Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross Organ Bridge Transfer Learning for Lung Cancer Detection

Classification of medical CT scan images is a challenging task that requires high accuracy and sensitivity. In this paper, we propose a method based on VGG 19 and transfer learning for the classification of normal and tumor of lungs from CT scan images. A popular deep convolutional neural network for image identification applications is VGG19. VGG 19 is used as a feature extractor and the last layers are fine-tuned to adapt to our task. We also use transfer learning to leverage the knowledge learned from other datasets, such as chest CT images for different types of lung cancer, viz. adenocarcinoma, basal cell carcinoma, squamous cell carcinoma and kidney dataset. We use a public dataset to assess our methodology. Our findings demonstrate the great accuracy and sensitivity of our approach. Using CT scan pictures, our technique may be helpful for the early identification and diagnosis of lung cancer. In the area of medical imaging, convolutional neural networks (CNNs) in particular have shown promising results in picture categorization tasks. Deep learning techniques, however, face difficulties because of the medical domain's lack of appropriate labelled data. In our study, we provide a novel transfer learning-based approach to medical picture categorization. By using a bridged database created using the same medical imaging modality as the target database, we utilize modality-bridge transfer learning to overcome the domain imbalance between the source and target datasets. Even with a small number of labelled target medical pictures, we are able to obtain good classification accuracy by learning from the source to the bridge and then from the bridge to the target. In order to extract deep characteristics and provide highly accurate findings, our research uses VGG19, a popular deep convolutional neural network, to improve the classification performance of pictures of lung cancer.
Is There a Positive Relationship between Stock Market Volatility and the Equity Premium?

This paper investigates whether evidence for a positive relationship between stock market volatility and the equity premium is more decisive when the volatility feedback effects of large and persistent changes in market volatility are taken into account. The analysis has two components. First, a log- linear present value framework is employed to derive a formal model of volatility feedback under the assumption of Markov-switching market volatility. Second, the model is estimated for a variety of assumptions about information available to economic agents. The empirical results suggest the existence of a negative and significant volatility feedback effect, supporting a positive relationship between stock market volatility and the equity premium.
Review of the male genital lichen sclerosus and urethral involvement

Lichen sclerosus (LS) previously known as balanitis xerotica obliterans, is a chronic, progressive lymphocyte mediated skin disease. It shows a predilection for the male genital area and may involve in external urinary meatus, penile urethra and even bulbar urethra causing stricture. Male genital lichen sclerosus (MGLSc) may lead to a significant impairment of urinary and sexual function; and it has also been associated with a risk of malignant transformation. The epidemiology, natural history, histological features and possible aetiology of this disease have been researched in recent years. The direct relationship between MGLSc and penile squamous cell carcinoma (SCC) remains to be established. Biopsy of the initial lesion for definitive diagnosis and long-term follow up of affected patients are well established. The aetiology of the disease is incompletely characterized to date. The goals of the treatment are to alleviate symptoms and discomfort, prevent anatomical changes such as urethral stricture and prevent malignant transformation. Topical corticosteroid and circumcision can be curative if the disease is treated early when still localized. Severe MGLSc and urethral involvement may require extensive treatment. Various surgical procedures like simple meatotomy, staged urethroplasty using oral mucosa and complicated urethral reconstruction have been widely used depending on the length and severity of urethral stricture. Biopsy and long-term surveillance are recommended. Further research is needed to improve the prevention, understanding and treatment of this challenging condition.
An Optimized Approach for Tumor Detection using Co-Occurrence Matrix and Back Propagation Neural Network

Brain tumours are a significant contributor to mortality rates in the population. The early detection and accurate classification of a tumour can significantly enhance the likelihood of survival. Magnetic resonance imaging (MRI) is presently an essential diagnostic modality in the investigation of the human brain. A brain tumour is characterized as an anomalous proliferation of cells within the brain or the central spinal canal. The present paper introduces the Backpropagation network as an artificial neural network methodology. The classification of tumour types in CT or MRI images of patients with pituitary, glioma, and meningioma tumours is facilitated through its utilization. The given paper involves implementation of image augmentation and normalization techniques, followed by the extraction of image features through the utilization of Gray Level Co-occurrence Matrix (GLCM). The findings involve two distinct modes, namely Training/Learning and Testing/Recognition, which ultimately yield a classified output. The results present linear relationship between epochs and number of iterations thereby validating the proposed approach.
Optimization Study on Structure of Null-Flux Coils in Electrodynamic Suspension System

