Miracle-dz/UniFAR-FTUs huggingface dataset:
WoundArch: A Hybrid Architecture System for Segmentation and Classification of Chronic Wounds

: Every year, millions of people are affected by wounds worldwide. The wound treatment process is costly and requires the nurse to perform activities during patient care: tissue classification and calculation of the wound area. Thus, this work proposes to build a hybrid computer system with two configurations to support wound care. The first configuration uses a smartphone to perform the capture, segmentation and classification of the wound images. The other configuration has a client-server architecture, the images are captured and segmented in the application and sent, via the internet, to the web server, which is responsible for classifying the tissue of the wounds. The proposed methodology is the segmentation of images using the watershed algorithm and classification of tissues in Necrosis, Granulation or Crushing. fulfilled. To evaluate the application, experiments were performed with 20 images of wounds and the system was evaluated in two architectures: client and client-server. The results show that the client-server reached accelerations of up to 3.2 times in relation to the client-only architecture. The client-server architecture also saves energy and space in the client units, increasing the uptime of smartphones, in addition to reducing the storage load of the same.
Automatic Brain White Matter Hypertinsities Segmentation using Deep Learning Techniques

White Matter Hyperintensities (WMH) are lesions observed in the brain as bright regions in Fluid Attenuated Inversion Recovery (FLAIR) images from Magnetic Resonance Imaging (MRI). Its presence is related to conditions such as aging, small vessel diseases, stroke, depression, and neurodegenerative diseases. Currently, WMH detection is done by specialized radiologists. However, deep learning techniques can learn the patterns from images and later recognize this kind of lesions automatically. This team participated in the MICCAI WMH segmentation challenge, which was released in 2017. A dataset of 60 pairs of human MRI images was provided by the contest, which consisted of T1, FLAIR and ground-truth images per subject. For segmenting the images a 21 layer Convolutional Neural Network-CNN with U-Net architecture was implemented. For validating the model, the contest reserved 110 additional images, which were used to test this method’s accuracy. Results showed an average of 78% accuracy and lesion recall, 74% of lesion f1, 6.24mm of Hausdorff distance, and 28% of absolute percentage difference. In general, the algorithm performance showed promising results, with the validation images not used for training. This work could lead other research teams to push the state of the art in WMH images segmentation.
Independently Moving Object Trajectories from Sequential Hierarchical Ransac

: Safe robot navigation in a dynamic environment, requires the trajectories of each independently moving object (IMO). We present the novel and effective system Sequential Hierarchical Ransac Estimation (Shire) designed for this purpose. The system uses a stereo camera stream to ﬁnd the objects and trajectories in real time. Shire detects moving objects using geometric consistency and ﬁnds their trajectories using bundle adjustment. Relying on geometric consistency allows the system to handle objects regardless of semantic class, unlike approaches based on semantic segmentation. Most Visual Odometry (VO) systems are inherently limited to single motion by the choice of tracker. This limitation allows for efﬁcient and robust ego-motion estimation in real time, but preclude tracking the multiple motions sought. Shire instead uses a generic tracker and achieves accurate VO and IMO estimates using track analysis. This removes the restriction to a single motion while retaining the real-time performance required for live navigation. We evaluate the system by bounding box intersection over union and ID persistence on a public dataset, collected from an autonomous test vehicle driving in real trafﬁc. We also show the velocities of estimated IMOs. We investigate variations of the system that provide trade offs between accuracy, performance and limitations.
The equilibrium shapes of small liquid droplets in solid–liquid phase mixtures: metallic h.c.p. and metalloid systems

Solid–liquid interfacial free energy is shown to be highly anisotropic in alloys of some h. c. p. metals and of two metalloids. This anisotropy is evaluated from a study of the equilibrium shapes of small droplets of alloy liquid entrained within solid grains. Factors affecting the attainment of equilibrium in such droplets are considered, and γ-plots are obtained for solid–liquid interfaces in the h. c. p. metals zinc and cadmium. In the magnesium alloys studied, spherical droplet shapes were observed, indicative of essentially isotropic interfacial energies. Droplet shapes were also analysed in some alloys of bismuth and of antimony. The results are related to current ideas of solid–liquid interfacial structure.
On methods of shortening ElGamal-type signatures

