Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Detection of Decision-Making Style, Based on Users' Online Mouse Pointer Activity

: Decision-making ( DM ) and online activity go hand in hand in many domains of everyday life (e.g., consumer behaviour, financial and investment choices, career development, health and psychological well-being). DM style refers to consistent behavioural patterns in the way different individuals approach DM situations. In this study, we explored the feasibility of inferring DM style from the trace of mouse cursor (or pointer) activity that users generated while performing an online task (the task required no explicit DM). We focussed on maximizing and satisficing DM style. Based on a set of spatial, temporal and spatial-temporal features that were extracted from mouse activity data and on measures of DM style assessed with a conventional self-report questionnaire, we modelled DM style in a supervised machine learning approach. The results show that the models of DM style have between good and high predictive accuracy. Guided by these results, we propose that this mouse-based method might play a useful role in computational recognition of DM style and merits further development. Future work will test the ability of pointer-based models to meaningfully link psychological measures of DM style to objective measures and outcomes of real-world DM situations.
Internal noise stochastic resonance in a circadian clock system

We have studied the influence of internal noise on a circadian clock system using stochastic simulation methods and chemical Langevin equations. It is found that internal noise can induce circadian oscillations, when the corresponding deterministic system does not oscillate. The performance of the noise induced circadian oscillation undergoes a maximum with the variation of the internal noise level, showing the occurrence of internal noise stochastic resonance. Since the magnitude of the internal noise is changed via the variation of the system size, these phenomena also demonstrate a kind of system size resonance.
Time-course analysis of change in display color-appearance based on ambient light change

This study investigates the time course of changes in display color appearance according to changes in ambient lighting. Psychophysical experiments were conducted to observe brightness under lighting transitions in illuminance and the perceived white point under lighting transitions in correlated color temperatures. The result shows that significant change in the color appearance occurs immediately after ambient lighting changes. In addition, the initial change in color appearance depends on the lighting transition characteristics. The continuous change in color appearance was observed when ambient lighting was changed to brighter or lower CCT. On the other hand, a significant abrupt change at the initial stage of adaptation was observed when ambient lighting was changed to darker or higher CCT.
Extended Head Pose Estimation on Synthesized Avatars for Determining the Severity of Cervical Dystonia

: We present an extended head pose estimation algorithm, which is trained exclusively on synthesized human avatars. Having five degrees of freedom to describe such head poses, this task can be regarded as being more complex than predicting the absolute rotation only with three degrees of freedom, which is commonly known as head pose estimation. Due to the lack of labeled data sets containing such complex head poses, we created a data set, consisting of renderings of avatars. With this extension, we take a step towards an algorithm that can make a qualitative assessment of cervical dystonia. Its symptomatic consists of an involuntary twisted head posture, which can be described by those five degrees of freedom. We trained an EfficientNetB2 and evaluated the results with the mean absolute error (MAE). Such estimation is possible, but the performance works differently well for the five degrees of freedom, with an MAE between 1.71° and 6.55°. By visually randomizing the domain of the avatars, the gap between real subject photos and the simulated ones might tend to be smaller and enables our algorithm being used on real photos in the future, while being trained on renderings only.
Extended cone-beam reconstruction using Radon transform

We extend Grangeat's formula (1991) so that partially measured cone-beam projections can be incorporated into image reconstruction in a successful way. The extension is based on a simple fact that multiple partially measured projections may be combined to compute the 3-D Radon data inaccessible from any single orbit point. Furthermore, we show a few examples for which Grangeat's formula does not provide a method for exact reconstruction but the extended formula does.
Bayesian Iterative Closest Point for Shape Analysis of Brain Structures

: Machine learning in medical image analysis has proved to be a strategy that solves many problems emerging from the variability in the physician’s outlines and the amount of time each physician spends analyzing each image. One of the most critical medical image analysis approaches is Medical Image Registration which has been a topic of active research for the last few years. In this paper, we proposed a Bayesian Optimization framework for Point Cloud Registration for shape analysis of brain structures. Here, we rely on a modified version of the Iterative Closest Point (ICP) algorithm. This approach built a black box function that receives input parameters for performing an Point Cloud transformation. Then, we used a similarity metric that shows the performance of the transformation. With this similarity metric, we built a function to define a Bayesian strategy that allows us to find the global optimum of the similarity metric-based function. To this end, we used Bayesian Optimization, which performs global optimization of unknown functions making observations and performing probabilistic calculations. This model considers all the previous observations, which prevents the strategy from falling into an optimal local, as often happens in strategies based on classical optimization approaches such as Gradient Descent. Finally, we evaluate the model by performing a point cloud registration process corresponding to brain structures at different time instances. The experimental results show a faster convergence towards the global optimum and building. Besides, the proposed model evidenced robust optimization results for registration strategies in point clouds.
Active acoustic classification of underwater targets based on scattering resonances.

