Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Residual Network Architecture on Covid-19 Chest x-ray Classification

Convolutional Neural Network (CNN) has proven with good performance in the area of feature extraction. Classification of medical images is often faced with the lack of sufficient amounts of data. Therefore, Transfer Learning can be applied to overcome these problems. Chest x-ray data are complex and require deeper layers for specific features. Resnet built with deep layers specifically focuses on problems that often occur in high-depth architectures, which are prone to decreased accuracy and training errors. Some of the aspects are able to affect the performance of the model such as the depth of convolution layers and training procedures, which include data splitting technique and Optimizers. In this study, the Hold Out data splitting and k-fold cross validation of 5 folds with Optimizer Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD) on the Resnet-50 and Resnet-101 architectures. The training procedure was applied to 15143 Chest x-ray images measuring 224x224 pixels with parameters epoch 50 and batch size 100. The best value was obtained using k-fold cross validation on Resnet-50 using the SGD optimizer with 99% accuracy.
Constraints on computational models of auditory scene analysis, as derived from human perception

Auditory scene analysis (ASA) groups the sensory evidence derived from a mixture of sound sources and derives separate descriptions of the individual sources. There are two possible ways of doing this:(a) using knowledge of the properties of certain classes of sounds, such as speech, in a top-down fashion;(b) using, in a bottom-up manner, those properties in the input (such as harmonic relations) that are typically found when two or more sound sources (e.g., voice and footsteps) affect the input at the same time. Some properties of the ASA done by humans are discussed and proposed as specifications for a computational model: the adding up of evidence, the coherence of the ASA system, the combining of top-down and bottom-up constraints, as illustrated in the duplex perception of speech, and the propagation of influences across the auditory field.
Multi-model Comparison of Cardiac Segmentation Model for Angiocardiography by Deep Learning

Angiocardiography is a key to the evaluation of cardiac function. The index of left ventricular ejection fraction (LVEF) also helps interpret cardiac hypertrophy, valve regurgitation, and regional myocardial systolic dysfunction for medical decision-making. However, it is difficult and time-consuming to calculate the LVEF. Therefore imaging techniques become much more important for assistance. In this research, three neural networks including DenseNet, EfficientNet, and ResNet are introduced for cardiac area calculation. EfficientNet turns out to be the best model with a mean dice accuracy of 0.91. The other two models also show significant performance with 0.958 for DenseNet and 0.955 for ResNet. With these network models, physicians can select and calculate the area of the heart. With the time-series images of cardiac catheterization, the size of the entire cardiac systole can be determined automatically. Therefore, it greatly helps medical professionals diagnose abnormal cardiac systole problems immediately.
Global Convergence of Noisy Gradient Descent

Noise plays an important role in the gradient-based optimization methods, and a series of numerical experiments have demonstrated that adding gradient noise improves learning for neural networks. However, the mathematical interpretation of the noise remains a challenge. In this paper, we show that, the noise variation can be regarded as a smoothing factor, and we prove that, under certain conditions, a noisy gradient descent (NG) enjoys linear global convergence in expectation sense. We contribute to this problem by introducing an intermediate which connect the NG method to the smoothed function. On the one hand, this connection reveals that applying the NG method to a function is the same as applying the gradient method to the corresponding function smoothed by the noise; and on the other hand, it allows us to establish the convergence behavior of the NG in a global sense. Moreover, we also consider what conditions make the global minimizer of the smoothed function not far from the original global minimizer.
Control Strategy of Isolated Bi-Directional AC/DC Converter Controlled by Single Phase Shift Control

A single-phase-shift control (SPS) plus variable-frequency control (VF) control strategy for isolated bidirectional AC/DC converters is proposed. The isolated bidirectional AC/DC converter topology consists of a dual active bridge (DAB) and a synchronous rectifier (SR). The proposed strategy converts the non-linear relationship between shift compared to output power in single phase shift control into a linear one by means of frequency control, making the control simple. The transfer function of the control is obtained by modelling the DAB small signal system and the optimal control parameters are obtained after frequency domain analysis. At the same time, the control method can effectively reduce the switching losses and the smaller value of the intermediate capacitor parameter reduces the size of the converter. The control principle of this isolated bi-directional AC/DC converter is described and analyzed in detail, and finally the correctness and effectiveness of the proposed control strategy is verified by simulation.
A Two-Stage Pricing Study of Product Line Considering Value-Added Services

With the advancement of society and technology, consumers are becoming more personalized and more willing to buy new products. To meet the diverse needs of consumers, the design and development of product lines have become an important strategic issue of enterprises. Based on the consumer choice model, this paper aims at the design and development of product lines and the purchase behavior of consumers. A two-stage pricing model is constructed under the condition of bundled sales of products and services. This paper analyzes the impact that enterprises should consider products with value-added services and consumers’ purchasing behavior on product line two-stage pricing. Research shows that the level of product value-added services and the degree of enterprise strategy will have an impact on the price of the product line and the enterprise’s profit. When the service level is higher, the enterprise’s product line price and profit will increase, and when the enterprise discount higher, the enterprise’s total profit and product line price will decrease.
Security and Privacy Issue in Internet of Things, Smart Building System: A Review

