Miracle-dz/UniFAR-FTUs huggingface dataset:
New methods for video data pre-processing to improve the quality of computer vision systems

Subject of study. A method of assessing changes in video data brightness that appear in a vision system under effect of external disturbance, and algorithms stabilizing the received video stream parameters are proposed. An assessment of video data quality as the result of data preliminary processing is given. Work objective is to create a methodology of objective analysis for the principal parameters variability in input data of video surveillance systems under negative factors influence and to develop effective algorithms to stabilize the parameters of the received video stream; it is also practical to evaluate the preprocessing quality and check the reliability of the proposed method on real video records. Tasks: when analyzing video recordings or real time video, determine the most universal, stable and informative quality indicator required for objective assessment of the video data quality under various shooting conditions and scene illumination features. It is also necessary to develop and implement programmatically algorithms for stabilizing video parameters. Methods used. Statistical analysis and treatment for the parameters of the analyzed video sequence as a random spatio-temporal process; algorithms to process video data with methods of digital filtering and adaptive stabilization of video stream brightness parameters. End results. It has been shown that the steadiest indicator of video stream quality is the average frame brightness (AFB). A universal algorithm for video data spatio-temporal processing of is proposed that forms the AFB values sequence from the original video stream; algorithms for filtering and stabilizing the brightness of a video stream are also given. Conclusions. Scientific novelty of the results obtained: A new method of objective analysis and parameters evaluation for the initial video surveillance data, and algorithms for filtering and stabilizing the video stream brightness are proposed. The performance of the proposed algorithms has been verified on real video surveillance data. The proposed algorithms are implemented in the Python software environment with using the OpenCV library functions.
On the Prediction of Hardware Security Properties of HLS Designs Using Graph Neural Networks

High-level synthesis (HLS) tools have provided significant productivity enhancements to the design flow of digital systems in recent years, resulting in highly-optimized circuits, in terms of area and latency. Given the evolution of hardware attacks, which can render them vulnerable, it is essential to consider security as a significant aspect of the HLS design flow. Yet the need to evaluate a huge number of functionally equivalent designs of the HLS design space challenges hardware security evaluation methods (e.g., fault injection - FI campaigns). In this work, we propose an evaluation methodology of hardware security properties of HLS-produced designs using state-of-the-art Graph Neural Network (GNN) approaches that achieves significant speedup and better scalability than typical evaluation methods (such as FI). We demonstrate the proposed methodology on a Double Modular Redundancy (DMR) countermeasure applied on an AES SBox implementation, enhanced by diversifying the redundant modules through HLS directives. The experimental results show that GNNs can be efficiently trained to predict important hardware security metrics concerning fault attacks (e.g., critical and detection error rates), by using regression. The proposed method predicts the fault vulnerability metrics of the HLS-based designs with high R-squared scores and achieves huge speedup compared to fault injection once the training of the GNN is completed.
A Beam Optimization Technique for Space-borne Phased Array SAR

Intensive research is currently concentered at the field of space-borne phased array Synthetic Aperture Radar (SAR) Systems for multimode application. To meet the requirement of multimode SAR system, many antenna pattern types based on phased array SAR system is required. This paper introduces a beam optimization technique for space-borne phased array SAR antenna, which involves large planar two-dimensional arrays. This technique is achieved by using a novel mixed genetic algorithm. The performances of using mixed genetic algorithm are verified by the measured antenna pattern results, and the design of this phased array SAR system can be directly applied for complex multimode SAR system.
Analysis Of Current Approaches to Modernizing the Electric Power Scheme of Diesel Generator Transport

The work is devoted to the analysis of modern approaches to modernisation of the scheme of power supply of diesel-generator transport and on this basis to the description of the optimal design of such a system. The aim of the work is to analyse modern approaches to modernisation of the scheme of power supply of diesel-generator transport and to develop an energy-efficient structure of such a system. The object of the study is the design of the traction power transmission of a diesel train. The subject of the study is the control system of traction power transmission of diesel trains. The above task of designing an upgraded automatic control system is the main part of the overall task of improving the efficiency of traction power transmission of a diesel train. The paper shows that the synthesized automatic control system should be optimized according to certain principles. In particular, the parametric principle is taken into account, according to which with the chosen structure of the control system and the known functional influences on the traction power transmission such parameters are defined, at which the given quality indicators of transients (indicators of traction inverter operation) are satisfied; the structural principle, according to which the system is designed so that it contains the minimum necessary number of elements, easily physically realized for full operation, is taken into account; functional principle is taken into account, according to which the control laws, and according to them the structure and numerical values of the parameters of the control system are determined. As a result, an upgraded control system of the diesel train traction axle on the basis of subordinate control systems without a speed sensor is developed and substantiated, which allows the movement of a diesel train with a given torque or speed, as well as provides the ability to move the diesel train when the general system fails. control of movement.
Factors impacting online loyalty to the Grab ordering app among Malaysia Kelantanese Customers in Post Covid-19 Pandemic