This paper concerns the neural network based active acoustic classification of underwater targets. The feature vectors utilized in the training of the network are based on the scattering resonance parameters extracted from the echo return. It is expected that these parameters constitute close to an ‘‘optimum’’ set of feature vectors in that they greatly reduce the dimensionality of the echo return while preserving the ‘‘unique’’ aspects related to target identity. The classification performance of a probabilistic neural network classifier is evaluated for several different SNR levels for both monostatic and bistatic scattering configurations. The performance of several different resonance extraction algorithms (including the constrained total least squares technique and the SVD ‘‘Prony’’) is evaluated in varying levels of SNR. The classification study is based on echo returns synthesized from T‐matrix solutions for five elastic targets (three spheres and two finite cylinders).
Extended envelopes around Galactic Cepheids I. Carinae from near and mid-infrared interferometry with the VLTI

We present the results of long-baseline interferometric observations of the bright southern CepheidCarinae in the infrared N (8−13 µm) and K (2.0−2.4 µm) bands, using the MIDI and VINCI instruments of the VLT Interferometer. We resolve in the N band a large circumstellar envelope (CSE) that we model with a Gaussian of 3R� (≈500R� ≈ 2−3 AU) half width at half maximum. The signature of this envelope is also detected in our K band data as a deviation from a single limb darkened disk visibility function. The superimposition of a Gaussian CSE on the limb darkened disk model of the Cepheid star results in a significantly better fit of our VINCI data. The extracted CSE parameters in the K band are a half width at half maximum of 2R� , comparable to the N band model, and a total brightness of 4% of the stellar photosphere. A possibility is that this CSE is linked to the relatively large mass loss rate ofCar. Though its physical nature cannot be determined from our data, we discuss an analogy with the molecular envelopes of RV Tauri, red supergiants and Miras.
Robust image features: concentric contrasting circles and their image extraction

Many computer vision tasks can be simplified if special image features are placed on the objects to be recognized. A review of special image features that have been used in the past is given and then a new image feature, the concentric contrasting circle, is presented. The concentric contrasting circle image feature has the advantages of being easily manufactured, easily extracted from the image, robust extraction (true targets are found, while few false targets are found), it is a passive feature, and its centroid is completely invariant to the three translational and one rotational degrees of freedom and nearly invariant to the remaining two rotational degrees of freedom. There are several examples of existing parallel implementations which perform most of the extraction work. Extraction robustness was measured by recording the probability of correct detection and the false alarm rate in a set of images of scenes containing mockups of satellites, fluid couplings, and electrical components. A typical application of concentric contrasting circle features is to place them on modeled objects for monocular pose estimation or object identification. This feature is demonstrated on a visually challenging background of a specular but wrinkled surface similar to a multilayered insulation spacecraft thermal blanket.
Radiative Heating Rates for Saharan Dust

Abstract A combined longwave and shortwave radiative transfer model was used to determine effects of Saharan dust on the radiative fluxes and heating/cooling rates in the atmosphere. Cases are treated for cloud-free and overcast conditions over the ocean and for cloud-free sky over desert. A benchmark comparison, made for the cloud-free ocean case between our calculations and those from Wiscombe’s detailed model, yielded results which were in close agreement. For moderately heavy dust amounts commonly measured over the Sahara and the eastern tropical Atlantic Ocean, typical calculated aerosol heating rates for the combined longwave and shortwave spectrum were in excess of 1 K day−1 for all three cases for most of the atmosphere beneath the top of the dust layer (500 mb). For the ocean case, maximum heating rates are found near the level of maximum concentration (700 mb), and also near the surface beneath the Saharan air layer (below 900 mb). Net fluxes determined at the top of the atmosphere for the ocean.
Enhanced two-beam coupling in colloids of ferroelectric nanoparticles in liquid crystals

We report on the first, to the best of our knowledge, studies of photorefraction in nematic liquid crystal (LC) doped with nanoferroelectric particles. We found the strong enhancement of two-beam coupling in the colloid of ferroelectric nanoparticles in LC. The effect originated from an increased birefringence of the colloid and a stronger LC reorientation torque. Our measurements allowed us to suggest that increased birefringence is caused by the contribution of polarizability anisotropy of the ferroelectric particles. Stronger reorientation torque is caused by the permanent dipole moment of the particles contributing to the dielectric anisotropy of the colloid eacol. The enhancement of two-beam coupling in LCs by doping with ferroelectric nanoparticles at extremely small concentration shows the strong potential of ferroelectric nanoparticles for improving the optical response of LCs, especially for those materials where a method of chemical synthesis has reached its limit.
Sampling-Distribution-Based Evaluation for Monte Carlo Rendering

