Miracle-dz/UniFAR-FTUs huggingface dataset:
Stress Detection from Surface Electromyography using Convolutional Neural Networks

The study of stress and its implications has been the focus of interest in various fields of science. Many automated/semi-automated stress detection systems based on physiological markers have been gaining enormous popularity and importance in recent years. Such non-voluntary physiological features exhibit unique characteristics in terms of reliability, accuracy. Combined with machine learning techniques, they offer a great field of study of stress identification and modelling. In this study, we explore the use of Convolutional Neural Networks (CNN) for stress detection through surface electromyography signals (sEMG) of the trapezius muscle. One of the main advantages of this model is the use of the sEMG signal without computed features, contrary to classical machine learning algorithms. The proposed model achieved good results, with 73% f1-score for a multi-class classification and 82% in a bi-class classification.
Cluster Kernel Reinforcement Learning-based Kalman Filter for Three-Lever Discrimination Task in Brain-Machine Interface

Brain-Machine Interface (BMI) translates paralyzed people's neural activity into control commands of the prosthesis so that their lost motor functions could be restored. The neural activities represent brain states that change continuously over time which brings the challenge to the online decoder. Reinforcement Learning (RL) has the advantage to construct the dynamic neural-kinematic mapping during the interaction. However, existing RL decoders output discrete actions as a classification problem and cannot provide continuous estimation. Previous work has combined Kalman Filter (KF) with RL for BMI, which achieves a continuous motor state estimation. However, this method adopts a neural network structure, which might get stuck in local optimum and cannot provide an efficient online update for the neural-kinematic mapping. In this paper, we propose a Cluster Kernel Reinforcement Learning-based Kalman Filter (CKRL-based KF) to avoid the local optimum problem for online neural-kinematic updating. The neural patterns are projected into Reproducing Kernel Hilbert Space (RKHS), which builds a universal approximation to guarantee the global optimum. We compare our proposed algorithm with the existing method on rat data collected during a brain control three-lever discrimination task. Our preliminary results show that the proposed method has a higher trial accuracy with lower variance across data segments, which shows its potential to improve the performance for online BMI control. Clinical Relevance— This paper provides a more stable decoding method for adaptive and continuous neural decoding. It is promising for clinical applications in BMI.
A Comparison of Flow- and Pressure-Controlled Infusion Strategies for Microneedle-based Transdermal Drug Delivery

Microneedle-based transdermal drug delivery is considered an attractive alternative to conventional injections using hypodermic needles due to its minimally invasive and painless nature; this has the potential to improve patient adherence to medication regimens. Hollow microneedles (MNs) are sharp, sub-millimeter protrusions with a channel that serves as a fluidic interface with the skin. This technology could be coupled with micro-pumps, embedded sensors, actuators and electronics to create Micro Transdermal Interface Platforms - smart, wearable infusion systems capable of delivering precise microdoses over a prolonged period. Using 500 µm tall hollow microneedles, ex-vivo human skin and a customized application/retraction device, this work focuses on comparing two infusion control strategies, namely ‘set pressure’ (SP) and ‘set flow’ (SF) infusion. It was found that flow-controlled infusion was capable of delivering higher volumes than pressure-driven delivery, and a mean volume of 3.8 mL was delivered using a set flowrate of 50 µL/minute. This suggests that flow driven delivery is a better control strategy and confirms that MN array retraction is beneficial for transdermal MN infusion.
Malware and Piracy Detection in Android Applications

With the exploding growth in the number of Android applications, software malware and piracy incidents are among the major concerns to be addressed for a healthy marketplace. In this work, a permission-based malware detection system is developed to identify malware infected applications using the permissions accessed by applications. We also re-implement Juxtapp for malware and piracy detection based on the underlying opcodes. The performance of these approaches is evaluated on a large dataset containing original, pirated and malware infected applications extracted from AndroZoo and KuafuDet. Experimental results demonstrate that the permission-based malware detection system generally performs better than the opcode-based approach. In addition, Juxtapp can be used to detect software piracy, and may be used in conjunction with a permission-based malware detection system to accurately identify malware infected and pirated applications.
Fundus GAN - GAN-based Fundus Image Synthesis for Training Retinal Image Classifiers