This research examined how significant factors including information quality, payment convenience, and social media involvement influence customer loyalty to the Grab smartphone app among customers post Covid-19. Customer loyalty is influenced by various elements and antecedents, including values, experience, security, technological adoption, and behavioural control, according to previous research. Customers in Kelantan who were regular users of the Grab Ordering mobile application were given questionnaires as part of this study, which took a quantitative approach. SPSS version 24 was used to analyse the data. The analysed data show that the three criteria of information quality, convenience of payment, and social media all have a significant impact on customer recurrent use of the Grab Ordering App. The results further provide useful information for recognising and improving services, comfortable environment, and customer needs.
Towards Automated Fact-Checking: An Exploratory Study on the Detection of Checkable Statements in Spanish

Nowadays, digital media allows important facts to be turned and twisted both knowingly and unknowingly, leading to the spread of misinformation to a continuously growing audience with little or no consequences. Thus, it has become crucial to verify (or fact-check) the authenticity of the shared information. One of the critical tasks in such verification process is determining which statements could be fact-checked. The automatization of this task would reduce human bias in the selection of checkable statements and save valuable time, thus helping to increase the coverage of the verification, and, potentially, their effectiveness. Although detecting checkable statements has received attention in the literature, most techniques have focused only on English. In this context, this study evaluates the performance of different approaches for detecting checkable statements in Spanish. Experimental evaluation showed promising results, achieving similar performance to the state-of-the-art techniques for the English language.
Formation of Nanopores in Anodic Oxidized Aluminium Affected by Carbon Nanodots

Formation of nanopores in anodic oxidized aluminium in (AOA) a solution of oxalic acid containing carbon nanodots (CNDs) was investigated. The size of pores is 20–40 nm, the thickness of pore walls is up to 50 nm. When the carbon addition is absent, these values are 50–100 nm and 30 nm respectively. The effect of CNDs is explained from the point of view of their adsorption on AOA, preliminarily the nanoparticles were researched using UV-VIS spectroscopy, dynamic laser scattering (in aggregated state), Raman and FTIR spectroscopy. Highly disordered structure of CNDs has been found. Adsorption isotherm obeys Temkin model. Crystallization of AOA under hydrothermal conditions at 200° C is suggested.
Unscented Kalman Filter for Estimation of Vehicle Velocity in Real Time

Vehicle velocity estimation systems can be utilized in a variety of automotive applications. Focusing Primarily on practicality, this paper presents a new method for estimating vehicle velocity in real time using low-cost sensor fusion combining a global positioning system (GPS) and an inertial navigation system (INS). In this paper, an unscented Kalman filter (UKF) with excellent estimation accuracy and robustness against model nonlinearity is developed for vehicle velocity estimation. It is a practical solution that can be easily implemented in mass-produced vehicles due to its high availability, high estimation accuracy and high robustness against model nonlinearity. The estimation performance of the proposed UKF is verified through experimental results using a test vehicle. Finally, the effectiveness of the proposed estimation algorithm can be confirmed through a comparative study.
Prediction of Autonomous Vehicle Trajectories in Turnaround Scenarios

Trajectory prediction of road agents plays important role in road traffic safety. Both autonomous vehicles and roadside units can benefit from it. Other than the prediction accuracy, many design constraints should be taken into consideration like the simplicity of the model. A simple model will be executed using the limited available resources of IoT units in real-time. In this paper, a model based on a Graph Neural Network is proposed that uses both the spatial and temporal information for trajectory prediction in a fully context-aware environment. The model solves the normal issues of Deep Graph Neural Networks like over-smoothing. It also predicts the trajectory of all the surrounding agents collectively without the need to iterate it. Also, the computational workload can be distributed among road agents using their communication capabilities to cooperate in predicting the trajectories.
Improvement of Perovskite Nanocrystals Stability by Incorporation into Polymer Cross-Linked Systems

in this research, we investigated composites of perovskite nanocrystals with polymer networks to enhance optical properties and stability of nanocrystals for further practical applications (e.g. energy harvesting, scintillation detection and imaging instruments). These results demonstrate different options for improving the stability and luminescence of perovskite nanocrystals of different compositions by incorporation in polymer cross-linked systems for prevention their degradation with reduction of different ambient conditions impact.
Strategic Quantization of a Noisy Source

