Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Reliability Of Structural Brain Connectivity With Outlier Adjusted Tractogram Filtering

Diffusion-weighted magnetic resonance imaging tractography is used to represent brain structures but it has limited specificity. Tractogram filtering is proposed to fix this by utilizing e.g. microstructural information to find which streamlines are essential in respect to the original measurements. However, filtered results can be biased if the measurements are unreliable due to partial voluming or artifacts e.g. due to subject motion. We propose augmenting filtering methods with outlier information to adjust for such unreliability. We implemented this in the Convex Optimization modelling for Microstructure Informed Tractography (COMMIT) framework to conduct experiments on data from a synthetic fiber phantom and the Human Connectome Project. Our results demonstrate that the newly augmented COMMIT provides more precise estimations of intra-axonal signal fractions than the original algorithm when diffusion-weighted images are affected by artifacts. Furthermore, we argue this approach could be highly beneficial for clinical studies with limited resolution and numerous unreliable measurements.
A Failed attempt at creating Guidelines for Visual GUI Testing: An industrial case study

Software development is governed by guidelines that aim to improve the code’s qualities, such as maintainability. However, whilst coding guidelines are commonplace for software, guidelines for testware are much less common. In particular, for GUI-based tests driven with image recognition, also referred to as Visual GUI Testing (VGT), explicit coding guidelines are missing.In this industrial case study, performed at the Swedish defence contractor Saab AB, we propose a set of coding guidelines for VGT and evaluate their impact on test scripts for an industrial, safety-critical system. To study the guidelines’ effect on maintenance costs, five representative manual test cases are each translated with and without the proposed guidelines in the two VGT tools SikuliX and EyeAutomate. As such, 20 test scripts were developed, with a combined development cost of more than 100 man-hours. Three of the tests are then maintained by one researcher and two practitioners for another version of the system and costs measured to evaluate return on investment. This analysis is complemented with observations and interviews to elicit practitioners’ perceptions and experiences with VGT.Results show that scripts developed with the guidelines had higher maintenance costs than scripts developed without guidelines. This is supported by qualitative results that many of the guidelines are considered inappropriate, superfluous or unnecessary due to the inherent properties of the scripts, e.g. their natural small size, linear flows, natural separation of concerns, and more. We conclude that there are differences between VGT scripts and software that prohibit direct translation of guidelines between the two. As such, we consider our study as a failure but argue that several lessons can be drawn from our results to guide future research into guidelines for VGT and GUI-based test automation.
Texture Analysis Of T1-Weighted Turbo Spin-Echo Mri For The Diagnosis And Follow-Up Of Collagen Vi-Related Myopathy

Muscle texture analysis in Magnetic Resonance Imaging (MRI) has revealed a good correlation with typical histological changes resulting from neuromuscular disorders. In this research, we assess the effectiveness of several features in describing intramuscular texture alterations in cases of Collagen VI-related myopathy. A T1-weighted Turbo Spin-Echo MRI dataset was used $(\mathrm{N}_{subj}\,=26)$, consisting of thigh scans from subjects diagnosed with Ullrich Congenital Muscular Dystrophy or Bethlem Myopathy, with different severity levels, as well as healthy subjects. A total of 355 texture features were studied, including attributes derived from the Gray-Level Co-occurrence Matrix, the Run-Length Matrix, Wavelet and Gabor filters. The extracted features were ranked using the Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm with Correlation Bias Reduction, prior to cross-validated classification with a Gaussian kernel SVM.
A Parallel Two-stage Sequential Operation Simulation Framework Based on MTUC and STUC

The increasing complexity of power system has brought challenges to power system planning. Conventional production simulation methods have gradually become difficult to quickly and accurately simulate the operation of the power system. The sequential operation simulation method based on SCUC can better meet the simulation demand in the power system planning field. We proposed a two-stage sequential operation simulation framework that uses a parallel strategy, which could simulate the operation quickly and accurately. The coordination between the MTUC model simulating medium-term scheduling and the STUC model simulating day-ahead scheduling brought improvements in computational overhead. In the STUC model, HVDC tie-lines were modeled as schedulable components, so planned operation curves that were more suitable for power system operation could be obtained on the basis of traditional curves. Finally, the improved XJTU-ROTS2017 test system was used to verify the effectiveness and superiority of the method proposed in this paper.
A Geometrically-Constrained Deep Network For Ct Image Segmentation