The Internet of Things is making an impact in a variety of fields, including healthcare, e-government, smart grid, smart farming, smart building, transportation, and so on. The evolution of the Internet of Things is also directly proportional to security and privacy concerns. Security and privacy issues in the Internet of Things world have been a hot topic among researchers in latest days. The Internet of Things' broader and faster development allows for more significant opportunities for security issues. Starting with attacks on IoT applications themselves, and progressing to attacks on user data, specifically on smart building application users. The study examined security and privacy issues in IoT devices using literature reviews. The review paper method was used by the researchers to complete the study using previous literature. The results of this study show that in its application, by connecting and integrating systems, the level of attacks and vulnerabilities will increase, so that in the future, there is a need for ways that can reduce these risks, one of which is by providing service systems that have high levels of security capabilities.
Toward Detection of Exoplanetary Rings via Transit Photometry: Methodology and a Possible Candidate

The detection of a planetary ring of exoplanets remains one of the most attractive, but challenging, goals in the field of exoplanetary science. We present a methodology that implements a systematic search for exoplanetary rings via transit photometry of long-period planets. This methodology relies on a precise integration scheme that we develop to compute a transit light curve of a ringed planet. We apply the methodology to 89 long-period planet candidates from the Kepler data so as to estimate, and/or set upper limits on, the parameters of possible rings. While the majority of our samples do not have sufficient signal-to-noise ratios (S/Ns) to place meaningful constraints on ring parameters, we find that six systems with higher S/Ns are inconsistent with the presence of a ring larger than 1.5 times the planetary radius, assuming a grazing orbit and a tilted ring. Furthermore, we identify five preliminary candidate systems whose light curves exhibit ring-like features. After removing four false positives due to the contamination from nearby stars, we identify KIC 10403228 as a reasonable candidate for a ringed planet. A systematic parameter fit of its light curve with a ringed planet model indicates two possible solutions corresponding to a Saturn-like planet with a tilted ring. There also remain two other possible scenarios accounting for the data; a circumstellar disk and a hierarchical triple. Due to large uncertain factors, we cannot choose one specific model among the three.
Analytic few-photon scattering in waveguide QED

We develop an approach to light-matter coupling in waveguide QED based upon scattering amplitudes evaluated via Dyson series. For optical states containing more than single photons, terms in this series become increasingly complex, and we provide a diagrammatic recipe for their evaluation, which is capable of yielding analytic results. Our method fully specifies a combined emitter-optical state that permits investigation of light-matter entanglement generation protocols. We use our expressions to study two-photon scattering from a Λ -system and find that the pole structure of the transition amplitude is dramatically altered as the two ground states are tuned from degeneracy.
Learning Hierarchical Traversability Representations for Efficient Multi-Resolution Path Planning*

Path planning on grid-based obstacle maps is an essential and much-studied problem with applications in robotics and autonomy. Traditionally, in the AI community, heuristic search methods (e.g., based on Dijkstra, $\text{A}^{*}$, or random trees) are used to solve this problem. This search, however, incurs a high computational cost that grows with the size and resolution of the obstacle grid and has to be mitigated with effective heuristics to allow path planning in real-time. This work introduces a learning framework using a deep neural network with a stackable convolution kernel to establish a hierarchy of directional traversability representations with decreasing resolution that can serve as an efficient heuristic to guide a multi-resolution path planner. This path planner finds paths efficiently, starting on the lowest resolution traversability representation and then refining the path incrementally through the hierarchy until it addresses the original obstacle constraints. We demonstrate the benefits and applicability of this approach on datasets of maps created to represent both indoor and outdoor environments to represent different real-world applications. The conducted experiments show that our method can accelerate path planning by 40% in indoor environments and 65% in outdoor environments compared to the same heuristic search method applied to the original obstacle map, which demonstrates the effectiveness of this method.
Event-Triggered Quantized Kernel Least Mean Square Algorithm

The classical quantized kernel least mean square (QKLMS) algorithm curbs the linear growth of network structure effectively. However, the filtering accuracy of QKLMS highly depends on the predefined quantization threshold. To improve the filtering accuracy dynamically, an event-triggered mechanism is therefore designed to adjust the quantization threshold adaptively. The designed trigger updates the quantization threshold by monitoring the convergence rate of the learning curve. Applying the event-triggered mechanism into QKLM-S generates a novel event-triggered QKLMS (ETQKLMS) algorithm. In addition, the convergence analysis of quantization threshold is carried out. Examples of Chua's chaotic time series prediction is presented to verify the effectiveness of ETQKLMS.
Image Generation Network Model based on Principal Component Analysis