In the null-flux electrodynamic suspension (EDS) system, the structural parameters of the null-flux coils (NFCs) have a significant impact on the electromagnetic force performance of the system. According to the specific index requirements of the null-flux EDS system, the mathematical model of the null-flux EDS is combined with swarm intelligence algorithms such as particle swarm optimization (PSO) algorithm and genetic algorithm (GA) to optimize the structural parameters of the NFCs, which can improve the performance of the system.
Agile Design of Innovation and Entrepreneurship System of College Students Based on Micro-service Architecture

The research is based on the perspective of microservice architecture construction, and taking an innovation and entrepreneurship system of college students as an example. The study is devoted to completing Agile development process for this system with technology of micro-service architecture and Scrum model, and describes a feasible process of Agile design and implementation for the first Sprint version. Feedback on application for college students of software engineering major in experimental university is positively, which is an effective approach for promoting their innovative and pioneering abilities.
Federated Learning and Decentralized IoT Cooperation

In an effort to lessen the difficulties caused by large-scale transfer of data on mobile phone networks, edge buffering tech has emerged as a result of the introduction of Internet of Things (IoT) apps by developers and carriers. Current optimization-based methods often struggle to adjust to changing circumstances. In order to solve these problems, most modern learning-based technologies are presented in a centralized way, which wastes network resources on refresher as well as data sharing. Study conducted present the Coordinated Deep Regeneration Edge Caching System (FADE) in the above setting. FADE enables decentralized cooperative development of a common prediction model among base stations. Base stations start local education with their initial sets of parameters and then upload almost perfect local circumstances to take part in international training sessions. Study conducted prove that convergence of FADE, displaying the intended convergence features. FADE demonstrates a considerable improvement over centralized deep training and reinforced approaches via trace-driven trials, resulting in a 92% decrease in performance deterioration and a mean 60% lower system costs. Especially, it outperforms the state-of-the-art methods in this field.
On-Line Micro-Weighing for Sterile Preparations Based on Error Compensation of Laminar Wind

High-precision micro-weighing is an important means to maintain the accuracy of powder filling volume in the pharmaceutical industry. However, the laminar wind used to keep the production area sterile causes weight misalignment continuously. This paper makes attempt to adaptively compensate the net weight errors brought by the dynamic laminar wind. We found an irregular fact that it is hard to use a uniform probability distribution to represent this net weight errors on all production lines. First, a criterion of window maximum mean discrepancy (WMMD) was set up to obtain the irregular distribution, then a production line adaptive error compensation method (AEC) was proposed by the WMMD-based distribution learning, to drag the interfered weight back to the actual net weight cleverly. Experiments carried out on two actual production lines, obtaining improvements of RMSE from 13.06 mg to 1.99 mg and from 10.48 mg to 0.62 mg respectively, prove that our method is workable to suppress the laminar wind.
Microvilli Semantic Segmentation in Microscopy Images Using a Visual Learning Pipeline

Data-driven medicine for diagnostic and therapy decision-making has experienced a rising development, due to new technologies such as microscopy imaging for cell and tissue characterization. The pattern recognition of microscopic images is a solid practice to study cell conditions. But the manual annotation is an extensive task, where automating it with computational tools acquires relevance within disease research and diagnosis. We propose a visual learning pipeline that quantifies and classifies without human supervision microvilli on the surface membrane of astrocytes. Our application is a two-step solution that combines computer vision techniques to segment the subcellular structures and an unsupervised learning model to classify them using geometry data. Running an experimental benchmarking of the annotation-free system, we determined that the k-means model as the unsupervised classifier showed the best evaluation metrics. The system provided a maximum clustering score of 91.05% with principal components analysis. And an average score of 84.50% using non-negative matrix factorization, but with the highest metrics dispersion in both cases.
An Online Education Effect Assessing Method Based on YOLOv8 and Vision Transformer

Due to the rapid growth of online education worldwide, assessing the learning effectiveness of students during online classes has become increasingly challenging for teachers. In this paper, a method of assessing online education effect based on YOLOv8 and Vision Transformer is proposed. Firstly, the drowsiness state of students in an online teaching unconstraint environment is estimated by using YOLOv8, and then the gaze direction of awake students is estimated by using Vision Transformer to assess the students' attention levels during online education. The results from YOLOv8 are determined based on the calculated probabilities of drowsiness or wakefulness. And then gaze estimation method proposed in this paper was compared with state-of-the-art methods on the MPIIFaceGaze and Gaze360 datasets in which the angular errors of gaze estimation are 4.58° and 12.27°, respectively. We conducted experiments and analysis on a self-made dataset, from which the results demonstrate the feasibility of our method in an unconstrained environment.
A unified framework for subspace face recognition