Two major challenges in applying deep learning to develop a computer-aided diagnosis of fundus images are the lack of enough labeled data and legal issues with patient privacy. Various efforts are being made to increase the amount of data either by augmenting training images or by synthesizing realistic-looking fundus images. However, augmentation is limited by the amount of available data and it does not address the patient privacy concern. In this paper, we propose a Generative Adversarial Network-based (GAN-based) fundus image synthesis method (Fundus GAN) that generates synthetic training images to solve the above problems. Fundus GAN is an improved way of generating retinal images by following a two-step generation process which involves first training a segmentation network to extract the vessel tree followed by vessel tree to fundus image-to-image translation using unsupervised generative attention networks. Our results show that the proposed Fundus GAN outperforms state of the art methods in different evaluation metrics. Our results also validate that generated retinal images can be used to train retinal image classifiers for eye diseases diagnosis. Clinical Relevance— Our proposed method Fundus GAN helps in solving the shortage of patient privacy-preserving training data in developing algorithms for automating image- based eye disease diagnosis. The proposed two-step GAN- based image synthesis can be used to improve the classification accuracy of retinal image classifiers without compromising the privacy of the patient
An Entourage Approach to the Contraction Principle in Uniform Spaces Endowed with a Graph

In this paper, we study Banach contractions in uniform spaces endowed with a graph and give some sufficient conditions for a mapping to be a Picard operator. Our main results generalize some results of [J. Jachymski, "The contraction principle for mappings on a metric space with a graph", Proc. Amer. Math. Soc. 136 (2008) 1359-1373] employing the basic entourages of the uniform space.
Predicting Evapotranspiration (ETo) by Training Regression Models

Evapotranspiration is a critical natural process in agriculture. Every farmer has to deal with this in every stage of crop development. Proper estimation of soil and leaf’s-based Evapotranspiration (ETo) is required to be done. Traditionally it is being done based on climate forecasting only, but nowadays, many new techniques have been shown up by which this can be estimated more accurately. In this study, machine learning and deep learning models are being used to determine the ETo. Regression techniques are preferred over classification so that every possible value can be taken as an input for accuracy. Nowadays, many algorithms have proved their efficiency with the specific dataset. There is no unique algorithm that has established itself as suitable for every type of dataset. This study will help to find the best algorithm for getting accurate predictions on ETo. A few famous regression algorithms are being opted for this study, and the best resultant algorithm is determined for a given dataset. The highlighted algorithm based on the experiment should only be followed if similar climate attributes are encountered for any research and study.
Physics-Based Sound Synthesis of the Piano

The present work is about the synthesis of piano sound based on the grounds of physical principles. For that, rst the acoustical properties of the piano have to be understood, since the underlying physical phenomena establish the framework for the model-based sound synthesis. Therefore, the di erent parts of the piano were measured and analyzed. The groundwork of the piano model lies in the digital waveguide modeling of the string behavior. Accordingly, the digital waveguide string model is thoroughly discussed and analyzed. The mathematical equivalence of the digital waveguide and the resonator bank is also presented. The partition of the piano model follows the principles of the sound production mechanism of the real piano. The hammer is modeled by nonlinear interaction. The discontinuity problem arising when connecting the hammer to the string is investigated and new solutions for its avoidance are proposed. The instability problems of the hammer model are overcome by a novel multi-rate implementation. The possible use of a nonlinear damper model is also discussed. The string simulation is based on the digital waveguide. For beating and two-stage decay, a new parallel resonator bank structure is proposed. The soundboard model consists of a feedback delay network with shaping lters. A new technique is presented to reproduce the attack noise of the piano sound in an e cient and physically meaningful way. Concerning the implementation issues, a multi-rate piano model is proposed, which resolves the problem of di erent computational loads presented by the string models of the low and high register. Additionally, the calibration of the piano model is described. A new loss lter design algorithm is presented for the calibration of the digital waveguide. The new technique minimizes the error of the resulting decay times and also ensures the stability of the feedback loop. For the one-pole lter as a special case, a novel lter design technique is proposed. It is founded on the new theoretical results of the Appendix concerning the decay times of a feedback loop containing the one-pole loop lter. A robust technique for the measurement of beating and two-stage decay is presented. This is used for the calibration of the parallel resonator bank. The methods and techniques proposed here are described with the application to piano sound synthesis. Nevertheless, most of them can be exploited for the e cient synthesis of other musical instruments as well.
Unaligned Multimodal Sequences for Depression Assessment From Speech

A growing area of mental health research pertains to how an individual's degree of depression might be automatically assessed through analyzing multimodal-based objective markers. However, when combined with machine learning, this research can be challenging due to the existence of unaligned multimodal sequences and the limited amount of annotated training data. In this paper, a novel cross-modal framework for automatic depression severity assessment is proposed. The low-level descriptions (LLDs) from multiple clues (such as text, audio and video) are extracted, after which multimodal fusion via cross-modal attention mechanism is utilized to facilitate the learning of more accurate feature representations. For the features extracted from each modality, the cross-modal attention mechanism is utilized to continuously update the input sequence of the target mode, until the score of the patient's health questionnaire (PHQ-8) can finally be obtained. Moreover, Self-Attention Generative Adversarial Networks (SAGAN) is employed to increase the amount of training data available for depression severity analysis. Experimental results on the depression sub-challenge dataset of the Audio/Visual Emotion Challenge (AVEC 2017 and AVEC 2019) demonstrate the effectiveness of our proposed method.
MVD-Net: Semantic Segmentation of Cataract Surgery Using Multi-View Learning

