Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Personality Traits of Team Athletes in Virtual Reality

Assessment of personality traits is highly relevant in team sports in order to analyze the performance of an athlete under pressure when in competitive situations, for team-strategic decisions, to optimize command transmission, and ultimately to understand top-level performers. It further facilitates the development and application of personalized exercises, coaching to improve performance in competition, and can be considered a valuable criterion for talent scouting and development. The current state of the art method to assess personality traits in sports relies on validated questionnaires. However, these often provide non-sport-specific, subjective self-reported information and lack the ability to measure how these characteristics are reflected in context-based performance.We developed a virtual reality (VR) tool for the assessment of personality traits in team sports, in our case for soccer. An evaluation of this tool within a study with 24 subjects yielded a benchmark of its immersion through user experience and provided an objective description of athletes’ personalities based on performance indicators extracted from activity-tracking. Within the tool, we implemented two realistic virtual soccer environments to assess the motivational orientation of soccer players (i.e. action-and state-orientation) which we discerned from the gold standard questionnaire.Results show that user experience and presence of the implemented virtual environments scored significantly higher compared to benchmark measurements. Additionally, a significant difference between the two groups of action and state-oriented athletes could be observed. Measures of failure rate, pass accuracy, number of perceived opponents, and achieved bonus goals are parameters that differ significantly among the two athlete groups. These findings show that VR technology is applicable for the assessment of athletes’ motivational orientation and thus demonstrate the feasibility of virtual environments as functional game scenario-based assessment tools for athletes.
Learning and Evaluation Methodologies for Polyphonic Music Sequence Prediction With LSTMs

Music language models play an important role for various music signal and symbolic music processing tasks, such as music generation, symbolic music classification, or automatic music transcription (AMT). In this article, we investigate Long Short-Term Memory (LSTM) networks for polyphonic music prediction, in the form of binary piano rolls. A preliminary experiment, assessing the influence of the timestep of piano rolls on system performance, highlights the need for more musical evaluation metrics. We introduce a range of metrics, focusing on temporal and harmonic aspects. We propose to combine them into a parametrisable loss to train our network. We then conduct a range of experiments with this new loss, both for polyphonic music prediction (intrinsic evaluation) and using our predictive model as a language model for AMT (extrinsic evaluation). Intrinsic evaluation shows that tuning the behaviour of a model is possible by adjusting loss parameters, with consistent results across timesteps. Extrinsic evaluation shows consistent behaviour across timesteps in terms of precision and recall with respect to the loss parameters, leading to an improvement in AMT performance without changing the complexity of the model. In particular, we show that intrinsic performance (in terms of cross entropy) is not related to extrinsic performance, highlighting the importance of using custom training losses for each specific application. Our model also compares favourably with previously proposed MLMs.
Identification of Indian Mangrove Mud Crab Genus Scylla spp. (Brachyura: Portunidae) using RAPD and PCR-RFLP Markers

ABSTRACT Identification of commonly available Indian mud crabs of the genus Scylla was carried out using RAPD and PCR-RFLP to resolve the taxonomic ambiguity of the genus. Samples were collected from western, eastern, and Andaman coastal waters of India. A total of 16 potential species-specific RAPD markers were identified from 179 individuals using 4 arbitrary primers (OPA2, OPA14, UBC122, and UBC456). Three PCR-RFLP markers were developed that could be used to confirm taxonomic status of Indian mud crab species. The species-specific RAPD markers and the digestion pattern of PCR-RFLP analysis indicate conclusively that only 2 species of mud crabs (Scylla serrata and Scylla olivacea) are commonly present in Indian coastal waters.
Document analysis at AGH University of Science and Technology

The use of modern archeometric techniques requires the use of appropriate equipment. The development of several tools used in the study of historical artifacts and art objects, manuscripts in particular, is the goal of the work presented below. The publication presents new developments for three research methods: nondestructive microfading testing of the objects’ lightfastness, reflectance transformation imaging to visualize texture of the object and multispectral imaging applied to enhance damaged writing.
Reset Variability in Backfilled Resistive Random Access Memory and Its Correlation to Low Frequency Noise in Read

Fast and stable switching between states is one of the key factors for the success resistive random access memory (RRAM) development. In an array, wide reset efficiency variation in RRAM cells is found to link to the characteristics of its low frequency noise (LFN) in bit-cell current. Through Monte Carlo simulation on randomly placing conductive filaments (CF), LFN characteristics correspond to the densities of the CF in the RRAM film. Further correlations between LFN features and the reset efficiency are found. In addition, CF topography are found to change after long term cycling tests. A trimming process is proposed to minimize the impacts of stochastic CF generation, leading to increase reset speed.
Hand Gesture Recognition for Sign Language Using 3DCNN

