Slovakia

&quot;An unsolved problem in the domain of Internet Accessibility, which is a stream in Human Computer Interface (HCI), is how cognitively challenged or visually impaired users access a service. This paper proposes an assistive technology for disabled users in the form of a Randomized Authentication Gateway (RAG) mechanism corresponding to a challenge-pool. The challenge-pool contains different stand-alone Accessibility Protocols that are suitable for different situations. Holistically,
we believe that the system works the finest when it is used as a single federated interface. The RAG manager picks up random challenges from the challenge-pool and the client, who is looking to access a particular service, is subjected to these challenges. If the client successfully completes them, he is authenticated as a valid user of the intended service. This paper discusses the audio-based, pressure-based and gesture-based mechanisms for seamless authentication of challenged users. We have defined this problem in terms of Accessibility Deficiency and Bot Indicator. The proposed system is only 23% accessibility deficient and covers the use cases of partially blind, completely blind, color blind and users suffering from diseases that affects their comprehension skills, like dyslexia. The research focuses on a keyboard-friendly environment where the fit or challenged users will have complete control over the interface using the one-click functionality. This work is our way to ensure that no restriction is imposed on any user while surfing through the internet.&quot;

IWSSIP 2021

Secure Web Accessibility Protocol for Challenged Users

secured authenticatio

accessibility protocol

web assistive technology

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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AAMAS 2021

"An unsolved problem in the domain of Internet Accessibility, which is a stream in Human Computer Interface (HCI), is how cognitively challenged or visually impaired users access a service. This paper proposes an assistive technology for disabled users in the form of a Randomized Authentication Gateway (RAG) mechanism corresponding to a challenge-pool. The challenge-pool contains different stand-alone Accessibility Protocols that are suitable for different situations. Holistically,
we believe that the system works the finest when it is used as a single federated interface. The RAG manager picks up random challenges from the challenge-pool and the client, who is looking to access a particular service, is subjected to these challenges. If the client successfully completes them, he is authenticated as a valid user of the intended service. This paper discusses the audio-based, pressure-based and gesture-based mechanisms for seamless authentication of challenged users. We have defined this problem in terms of Accessibility Deficiency and Bot Indicator. The proposed system is only 23% accessibility deficient and covers the use cases of partially blind, completely blind, color blind and users suffering from diseases that affects their comprehension skills, like dyslexia. The research focuses on a keyboard-friendly environment where the fit or challenged users will have complete control over the interface using the one-click functionality. This work is our way to ensure that no restriction is imposed on any user while surfing through the internet."

technical paper


IWSSIP is an international conference that brings together researchers and developers from both academia and industry reporting the latest scientific and theoretical advances to discuss and debate major issues and state-of-the-art aspects on systems, signals and image processing.

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Sparse representation and compressive sensing theory is wide used in image processing with the core idea that given a transformation that describes a signal in sparse domain can be achieved a moore compact and thus compressed form of the signal through optimization. In this research discrete orthogonal moments transforms are applied in sparse representation and compressive sensing theory under the implementation of transformed-based compressive sensing. Finally a comparison take place between image moments transforms and DCT in compression and denoising. 

Moment transform-based Compressive Sensing in Image Processing

Particle colliders are machines built to probe fundamental questions in physics. The properties of the produced particles are measured by complex experiments which use a wide variety of devices, such as the calorimeter system. In the ATLAS experiment at LHC, the Tile Calorimeter (TileCal) comprises about 10,000 readout channels that amplify, shape, and sample each signal every 25 ns. Since LHC collisions occur every 25 ns, and due to the increase of the luminosity level, signals from adjacent collisions may be read out within the same TileCal readout window, deforming the expected signal, and degrading the energy estimation efficiency. Therefore, this work compares the performance of the currently available methods for TileCal energy estimation using LHC collision data. Different pile-up conditions are considered. The results show that the performance in terms of the uncertainty of the energy estimation can be improved up to 35% in high occupancy readout channels.

Energy Reconstruction Techniques in TileCal under High Pile-up Conditions

Automatic cardiac MRI segmentation, including left and rightventricular endocardium and epicardium, has an essential role in clinical  diagnosis  by  providing  crucial  information  about  cardiac  function. Determining heart chamber properties, such as volume or ejection fraction,  directly  relies  on  their  accurate  segmentation.  In  this  work,  we propose a new automatic method for the segmentation of myocardium, left, and right ventricles from MRI images. We introduce a new archi-tecture that incorporates SERes blocks into 3D U-net architecture (3DSERes-U-Net). The SERes blocks incorporate squeeze-and-excitation operations into residual learning. The adaptive feature recalibration ability of squeeze-and-excitation operations boosts the network’s representational power while feature reuse utilizes effective learning of the features,which  improves  segmentation  performance.  We  evaluate  the  proposedmethod on the testing dataset of the MICCAI Automated Cardiac Diag-nosis Challenge (ACDC) dataset and obtain highly comparable resultsto the state-of-the-art methods

Segmentation and Quantification of Bi-Ventricles and Myocardium Using 3D SERes-U-Net

This presentation evaluates results of an ongoing research employing novel offline end-to-end speech recognition models for Slovak language. Transition to ESPnet's Transformer neural network architecture is introduced along with a brief theoretical introduction to end-to-end based speech recognition.