Semantic segmentation of surgery scenarios is a fundamental task for computer-aided surgery systems. Precise segmentation of surgical instruments and anatomies contributes to capturing accurate spatial information for tracking. However, uneven reflection and class imbalance lead the segmentation in cataract surgery to a challenging task. To desirably conduct segmentation, a network with multi-view decoders (MVD-Net) is proposed to present a generalizable segmentation for cataract surgery. Two discrepant decoders are implemented to achieve multi-view learning with the backbone of U-Net. The experiment is carried out on the Cataract Dataset for Image Segmentation (CaDIS). The ablation study verifies the effectiveness of the proposed modules in MVD-Net, and superior performance is provided by MVD-Net in the comparison with the state-of-the-art methods. The source code will be publicly released.
Link Weight Prediction in Directed Networks Based on Graph Convolution Network

Aiming to predict the strength of the connection between two nodes in the networks, link weight prediction is a relatively new topic in network science that has attracted more attention recently. Many previous studies focused on undirected networks. However, many real-world networks are directed networks and the weights of links are different when they are in different directions. In this study, we provide a regression model that can learn features from directed networks automatically instead of using predefined indices. Thus we proposed DLWP-GCN, a directed regression model designed for link weight prediction based on graph convolution network, which utilizes graph convolution network to extract node features and edge features from the subgraph and its corresponding line subgraph. We conduct experiments on 6 networks in the real world to evaluate DLWP-GCN with several baseline methods. Experiment results show that DLWP-GCN outperforms other baseline methods and is robust. Furthermore, we find that node features and edge features can complement each other, and utilizing both features results in better performance.
Crack segmentation enhancement using an adaptive hyper-parameter tuning scheme configured by a novel crack type clustering method

In the past few years, computer vision algorithms have profoundly impacted areas where image and video data are used as input data. Crack detection is one of the areas where researchers have applied countless deep learning architectures to effectively detect cracks on the surface of concrete structures and pavements. A class imbalance arises when applying crack detection at the pixel level because crack pixels occupy a small portion of the images and a small portion of the dataset. To solve this problem, researchers have proposed different approaches that most are focused on using weighted loss functions. A crack segmentation algorithm with a weighted loss function applies more weights (attention) on crack pixels during loss calculation. Even though this approach slightly improves the performance of the entire dataset, it does not include the crack type in the process of loss weighting. We explore that an adaptive weighting factor depending on the crack type results in a crack segmentation performance boost. Therefore, in this paper, we first propose an evaluation approach based on crack-type clustering. Second, an adaptive hyper-parameter tuning scheme based on the proposed clustering method is employed to improve the crack segmentation performance. Results show that applying the new hyper-parameter tuning approach improves the crack segmentation performance over the unseen data compared to the state-of-the-art approaches. Moreover, the new clustering-based evaluation approach gives in-depth insights into the performance of the crack segmentation algorithm over different crack types.
3D Compton Imaging of Distributed Sources around the Chernobyl Nuclear Power Plant

Portable radiation detection systems can be equipped with contextual sensors to allow free-moving 3D gamma-ray imaging in a method called scene data fusion (SDF). The scene information provided by the contextual sensors can be used to enable 3D imaging and constrain image reconstruction to improve imaging accuracy and computational efficiency and visualization. SDF could be a useful tool in many applications, including radiation mapping for accident assessment and remediation. To demonstrate this concept, Polaris-LAMP, a commercially available detector that has been integrated with contextual sensors, was operated in some areas in and around the Chernobyl Nuclear Power Plant (ChNPP). Here, we present results employing Compton image reconstruction in combination with SDF of data that were collected over series of 20-50 minute dynamic measurements around Pripyat, a nearby town which has been abandoned since the accident in 1986 and in some locations within ChNPP. With the SDF enabled Polaris-LAMP it was possible to create 3D maps of gamma-ray sources and to successfully identify hot spot candidates of Cs-137 on key features of the scenes investigated, demonstrating the usefulness of SDF in the mapping of unknown distributed source environments. In addition, the reconstructed 3D scenes provide important context and visualization of structures and objects.
Fully 3D Scatter Estimation in Axially Long FOV PETCT Scanners: Residual Estimation Approach