Recently, automatic hand gesture recognition has gained increasing importance for two principal reasons: the growth of the deaf and hearing-impaired population, and the development of vision-based applications and touchless control on ubiquitous devices. As hand gesture recognition is at the core of sign language analysis a robust hand gesture recognition system should consider both spatial and temporal features. Unfortunately, finding discriminative spatiotemporal descriptors for a hand gesture sequence is not a trivial task. In this study, we proposed an efficient deep convolutional neural networks approach for hand gesture recognition. The proposed approach employed transfer learning to beat the scarcity of a large labeled hand gesture dataset. We evaluated it using three gesture datasets from color videos: 40, 23, and 10 classes were used from these datasets. The approach obtained recognition rates of 98.12%, 100%, and 76.67% on the three datasets, respectively for the signer-dependent mode. For the signer-independent mode, it obtained recognition rates of 84.38%, 34.9%, and 70% on the three datasets, respectively.
Minimal Embodiment: Effects of a Portable Version of a Virtual Disembodiment Experience on Fear of Death

Body ownership illusions are the basis of various virtual reality (VR) applications. However, such illusions frequently require elaborate tracking systems, which make deployment in clinical settings challenging. Therefore, we build on Bourdin and colleagues’ former work [3] to trial a virtual out-of-body experience (OBE, individuals view themselves situating outside their physical bodies) with ”minimal embodiment. ” We used a consumer system consisting of a 3 degrees of freedom (DOF) headset and its associated 3 DOF hand tracker. In an exploratory study, we compared participants’ fear-of-death (FOD) after undergoing either a control condition (participants remained in control of the avatar body) or a disembodiment condition (participants drifted out of the avatar body and lost visuotactile contact with their avatar). Results revealed an indirect effect of perceived embodiment increasing FOD through a heightened degree of reported OBE in the experimental condition. We discuss limitations and possible extensions of this study, and the future of using simple VR tools in healthcare settings.
Chromosome Numbers in Pandanus, Sparganium and Typha

Chromosome numbers of some species in Pandanus, Sparganium and Tzypha are reported, as given in the following table. Basic number is 15 in both Sparganiumz and Typha and that of Pandanus remains undecided. Somatic chromosomes are generally of short rod- or dot-shaped in all species studied.
Cross Model Deep Learning Scheme for Automatic Modulation Classification

Deep Neural Networks (DNNs) have achieved remarkable accuracy improvements for automatic modulation classification. However, the employed networks often have millions of parameters and need very high computation, which makes it difficult to deploy these models on portable devices with limited resources. We propose a cross model deep learning scheme to build a lightweight deep network for accurate modulation classification. Firstly, a large Hybrid DNN (HDNN) that is composed of convolutional and recurrent layers is constructed and trained for automatic and accurate classification of signals. Then we build a smaller Layered Resnet Network (LRN) with shallow layers and few nodes. The HDNN and LRN are taken as a Teacher Model (TM) and a Student Model (SM) respectively. Finally, a knowledge distillation method is proposed to guide the learning of the SM, by formulating a teaching loss from the prediction of the TM to train the SM. The performances of the proposed HDNN and LRN are investigated on the public RadioML2016.10a and RadioML2016.10b data sets. The experimental results show that the trained HDNN presents state-of-the-art classification results and the LRN trained in this scheme takes only about a sixth of the HDNN’s inference time and consumes only 472.3KB for storage, with a slight accuracy decrease compared with the large HDNN.
Potential Effects of Dynamic Parallax on Eyesight in Virtual Reality System

Virtual desktop office and VR theater become popular. Using HMD at high frequencies and for a long time has become an indispensable requirement. Whether this will affect the user’s eyesight is one of the major concerns. This paper presents an investigation of a dynamically changed parallax for 2D content in VR systems and potential effects on eyesight. By controlling the horizontal movement of the left and right views, parallax is continuously and slowly adjusted within comfort zone. This can alleviate the static fixation of the eyes to the fixed distance of the display, relieving visual discomfort and reducing the degree of visual impairment. The experimental results show that dynamic parallax can alleviate the visual discomfort of watching videos in HMD and reduce the potential adverse effects on eyesight to some extent. Moreover, the uniform variation parallax has a better effect than the jump parallax.
Nouns and Verbs in Professional Reporting of Extreme Events