: In this paper, we investigate the application of per-pixel difference metrics for evaluating Monte Carlo (MC) rendering techniques. In particular, we propose to take the sampling distribution of the mean (SDM) into account for this purpose. We establish the theoretical background and analyze other per-pixel difference metrics, such as the absolute deviation (AD) and the mean squared error (MSE) in relation to the SDM. Based on insights from this analysis, we propose a new, alternative, and particularly easy-to-use approach, which builds on the SDM and facilitates meaningful comparisons of MC rendering techniques on a per-pixel basis. In order to demonstrate the usefulness of our approach, we compare it to commonly used metrics based on a variety of images computed with different rendering techniques. Our evaluation reveals limitations of commonly used metrics, in particular regarding the detection of differences between renderings that might be difficult to detect otherwise—this circumstance is particularly apparent in comparison to the MSE calculated for each pixel. Our results indicate the potential of SDM-based approaches to reveal differences between MC renderers that might be caused by conceptual or implementation-related issues. Thus, we understand our approach as a way to facilitate the development and evaluation of rendering techniques.
Fast Heuristic for Ricochet Robots

: In this contribution, we describe a fast heuristic for a logical game called Ricochet Robots in which multiple robots cooperate in order to reach a goal. The heuristic recursively explores a restricted search space using subgoals that correspond to interactions of two robots. Subgoals are expanded according to an estimated length of a complete solution, which makes the algorithm reminiscent of the A* algorithm. The estimated length is a lower bound of the length of the real solution, and this allows us to prune subgoals using the best solution found thus far. After eliminating all remaining subgoals, we are guaranteed that the best solution found is the shortest solution from the restricted search space. Moreover, we show that the restricted search space contains a large portion of optimal solutions of the empirical distribution of 1 million random problems. We believe that the presented ideas should generalize to other search problems in which multiple independent agents could block or help each other.
XMeDNN: An Explainable Deep Neural Network System for Intrusion Detection in Internet of Medical Things

: The rapid growth in the adoption of Internet of Things in various areas of our daily lives makes it an interesting target to malicious actors. One area that is witnessing accelerating growth in smart device adoption is health and medical services. The growth in Internet of Medical Things is triggering increased interest in privacy, conﬁdentiality, and intrusion detection. In this paper, we present a deep neural network designed to detect attacks on Internet-of-Things devices in medical settings. The proposed system was tested using WUSTL-EHMS-2020 dataset. Tests showed that the proposed deep learning system can deliver excellent performance with accuracy 97.578%, and a false-positive rate of 3.12%.
Deep Learning Semi-Supervised Strategy for Gamma/Hadron Classification of Imaging Atmospheric Cherenkov Telescope Events

: The new Cherenkov Telescope Array (CTA) will record astrophysical gamma-ray events with an energy coverage range, angular resolution, and ﬂux sensitivity never achieved before. The Earth’s atmosphere produces Cherenkov’s light when a shower of particles is induced by a high-energy particle of astrophysical origin (gam-mas, hadrons, electrons, etc.). The energy and direction of these gamma air shower events can be reconstructed stereoscopically using imaging atmospheric Cherenkov detectors. Since most of CTA’s scientiﬁc goals focus on identifying and studying Gamma-Ray sources, it is imperative to distinguish this speciﬁc type of event from the hadronic cosmic ray background with the highest possible efﬁciency. Following this objective, we designed a competitive deep-learning-based approach for gamma/background classiﬁcation. First, we train the model with simulated images in a standard supervised fashion. Then, we explore a novel self-supervised approach that allows the use of new unlabeled images towards a method for reﬁning the classiﬁer using real images captured by the telescopes. Our results show that one can use unlabeled observed data to increase the accuracy and general performance of current simulation-based classiﬁers, which suggests that continuous improvement of the learning model could be possible under real data conditions.
3D Mapping of Indoor Parking Space Using Edge Consistency Census Transform Stereo Odometry

