Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automatic Deep Reinforcement Learning Based Credit Scoring Model using Deep-Q Network for Classification of Customer Credit Requests

Credit risk assessment is a very crucial task for every firm. Especially, companies which give goods or services to their customers to be paid back on a later date or gives loans need to have an efficient credit risk assessment system to avoid financial losses. For accurate assessment of credit risk, precise credit scoring models are needed which the firms may use as a decision-support tool for making lending decisions. Approving credit to bad customers or denying credit to potential customers both can incur profit losses for the firm. Several researchers have addressed this credit risk assessment problem previously by building credit scoring models using various machine learning algorithms. But the performance of these models gets affected due to the skewed nature of the credit scoring data and the hidden correlations between the data features. It has been noted from literature that the credit scoring models are sensitive to the highly imbalanced class ratio which exists in credit scoring datasets. We address these challenges in this paper by proposing a deep-Q network based reinforcement learning model. The model uses two reward functions to help the model learn the optimal policy of detecting bad customers and maintain a balance between the credit approval and decline rate. We have then compared our DQN model performance with other classification models for the same dataset to demonstrate the effective utility of our model in improving the effective lending decisions.
Ensemble AI Fault Diagnosis Model Using Ultrasonic Microphone

the earliest method for predicting industrial faults is through ultrasonic diagnostics. This study focuses on analyzing a dataset comprising 10 main faults, from which 29 different features are extracted in both time and frequency domains. To reduce dimensions, the t-SNE method is utilized, which effectively distinguishes between the classes. Four predictive models, namely k-nearest Neighbor (kNN), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM), are then applied. To tackle the classification problem, a stacking classifier based on a meta-classifier, which combines multiple classification models, is introduced. The proposed method achieves an accuracy approximately 6% higher with lower variations over 5 cross folds, exhibiting superior performance compared to other approaches.
Deep Semantic Segmentation of Echocardiographic Images using Vision Transformers

Deep learning (DL) methods have revolutionized image segmentation by providing tools to automatically identify structures within images, with high levels of accuracy. In particular, Convolutional Neural Networks (CNN), such as the U-Net and its variants, have achieved remarkable results in many segmentation tasks. Only recently, Vision Transformer (ViT)-based models have emerged and in some cases demonstrated to outperform CNNs in semantic segmentation tasks. However, transformers typically require larger amounts of data for training as compared to CNNs. This can result in a significant drawback given the time-consuming nature of collecting and annotating data, especially in a clinical setting. In particular, only a few studies involved the application of ViT networks for ultrasound image segmentation.In this study, we propose one of the earliest applications of ViT-based architectures for segmenting the left heart chambers in 2D echocardiographic images. Indeed, the identification of cardiac structures, like e.g. heart chambers, can be used to derive relevant quantitative parameters, such as atrial and ventricular volumes, the ejection fraction, etc.We trained and tested several ViT models using the publicly available CAMUS dataset, composed by cardiac image sequences from 500 patients, with the corresponding mask labels of the left ventricle and left atrium. ViT networks performances were then compared to an implementation of the U-Net. We demonstrate how, on this type of data, recent ViT variants can reach and even outperform CNNs, despite the limited data availability.
Hybrid Integration of Active III-V on Standard SOI Photonic Platform Based with GACC Methods

The hybrid integrated photonic platform with grating-assisted codirectional coupler (GACC) is proposed to improve light coupling from an active III-V device to a standard SOI waveguide. The coupling efficiency could achieve 90% even when the III-V and SOI chips are bonded with a 100-nm thick adhesive layer.
neuroAIx: FPGA Cluster for Reproducible and Accelerated Neuroscience Simulations of SNNs