This corpus-based study tested whether linguistic devices that can evoke negative emotions are featured in Terrorist Attack news articles to a greater extent, in comparison to articles on Natural Disasters and Human Accidents. A total of 210 news reports were analysed in terms of linguistic categories. The results showed that news reports on Terrorist Attacks display the distinctive patterns of use in the distribution of nouns and verbs than the other two categories.
Evaluating a Slam-Based Mobile Mapping System: a Methodological Comparison for 3D Heritage Scene Real-Time Reconstruction

Surveying techniques have increased their productivity, improving the amount and the quality of Cultural Heritage data for documentation purposes. The reason of this improvement is twofold: on one hand, CH related researches gained more and more attention given the tremendous importance they represent for the whole mankind. On the other, their knowledge and accurate documentation is urgent. Even if there are several acquisition techniques for each scale of representation, there is not still the winning solution for every condition; the consequence is that most of the surveying projects related to large and complex sites integrate multiple sensors and techniques [1]. Towards this direction, producers are introducing in the market sophisticated tools which integrates multiple sensors in an ”all in one solution” that in the literature are defined as combination of methods. One of them, recently introduced, is the SLAM (Simultaneously Localization and Mapping) technology, that has introduced a new system to acquire millions of 3D points in a short time and with good accuracy also in indoor location. In this light, the work that will be described in the article deals with the comparison of dense point clouds obtained with different acquisition techniques (namely TLS and Mobile Mapping), using as a case study a Roman Amphitheater, since, given its complexity, represents a challenging benchmark for such study. For a more detailed documentation of the site and to cope with the lack of imagery information from these latter techniques, a drone flight was performed. The main contributions of the article are: i) testing in a real case scenario a novel tool which integrates different sensors, assessing its suitability for archaeological settings; ii) validating its accuracy w.r. t. other well established techniques; iii) performing the complete mapping of an important archaeological sites, combining terrestrial and aerial acquisition techniques.
On the Resilience of the NFV-MANO: An Availability Model of a Cloud-native Architecture

With Network Function Virtualization (NFV), the management and orchestration of network services require a new set of functionalities to be added on top of legacy models of operation. Due to the introduction of the virtualization layer and the decoupling of the network functions and their running infrastructure, the operation models need to include new elements like virtual network functions (VNFs) and a new set of relationships between them and the NFV Infrastructure (NFVI). The NFV Management and Orchestration (MANO) framework plays the key role in managing and orchestrating the NFV infrastructure, network services and the associated VNFs. Failures of the MANO hinders the network ability to react to new service requests or events related to the normal lifecycle operation of network services. Thus, it becomes extremely important to ensure a high level of availability for the MANO architecture. The goal of this work is to model, analyze, and evaluate the impact that different failure modes have on the MANO availability. A model based on Stochastic Activity Networks (SANs), derived from current standard-compliant microservice-based implementations, is proposed as a case study. The case study is used to quantitatively evaluate the steady-state availability and identify the most important parameters influencing the system availability for different deployment configurations.
Identification of Potential Risk Factors of Diabetes for the Qatari Population

Large-scale cohorts are established in different regions of the world to identify the complex interaction of genetic, environmental, and lifestyle-related factors that may contribute to chronic diseases including diabetes. Qatar Biobank (QBB) is the largest repository for cohort study specific to the Qatari population. There are few studies based on the QBB cohort, which highlighted multiple risk factors responsible for diabetes in the Qatari population. However, no comprehensive research has been done using machine learning techniques to identify key factors that may contribute to diabetes specific to the Qatari population. We developed several machine-learning models using QBB data to classify diabetic patients from the non-diabetic participants forming the control group for this study. From the roster of several hundred measurements, we identified 25 potential risk factors that might be influential in distinguishing diabetic patients from nondiabetic participants. From the identified risk factors, we ranked HbAlc, Glucose, and LDL-Cholesterol as the most influential risk factors. Using these risk factors, we also developed several machine-learning models to classify diabetic subjects from healthy subjects. Overall, the classifiers achieved ~0.85 F1-score in classifying diabetic subjects from non-diabetic subjects. Further investigation will pave the way for the inclusion of the identified risk factors into the standard diabetes screening process of the Qatari population.
Frequency Split Improvement of Fused Silica Micro Shell Resonator Based on Suppression of Geometric Harmonic Error

This paper demonstrates the impact of harmonic error on the natural frequency of micro shell resonator (MSR). Parameterized 3D model is established to analyze the frequency split of resonator with deviation along the circumferential and height direction. Both the n=1 tilt mode and n=2 wineglass modes are considered. The simulation results show that frequency split significantly increases as the growth of the 2nd and 4th harmonic errors. A rotary platform is used to reduce the harmonic error of the resonator by improving symmetry of the shell structure. Finally, more than 10 samples are electrostatically characterized and well matched with the simulation results.
Analysis of Effective Sleep through Logistic Regression

