Miracle-dz/UniFAR-FTUs huggingface dataset:
On Enhancing Error-Tolerability of Videos via Re-Encoding with Adaptive I-Frame Insertion

Videos are expected to be widely used in IoT or AI applications. The quality of videos are thus crucial for the success of these technologies. However, noises during transmission of videos, or aging of video processing or storage related circuits may significantly degrade the quality of videos, making the video become unacceptable. In this work we investigate the issue of video quality (error-tolerability) enhancement. Different from the previous work that mainly focuses on noisy videos, this work considers erroneous videos generated by faulty circuits. Single stuck-at faults are injected to an H.264 decoding circuit for generating erroneous videos. We find that adaptive insertion of I-frames is an attractive solution. This method first examines the quality of the current videos and accordingly suggests re-encoding of future videos with a proper number of additional I-frames. An error-tolerability enhancement flow for videos is proposed, which integrates video quality grading and determination of the suggested number of I-frames to be inserted. Limitations and implementation of this flow are also discussed. Experimental results on a total of 81,412 erroneous videos show that more than 90% of unacceptable erroneous videos whose quality is within a specified range become acceptable by applying the proposed flow.
Understanding Several Adaptive Filter Algorithms Based on the Weight-update Strategy

This paper describes the comparison between adaptive filtering algorithms including least mean square (LMS), normalized least mean square (NLMS), recursive least square (RLS) and Kalman algorithm. We attempt to study their nonobvious nature which are often overlooked from the strategy how they adaptively update the weight. This joint perspective on adaptive filtering algorithms may help the intellectually curious reader expand thinking and deepen understanding.
Validation of Insole-based Gait Analysis System in Young Children with a Neurodevelopmental Disorder and Autism Traits

Motor impairments are prevalent in children with neurodevelopmental disorders (NDD) and/or autism spectrum disorders (ASD) and often impact their development and quality of life. Early autism diagnosis can ameliorate lifetime outcomes by enabling earlier access to treatment. However, conventional clinical evaluation methods present some limitations due to high phenotypical heterogeneity within the autism spectrum and NDDs. Based on expanding research reporting high prevalence of motor impairments, there is a compelling need to develop quantitative methods to measure behavioral and motor-related phenotypes. Research focusing on gait analysis in individuals with ASD point to distinctive gait features. Thus, precise gait measures may contribute to more refined phenotype measures in NDD and ASD.This paper introduces the PediaSole, a minimally obtrusive insole-based system capable of measuring spatiotemporal gait parameters in pediatric populations. Accuracy of the system was validated against gold-standard equipment in neurotypical children and children with autism traits of genetic etiology, aged 3-12 yrs. Results indicated a good level of agreement between the PediaSole and the reference equipment. Additionally, 3 gait parameters (normalized stride velocity, stride time, and stance percentage) showed significant correlations with a well-established clinical measure of motor function, indicating the potential of this technology for clinical applications.
Classification of Brain MRI Images Using Convolution Neural Network and Transfer Learning

Magnetic Resonance Image (MRI) is a non intrusive procedure for obtaining high resolution images of living tissues. Varieties of tissue contrast images that MRI modalities produce provide important structural information, enabling diagnosis and segmentation of tumors. In this paper we come up with an integrated model of Convolutional Neural Network and Transfer learning for automated binary classification of brain MRI images. We use a sixteen layer pretrained network, to distinguish normal and abnormal images. Our experiments suggest good accuracy in classification on test data, and thus will turn out to be useful for efficient automated diagnosis.
Unidirectional Emission of Active Eccentric Microring Cavities

Calculations of spectra, thresholds, and emission directivities of laser modes for active eccentric microring cavities are based on the lasing eigenvalue problem, which was reduced to the system of Muller boundary integral equations, and was discretized by the Galerkin method with trigonometric basis. In computational experiments, we demonstrate unidirectional emission of lasing, which is achievable with some assumptions on the position of the air hole in the cavity and its radius. In this work we obtain high directivity for unidirectional emission with the one pronounced ray in the directional pattern.
Scenario-based Testing of a Ship Collision Avoidance System

We propose a method for scenario-based testing of maritime collision avoidance systems. The goal is to test an autonomous agent in scenarios that can lead to an unacceptable risk of collision or may clearly not comply with the International Regulations for Preventing Collisions at Sea (COLREGs). Our method is based on the use of a discriminating artificial neural network that is trained online while performing the testing of the agents. Our experimental results show that the proposed algorithm generates test suits composed mostly of challenging scenarios. This allows us to validate quickly if the agent under test can perform the collision avoidance maneuvers safely while abiding the COLREGs.
Developing a Multiple Testing Procedure in the D-Posterior Approach using the R Software Environment