The complex mechanisms and impressive learning capabilities of the human brain inspire the search for ever more efficient neural network models. Investigating the specific nature of biological neural networks in accelerated simulations with hundreds of millions of synapses, however, pushes even the most sophisticated systems to their limits. At the same time, the inherent chaotic nature of spiking networks and their typically distributed simulation complicates reproducibility, which is a major concern in both research and engineering. With HPC systems incrementally surpassing biological real-time in the simulation of neuroscience networks of significant size and complexity, novel computing architectures are required to reach disruptive speed-ups while assuring determinism and providing flexibility for continuous adaptations to new insights from neuroscience.To this end, we developed the neuroAIx FPGA cluster, based on off-the-shelf AMD/Xilinx FPGAs. It integrates a proprietary ultra-low-latency communication architecture and a custom short-range synchronization algorithm that brings worst-case source-to-destination spike latency down to 2 microseconds. Together with a latency-hiding memory-prefetch procedure, it enables deterministic simulations of the widely-used cortical microcircuit model, with nearly 80k neurons and 300M synapses, with an acceleration factor of 20x compared to biological real-time. This surpasses all existing systems, outperforming non-FPGA platforms by 10x, while providing the flexibility needed to explore neuro-inspired algorithms in the future.
An AI-Enhanced Process Mining Framework for Software Process Insights

Using process mining to gain insight of software processes presents several challenges, especially in preparing good-quality input data for the mining algorithm from the low-level data recorded by non-process-aware systems, such as version control systems. This paper proposes a process mining framework integrating AI techniques to enhance and simplify the execution of process mining tasks in this context. First, we implement a learning process to build an ontology capturing the knowledge of the analyzed processes' domain. Then this domain ontology is used in constructing an event log that enables an effective process discovery and analysis. The domain ontology plays the role of a domain expert in matching low-level events to high-abstraction and meaningful process activities. We implemented and experienced the proposed framework to extract process knowledge of e-commerce web applications development.
Detection of Parkinson's Disease Using Machine Learning: A Case Study

Detecting Parkinson's disease is a complex task that comprises of examining different aspects of the body, mind, and the nervous system. But researchers have discovered a new way to use speech metadata to diagnose people with Parkinson's disease by extracting features from people's voices. In this paper, we have applied Naive Bayes, Logistic Regression, Gradient Boosting, Random Forest, Multilayer Perceptron and Stacked Classifiers to evaluate different learning models for detection and the Stacked Classifier and Gradient Boosted models reached the highest accuracy with 89.47% and 88.15%, respectively. The multilayer perceptron model has the lowest accuracy with 84.21%. Therefore, the Stacking Classifier and Gradient Boosting models were found to be optimal for similar data. This study provides insight into the selection of an appropriate machine learning models for accurate prediction.
Fabrication of Superhydrophobic Carbon Fiber via Laser Interference

This paper proposes a method of fabricating micro/nanostructures on stainless steel surfaces using direct laser interference lithography, followed by template hot pressing. This method leaves micro/nanometric structures on carbon fibers, which, after treatment with fluorosilane, can construct robust carbon fibers with excellent mechanical properties and superhydrophobic performance on a stainless steel substrate. The novelty of this method lies in the simplicity of the preparation process and the powerful superhydrophobic capability of the carbon fibers, which can be produced on a large scale. This solves the issues of high preparation costs, complex processes, inherent performance degradation, and difficulties in mass production inherent in traditional fabrication techniques. The results demonstrate that the superhydrophobic capability of the carbon fibers prepared using this method is significantly improved compared to untreated surfaces, with contact angles reaching up to 164°
Using Autoencoders to Visualize Big Environmental Audio

Traditional ecological survey methods are expensive and time-consuming as they typically involve a domain expert in the field. As a result of this, continuous audio recordings are playing an ever more critical role in conservation and biodiversity monitoring. However, listening to these recordings is often infeasible, as they can be thousands of hours long. The knowledge of domain experts can be efficiently leveraged using visualization. Traditionally, spectrograms are used to visualize audio data. However, there is a limit to the duration of audio that can fit on a computer screen as a spectrogram. Several techniques have been adapted to overcome this. Each of these techniques has its own set of advantages and disadvantages that make it ideal for some situations but unsuitable for others. In this paper, we propose a novel visualization based on embeddings produced by Frequency Preserving Autoencoders. We evaluate by visually comparing with traditional spectrogram and spectral index false-color spectrogram, using human-generated annotations as a baseline. We find that calls from some species are more consistently visible in our autoencoder-based visualization, while others are more clearly visible in the alternative visualizations. This novel visualization presents a new opportunity to assist ecologists in identifying bird calls in long-duration audio, if calls are difficult to identify using other methods.
SIDVis: Designing Visual Interactive System for Analyzing Suicide Ideation Detection