Transition from hybrid to end-to-end speech recognition in Slovak utilizing Transformer neural network architecture

In this paper, a simple IIR filter is used in system identification. A uniform white sequence is used as an input signal for the unknown system. A noise white sequence signal, which is not correlated with the input signal, is added to the system output. An illustrative example is solved, and the optimization is performed using the Simplex method by Nelder and Mead. A comparison is done to results by a genetic algorithm and a simulated annealing algorithm. It is demonstrated, that a gradient-based algorithm gets stuck in a local minimum. The obtained result confirms the efficiency and efficacy of this approach. 

Adaptive IIR Filtering for System Identification applying the method by Nelder and Mead

"Atrial fibrillation (AF) is a cardiovascular disease identified as one of the main risks
factors for stroke. The majority of strokes due to AF are caused by clots originating in the left atrial appendage (LAA). LAA occlusion is an effective procedure for reducing stroke risk.  Planning the procedure using pre-procedural imaging and analysis has shown benefits. The analysis is commonly done by manually segmenting the appendage on 2D slices. Automatic LAA segmentation methods could save an expert's time and provide insightful 3D visualizations and accurate automatic measurements to aid in medical procedures. Several semi- and fully automatic methods for segmenting the appendage have been proposed. This paper provides a review of automatic LAA segmentation methods on 3D and 4D medical images, including CT, MRI, and echocardiogram images. We classify methods into heuristic and model-based methods, as well as semi- and fully-automatic methods. We summarize and compare the proposed methods, evaluate their effectiveness, present current challenges in the field and approaches to overcome them."

Left Atrial Appendage Segmentation and Analysis in 3D and 4D medical images

This presentation shows the results of our research based on the comparison of approaches of automatic detection and localization of an optic disc and macula using on deep learning. Information about their position helps to diagnose diabetic retinopathy - a disease that causes visual impairment and leads to blindness. Detecting the symptoms of this disease manually is a demanding process, but thanks to advances in computer vision, it can be automated. Compared methods base on Faster R-CNN, YOLO and SSD detectors are used to detect the optic disc and macula in the eye fundus images. We test the methods on three publically available datasets of fundus images - “DRIVE“, “IOSTAR“ and “ODIR“. We created own dataset containing 500 fundus images as a combination of mentioned databases, where the ground truth position of the macula and optic disc were marked by an ophthalmologist.

Detection of optic disc and macula in retinal images using deep learning methods

The motivation for the research in this paper is gathering valuable information to develop a DFT-based steganographic method using an existing DFT-based watermarking technique. Specifically, different image dimensions will be tested to find out how the difference in dimension affects the ability to decode a watermark from an image successfully. The goal is to determine the smallest possible image dimension that still produces successful decoding. Determining that image dimension is critical to examine to develop a steganographic method with the highest capacity possible. Several experiments are conducted to draw conclusions in terms of image quality, detection rate, and robustness. In this research, tests have been conducted in the digital domain exclusively. The results show that image dimensions play a critical role in watermark detection, and further development of the method is needed in order to be able to use images of smaller dimensions. The watermarking method showed robustness for several image attacks and showed enhanced performance when specific filters are applied on images.

Analysis of the DFT-based Watermarking Method for Image Steganography

Current wireless communication in ad-hoc networks requires a so-phisticated approach for the data location, retrieval, and transfer, to use the con-nection availability appropriately. Data are commonly stored in the databases spread across the network or located in each node. This paper deals with the current techniques of data location and retrieval using the index set. The main limitation is the availability and suitability of the index for the particular query request. Our proposed solution is based on the multi-indexing layer, by which the several indexes can be used to a single query in parallel, followed by the merging operation using bitmap index mapping. Thanks to the proposed archi-tecture, data location and efficiency of the whole system can be ensured. The robustness and scalability of the solution can be reached, as well.

Using multi-index database layer in ad-hoc communication networks

This article describes a method for detection of cognitive defects based on eye tracking during reading. The aim of this research is to pursue and extend the experiments conducted in Sweden by introducing Conventional signal theory. The dataset used for experiment was acquired by the authors of the research article Screening for Dyslexia Using Eye Tracking during Reading. The provided data consist of 185 subjects divided into two groups. The first group comprises of 88 low risk (LR) subjects and the second group comprises of 97 high risk (HR) subjects. Our measurements achieved a classification accuracy score 86.164% by classifying the subjects into the correct groups.

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Moment transform-based Compressive Sensing in Image Processing

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