In the field of Artificial Intelligence, a large and densely annotated dataset is required for training making it a time and resource-expensive task. In this paper, we propose an image generation network model that keeps the training examples at a minimal level. The proposed model gives additional feature maps to the input value (latent space) of the DCGAN model, which is an adversarial image generation model using a convolutional neural network. To solve the problem that the neural network model cannot generate clear images in case of lack of training data, one additional feature map was added to the input value of the generation model, latent space. The feature map was extracted from 2,000 images of the CelebA dataset consisting of human face images through principal component analysis. We used 3,838 Large-Age-Gap datasets and one feature image for training. Compare to the previous model which uses 200,000 images, the proposed model generates more natural facial images with only 3,829 examples and the error rate is significantly reduced than the previous model at the beginning of the model training.
Brain Cell Segmentation and Detection from the LIVECell Dataset Using Deep Learning with the EfficientDet Model

Neurological disorders are a common cause of death and disability in the world. One of the difficulties is that the response to treatment is hard to evaluate and quantify. Neuronal cells could be reviewed using light microscopy modality. However, it is challenging and time-consumption for detecting and segmenting even with experts. Therefore, accurate instance segmentation of cells is crucial. With the advancement of computer vision and deep learning, the detection and segmentation of cells with the aid of computers might lead to the resolution of these issues. This article presents methodological segmentation and brain cell markers derived from the LIVECell dataset. LIVECell dataset is a huge, high-quality, hand-annotated, skillfully constructed dataset including 1,686,352 individual cells in a variety of cell formats and density cultures. The proposed training model using the EfficientDet model is a model that can perform segmentation training and mark individual cell units by setting bounding boxes, which makes optimal use of LIVECell data without the need to use the single modeling two for two tasks. As a result, the intersection over union (IoU) is between 50.0% and 80.0%, with a mean accuracy precision(AP) of 58.824% and an average false-negative ratio (AFNR) of 56.566%, relying on the bounds segment bound box to mark separate objects with IoU values. This result may open up future functional studies on the individual kernel differences in performance and architecture of neural networks for segmentation implementation.
Classification of Motion Sickness Levels using Multimodal Biosignals in Real Driving Conditions

Motion sickness is an unpleasant physiological response to situations involving the perception of motion. Research on motion sickness focuses on its manifestation by analyzing biosignals to observe physiological changes coinciding with the perception of motion sickness. Meanwhile, multimodal data fusion has gained attention for its ability to reflect the multimodality of real-life tasks and enhance the robustness of machine learning models. In this study, we aimed to find a deep learning-based multimodal framework for integrative analysis of multiple biosignals with the highest performance in classifying the level of carsickness. To do so, we first generated a dataset consisting of five different types of biosignals collected under real driving conditions: electroencephalogram (EEG), electrocardiogram (ECG), respiration (RESP), photoplethysmogram (PPG), and galvanic skin response (GSR). Then, we compared six deep learning-based unimodal classification models which have shown competency in signal classification. Lastly, we compared four different fusion methods for multimodal classification frameworks using either all five biosignals or three signals, which include RESP, ECG, and PPG. As a result, we found out that the fusion method combining self-attention and the tensor fusion network outperformed other unimodal and multimodal models with categorical accuracy of 76.26 % regardless of the number of biosignals used.
Learning to measure resistance noise demystifies the ubiquitous 1/f excess noise

To study resistance noise (DeltaR) by a spectrum analyzer we must convert this noise into a noise voltage (DeltaV) at the reach of such generalized voltmeter. Whenever a current Iconv is set in a resistor to convert its resistance noise into noise voltage by Ohm's Law: DeltaV=DeltaR*Iconv, the converted noise thus obtained does not track DeltaRte (its resistance noise in Thermal Equilibrium, TE) but DeltaR, that is: a resistance noise out of TE due to Iconv itself. Thus, backgating noises in the channel of resistors (i. e. Field-Induced Resistance Noise, FIRN) found by this method always are noises out of TE. The way the Lorentzian DeltaRte of a resistor is converted by Iconv into nine decades of resistance noise DeltaR with 1/f spectrum is the lesson we give on this unexpected spectral change that we could express as: "To measure is to disturb, particularly in resistance noise measurements".
Acoustic and aerodynamic characteristics of ejectives in Amharic

