. However, the deep learning methods require sufficient labeled samples to improve performance, which is unfeasible in practice. The training labels are usually limited in HSIs that need to be classified (namely target domain), while other available labels in multisource HSIs (namely source domain) are not utilized effectively. To mitigate these issues, an attention multisource fusion method of few-shot learning (AMF-FSL) is proposed for small-sized HSI classification. AMF-FSL is an implementation of few-shot learning (FSL) in the meta-learning field, which can transfer the learned ability of classification from multiple source data to target data. The process of learning to classify in AMF-FSL is not restricted by the traditional requirement of the same distribution between the source and target domains, which can learn from the source domain and apply it to a different distribution in the target domain. Moreover, the multisource domain adaption in AMF-FSL has the capacity of extracting features from fused homogeneous and heterogeneous data in the source domain, which can improve the generalization of the classification model in the cross domains. Specifically, the multisource domain adaption contains three modules, namely the target-based class alignment, domain attention assignment, and multisource data fusion, which are responsible for aligning the class space, paying band-level attention, and merging the distributions of homogeneous and heterogeneous data in the source domain. The experimental results demonstrate the effectiveness of the multisource domain adaption and the superiority of AMF-FSL over other state-of-the-art methods in small-sized HSI classification.
Development of Body Rotation Mechanism for Wheeled Robot to Enhance Trafficability and Overcome Mobility Limitation

A wheeled robot operating on various complex terrains with scattered obstacles and steep slopes must be capable of surmounting obstructions and coping with the extreme driving environment. This paper proposes a body rotation mechanism that controls the load distribution on the robot wheel for the robot to surmount rocky obstacles and steadily ascend deformable slopes. This work formulates a robot dynamics model based on the wheel–complex terrain interaction model to analyze the mechanical effect of the proposed body rotation mechanism. Moreover, an optimal body rotation configuration integrating the robot dynamics model and non-dominated sorting genetic algorithm-II is obtained to choose the appropriate body rotation control strategy. The numerical analysis results conclusively prove the effectiveness of the proposed mechanism. The robot with its fabricated platform is field tested by allowing it to surmount a rocky obstacle and ascend a deformable slope. The results indicate that the proposed body rotation mechanism is an effective approach for enhancing the mobility of a wheeled robot in traversing complex terrains.
Virtual Reality for Exposure Therapy

Since its invention, virtual reality (VR) reached high level of maturity and thus many application areas opened. During last decade, there were performed many studies focused on use of VR in mental health care, in particular in the therapy of specific phobias. In the paper, we describe design and development of a VR application for the VR exposure therapy of specific phobias, agoraphobia and social phobias. The VR application is completed with a biofeedback system. The developed system was first tested with volunteers recruited from personnel of the National Institute of Mental Health. Currently a clinical study is being performed. First results confirm the feasibility of the VR exposure therapy application in phobic participants. In addition, the VR flexibility in development of new scenarios shows potential advantage over other approaches to the therapy.
Detection of Fictitious Accounts Registration

This paper deals with the task of classification of accounts registered in e-commerce systems. The authors consider solving this problem with different machine learning algorithms and LSTM recurrent neural network. A brief description of machine learning algorithms and LSTM neural network used during the research is given in the paper. The dataset and algorithm of its preprocessing are described, additional features are introduced. The paper presents the results of numerical experiments: confusion matrices and classification metrics received with considered algorithms. Comparing of the models has been conducted on the basis of received metrics. The algorithm that provides the best metrics is selected. The SMOTE and ADASYN resampling algorithms are applied to the dataset, the received classification metrics are provided. The methods of improving classification results are proposed.
Protective effect of selenium and zinc against cadmium toxicity in SHSY-5Y neurons

Cadmium is a widespread heavy metal environmental toxicant and pollutant. Cadmium toxicity affects many tissues and organs including the central nervous system and cadmium exposure has been related to many neurodegenerative diseases (1). Cadmium-induced toxic effects include oxidative damage, apoptosis, interference with calcium/zinc-dependent mechanisms, inhibition of cellular respiratory processes and others, but the underlying mechanisms of cadmium neurotoxicity are not completely understood (2). On the other hand heavy metal toxicity can be counteracted by bioelements such as zinc, selenium and others mainly through the induction of metallothionein expression levels and other antioxidant pro-teins (3). Human neuroblastoma SHSY-5Y cell line, in both the undifferentiated and neuronal-like differentiated state, were used in this study to better elucidate the mechanisms un-derlying the protective effect of zinc and selenium against the cadmium neurotoxicity. Toxic effects of cadmium chloride (10 mM, 24h) observed by cell viability assay, western blot anal-ysis of Bax and Gap-43, and immunostaining of b3 tubulin and cytochrome c proteins, were reverted to control values by a 24h-pretreatment with zinc chloride (50 mM) both in undiffer-entiated and differentiated neurons. Interestingly, the reverting effect of a 24h-pretreatment with sodium selenite (100 nM) against cadmium toxicity, was observed only in undifferentiat-ed neurons. In conclusion we can hypothesize that in undifferentiated and differentiated SHSY-5Y neurons, the protective effects of zinc and selenium compounds against cadmium toxicity depend on the activation of partially common signalling pathways. Moreover sodium selenite don﻿t exert a significant protective effect in differentiated SHSY-5Y demonstrating that the differentiated and undifferentiated phenotypes show different responses.
Analysis of Multisensory-Motor Integration in Olfactory Navigation of Silkmoth, Bombyx mori, using Virtual Reality System

Most animals survive and thrive due to navigation behavior to reach their destinations. In order to navigate, it is important for animals to integrate information obtained from multisensory inputs and use that information to modulate their behavior. In this study, by using a virtual reality (VR) system for an insect, we investigated how an adult silkmoth integrates visual and wind direction information during female search behavior (olfactory behavior). According to the behavioral experiments using the VR system, the silkmoth had the highest navigation success rate when odor, vision, and wind information were correctly provided. However, we found that the success rate of the search significantly reduced if wind direction information was provided that was incorrect from the direction actually detected. This indicates that it is important to acquire not only odor information, but also wind direction information correctly. In other words, Behavior was modulated by the degree of co-incidence between the direction of arrival of the odor and the direction of arrival of the wind, and posture control (angular velocity control) was modulated by visual information. We mathematically modeled the modulation of behavior using multisensory information and evaluated it by simulation. As a result, the mathematical model not only succeeded in reproducing the actual female search behavior of the silkmoth, but can also improve search success relative to the conventional odor source search algorithm.