Incorporating prior knowledge into a segmentation process, whether it be geometrical constraints such as volume penalisation, (partial) convexity enforcement, or topological prescriptions to preserve the contextual relations between objects, proves to improve accuracy in medical image segmentation, in particular when addressing the issue of weak boundary definition. Motivated by this observation, the proposed contribution aims to include geometrical constraints in the training of convolutional neural networks in the form of a penalty in the loss function. These geometrical constraints take several forms and encompass level curve alignment through the weighted total variation component, an area penalisation phrased as a hard constraint in the modelling, and an intensity homogeneity criterion based on a combination of the standard Dice loss with the piecewise constant Mumford-Shah model. The mathematical formulation yields a non-smooth non-convex optimisation problem, which rules out conventional smooth optimisation techniques and leads us to adopt a Lagrangian setting. The application falls within the scope of organ-at-risk segmentation in CT (Computed Tomography) images, in the context of radiotherapy planning. Experiments demonstrate that our method provides significant improvements over existing non-constrained approaches.
RiverFuzzRL - an open-source tool to experiment with reinforcement learning for fuzzing

Combining fuzzing techniques and reinforcement learning could be an important direction in software testing. However, there is a gap in support for experimentation in this field, as there are no open-source tools to let academia and industry to perform experiments easily. The purpose of this paper is to fill this gap by introducing a new framework, named RiverFuzzRL, on top of our already mature frame-work for AI-guided fuzzing, River. We provide out-of-the-box implementations for users to choose from or customize for their test target. The work presented here is performed on testing binaries and does not require access to the source code, but it can be easily adapted to other types of software testing as well. We also discuss the challenges faced, opportunities, and factors that are important for performance, as seen in the evaluation.
Steep Subthreshold Swing in Double Gate NCFET:A Simulation Study

The rapid increase in interest on Negative capacitance Field Effect Transistors(NCFETs) is due to its Low power applications. NCFETs stepsup the voltage between the oxide and ferroelectric capacitance due to reversing (i.e, amplification) effect of a ferroelectric (fe) layer[1]. These are designed in a way such that its Subthreshold swing overcomes the boltzmann limit. According to the Boltzmann theorem, carriers that can cross the barrier and increase current by a decade need a gate voltage increase of at least 60 mV[2]. This paper presents a new approach for designing NCFETS, where the target is a low subthreshold swing. The proposed model is validated against several experimental data for an double-gate FET architecture.
External Factors Influencing Production Planning and Control System Acceptance: A Meta-Analytical Approach

The number of information systems as well as the respective range of functionalities used in the area of production planning and control (PPC) increases steadily. Reason for this is given by the rising complexities of production processes and competitive conditions, leading to challenges in the daily decision-making of the employees involved in the process. However, contrary to the performance promises associated with the introduction of information systems, the systems used frequently fall short of expectations. The causes for this are, besides well-known technical problems, often found in underestimated challenges of user acceptance. This paper thus addresses the question of the external factors relevant for an acceptance-based use of PPC-systems. For this purpose, the authors conducted a meta-analysis, focusing on previous studies analyzing the use of PPC-systems applying the technology acceptance model. As a result, the analysis revealed the most frequently identified and validated external factors influencing the user acceptance of system usage in PPC. However, the analysis also shed light on shortcomings regarding the current degree of examination of APS and ME-systems.
Pedestrian Street-Cross Action Recognition in Monocular Far Infrared Sequences

The early recognition and understanding of the actions performed by pedestrians in traffic scenes leads to an anticipation of pedestrian intentions in advance and helps in the process of collision warning and avoidance in the context of autonomous vehicles. An environment with low visibility conditions such as night-time, fog, heavy rain or smoke increases the number of difficult situations in traffic. A complete and original model for assessing if a pedestrian is engaged in a street cross action using only infrared monocular scene perception is proposed in this paper. The assessment of a street cross action is done by the time series analysis of features like: pedestrian motion, position of pedestrians with respect to the drivable area and their distance with respect to the ego-vehicle. The extraction of these features emerges from the combination of a deep learning based pedestrian detector with an original tracking algorithm, a semantic segmentation of the road surface and a time series long-short term memory network based action recognition. In order to validate the proposed method we introduce a new dataset named CROSSIR. It is formed of pedestrian annotations, action annotations and semantic labels for the road. The CROSSIR dataset is suitable for several common computer vision algorithms: (1) pedestrian detection and tracking algorithms because each pedestrian has a unique identifier over the frames in which it appears; (2) pedestrian action recognition; (3) semantic segmentation of the road pixels in the infrared image.
Analytical Approach for Multiple PV Placements in Radial Distribution System under Balanced and Unbalanced Load Condition

Development of Distributed Generation (DG) in the distribution network renders various economic and technical benefits to the electrical grid. Contrarily, optimal placement and DGs sizing is a challenging problem which otherwise can cause a threat to the system and would decrease the overall system performance. This paper extracts a novel analytical approach for optimal placement of suitable size Distributed Generation (DG) based on deviation in voltage stability and voltage profile index to bring down the overall power loss thereby improving the voltage stability and voltage profile of the system. In this research work PV is considered as DG. The proposed index has been carried out on benchmark IEEE 12bus Radial Distribution System using Matlab/Simulink software. Results indicate the ideal location and siting of DGs which proves for overall system performance.
Memory-Efficient Neural Network For Non-Linear Ultrasound Computed Tomography Reconstruction