This paper invetsigates the main phonetic characteristics that distinguishes ejectives from pulmonic sounds in Amharic. In this language, there are five ejectives that can be phonemically singleton or geminate. Duration measurements have been made in intervocalic position for pulmonic stops and for each type of ejective, taking into account the overall duration and VOT. Results show that ejective stops have a higher amplitude burst than pulmonic stops. The duration of the noise is shorter for ejective fricatives compared to pulmonic fricatives. At the end of ejective fricatives, there is a 30‐ms glottal lag that is not present in pulmonic fricatives. Geminate ejectives are realized by delaying the elevation of the larynx. This can be observed on the spectrographic data by an increase of the noise at the end of the geminate ejectives. Aerodynamic data have been collected in synchronization with the acoustic recordings. The main observations are that pharyngeal pressures values are much higher than what is .
Denoising Autoencoder and Weight Initialization of CNN Model for ERP Classification

Brain-Computer Interface (BCI) systems have a great impact on improving people’s lives. One of the popular BCI implementations is the Event-Related Potential (ERP)-based spelling system which decodes electroencephalogram (EEG) signals to identify a target character. The effectiveness of BCI systems highly depends on the single trial decoding accuracy; however, the EEG signals are contaminated with diverse artifacts which leads to a poor signal-to-noise ratio. Therefore, various filtering algorithms (e.g., FFT, CSP, Laplacian, PCA) have been applied to find the optimal subset of feature spaces in the temporal and spatial domains. These preprocessing steps could efficiently discard the artifacts and have shown superior performance with typical linear classifiers. However, there is a risk that the informative subspace can be also eliminated by the unsupervised learning process, and this algorithm is not proper to be employed in the end-to-end deep-learning architectures where all modules are differentiable. This study aims to propose a generalized deep neural network model by denoising the ERP signals and initializing the Convolutional Neural Network (CNN) model parameters based on the autoencoder. Proposed CNN models indicate - 98.2% spelling performance and - 91.5% single trial accuracy which outperformed the state-of-the-art CNN models.
Managing Inconsistency With an Optimal Distribution of Information Granularity in Fuzzy Preference Relations

In decision-making with fuzzy preference relations, pairwise comparisons among all the attributes or alternatives in question are done by decision-makers to generate a matrix. The validity and reliability of the decision made is ensured if the consistency of this matrix is high, which usually requires alterations of the entries of the matrix. This research introduces a new consistency improvement procedure by allowing an information granularity level in the decision-making process. Concretely, to improve the consistency, it invokes a process of an optimal information granularity distribution across the related alterations of the entries of the matrix. To demonstrate its effectiveness, we complete some detailed experiments.
Deep Generative Networks for Algorithm Development in Implantable Neural Technology

Electrical stimulation of deep brain structures is an established therapy for drug-resistant focal epilepsy. The emerging implantable neural sensing and stimulating (INSS) technology enables simultaneous delivery of chronic deep brain stimulation (DBS) and recording of electrical brain activity from deep brain structures while patients live in their home environment. Long-term intracranial electroencephalography (iEEG) iEEG signals recorded by INSS devices represent an opportunity to investigate brain neurophysiology and how DBS affects neural circuits. However, novel algorithms and data processing pipelines need to be developed to facilitate research of these long-term iEEG signals. Early-stage analytical infrastructure development for INSS applications can be limited by lacking iEEG data that might not always be available. Here, we investigate the feasibility of utilizing the Deep Generative Adversarial Network (DCGAN) for synthetic iEEG data generation. We trained DCGAN using 3-second iEEG segments and validated synthetic iEEG usability by training a classification model, using synthetic iEEG only and providing a good classification performance on unseen real iEEG with an F1 score 0.849. Subsequently, we demonstrated the feasibility of utilizing the synthetic iEEG in the INSS application development by training a deep learning network for DBS artifact removal using synthetic data only and demonstrated the performance on real iEEG signals. The presented strategy of on-demand generating synthetic iEEG will benefit early-stage algorithm development for INSS applications.
Multi-scale adaptive learning network with double connection mechanism for super-resolution on agricultural pest images

Accurate recognition of pests can effectively reduce the negative impact of pests on the agricultural economy. The unprofessional shooting ways result in low-resolution images, which seriously influences the accuracy of pest recognition. The super-resolution reconstruction method can transform low-resolution images into high-resolution images. The existing super-resolution reconstruction networks have the problems such as feature missing, low feature utilization, and a large amount of computation. The multi-scale adaptive learning network (MALNet) proposed in this paper is based on residual and dense connections and consists of four modules: rear-compensation enlargement module (RCEM), low-frequency feature building module (LFFBM), multi-scale details representation module (MsDRM), and feature channels correction module (FCCM). First, the low-frequency contour feature is built by LFFBM, and MsDRM extracts local detail features of the pest. After that, FCCM enhances important features, being fused with the enlarged feature map generated by RCEM. The experimental results show that, compared with other reconstruction networks, MALNet achieves an average improvement of 0.26 dB and 0.006 in peak signal- to-noise ratio and structural similarity.