PCA, LDA, and Bayesian analysis are the three most representative subspace face recognition approaches. In this paper, we show that they can be unified under the same framework. We first model face difference with three components: intrinsic difference, transformation difference, and noise. A unified framework is then constructed by using this face difference model and a detailed subspace analysis on the three components. We explain the inherent relationship among different subspace methods and their unique contributions to the extraction of discriminating information from the face difference. Based on the framework, a unified subspace analysis method is developed using PCA, Bayes, and LDA as three steps. A 3D parameter space is constructed using the three subspace dimensions as axes. Searching through this parameter space, we achieve better recognition performance than standard subspace methods.
Inferring catchment flow path responses using a data-driven model: an exploratory study based on a generalized additive model

ABSTRACT Identifying physical catchment processes from streamflow data, such as quick- and slow-flow paths, remains challenging. This study is designed to explore whether a flexible nonparametric regression model (generalized additive model, GAM) can be used to infer different flow paths. This assumes that the data relationship in data-driven models is also a reflection of catchment physical processes. The GAM, using time-lagged flow covariates, was fitted to synthetic rainfall–runoff data simulated using simple linear reservoirs. Partial plots of the time-lagged covariates show that the model could differentiate simple and more complex flow paths in simulated synthetic data with short and long memory systems and varying between dry and wet climates. Further analysis of data from real catchments showed that the model could differentiate catchments dominated by slow flow and by quick flow. Therefore, this study indicates that GAM can be used to identify catchment storages and delay processes from streamflow data.
Experimental Study on Key Generation for Physical Layer Security in Wireless Communications

This paper presents a thorough experimental study on key generation principles, i.e., temporal variation, channel reciprocity, and spatial decorrelation, through a testbed constructed by using wireless open-access research platform. It is the first comprehensive study through: 1) carrying out a number of experiments in different multipath environments, including an anechoic chamber, a reverberation chamber, and an indoor office environment, which represents little, rich, and moderate multipath, respectively; 2) considering static, object moving, and mobile scenarios in these environments, which represents different levels of channel dynamicity; and 3) studying two most popular channel parameters, i.e., channel state information and received signal strength. Through results collected from over a hundred tests, this paper offers insights to the design of a secure and efficient key generation system. We show that multipath is essential and beneficial to key generation as it increases the channel randomness. We also find that the movement of users/objects can help introduce temporal variation/randomness and help users reach an agreement on the keys. This paper complements existing research by experiments constructed by a new hardware platform.
Evaluation of User Interface, User Experience, and Usability in Software Through Electroencephalography (EEG) Signal Detection: A Mapping Review

As the digital landscape continues to evolve through the widespread use of software, understanding how users interact with software becomes increasingly important. This article presents the findings of a literature review that explores the use of Electroencephalography (EEG) signals to assess user interfaces, user experience, and/or software usability. We used the mapping review method in this research with the main stages including: 1) Establishing research questions; 2) Source selection; 3) Identifying keywords; 4) Carrying out a screening process; and 5) Extracting and synthesizing data. As a result, we determined 16 relevant papers as primary sources. We highlight the distribution of papers from various databases and their suitability for the period January 2013 to May 2023. In addition, we find that traditional EEG devices, standard five frequency bands, 32 electrodes, signal classification techniques such as Linear Discriminant Analysis (LDA), Frequency Fourier Transform (FFT), and Power Spectrum Density (PSD), gaming software as research subjects, and user experience evaluation questionnaires are most widely used in research practice. The results of this literature review provide comprehensive insight into the latest developments in the use of EEG signals to evaluate software in terms of the field of Human-Computer Interaction, to reduce the factors of subjectivity, bias, efficiency, and accuracy of evaluation results. These insights can serve as a basis for further research and the development of more effective methods and tools for improving software quality.
Evaluating Impact of Wearing Masks in Face Recognition Using Deep Learning Algorithms

Automated and contactless face recognition is a widely used machine learning technology for identifying people which has been applied in scenarios like secure login to electronic devices, automated border control, community surveillance, tracking school attendance. The use of face masks has become essential due to the global spread of COVID-19, raising concerns about the performance of recognition systems. Conventional face recognition technologies were primarily designed to work with unmasked faces, and the widespread use of masked face images significantly degrades their performance. To address this understudied issue, we evaluated the performance of six deep learning models, namely, VGG-16, AlexNet, GoogleNet, LeNet, ResNet-50, and FaceNet on masked and unmasked face images. We aim to find out if deep learning models struggle with masked face recognition and identify the models that mitigate the impact of masked face images. We track, and report miss rates for both masked and unmasked images, along with performance metrics like accuracy and F1 scores in this paper.
ConstructNet: A Deep Learning Object Detector for Construction Site Surveillance