Deep neural networks have proven to excel classical medical image reconstruction techniques. Some networks are based on fully connected (FC) layers to achieve domain transformation such as from the data acquisition domain to the image domain. However, FC layers result in huge numbers of parameters which take a lot of GPU memory. Hence, they do not scale well, and the overall performance is limited. For ultrasound computed tomography (USCT) application, we propose a memory-efficient convolutional network that reconstructs images from the frequency domain to image domain with much less parameters compared with multilayer perceptron, by using data-driven learning. Extensive experiments demonstrate that our method achieves high reconstruction quality. It improves the structural similarity measure (SSIM) from 0.73 to 0.99 when compared with state-of-the-art reconstruction methods in this field while reduces 2/3 parameters when compared with deep neural network with FC layers to reconstruct images from frequency domain to image domain.
Innovative Selection Method for Internal Logistics Concepts Based on Material Data

Production logistics for automobile manufacturing needs to adapt to new production layouts and methods within the upcoming years. Based on the continuing growth of individualization of vehicles in the premium segment and alternative drive concepts, economical productions, based on Henry Ford's conveyor system, will become difficult. Therefore, new flexible and adaptable production logistics concepts will be used to achieve the goal of a flexible and changeable automotive production. However, manufacturers have to decide, which logistic concept is best for each assembly station. The purpose of this paper is to show an innovative method, which contains requirements for a logistics concept e.g. the selection of an individual concept for each production station and considers respective requirements and costs resulting for the use of respective logistics concepts.
Feature Extraction for Non-intrusive Load Monitoring System

Compared with traditional intrusive load monitoring system, the non-intrusive load monitoring system only needs to install a monitoring equipment at the entrance of the power supply. In this paper, we focus on the feature extraction for non-intrusive load monitoring system. By extracting features from the monitored voltage and current and training them in machine learning model, we can identify the types and operation states of different loads. Meanwhile, these features reflect the electrical characteristics (e.g., power) and can be used to detect the events (i.e., switch of operation states) for every load. Specifically, the features are obtained via time and frequency domain analysis. In time domain, energy ratio is calculated to detect the events during the operation of loads. Besides, active and reactive power, power factor and current peak coefficient are also calculated. To identify the load with current peak distortion, we innovatively propose a position ratio, defined as the ratio of the ramp-off time over the ramp-up time for a period of current waveform. In frequency domain, harmonic and total harmonic current distortion are extracted. Apart from this, the harmonic ratio such as the ratio of third harmonic to fundamental wave is proposed for load identification. It can be seen that, for resistive load such as heating kettle, the harmonic ratio is quite close to zero; for inductive load such as washing machine, the harmonic ratio is close to 1.
Effect of Facial Expression Categories and Calculation Methods on Automatic Emotion Recognition

Automatic classifiers are becoming increasingly popular for emotion recognition purposes. In order to compare the accuracy of seven commercially available classifiers, 468 video recordings of posed facial expressions supposedly displaying anger, disgust, fear, happiness, sadness or surprise were processed. Four different calculation methods to identify the emotion recognised were compared. A generalised linear mixed model reveals significant differences in automatic classifiers, in the type of emotions displayed, and in calculation methods. Some emotions such as happiness, sadness and surprise appear to lead to relatively high recognition accuracy, whereas anger, disgust and fear lead to low recognition accuracy. Finally, differences between the calculation methods are observed, as the Matching Score method indicates lower accuracy compared to the three other methods. These results support the necessity to establish standards for automatic classification of facial expression.
Design of Data Compression Mechanism in Cache Memory of Elbrus Processors

Higher capacity of cache memory increases Hit Rate at the cost of power consumption and die area. Hardware data compression in cache allows increasing its effective capacity without significant negative effects on these characteristics. This work discusses the realization of data compression in L3 cache memory of modern processors with “Elbrus” architecture. One of the most efficient and at the same time fast compression algorithms is BDI (Base-Delta-Immediate) algorithm. Its modification, called BDI*-HL (Base-Delta-Immediate Modified, Half-Line), is selected for implementation. To achieve an increase in effective capacity, the principles of joint placement of compressed cache lines are introduced and implemented. L3 cache memory is modified to correctly work with compressed lines. A testing bench is developed to evaluate the effectiveness of the designed mechanism. The developed solution does not require any significant changes in the structure of cache memory, does not affect the principles of eviction policy and the coherency protocol and does not change values of working frequency and cache die area.
Quaterni-On: Calibration-free Matching of Wearable IMU Data to Joint Estimates of Ambient Cameras