This paper is concerned with strategic quantization of a noisy source where the encoder, which observes the remote source through a noisy channel, and the decoder, with distortions defined over the remote source, have misaligned objectives. We show that as in the classical noisy source setting, this indirect source coding problem can be transformed to direct source coding with an equivalent distortion measure defined as the conditional expectation of the original distortion measure conditioned over the sensing channel output. On the design side, we extend the gradient-descent based method developed to solve the noiseless problem in recent work to the associated noisy setting. Finally, we present the numerical results that confirm the theoretical analysis in this paper. The codes associated with our numerical results are available at: https://tinyurl.com/allerton2023.
Approach to the Industrial Automation Systems Design, Taking into Account the Properties of Electric Drives

The purpose of the study was to extend the previously created methodology for designing electromechanical systems to design of manufacturing process control systems. This methodology is based on SysML language, conceptual graphs and artificial intelligence software tool CoGui. It involves the construction of an information model of design project that contains knowledge about the requirements for the system being designed and a knowledge base of available technical solutions. Redundant information model of possible solutions and the constraints set by the designer is used for transformation design knowledge models to final design solutions. The methodology is verified by evaluating the degree of consistency and feasibility of design solutions for typical examples of industrial automation systems.
Radar-based Recognition of Activities of Daily Living in the Palliative Care Context Using Deep Learning

The accurate detection and quantification of activities of daily life (ADL) are crucial for assessing the health status of palliative patients to allow an optimized treatment in the last phase of life. Current evaluation methods heavily rely on subjective self-reports or external observations by clinical staff, lacking objectivity. To address this limitation, we propose a radar-based approach for recognizing ADLs in a palliative care context. In our proof of concept study, we recorded five different ADLs relevant to palliative care, all occurring within a hospital bed, from N=14 healthy participants (57% women, aged 28.6 ± 5.3years). All movements were recorded using two frequency-modulated continuous wave radar systems measuring velocity, range, and angle. A convolutional neural network combined with long short-term memory achieved a classification accuracy of 99.8 ± 0.4% across five cross-validation folds. Furthermore, we compare our initial approach, which takes into account all dimensions of the available radar data, to a simplified version, where only velocity information over time is fed into the network. While these results demonstrate the high potential of radar-based sensing to automatically detect and quantify activities in a palliative care context, future work is still necessary to assess the applicability to real-world hospital scenarios.
An Antenna-Filter-Antenna Enabled Dual-Band Dual-Circularly-Polarized Transmit-Array Antenna

A dual-band dual-circularly-polarized (dual-CP) transmit-array (TA) antenna enabled by antenna-filter-antenna structure and shared-aperture arrangement is proposed. The four generated beams are able to independently point to arbitrary directions in the far field. A dual-band dual-CP antenna array composed of four dual-linearly-polarized metasurface antenna elements and a wideband feed network is designed and utilized as the feeding source. An integrated TA antenna capable of providing the four-color reuse scheme is synthesized and verified. The low-band dual-CP beams own a measured joint gain variation and axial ratio smaller than 3 dB bandwidth of wider than 5.6%, while this bandwidth is about 3.6% for the high-band dual-CP beams. The designed TA antenna can be applied for applications such as satellite communications.
Uncovering Bias in ASR Systems: Evaluating Wav2vec2 and Whisper for Dutch speakers

It is crucial that ASR systems can handle the wide range of variations in speech of speakers from different demographic groups, with different speaking styles, and of speakers with (dis)abilities. A potential quality-of-service harm arises when ASR systems do not perform equally well for everyone. ASR systems may exhibit bias against certain types of speech, such as non-native accents, different age groups and gender. In this study, we evaluate two widely-used neural network-based architectures: Wav2vec2 and Whisper on potential biases for Dutch speakers. We used the Dutch speech corpus JASMIN as a test set containing read and conversational speech in a human-machine interaction setting. The results reveal a significant bias against non-natives, children and elderly and some regional dialects. The ASR systems generally perform slightly better for women than for men.
Modelling Android applications through static analysis and systematic exploratory testing