Suicide is a critical global issue that demands a comprehensive examination of factors such as mental illness, substance abuse, financial stress, and trauma. Effectively identifying individuals at risk is vital for intervention and prevention efforts. However, distinguishing suicidal ideation (SID) from non-suicidal language poses challenges. Existing research has addressed this issue, but limited attention has been given to visually interpretable and interactive systems tailored for SID. This study contributes to responsible AI by leveraging deep learning and machine learning techniques to enhance SID detection, enabling proactive interventions and support. In this paper, we introduce SIDVis, an interactive visualization system that improves performance and interpretability at the same time. The rigorous evaluation demonstrates that SIDVis not only outperforms existing methods in terms of accuracy but also provides an explanation for the responsible use of the underlying AI approach, demonstrating its potential to improve SID detection and intervention strategies.
Google Trends™ Dynamics for the Guidance of Open-Source Intelligence: Augmentation of Social-Media and Survey Surveillance of Population Mental Health

The unfolding of the COVID-19 outbreak was an unprecedented and unanticipated opportunity to understand how a sudden global shock modulates people's online searches when seeking information about their emotional well-being. It has also illustrated how public health surveillance systems were essential for tracking diseases' spatial and temporal dynamics and shaping rapid public policy changes. The present paper validates a data mining and processing framework which examines how digital epidemiology and machine learning reveal trends in human mental health and psychological distress expression variability. We present results obtained in two research exploring the relationship between Google Trends time-series in the digital surveillance of search engines during the pandemic and a selection of social media feeds and official UK well-being surveys. The generated body of evidence shows how data science can provide robust, finely grained, and replicable evidence on mental health measures at the population level. In the future, the digital surveillance analytics validated here can be rapidly deployed for crisis management and allow early detection of distress signals to better manage communication and policy action at population level.
Measurement and Analysis of Longitudinal and Transversal Effective Piezoelectric Coefficients (d33,f and e31,f) in 100 nm-500 nm Sc0.3Al0.7N films

High-quality piezoelectric ScAlN thin films with thickness under 500 nm are essential for achieving acoustic filters for 5G and 6G communications. In this work, 8-inch and 30% Sc doped AlN thin films with thickness of 100 nm, 300 nm, and 500 nm have been deposited by single target sputtering. The wafer-level piezoelectric coefficients, including d33,f and e31,f, have been characterized to evaluate the film quality and uniformity of the films. The mean values of the d33,f are 6.07 pm/V, 6.08 pm/V and 6.79 pm/V for 100 nm, 300 nm, and 500 nm films. The mean values of the e31,f are -1.16 C/m2, -0.99 C/m2, and -1.23 C/m2 for 100 nm, 300 nm, and 500 nm films. The patterns of the wafer map are discussed with more film characterization results and the possible reasons leading to the observed distribution are further discussed.
Comparison of Effectiveness of Heart Rate Variability Indices in Differentiation of Atrial Fibrillation from Sinus Rhythm