The construction industry is increasingly recognizing the imperative for automation, and one promising application lies in employing deep learning models for monitoring and surveillance of construction sites. Deep learning object detection algorithms have demonstrated their ability to swiftly identify a wide range of objects within images and videos. This paper introduces a novel architecture, ConstructNet, tailored for object detection of construction site machinery. Since the images of construction machines have large bounding boxes, Construct-Net focuses on detecting medium and large-sized objects. Our approach combines an FCOS-style architecture, a slim neck design paradigm, and CIOU loss. On the Alberta Construction Image Dataset (ACID), our model achieves an mAP of 0.71, whereas YOLOv8n has an mAP of only 0.65. ConstructNet is also compact, with a model size of only 13.58MB.
Energy Cost Analysis for Optimally Controlling Structural Brain Networks

In this paper, we investigate the problem of energy cost in controlling brain networks, in which the difficulty of achieving brain state transitions is quantified from the perspective of energy cost. Among them, we utilize the controllability Gramian matrix, an energy related controllability metric. We estimate a new upper bound on the minimum eigenvalue of the controllability Gramian matrix to obtain the relationship between the control energy and the number of control nodes. Simulation examples of brain networks verify the validity of the results. The results can provide a useful tool to characterize the energy cost of controlling brain networks and make predictions about the energy required to complete different control tasks of brain networks, thus providing potential theoretical support for regulatory interventions in many brain diseases, such as Schizophrenia, Parkinsons Disease, Alzheimer's Disease (AD), and so on.
Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms

In the context of binary classification, we define disagreement as a measure of how often two independently-trained models differ in their classification of unlabeled data. We explore the use of disagreement for error estimation and model selection. We call the procedure co-validation, since the two models effectively (invalidate one another by comparing results on unlabeled data, which we assume is relatively cheap and plentiful compared to labeled data. We show that per-instance disagreement is an unbiased estimate of the variance of error for that instance. We also show that disagreement provides a lower bound on the prediction (generalization) error, and a tight upper bound on the "variance of prediction error", or the variance of the average error across instances, where variance is measured across training sets. We present experimental results on several data sets exploring co-validation for error estimation and model selection. The procedure is especially effective in active learning settings, where training sets are not drawn at random and cross validation overestimates error.
Total cholesterol/HDL cholesterol ratio vs LDL cholesterol/HDL cholesterol ratio as indices of ischemic heart disease risk in men: the Quebec Cardiovascular Study.

BACKGROUND Total cholesterol (TC)/high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C)/HDL-C ratios are used to predict ischemic heart disease risk. There is, however, no consensus on which of these 2 indices is superior. The objective of the present study was to present evidence that the LDL-C/HDL-C ratio may underestimate ischemic heart disease risk in overweight hyperinsulinemic patients with high triglyceride (TG)-low HDL-C dyslipidemia. METHODS A total of 2103 middle-aged men in whom measurements of the metabolic profile were performed in the fasting state were recruited from 7 suburbs of the Quebec metropolitan area. RESULTS The relationship of LDL-C/HDL-C to TC/HDL-C ratios was examined among men in the Quebec Cardiovascular Study classified into tertiles of fasting TG levels. For any given LDL-C/HDL-C ratio, the TC/HDL-C ratio was higher among men in the top TG tertile (>168 mg/dL [>1.9 mmol/L]) than in men in the first and second TG tertiles. Adjustment of the TC/HDL-C ratio for LDL-C/HDL-C by covariance analysis generated significant differences in average TC/HDL-C ratios among TG tertiles (P<.001). Greater differences in features of the insulin resistance syndrome (insulinemia, apolipoprotein B, and LDL size) were noted across tertiles of the TC/HDL-C ratio than tertiles of the LDL-C/HDL-C ratio. CONCLUSION Variation in the TC/HDL-C ratio may be associated with more substantial alterations in metabolic indices predictive of ischemic heart disease risk and related to the insulin resistance syndrome than variation in the LDL-C/HDL-C ratio.
Impact of Virtual Arm Impedance on Small-Signal Stability of Modular Multilevel Converters

Inclusion of virtual impedance is an effective approach to mitigate small-signal oscillations in power converters without increasing manufacturing cost. This paper utilizes the second-harmonic circulating current in a modular multilevel converter (MMC) to generate virtual arm impedance and enhance system stability. The proposed method includes both resistance and reactance components, which can be combined to emulate a virtual impedance with flexible magnitude and phase. The active damping effect of different virtual impedance magnitude and phase is compared by both theoretical impedance-based stability analysis and experimental results. It is shown that the virtual reactance is the predominant component in improve system stability, of which the optimal value can be derived according to system stability margin. Understanding the stability impact of magnitude and phase of the virtual arm impedance can facilitate the design of active damping schemes of MMCs under specific grid conditions.