Sleep is something that affects the way of life of humans. Whether it is about education, work, driving a vehicle, or even the health problems caused by insufficient rest, such as heart disease, diabetes, obesity, and more, especially nowadays, the disease that affects sleep. Asleep is caused by social pressures such as insomnia, depression, and most people with this disease tend to be unaware of it. Some cases, the condition may lead to suicide. Which can be seen from the news of the deaths of many celebrities For the study method The researchers collected sleep data from 16 participants aged 20 - 60 (Patient 8 Normal 8) for 40 days using a smart watch connected to the application for analysis. information Using frequency, percentage, chi-square test, analyzing factors related to quality of sleep and predicting sleep quality by using logistic regression with Spyder. The factors include age range related to the quality of deep sleep. (p <0.05) The model result is shown at 84% . 0 ROC Curve is used to present the model’s results.
An Improvement of Support Vector Machine Imputation Algorithm Based on Multiple Iteration and Grid Search Strategies

Data missing is a vitally important issue that influences the classification results in medical field. This paper proposes an improved support vector machine (SVM) imputation algorithm by using strategies of pre-imputation, multiple iteration and grid search (IG-SVMI). Based on the experimental performance, nine UCI datasets and two real datasets are used to compare the proposed algorithm with four existing imputation algorithms (RFI, KNNI, CCMVI and orthogonal coding SVMI). The datasets are considered into two types of originally containing missing value and randomly auto-generating missing of complete dataset. Classification accuracy and NRMSE are used as parameters to judge the efficient of the proposed IG-SVMI algorithm. The experiments have shown that the proposed IG-SVMI algorithm can achieve better results than the benchmark approaches.
Collision-Free Speed Alteration Strategy for Human Safety in Human-Robot Coexistence Environments

This paper presents a novel real-time collision-free speed alteration strategy using a danger index and an elite real-coded genetic algorithm (ERGA) for environments in which humans and robots coexist or cooperate, in order to guarantee the safety of an operator who works with a collaborative robot. A danger index based on ellipsoid modeling of the operator and robot describes the degree of safety during human–robot interactions. The ERGA and a penalty function are used to solve the constrained nonlinear optimization problem to change the handling speed of the robot. Comparative simulation results show the superiority of the proposed method by comparing to two existing methods. The applicability of the proposed method is verified using two experiments involving a 6-DoF industrial manipulator with an EtherCAT network protocol, an RGB-D sensor and a real-time operation system.
Real-Time Scheduling for Electric Vehicles Charging/Discharging Using Reinforcement Learning

With the increase in Electric Vehicles (EVs) penetration, their charging needs form an additional burden on the grid. Thus, charging coordination is necessary for safe and efficient EV use. The scheduling of EVs is especially essential in Vehicle-to-Grid schemes, where EVs energy cost can be reduced by intelligently managing the charging and discharging according to real-time prices and owners' needs. Further, utilities can perform load shifting by price-based demand-response programs. However, the scheduling problem is challenging in the presence of multiple unknown variables, such as real-time prices, commuting behavior, and energy needs. Most of the scheduling techniques attempt to model the system uncertainties (e.g., forecast prices) and plan accordingly. In this paper, we propose the use of a model-free Reinforcement Learning (RL) technique that can deduce a charging/discharging strategy without requiring any model of the system. We evaluate the performance of the proposed system with real-world data and investigate the learned charging/discharging pattern to show the effectiveness of the proposed method.
Optimization of MEMS-Gyroscope Calibration using Properties of Sums of Random Variables

We report a new approach for the problem of the automated calibration of offsets of a MEMS-gyrospcope. The developed method is a fast, direct and memory-efficient approach to the problem. The method uses statistical information from the time series of gyroscopic sample data and combines it with the desired fractional accuracy of the gyroscopic offsets. We show that by using the statistical information contained in the time series the process of calculating the gyroscopic offsets can be significantly optimized. We applied the method proposed in this paper to existing data used in generic algorithms. We have been able to speed up the calibration of gyroscopic offsets significantly. For the data used to benchmark against in this publication our proposed algorithm was about 50 times faster. The method also works on-the-fly in that the statistical information needed is updated every time a new measurement is available. This implies that our suggested method is also very memory efficient making it especially useful when re-calibration needs to be done often or on-chip. We have developed and published a C/C++ code which enables the application of our method to various possible application scenarios.