Miracle-dz/UniFAR-FTUs huggingface dataset:
System Monitoring with Metric-Correlation Models

Modern software systems expose management metrics to help track their health. Recently, it was demonstrated that correlations among these metrics allow errors to be detected and their causes localized. Prior research shows that linear models can capture many of these correlations. However, our research shows that several factors may prevent linear models from accurately describing correlations, even if the underlying relationship is linear. Common phenomena we have observed include relationships that evolve, relationships with missing variables, and heterogeneous residual variance of the correlated metrics. Usually these phenomena can be discovered by testing for heteroscedasticity of the underlying linear models. Such behaviour violates the assumptions of simple linear regression, which thus fail to describe system dynamics correctly. In this paper we address the above challenges by employing efficient variants of Ordinary Least Squares regression models. In addition, we automate the process of error detection by introducing the Wilcoxon Rank-Sum test after proper correlations modeling. We validate our models using a realistic Java-Enterprise-Edition application. Using fault-injection experiments we show that our improved models capture system behavior accurately.
Social competitiveness associated with rapid fluctuations in sperm quality in male fowl

When females copulate with multiple males, paternity is determined by the competitive ability of a male to access females and by the ability of its ejaculates to out-compete those of other males over fertilization. The relationship between the social competitiveness of a male and the fertilizing quality of its sperm has therefore crucial implications for the evolution of male reproductive strategies in response to sexual selection. Here, we present a longitudinal experimental study of the relationship between social status and sperm quality. We monitored sperm quality in socially naive male domestic fowl, Gallus gallus domesticus, before and after exposure to a social challenge which comprised two stages. In the first stage, social dominance was established in male pairs divergent in sperm quality, and in the second, social status was experimentally manipulated by re-shuffling males across pairs. We show that sperm quality fluctuates within males both before and after a social challenge. Importantly, such fluctuations followed consistently different patterns in males that displayed different levels of social competitiveness in the social challenge. In particular, following the social challenge, sperm quality dropped in males that won both contests while the sperm quality of males that lost both contests remained constant. Together, these results indicate that males of different social competitiveness are predisposed to specific patterns of fluctuations in sperm quality. These rapid within-male fluctuations may help explain the recent findings of trade-offs between male social and gametic competitive abilities and may help maintain phenotypic variability in these traits.
A Novel 180° Phase Difference Wideband UFPD Without Extra Phase Delay Structure

In this paper, a 180° phase difference wideband bandpass unequal filtering power divider (UFPD) without an extra phase delay structure is presented. It consists of a three-order Chebyshev impedance transformer (IT), a quarter wavelength impedance transformer, an isolation structure and a short-stub. The proposed UFPD has a flat phase difference response between two output ports without the need for an extra phase delay structure. For verification, a 2:1 bandpass UFPD with a 180° phase difference has been designed, fabricated and measured. The UFPD could achieve a 39 % fractional bandwidth (FBW) and a $182^{\circ} \pm 1^{\circ}$ phase difference in passband. The return loss (RL) is below -12.3 dB, and the isolation level is below -17.5 dB in passband. Measured and simulated results are matched well.
An Overview of the Approaches for Generating Test Data in the Context of the Quality Assurance of Big Data Applications

With society’s increasing data production and the corresponding demand for systems that are capable of utilizing them, the big data domain has gained significant importance. However, besides the systems’ actual implementation, their testing also needs to be considered. For this, oftentimes, proper test data sets are necessary. This publication discusses several different approaches how these can be provisioned and, further, highlights the respective advantages, disadvantages, and suitable application scenarios. In doing so, researchers and practitioners that are implementing big data applications and need to test them, or who are generally interested in the domain, are supported in their own considerations on how to obtain test data.
Asymmetrical gradient coil for head imaging

This work presents a novel approach to develop dedicated transverse gradient coils for head imaging. The proposed coil design is based on the stochastic optimization of an asymmetrical stream function and improves the matching between the region‐of‐interest and the homogeneous gradient volume. Additionally, the electric field produced by these asymmetrical coils is 30% lower than that produced by standard symmetrical designs, which minimizes the risk of magnetostimulation of nerves in fast imaging techniques. A prototype of the asymmetrical gradient coil was built to test the method and magnetic field produced by the prototype was measured. Magnetic field measurements and electrical parameters of coils are in good agreement with theoretical calculations. Magn Reson Med 48:707–714, 2002. © 2002 Wiley‐Liss, Inc.
Detection of Defects in Work-in-Process Products Using Deep Learning: A Case of a Water Tap Production Line