Recently, various heart rate variability (HRV) indices have been used to differentiate normal sinus rhythm (SR) from atrial fibrillation (AF). This study compared the effectiveness of different HRV indices for detecting AF in 63,140 segments of 60-second electrocardiograms (ECGs). Exactly 31,627 SR and 31,513 AF 60-second ECGs were analyzed. Based on the receiver operating characteristic (ROC) analysis, we identified optimal cutoff values of HRV indices using Youden's criterion. Next, for each HRV index a simple threshold classifier was used to classify ECG segments as AF or SR by comparing the value of the HRV index with the cutoff value. The HRV indices were then compared for their diagnostic properties using several classification measures, including sensitivity, specificity, and accuracy. The groups of HRV parameters that demonstrated the most effective diagnostic properties were pRRx (percentage of differences between successive RR intervals greater than or equal to x ms), and pRRx% (percentage of relative RR interval differences greater than or equal to x%). The best area under curve (0.972), accuracy (95.3%) and diagnostic odds ratio (483) were found for pRR4%. This study highlights the potential of these HRV indices in detecting AF and could have significant implications for clinical practice.
A Wireless Body Sensor Network for Cardiorespiratory Monitoring During Cycling

In this work, a completely wearable Wireless Body Sensor Network (WBSN) for cardiorespiratory monitoring is presented. The WBSN consists of three Inertial Measurement Units (IMUs) for respiratory signal detection and a pulse oximeter for pulse rate measurements. The devices communicate using the ANT protocol, with a smartphone serving as the master node. The system was preliminarily validated against gold standard measurements using an experimental protocol involving cycling exercises. The results showed good agreement between the IMU-based system and the gold standard methods for both respiratory rate (RR) and pulse rate (PR) measurements, i.e., the COSMED K5 metabolic cart and a Garmin chest strap, respectively. The RR estimation had a mean difference of −0.85, with limits of agreement of [−6.76; +5.06]. The PR estimation had a mean difference of 1.80, with limits of agreement of [−8.80; +10.96]. The ad hoc filtering algorithm used for RR estimation based on the IMU data contributed to the accuracy of the system. While the validation results were positive, further studies with larger sample sizes and diverse activities are needed to confirm the validity of the system.
Photoacoustic imaging of vascular hyperpermeability

Background: Vascular hyperpermeability (VHP), an abnormal increase in the leakiness of the blood vessels, is common in many diseases and disorders, including cancer, infections, and inflammation, among others. This study aims to develop a real-time, non-ionizing photoacoustic method for imaging and quantitative analysis of vascular hyperpermeability. Toward this goal, we investigate the use of Evans blue (EB) as a marker of VHP. The hypothesis is that photoacoustic imaging (PAI) of EB's high-molecular-weight complex with serum albumin can allow a non-invasive, robust, and specific analysis of VHP.Methods: The studies were performed in mice with hepatocellular carcinoma implanted on both legs. Each animal acted as its control (microbubbles only), where ultrasound treatment induced hyperpermeability in one leg. PAI of HCC was performed before and after inducing hyperpermeability with ultrasound. The treatments included ultrasound alone and antivascular ultrasound (AVUS), combining ultrasound and microbubbles. The PA signal was unmixed for EB. The change in EB signal (ΔPA) induced by AVUS was compared with the treatment with ultrasound alone and a control group with no treatment. The image-derived PA measurements were compared with the Miles assay and histology performed on excised tumors.Results: PAI of the control group showed no difference in ΔPA between treated and untreated tumors: 0.14 ± 0.04 au versus 0.15 ± 0.07 au. The ultrasound-only group showed a small increase in ΔPA between the treated and the untreated groups: 0.14 ± 0.04 versus 0.18 ± 0.05. In contrast, the AVUS group showed a marked increase in ΔPA between the untreated and the treated groups: 0.18 ± 0.02 au versus 0.60 ± 0.19 au. EB concentration measured by Miles assay was highest in the AVUS group: 141 ± 48 ng/mg tissue compared to 61 ± 16 ng/mg tissue and 58 ± 24 ng/mg tissue in the ultrasound-only and the control group. Histology showed vascular dilation, vascular disruption, and extravasation of EB in the AVUS-treated group.Conclusion: PAI is a promising platform for imaging and quantitative assessment of VHP. Future studies observing a temporal change in EB signal could provide a platform for quantitative permeability assessment in various diseases.
Sensing Probe Development for Pulse Wave and Blood Pressure Detection using Fiber Optic Based Fabry-Perot Interferometer