: In this paper, we propose a real-time 3D mapping system for indoor parking ramps and spaces. Visual odometry is calculated by applying the proposed Edge Consistency Census Transform (ECCT) stereo matching method. ECCT works strongly in repeated patterns and reduces drift errors in the vertical direction of the ground caused by Kanade-Lucas-Tomasi stereo matching of VINS-FUSION algorithm. We propose a mobile mapping system that uses a stereo camera and 2D lidar for data set acquisition. The parking ramp and spaces dataset are obtained using the mobile mapping system and are reconstructed using the proposed system. The proposed system performs the 3D mapping of the parking ramp and spaces dataset that is obtained using the mobile mapping system. We present the error of the normal vector with respect to the ground of the parking space as a quantitative evaluation for performance comparison with the previous method. Also, we present 3D mapping results as qualitative results.
Step Towards Generalization: Fault Classification in Multivariate High-Frequency Data from Different Operating Regimes of Hydraulic Rock Drill System

: Hydraulic rock drills operate under harsh environments of excessive humidity and vibrations. In operation, the fundamental machine frequency is hampered by various loading disturbances created by the pressure waves generated during the rock drill application, which initiates faults at different times during a complete cycle of rock drilling. These faults include failure of internal parts, excessive channel openings and damaged parts, causing enough non-linearity in the pressure data generated. A fault in such machinery can multiply quite rapidly, leading to accidents like complete failure of the equipment and loss of life. Therefore, it is crucial to classify the fault and inform the operator of it. The fault classification challenge escalates further when the rock drill operates on previously unknown operating conditions. In the present work, we compare the performance of deep learning models like Long short-term memory, Convolutional Neural Network, and Residual Network to classify faults, whose signature is recorded in data generated at a frequency of 50kHz when a rock drill is in operation. We also demonstrate how the accuracy of models vary when the models are tested on unseen operating conditions. An overall analysis is provided to generalize a model for fault classification in industrial applications over contrasting operating conditions.
Farsighter: Efficient Multi-Step Exploration for Deep Reinforcement Learning

: Uncertainty-based exploration in deep reinforcement learning (RL) and deep multi-agent reinforcement learning (MARL) plays a key role in improving sample efficiency and boosting total reward. Uncertainty-based exploration methods often measure the uncertainty (variance) of the value function; However, existing exploration strategies either only consider the local uncertainty of the next immediate reward or estimate the uncertainty by propagating the uncertainty for all the remaining steps in an episode. Neither approach can explicitly control the bias-variance trade-off of the value function. In this paper, we propose Farsighter, an explicit multi-step uncertainty exploration framework. Specifically, Farsighter considers the uncertainty of exact k future steps and it can adaptively adjust k. In practice, we learn Bayesian posterior over Q-function in discrete cases and over action in continuous cases to approximate uncertainty in each step and recursively deploy Thompson sampling on the learned posterior distribution with TD(k) update. Our method can work on general tasks with high/low-dimensional states, discrete/continuous actions, and sparse/dense rewards. Empirical evaluations show that Farsighter outperforms SOTA explorations on a wide range of Atari games, robotic manipulation tasks, and general RL tasks.
PDIFT: A Practical Dynamic Information-Flow Tracker

: We present PDIFT, a hybrid dynamic information-ﬂow tracker. PDIFT is based on a thin hypervisor, which tracks information in coarse granularity but has a negligible performance overhead. PDIFT switches to its internally embedded emulator when the information is accessed and needs to be tracked with ﬁner granularity. Using the combination of a thin hypervisor with an embedded emulator, we achieved a signiﬁcant improvement in the overall performance. We believe that PDIFT can be used as an extension to the Android base DIFT systems that currently struggle with native code tracking.
Daily Pain Prediction in Workplace Using Gaussian Processes

: Work-related Musculoskeletal disorders (MSDs) account for 60% of sickness-related absences and even permanent inability to work in the Europe. Long term impacts of MSDs include “Pain chronification” which is the transition of temporary pain into persistent pain. Preventive pain management can lower the risk of chronic pain. It is therefore important to appropriately assess pain in advance, which can assist a person in improving their fear of returning to work. In this study, we analysed pain data acquired over time by a smartphone application from a number of participants. We attempt to forecast a person’s future pain levels based on his or her prior pain data. Due to the self-reported nature of the data, modelling daily pain is challenging due to the large number of missing values. For pain prediction modelling of a test subject, we employ a subset selection strategy that dynamically selects a closest subset of individuals from the training data. The similarity between the test subject and the training subjects is determined via dynamic time warping-based dissimilarity measure based on the time limited historical data until a given point in time. The pain trends of these selected subset subjects is more similar to that of the individual of interest. Then, we employ a Gaussian processes regression model for modelling the pain. We empirically test our model using a leave-one-subject-out cross validation to attain 20% improvement over state-of-the-art results in early prediction of pain.