Long axial field-of-view PETCT scanners provide very high sensitivity owing to large LOR acceptance angles. However, this poses challenges for robust scatter correction due to different contributions from single and multiple scatter in oblique segments. In this work we present a novel approach to estimate fully 3D scatter by means of estimating the residuals between the measured and modelled data. For this, the 2D SSS model based scatter along with 2D measured data was used to obtain the unscattered trues image estimate. The scatter for oblique segments was computed as the residual between the measured net trues and the unscattered trues estimate. A preliminary assessment was performed using experimental phantoms and clinical data acquired on the Biograph Vision Quadra (Siemens Healthineers). The experimental phantoms included the long cylindrical phantom with uniform activity and the oval phantom with cylindrical insert having a contrast of 200:1 between the insert and background and filled with F-18 FDG. In addition, an image quality phantom with I-124 solution was also used to assess the performance with prompt gamma coincidences contamination in the background. The clinical datasets included 25 patient studies using different radioisotopes. All data were reconstructed with TOF OP-OSEM (4 iterations, 5 subsets) using the maximum ring difference (MRD) of 85 with 2D scatter and MRD of 322 with the proposed 3D scatter. Scatter profiles from cylinder phantom with 3D scatter approach matched the tails for the corresponding net trues for different oblique segments. Image quantification differences for cylinder phantom centered and off-centered were lower with 3D scatter approach compared to 2D scatter approach. 3D scatter approach also gave consistently similar quantification for different count statistics. For high contrast phantom image quality was improved with the use of 3D scatter approach compared to the 2D scatter approach. For clinical datasets, it was found that 3D scatter gave higher quantitative estimates compared to 2D scatter. Preliminary results suggests that the proposed 3D scatter approach can provide robust and accurate scatter estimates without increasing the computational cost for the long axial field-of-view scanner.
Multimodal Diagnosis for Pulmonary Embolism from EHR Data and CT Images

Pulmonary Embolism (PE) is a severe medical condition that can pose a significant risk to life. Traditional deep learning methods for PE diagnosis are based on Computed Tomography (CT) images and do not consider the patient's clinical context. To make full use of patient's clinical information, this article presents a multimodal fusion model ingesting Electronic Health Record (EHR) data and CT images for PE diagnosis. The proposed model is based on multilayer perception and convolutional neural networks. To remove the invalid information in the EHR data, the multidimensional scaling algorithm is performed for feature dimension reduction. The EHR data and CT images of 600 patients are used for experiments. The experiment results show that the proposed models outperform existing methods and the multimodal fusion model shows better performance than the single-input model.
Image Based Vehicle-Trailer Angle Estimation

A trailer assist system is an Advanced Driver Assistance System. It enables a more effective maneuvering of a trailer towed by a vehicle. It is particularly useful in constraint scenarios such as parking, traffic jam, turns, etc. Traditionally, trailer assist systems are based on image processing methods or require specific sensors to measure the hitch angle. In this paper, we propose to estimate the hitch angle by making use of the existing rear camera of the vehicle and by using an image processing approach, thus avoiding the supplementary angle measurement sensor. To this extent, a dedicated database is collected and annotated. Two different methods are developed to predict this corresponding angle, the first method is based on a convolutional neural network and the second one is a deterministic image processing method. The two approaches are finally compared in terms of accuracy and execution time.
Modeling Neural Connectivity in a Point-Process Analogue of Kalman Filter

A neural encoding model describes how single neuron tunes to external stimuli as well as its connectivity with other neurons. The connectivity illustrates the neuronal interaction within populations in response to the shared latent brain states. Understanding these interactions is crucial to computationally predict the neural activity, which elucidates the neural encoding mechanism A computational analysis on the neural connectivity also facilitates developing more point process decoding model to interpret movement state from neural spike observations for brain machine interfaces (BMI). Most of the previous point process models only consider single neural tuning property and assumes conditional independence among multiple neurons. The connectivity among neurons is not considered in such a Bayesian approach to derive the state. In this work, we propose a point-process analogue of Kalman Filter to model the neural connectivity in a closed-form Bayesian filter. Neural connectivity corrects the posterior of the state given the multi-dimension observation, and a Gaussian distribution is used to approximate the updated posterior distribution. We validate the proposed method on simulation data and compared with traditional point process filtering with conditional independent assumption. The result shows that our method models the neural connectivity information and the single neuronal tuning property at the same time and achieves a better performance of the state decoding. Clinical Relevance - This paper proposes a closed-form derivation of a point process filter based on Gaussian approximations. It can model both single neuronal tuning property and the neural connectivity, which is potential to understanding the neural connectivity computationally.