A low-cost sensing probe based on fiber optic Fabry-Perot interferometer (FFPI) was designed and developed for pulse wave and blood pressure measurements. The FFPI system was comprised of the light source, sensing probe, and photodetector. To develop the sensing probe, four reflective materials and also three elastic materials were demonstrated to measure the reflectivity and elasticity, respectively. Furthermore, the probe was tested on the fingertip and neck to obtain the interference fringes with five times of repeatability. Besides, the peak detection and fringe counting techniques were employed to demodulate interference fringes into pulse waves. The results revealed that Aluminum-coated mirror provided the highest reflectivity, while a latex rubber balloon, the most elastic material, had a percentage of elongation of 199.25 mm and a Young's modulus of 2.85 MPa. To conclude, the sensing probe had the ability to detect heart pulses, paving the way for an affordable, painless, and user-friendly instrument.
SwinIR for Photoacoustic Computed Tomography Artifact Reduction

Photoacoustic (PA) imaging is an emerging hybrid medical imaging modality involving the optical excitation of chromophores - light-sensitive molecules like hemoglobin and lipids, to infer underlying vascular structure. Supplying them energy in the form of pulsed laser results in rapid successive thermoelastic expansion and contraction, resulting in the generation of ultrasound, which can then be measured using transducer arrays. Raw sensor data is represented in k-Space from which the Cartesian equivalent is reconstructed using rule-based algorithms. These reconstructions tend to be noisy and have artifacts, but the recent widespread adoption of deep learning has facilitated the post-processing of reconstructions to significantly improve them. UNet, in particular, has had a far-reaching impact on the medical imaging domain, and PA imaging has been no exception, seeing a myriad of solutions based on it. In this paper, we investigate the efficacy of replacing convolution-based feature generation for post-processing PA reconstructions with a Vision Transformer-based (ViT) approach owing to its recent success in computer vision. Specifically, we examine the ability of Shifted Window (Swin) ViTs to restore an artifact-free vascular image from an artifact-heavy image reconstructed using the time-reversal algorithm.
Energy-Efficient Computing Offloading Policy for MEC-assisted Power Sensor Network: A D3QN Approach

With the deep and close integration of massive renewable and controllable terminals into the next generation smart power grid, the amount of data and various applications with different quality requirements will increase greatly, which will be tough challenges for the power sensor network to provide real-time data collecting and processing, global monitoring, and quick response, etc. Meanwhile, due to the limitation in energy supplement and computing capacity, it is a second issue for the sensor network how to work in an energy-efficient way. In this paper, our work sets out to study a reinforcement learning (RL) based computing offloading policy in the mobile edge computing (MEC) assisted power sensor network. By jointly optimizing task scheduling and resource allocation, we propose a new scheme to optimize data latency and energy efficiency. The optimization problem is modeled as a Markov decision process (MDP), and Dueling Double Deep Q Network (D3QN) algorithm is employed to solve this MDP problem. Simulation results demonstrate the effectiveness of the proposed algorithm and its outperformance in reduction of data latency and energy consumption as well as convergence and stability, compared with other benchmarks.
Innovations in Urban Automation: Robotic Arm Integration in Smart City Environments

Automated self operating Fore limb helps to react like the human's arm. It enables perform multiple function just like human's hand. The rapid advancement of robotic arm technologies has introduced a new era of possibilities for their integration into various domains of smart city development. Robotic arms have the potential to revolutionize critical aspects of urban living, including waste management, infrastructure maintenance, public safety, and mobility. This paper presents a comprehensive review of the applications, benefits, challenges, and implications of incorporating robotic arms in smart city development. The paper explores the diverse range of tasks that robotic arms can undertake within a smart city context, showcasing their versatility and potential impact on efficiency, safety, and sustainability. It delves into the regulatory, ethical, and social considerations associated with the widespread adoption of robotic arms, addressing concerns such as privacy, job displacement, and equity.