Разработка схем подписи, порождающих подписи малого размера, - актуальная и нетривиальная криптографическая задача. Наиболее перспективными сейчас являются многомерные схемы и схемы на основе спариваний Вейля. Но криптографические механизмы, используемые в этих схемах, пока не поддерживаются большей частью криптографического программного обеспечения, что затрудняет их использование на практике. В настоящей работе предложены три способа укорачивания подписи для стандартных схем типа Эль-Гамаля и проанализировано их влияние на стойкость. Применение всех трех способов к схеме подписи ГОСТ с порядком подгруппы группы точек эллиптической кривой $q\in(2^{255},2^{256})$ позволяет уменьшить размер подписи с $512$ до $320$ бит, сохраняя достаточный уровень стойкости и приемлемое (для неинтерактивных протоколов) время генерации и проверки подписи.
Deepfake Detection using Capsule Networks and Long Short-Term Memory Networks

With the recent advancements of technology, and in particular with graphics processing and artificial intelligence algorithms, fake media generation has become easier. Using deep learning techniques like Deepfakes and FaceSwap, anyone can generate fake videos by manipulating the face/voice of target subjects in videos. These AI synthesized videos are a big threat to the authenticity and trustworthiness of online information and can be used for malicious purposes. Detecting face tampering in videos is of utmost importance. We propose a spatio-temporal hybrid model of Capsule Networks integrated with Long Short-Term Memory (LSTM) networks. This model exploits the inconsistencies in videos to distinguish real and fake videos. We use three different frame selection techniques and show that frame selection has a significant impact on the performance of models. The combined Capsule and LSTM network have comparable performance to state-of-the-art models and about 1/5th the number of parameters, resulting in reduced computational cost.
Threats and Opportunities for the Clinical Investigation of High-risk Medical Devices in the Context of the New European Regulations

: This position paper analyses the threats from the current situation of the clinical investigation to the expectations of the new European regulations focusing on high risk medical devices (HRMDs). We present also some opportunities to improve the feasibility and quality of clinical investigation. In summary, investigation protocols of medical devices, advised and authorized by the competent authorities, are few and heterogenous. There is a lack of quality in the existing studies, a lack of methodological knowledge and consequently high expectations for assistance from those involved in the design of clinical study protocols on HRMD. Guidance that is specific to the different type of devices is missing. Adaptive designs, pragmatic trial, usability methods, computer modeling and real world data are gaining more and more traction for assessing the safety and performance of high risk medical devices from a regulatory view- point.
Inertial-based Gait Analysis Applied to Patients with Parkinson Disease

People with Parkinson’s disease have a high incidence of falls due to motor difficulties. Recent studies have shown that PD patients can receive benefit from motor therapy based on cueing and feedback. This study describes a system based on a foot-mounted IMU for the calculation of gait parameters applied to different datasets of healthy elderly people, geriatric patients and patients with PD, in order to integrate it into a realtime acquisition system with application for tactile cueing. This system is divided into different steps: the identification of gait cycles and their events, the estimation of the path of the foot, which includes the estimation of the orientation of the foot, the application of methods to correct the error derived from the double integration of acceleration such as ZUPT, and finally the estimation of the different gait metrics. The results show that the algorithm developed is an accurate method for stride segmentation and is considered adequate to assess the gait metrics for gait evaluation of patients with motor difficulties.
CIBA: Continuous Interruption-free Brain Authentication

The performance of contemporary biometrics systems based on electroencephalography (EEG) suffers from a low signal to noise ratio due to the properties of the human EEG and the measurement on the scalp. There is a trade-off between accuracy and the time required for data acquisition. Additional time is needed to mount an EEG headset so that authentication requires several minutes, rendering it not very usable for most scenarios. In a scenario where an EEG headset is already worn for a different purpose, the setup time can be neglected and the time for data acquisition may be extended if it does not interfere with the subject’s actual task, allowing continuous authentication. However, most proposed EEG-based authentication systems require the user to perform a certain task during data acquisition, distracting the user from the actual task. We conceptualize an EEG-based continuous authentication scheme that does not require the user to perform a task in addition to working at a screen. We propose two approaches based on well known brain responses, SSVEP and ERP.
Practical Application of Maturity Models in Healthcare: Findings from Multiple Digitalization Case Studies

Maturity models (MMs) are widely applied means for describing a current status of development across numerous domains, but also within the healthcare sector. They offer orientation for systematic development and improvement regarding the aspect examined. However, experience from the practical application of MMs is scarcely described. Within this article two projects in which MMs were used in collaboration with practitioner groups from hospital environments are presented. The general project intentions, motivation for incorporating MMs and generated results are described. By deriving observations across the two cases, general tensions between healthcare practice and research concerning maturity modelling are identified. Additionally, the suitability of existing MMs to support especially hospitals in coping with the challenges of the digital transformation is discussed. This study’s findings may be incorporated into development and refinement of MMs and may thus contribute to increasing practical value created by such means.
MoCaCo: A Simulator Framework for Motion Capture Comparison