The article provides a method for developing a statistical criterion that satisfies given restrictions on the average proportion of wrong decisions, similar to the concept of pFDR in multiple testing. The statistical criterion uses programming capabilities in the R environment. The advantages of this method are shown in comparison with the Benjamini-Hochberg procedure.
Design of Intrusion Prevention System for OT Networks Using Deep Neural Networks

The Automation industries that uses Supervisory Control and Data Acquisition (SCADA) systems are highly vulnerable for Network threats. Systems that are air-gapped and isolated from the internet are highly affected due to insider attacks like Spoofing, DOS and Malware threats that affects confidentiality, integrity and availability of Operational Technology (OT) system elements and degrade its performance even though security measures are taken. In this paper, a behavior-based intrusion prevention system (IPS) is designed for OT networks. The proposed system is implemented on SCADA test bed with two systems replicates automation scenarios in industry. This paper describes 4 main classes of cyber-attacks with their subclasses against SCADA systems and methodology with design of components of IPS system, database creation, Baselines and deployment of system in environment. IPS system identifies not only IT protocols but also Industry Control System (ICS) protocols Modbus and DNP3 with their inside communication fields using deep packet inspection (DPI). The analytical results show 99.89% accuracy on binary classification and 97.95% accuracy on multiclass classification of different attack vectors performed on network with low false positive rate. These results are also validated by actual deployment of IPS in SCADA systems with the prevention of DOS attack.
What do developers talk about open source software licensing?

Free and open source software has gained a lot of momentum in the industry and the research community. Open source licenses determine the rules, under which the open source software can be further used and distributed. Previous works have examined the usage of open source licenses in the framework of specific projects or online social coding platforms, examining developers specific licensing views for specific software. However, the questions practitioners ask about licenses and licensing as captured in Question and Answer websites also constitute an important aspect toward understanding practitioners general licenses and licensing concerns. In this paper, we investigate open source license discussions using data from the Software Engineering, Open Source and Law Stack Exchange sites that contain relevant data. We describe the process used for the data collection and analysis, and discuss the main results that can be useful for developers, educators and license authors. Our results indicate that clarifications about specific licenses and specific license terms are required.
A student’s performance prediction method based on neural collaborative filtering

Along with the increasingly fast development of elearning and the transformation of the traditional classroom, novel technology should be applied in human learning domain, that is called smart learning. Since deep learning has been quickly developed in recent years, a diversity of prediction methods have been successfully applied in many domains. Most of the recent studies about student’s performance prediction mainly use machine learning methods like decision tree and k-nearest neighbors to discover the correlation between the student’s features and performances. In this work, we explore the application of deep learning in student’s performance prediction scenario. Inspired by some works in which the combination of deep learning with collaborative filtering is explored, we propose a novel method based on neural collaborative filtering for student’s performance prediction which, unlike other recent works about student’s performance prediction, does not require outer features of students. The main contribution of our work is that we explore a novel representation method for latent features in which the latent space is separated on several parts by their meanings which allows the model to learn a better latent representation for inference. Our results for real student’s score prediction show that our proposed methods can outperform existing models.
Development of Y STR Megaplex Assays

Y chromosome short tandem repeat markers have a number of applications in human identity testing including typing the perpetrator of sexual assault cases without differential extraction and tracing paternal lineages for missing person investigations. In order for Y STR systems to become more widely accepted within the forensic DNA typing community, population studies and robust assays are required. We have focused on developing new Y STR multiplexes that can provide a high degree of discrimination between unrelated males. The Y STR markers we are working with include the hexanucleotide repeat DYS448, pentanucleotides DYS438, DYS446, DYS447, DYS450, tetranucleotide repeat DYS19, DYS385 I/II, DYS389I/II, DYS390, DYS391, DYS393, DYS437, DYS439, DYS441, DYS442, GATA A7.1, GATA H4, G09411, trinucleotides DYS388, DYS392, DYS426, and the dinucleotide repeat YCAII. Primers for the markers DYS385, DYS389, and YCAII target duplicated regions of the Y chromosome and thus can produce two polymorphic peaks with each primer set. The multiplexes we are developing are the first to include all of the European “extended haplotype” (11 PCR products) in a single reaction. We have compared our results to the commercially available Y-Plex 6 kit from Reliagene that amplifies 6 Y STRs. In order to improve the power of discrimination for Y chromosome tests, we have developed strategies for rapidly preparing multiplex PCR assays that utilize both four and five dye chemistries for detection and permit simultaneous amplification of 20 or more Y STR markers in a single reaction. Primer design issues will be reviewed, as will our efforts to avoid any homology with X chromosome sequences. Prime rs have been redesigned from previously published work with these Y STR markers in order to make them more compatible in a multiplex amplification. Results will be shown using our new Y STR multiplex from 3 different laboratories. In addition, allele ranges for each of the Y STR markers have been well characterized in a diverse set of world samples.
Simultaneous Motion and Force Sensing for a Flexure Finger