Inertial measurement units, such as those embedded in many smartwatches, enable to track the orientation of the body joint to which the wearable is attached. Tracking the user's entire body would thus require many wearables, which tends to be difficult to achieve in daily life. This paper presents a calibration-free method to match the data stream from a body-worn Inertial Measurement Unit, to any body joint estimates that are recognized and located from a camera in the environment. This allows networked cameras to associate detected body joints from nearby humans with an orientation stream from a nearby wearable; This wearable's orientation stream can be transformed and complemented with its user's full body pose estimates. Results show that in multi-user environments where users try to perform actions synchronously, out of 42 joint candidates, our calibration-free method obtains a perfect match within 83 to 155 samples (2.8 to 5.2 seconds), depending on the scenario. This would facilitate a seamless combination of wearable- and vision-based tracking for a robust, full-body user tracking.
Direct extraction of solar cell model parameters using optimization methods

This article reviews and appraises various fitting parameter extraction methods for solar cells modeled with 5 parameters. The current of illuminated photovoltaic panels and solar cells are customarily described by the single diode model, which contains a diode, a current source, a series resistance and a parallel conductance. This simple model with only 5 parameters provides reasonable accuracy. In this work, we carry out a comparative review of several parameter extraction methods using many data points, since methods based on fewer data points are more prone to measurement error. Among the methods tested in this benchmark are the implicit fitting, the direct optimization of the current (vertical fit) using the Lambert W function, and the Co-content method, which is based on fitting the integral of current with respect to voltage. We also evaluate the direct optimization of the voltage (lateral fit) using the Lambert W function. The results demonstrate that, regarding convergence reliability for a large range of initial values, the best procedure is the Co-content method, which is based on the use of a quadratic equation for fitting. We tested the different methods on the same experimental data of a solar cell as well as PV modules measured by NREL.
Assessment of Map Construction in vSLAM

Vision-based Simultaneous Localization and Mapping (vSLAM) is a challenging task in modern computer vision. vSLAM is particularly important as mobile robotics application. It allows to localize the robot and build the map of unknown environment in 3D in real-time. During research and development of new methods, it needs extensive evaluation on trajectory and map quality compared to known methods. In this work we focus on map quality estimation. We develop the simulated ground-truth data in photo-realistic environment and introduce new metrics in order to estimate map quality. We evaluate neural network based vSLAM methods with our framework in order to show that it fits map quality estimation more than standard approaches. Open-source implementation of our map metrics is available at https://github.com/CnnDepth/slam_comparison
Gate Drive Optimization Framework with Electro-thermal Model of GaN HEMTs for High-Frequency DC-DC Converters

The gate drive circuitry design of Gallium Nitride High Electron Mobility Transistors (GaN HEMTs) is critical to power converter efficiency. The high switching frequency achievable by GaN means the gate drive circuitry must be designed to minimize parasitic inductance to prevent voltage overshoot. Additionally, the output current and characteristic capacitance of GaN transistors is dependent of the temperature of the device. This paper presents a framework utilizing an electro-thermal model for GaN devices to mitigate the benchtop tuning requirement of the gate drive resistors. The proposed electro-thermal model utilizes a Cauer approach in the equivalent circuit of a GaN switch. This adaptively changes the characteristics of the device, allowing for more accurate modeling in the design stage. The electro-thermal model is incorporated into a half-bridge model to simulate both the gate drive resistors as well as the parasitic inductances typical in a PCB design. With the full model, an optimization procedure is developed to find a Pareto efficient converter based upon desired switching frequency and bus voltage. Finally, the combined complete simulation and hardware experimentation are shown using a GaN half-bridge converter board operating as boost DC-DC converter for photovoltaic applications.
Spatial Decomposition For Robust Domain Adaptation In Prostate Cancer Detection

The utility of high-quality imaging of Prostate Cancer (PCa) using 3.0 Tesla MRI (versus 1.5 Tesla) is well established, yet a vast majority of MRI units across many countries are 1.5 Tesla. Recently, Deep Learning has been applied successfully to augment radiological interpretation of medical images. However, training such models requires very large amount of data, and often the models do not generalize well to data with different acquisition parameters. To address this, we introduce domain standardization, a novel method that enables image synthesis between domains by separating anatomy- and modality-related factors of images. Our results show an improved PCa classification with an AUC of 0.75 compared to traditional transfer learning methods. We envision domain standardization to be applied as a promising tool towards enhancing the interpretation of lower resolution MRI images, reducing the barriers of the potential uptake of deep models for jurisdictions with smaller populations.