With human motion capture being used in various research fields and the entertainment industry, suitable systems need to be selected based on individual use cases. In this paper we propose a novel software framework that is capable to simulate, compare, and evaluate any motion capturing system in a purely virtual way. Given an avatar as input character, a user can create an individual tracking setup by simply placing trackers on the avatars skin. The physical behavior of the placed trackers is configurable and extendable to simulate any existing tracking device. Thus it is possible e.g. to add or modify drift, noise, latency, frequency, or any other parameter of the virtual trackers. Additionally it is possible to integrate an individual inverse kinematics (IK) solving system which is steered by the placed trackers. This allows to compare not only different tracker setups, but also different IK solving systems. Finally users can plug-in custom error metrics for comparison of the calculated body poses against ground truth poses. To demonstrate the capabilities of our proposed framework, we present a proof of concept by implementing a simplified simulation model of the HTC vive tracking system to control the VRIK solver from the FinalIK plugin and calculate error metrics for positional, angular, and anatomic differences.
Player-Type-based Personalization of Gamification in Fitness Apps

This paper examines the effect of personalized gamification on an individual’s motivation in the context of fitness apps. In a first study, we evaluate the four categorization models "Bartle Player Types", "Big Five", "Hexad User Types", and "BrainHex" on their ability to predict individual gamification preferences of users and develop a new prediction model called “MoMo”. Bartle, BrainHex, and MoMo are validated empirically in a second study, employing off-the-shelf fitness apps with gamification elements. The results of both studies indicate that a prediction is possible using the categorization models. Among all models, MoMo performs best in predicting individual gamification preferences, followed by BrainHex. Results of the second study indicate that, although the models MoMo and BrainHex perform better in predicting the theoretical rating of gamification elements than the random model, the prediction of the real motivation value in a specific fitness app is more difficult. This may be due to the concrete implementation of the elements in the second study, and due to the general problem of (theoretically) rating gamification elements without having experienced them in a real application.
Morphological Classification of Heartbeats in Compressed ECG

The number of connected medical devices that are able to acquire, analyze, or transmit health data is continuously increasing. This has allowed the rise of Internet of Medical Things (IoMT). IoMT-systems often need to process a massive amount of data. On the one hand, the colossal amount of data available allows the adoption of machine learning techniques to provide automatic diagnosis. On the other hand, it represents a problem in terms of data storage, data transmission, computational cost, and power consumption. To mitigate such problems, modern IoMT systems are adopting machine learning techniques with compressed sensing methods. Following this line of research, we propose a novel heartbeat morphology classifier, called RENEE, that works on compressed ECG signals. The ECG signal compression is realized by means of 1-bit quantization. We used several machine learning techniques to classify the heartbeats from compressed ECG signals. The obtained results demonstrate that RENEE exhibits comparable results with respect to state-of-the-art methods that achieve the same goal on uncompressed ECG signals.
A Retinex Inspired Bilateral Filter for Enhancing Images under Difficult Light Conditions

This paper presents SuPeR-B, a novel, Retinex inspired colo r spatial algorithm to enhance images acquired under difficult light conditions, such as pictures containi ng dark and bright regions caused by backlight and/or local, not diffused spotlight. SuPeR-B takes as input a colo r image and improves its readability by processing its color channels independently in accordance with some pr inci les of the Retinex theory. Precisely, SuPeRB re-works the channel intensity of each pixels accounting f or differences computed both in the spatial and intensity domains. In this way, SuPeR-B acts as a bilater filt r. The experiments, carried out on a real-world dataset, shows that SuPeR-B ensures good enhancement resul ts, al o in comparison with other state-of-the-art algorithms: SuPeR-B improves the overall content of the ima ge, making the dark regions brighter and more contrasted, while lowering possible chromatic dominants o f he light.
Deep Learning Assisted Retinopathy of Prematurity Screening Technique

Retinopathy of Prematurity (ROP) is the leading cause of blindness in preterm babies worldwide. By using proper scanning and treatment, the effect of the blindness of ROP can be reduced. However, due to lack of medical facilities, a large proportion of these preterm infants remain undiagnosed after birth. As a result, these babies are more likely to have ROP induced blindness. In this paper, we propose a robust and intelligent system based on deep learning and computer vision to automatically detect the optical disk (OD) and retinal blood vessels and also classify the high severity (Zone-1) case of ROP. To test and validate the proposed system, we present empirical results using the preterm infant fundus images from a local hospital. Our results showed that the YOLO-V5 model accurately detects the OD from preterm babies fundus images. Further, the computer vision-based system accurately segmented the retinal vessels from the preterm babies fundus images. Specifically for the Zone-1 case of ROP, our system is able to achieve an accuracy of 83.3%.
On the Link between Mesh Size Adaptation and Irregular Vertices