Flexure graspers have been identified as a bioinspired evolution in robotics to adapt to unpredictable disturbances and uncertainties in unstructured environments. However, these soft robots with infinite degrees of freedom (DOFs) have brought in many technical challenges, among which is the sensing of spatially-distributed and time-varying force and displacement fields for feedback control with a finite number of nodal measurements. This paper proposes a simultaneous motion and force sensing method based on discrete strain data with an illustrative application to a flexure finger that can serve as an actuation and sensing unit in a robotic hand. The method is rigorously formulated by introducing the strain mode shapes and correlating continuous displacement and force/moment distributions with curvatures of deformation. Design analysis of the flexure robotic finger is presented and the compliant composite joints are fabricated via shape deposition with embedded strain gauges. The proposed sensing method is numerically verified with finite element analysis and experimentally validated on a prototype of the flexure finger.
A practical implementation of modern Distributed Control Systems in Electric Power Substations

This paper presents a practical implementation of a modern DCS in energy sector, applicable for electric power transmission and distributions substations.When dealing with aging infrastructure the reward from investing in modern automation solution can be substantial, with benefits such increasing productivity, cost savings, safety operations, scalable solution and a better integration in the modern IT facilities.The modern DCS systems evolved into a flexible and scalable automation solutions, which make them mandatory in the actual competitive energy business environment. Comparing to PLCs and traditional DCS, the modern DCS systems are designed to provide automation solution in a safe, reliable and cost-effectively manner.The main goal of this article is to present and suggest an optimal DCS design and functional solutions taking into consideration customer requirements, international and customer applicable norms, budget constraint, hardware and software limitations, integration of existing equipment, cybersecurity and low impact of the monitored and controlled process for extension and future growth.The principles presented in this paper can be found implemented in Romania mainly in the electric power transmission substations, owned by Romanian Transmission and System Operator CNTEE Transelectrica SA.
The Regulation of Neuronal Excitability and Nociception by Tonic GABAergic Inhibition

The mammalian central nervous system maintains a delicate balance between neuronal excitation and inhibition. Conventional synaptic inhibition is mediated through the transient activity of postsynaptic γ-aminobutyric acid (GABA) at type A GABA (GABAA) receptors. A subset of GABAA receptors is also located outside of inhibitory synapses. These extrasynaptic receptors generate a tonic inhibitory conductance in response to low concentrations of extracellular GABA. Tonic inhibition broadly suppresses neuronal activity and regulates many vital processes such as sleep, consciousness and memory formation. This thesis examines the physiological effects of tonic inhibition at the cellular level and in the behaving animal. This thesis also explores whether gabapentin, a commonly used sedative, anxiolytic, and analgesic drug, enhances tonic GABAergic inhibition. I hypothesize that: (1) tonic GABAA receptor activity reduces the intrinsic excitability of neurons; (2) the activity of tonically active GABAA receptors in spinal pain pathways attenuates nociception; and (3) tonic inhibition can be upregulated by gabapentin.
Automatic Stress Detection Using Wearable Sensors and Machine Learning: A Review

Stress is a feeling of being under abnormal pressure which comes from different aspects of our day to day life. Stress management is important during this modern era to keep up one's stress level low and reduce health risks as stress is one of the primary cause leading to major chronic health disorders. In this paper, we examine and review various stress detection approaches who uses low-cost wearable sensors for data collection and machine learning algorithms for predicting stress level of an individual. Researchers have found that stress level can be detected through some physiological measures like heart rate, heart rate variability and skin conductance. This paper aims to provide a comprehensive review on various stress detection techniques and gives a reliable guideline towards more efficient detection of stress.
Machine Learning approach for the classification of EEG signals of multiple imagery tasks