Mobile application development is a fast paced industry with frequent releases. While the development pace increases, so too does the need for automated test generation. Model-based test generation is one of the most common and successful approaches to support this need. Understanding and modelling the application under test is integral to producing comprehensive, dependable and effective tests. Unfortunately, mobile platforms such as Android, introduce a host of difficulties. Static analysis struggles with Android’s event-based nature and the growing variety of mechanisms available for developers to implement different features. Additionally, dynamic analysis, implemented by popular random test generators, is slow, inefficient, and limited by a lack of application knowledge.This paper introduces DroidGraph, a framework to generate a comprehensive control flow model of Android applications using traditional static analysis and efficient systematic exploratory tests. DroidGraph provides a detailed model of an Android application, from low level method statements to high level user interface structures. This model can be used to support automated test generation. We apply DroidGraph to 19 diverse apps and show that our efficient exploratory tests, on average, interact with 18% more of the app than commonly used random exploration in 345 less interactions. Integrating the dynamic analysis results provided by these tests complements our static analysis, and uncovers on average 51 more components and 49% more interface callback links in the application code.
Effect of Nanoconfining PLGA Shell on the Self-Assembly of Indigo Carmine

Molecular self-assembly, particularly under 3D spherical confinement, is driven by inherent properties of molecules, as well as the confining system, both play a dominant role in the final self-assembled structure. We recently proposed a novel approach for confinement, in which the core of polymeric nanocapsules was used to entrap organic dye molecules (e.g., indigo carmine), that subsequently organize into rolled up lamellar molecular sheets forming scroll structures. To examine the effect of the confining shell composed by polymeric encapsulating materials, the high molecular weight poly-lactide (PLA) was replaced with low molecular weight poly (lactide-co-glycolide) (PLGA). The PLGA based confining system was prepared by water-in-oil-in-water (W1/O/W2) double emulsions. Various techniques were used to analyze the physicochemical properties of the self-assembled dye loaded nanocapsules, in terms of particle morphology, size distribution, surface charge, core content, shell composition, loading capacity, and thermal stability. These techniques also served to evaluate the interactions of the dye and its lamellar nanosheets with the interior wall of the capsule shell. The results show, given the same molecular building unit for self-assembling, that the confining shell charge predominates the spatial growth and the direction of assembled organic dye molecules. This provides a further understanding of the polymeric nanocapsule cavities as an emerging viable platform for practical self-assembling applications.
Reducing Vulnerability in Critical Life Course Phases through Enhancing Human Capital

In this paper we examine selected types of vulnerability over the human life course with a specific focus on differentials not only by age and gender -- as is conventionally done in demography -- but also by level of education and with a focus on health. Starting with the newborn and vulnerability in terms of infant and child mortality, we refer to the level of education of the mother. In reference to young people's susceptibility to unemployment after leaving school, we take into account the education of the women and men themselves. Proceeding further in the life course, we next consider vulnerability to becoming disabled in the age group 30-74 according to the education level of the studied persons themselves. Finally, the last section studies differential vulnerability at the national level using the time series of deaths from disasters where the aggregate levels of education at a national level are being taken into account. We conclude that over the entire life cycle of individuals, the changes in behavior that tend to be associated with more education (of mothers or the persons themselves) can be viewed as a potent factor in reducing child mortality, reducing the risk for unemployment at young age, reducing the vulnerability to natural disasters, and finally reducing the risk of falling into disability. These general long-term benefits of near-term investments in education hold for individuals as well as for entire societies.
Investigation of Electrohydrodynamics Phenomenon of Nematic Liquid Crystals on The Parallelepiped Longitudinal-View Cells

The electrohydrodynamics phenomenon of nematic liquid crystals in parallelepiped sandwich cells has been successfully investigated. The results show hierarchical patterns include the Williams Domain (WD), which moves uniformly from the edge to the center of the cell (called translational flow)─fluctuating Williams Domain (FWD)─grid pattern (GP)─dynamic scattering mode (DSM). The phase diagram shows that the threshold voltage of each pattern increases exponentially with frequency. It correlates with the electrohydrodynamic phenomenon in the low-frequency region. The translational flow velocity in the Y-segment has also been calculated. It shows random values in the range of 0.04─0.1 micrometer per second.
An Intelligent Labeling and Dataset Generating Method for Desktop Application Element Object Detection

As an important part of automatic interaction with personal computers, the precision of locating desktop application widgets via object detection models has a significant impact on the effectiveness of the subsequent interacting operations. In order to obtain massive data to further fine-tune models pretrained on datasets like MS COCO to adapt it to the downstream desktop application widgets object detection task, we introduce a data generating method and develop a GUI data generator to generate 12k GUI screenshots with ground truth GUI widgets coordinates, and build a dataset called DAWOD (Desktop Application Widgets Object Detection). We show that model with domain-specific fine-tuning on DAWOD will be better at identifying and locating desktop application widgets than itself without fine-tuning.