In numerical simulations and computer graphics meshes are often required to have varying element sizes. High resolution, i.e. small elements, should be only used where necessary. The transition between element sizes requires introducing irregular vertices. In this work, we examine the occurance of irregular vertices in transition regions by setting up an advancing front triangulation that generates optimal transitions. We establish a relation between the appearance of irregular vertices and the properties of the size function and show that a linear transition between different element sizes can be achieved without any singularities on the interior of the transition. Therefore, we can optimize triangulations by setting transition fronts accordingly. These results are used to estimate properties of block-structured grids, e.g. how many blocks are required to represent a given domain correctly.
Reconstruction of Convex Polytope Compositions from 3D Point-clouds

Reconstructing a composition (union) of convex polytopes that perfectly fits the corresponding input point-cloud is a hard optimization problem with interesting applications in reverse engineering and rigid body dynamics simulations. We propose a pipeline that first extracts a set of planes, then partitions the input point-cloud into weakly convex clusters and finally generates a set of convex polytopes as the intersection of fitted planes for each partition. Finding the best-fitting convex polytopes is formulated as a combinatorial optimization problem over the set of fitted planes and is solved using an Evolutionary Algorithm. For convex clustering, we employ two different methods and detail their strengths and weaknesses in a thorough evaluation based on multiple input data-sets.
A Study on the Influence of Omnidirectional Distortion on CNN-based Stereo Vision

Stereo vision is one of the most prominent strategies to reconstruct a 3D scene with computer vision techniques. With the advent of Convolutional Neural Networks (CNN), stereo vision has undergone a breakthrough. Always more works attend to recover the depth information from stereo images by using CNNs. However, most of the existing approaches are developed for images captured with perspective cameras. Perspective cameras have a very limited field of view of around 60◦ and only a small portion of a scene can be reconstructed with a standard binocular stereo system. In the last decades, much effort has been conducted in the research field of omnidirectional stereo vision, which allows an almost complete scene reconstruction if the cameras are mounted at the ceiling. However, as omnidirectional images show strong distortion artifacts, most of the approaches perform an image warping to reduce the reconstruction complexity. In this work, we examine the impact of the omnidirectional image distortion on the learning process of a CNN. We compare the results of a network training with perspective and omnidirectional stereo images. For this work, we use AnyNet and a novel dataset of synthetic omnidirectional and perspective stereo images.
Three-step Alignment Approach for Fitting a Normalized Mask of a Person Rotating in A-Pose or T-Pose Essential for 3D Reconstruction based on 2D Images and CGI Derived Reference Target Pose

The 3D silhouette reconstruction of a human body rotating in front of a monocular camera system is a very challenging task due to elastic deformation and positional mismatch from body motion. Nevertheless, knowledge of the 3D body shape is a key information for precise determination of one’s clothing sizes, e.g. for precise shopping to reduce the number of return shipments in online retail. In this paper a novel three step alignment process is presented, utilizing As-Rigid-As-Possible (ARAP) transformations to normalize the body joint skeleton derived from OpenPose with a CGI rendered reference model in Aor T-pose. With further distance-map accelerated registration steps, positional mismatches and inaccuracies from the OpenPose joint estimation are compensated thus allowing for 3D silhouette reconstruction of a moving and elastic object without the need for sophisticated statistical shape models. Tests on both, artificial and real-world data, generally proof the practicability of this approach with all three alignment/registration steps essential and adequate for 3D silhouette reconstruction data normalization.
Multi-Stage Dynamic Batching and On-Demand I-Vector Clustering for Cost-effective Video Surveillance

In this paper, we present a cost-effective Video-Surveillance System (VSS) for face recognition and online clustering of unknown individuals at large scale. We aim to obtain Performance Indicators (PIs) for people flow monitoring in large infrastructures, without storing any biometric information. For this purpose, we focus on how to take advantage of a central GPU-enabled computing server, connected to a set of videosurveillance cameras, to automatically register new identities and update their descriptive data as they are reidentified. The proposed method comprises two main procedures executed in parallel. A Multi-Stage Dynamic Batching (MSDB) procedure efficiently extracts facial identity vectors (i-vectors) from captured images. At the same time, an On-Demand I-Vector Clustering (ODIVC) procedure clusters the i-vectors into identities. This clustering algorithm is designed to progressively adapt to the increasing data scale, with a lower decrease in its effectiveness compared to other alternatives. Experimental results show that ODIVC achieves state-ofthe-art results in well-known large scale datasets and that our VSS can detect, recognize and cluster in real time faces coming from up to 40 cameras with a central off-the-shelf GPU-enabled computing server.