Electroencephalogram (EEG) signals can be used to capture the electrical pattern generated on the surface of the human brain. The electrical activity in terms of EEG signals occurs in the brain as a result of any type of emotions, behavior, thoughts that occur in the human brain. These EEG signals can be utilized as an input to the Brain-Computer Interface (BCI) application. The proper feature extraction and classification of the extracted features are a very important step for BCI applications. In this paper, the collection and recording of EEG signals are done corresponding to eight imagery tasks. The extraction of the features is done in the form of absolute band powers, based on the logarithm of the Power Spectral Density (PSD) of the EEG data for each channel. The selected features are used for the classification and analysis of the captured EEG signals corresponding to the eight cognitive imagery tasks such as ‘forward’, ‘backward’, ‘left’, ‘right’, ‘hungry’, ‘food’, ‘water’, and ‘sleep’. The selected features are then used to train the Machine Learning (ML) models of Logistic Regression (LR), and Quadratic Discriminant Analysis (QDA). The classifiers are analyzed and investigated on several evaluation metrics such as the average accuracy of the classifiers, accuracy of each imagery tasks, precision, and recall. The average accuracy achieved by the classifier is 72.5%, and 74.7% for LR, and QDA respectively.
Performance Evaluation of Pattern Recognition Algorithms for Upper Limb Prosthetic Applications

Poly-articulated, myoelectric hand prostheses reproduce complex multi-degree of freedom movements, which are fundamental to effectively assist upper limb amputees in the execution of daily life activities. In this scenario, the control system consists in a pattern recognition algorithm translating the recorded electromyographic (EMG) activity into joint movements. However, the low decoding performance typically reached by the control system results in poor stability of the prosthetic device. In order to solve this issue, here we tested several state-of-the-art classifiers for decoding multi-joint hand movements from electromyographic recordings of arm muscles, collected from healthy subjects. Specifically, we tested: NonLinear Logistic Regression (NLR), Regularized Least-Square, Artificial Neural Network, Support Vector Machine, and Linear Discriminant Analysis. We aimed at minimizing the number of EMG electrodes (6 maximum) by optimizing each classifier in terms of the F1Score, and then we compared the performance of the classifiers. We found that the NLR algorithm achieved the best results with only 3 EMG electrodes. The optimized algorithms were then tested on three right arm amputees controlling a virtual hand. We obtained that algorithm’s performance was comparable with that obtained from healthy subjects. In particular, the NLR classifier achieved 99% correct classification for all the patients, indicating its potential effective use in prosthetic applications.
Sentiment analysis of preschool teachers’ perceptions on ICT use for young children

Sentiment analysis in gaining more attention as it is increasingly used in multiple domains, including in interpreting educational data. The article uses sentiment analysis technique to understand the early childhood educators reported beliefs (perception) on young children’s ICT use. The dataset was obtained from a comparative study of early childhood educators from two countries, Australia and Malaysia. The result shows a similar outcome where most teachers agreed upon the benefits of ICT use and conclude more positive sentiment polarity.This paper summarizes the findings using sentiment analysis as well as comparing it to the quantitative data obtained from the survey.
Detection of Deceit from Thermal Videos on Real Crime Database

Detection of deceit is a very difficult task even for the experts. The time delay and requirement of experts for the existing polygraph test have led to requirement of noninvasive methods like thermal imaging. The major hurdle in the creation of a database of deceit is the simulation of guilt and lie in the participants. Most of the previous works use mock crimes with enacted responses of the participants. Here a novel experimental protocol is suggested where a real crime of stealing occurs. The recording has been done in a concealed manner. Also, the participants for the experiment are people with a strong tendency and record of habitual stealing. The experiment has been conducted at Medinipur medical college and hospital, India and has been approved by the ethical committee. The blood flow rate at the forehead and periorbital regions are found out from the thermal data. The rate of increase in blood flow rate shows that deceit could be detected from the thermal signatures.
A Smart Ambulatory Cognitive State Taxonomy System Through EEG Signal Analysis

Human mental state classification using Electroen-cephalogram(EEG) signal is one of the dynamic investigating areas in developing intelligent machine frameworks. In this paper, we propose an ambulatory cognitive state classification system using statistical feature analysis. The aim is to develop a realtime system which captures human cognitive states through computer-mediated help based on physiological signals. The walking cognitive state estimator is developed using a realtime Augmented Cognition (AugCog) framework. Six different normality testing methods i.e. Kolmogorov-Smirnov, Anderson Darling, Cramer-Von Mises, Shapiro Wilk, Jarque-Bera, D'Ago-stino and Pearson's test are applied to achieve better accuracy. Box - Cox - transformation is also applied to ensure whether the data follows a normal distribution. We apply Fisher Discriminant Ratio (FDR) for selecting the most discriminative feature amongst all the statistical features (i.e. Mean, Median, Mode, Standard Deviation, Variance, Skewness and Kurtosis) obtained from the signal. Various machine learning methods are applied on the extracted features for training, testing and validation. Probabilistic neural network (P-NN) with Kurtosis feature is found to produces a maximum accuracy of 92.61 % as compared to other classifiers.