This paper presents the development of a deep learning model designed to identify two classes of object images, the work-in-process of a kind of tap. There were the good parts of316 images and the defective parts of320 images. Both classes of images were processed using the oversampling technique for data augmentation before transformation. Then, the processed data were applied to training three transfer learning models, consisting of ResN et50, MobileN et, and Xception. The results show an accuracy and F1-score of 100% for the ResN e50 model, while the augmentation can help improve the performance of the MobileNet and Xception models. Particularly, when evaluated with the test set, the accuracy can be improved from 98.96% to 99.67% for the MobileNet model, while it can be increased from 95.83% to 97.01 % for the Xception model. The accuracy rates were slightly improved, about 0.72% and 1.23% for MobileNet and Xception, respectively. This means that the augmentation approach can be used to improve the performance of transfer learning models. For the major contributions of this study, it could be extended to other types of taps, and then ResNet50, which is the best deep learning model in this study, can be applied for the development of a new station in a production line, for detection and rejection defects automatically.
The optimized Neural Networking scheme in Time Series Analysis for Detecting Depression

This study offers the Optimized Neural Networking scheme in time series analysis for detecting depression. The proposed scheme uses Deep learning strategies, including Convolutional Neural Networks, to analyze feature representations from time collection facts. The temporal records were extracted via convolution to enable robust prediction of melancholy. A real-global dataset of electronic fitness records was hired to evaluate the proposed scheme's performance. Results show that the proposed scheme performed a drastically excessive accuracy in detecting depression compared to conventional strategies, including linear regression and guide Vector devices. The proposed scheme can extract thrilling functions from time collection data that could efficaciously expect despair. The findings show the potential of applying deep mastering techniques for detecting melancholy and offer assurance for using this scheme in practice.
A Virtual Body Try-on Model Based on Edge Detection Algorithm

The target of image visual try-on is to transfer target garment to reference photograph. Current models generally aim to preserve the texture of the target garment while distorting it, and preserve the human features of the non-fitting parts as much as possible. However, when the human image appears occlusion or the human posture changes, it becomes very difficult to fit garment to human body and retain the texture of the garment. In terms of this issue, we present a lightweight network which combines the nonlinear characteristics of convolutional neural network and the characteristics of optimal edge operator. We verified the network on dataset, which achieved good results. Our structural similarity (SSIM) source of warp cloth is 0.8389, SSIM source of human try-on is 0.8130, Frechet Inception Distance (FID) source is 13.6506, all three data are better than mainstream model. Hence, our method can generate fitting images with better perceptual quality and richer detail.
IoT and ML based Smart Pill Dispenser (SPD) Application for Monitoring Elderly People

With the increase in health issues these days, a majority of the people are adhered to medication. There might be a chance that most of them may forget to take their medications as prescribed due to a variety of factors such as busy schedule, mental stress, health issues, and so on. As a result, it may take longer to recover from illness and origin for side effects as well, especially for elderly. Henceforth, it is necessary that the patient must take the relevant medications in the correct dosage and at the correct time. In this paper, an approach has been proposed using internet of things (loT) that guides the elderly people to take the proper medication on time. The proposed smart pill Dispenser (SPD) system is economical and effective. Also, a web application is integrated that collects the information about the diabetes and heart disease of each patient. Further, machine learning models are embedded in order to predict the chance of diabetes, heart stroke, and kidney disease for the patient. This would be economical and effective model to dispense the medicines on time to the elderly people.
Pre-Holocene Origin for the Coronopus navasii Disjunction: Conservation Implications from Its Long Isolation

Integration of unexpected discoveries about charismatic species can disrupt their well-established recovery plans, particularly when this requires coordinate actions among the different governments responsible. The Critically Endangered Coronopus navasii (Brassicaceae) was considered a restricted endemism to a few Mediterranean temporary ponds in a high mountain range of Southeast Spain, until a new group of populations were discovered 500 km North in 2006. Ten years after this finding, its management has not been accommodated due to limited information of the new populations and administrative inertia. In this study, DNA sequences and species distribution models are used to analyse the origin of the C. navasii disjunction as a preliminary step to reassess its recovery plan. Molecular results placed the disjunction during Miocene-Pleistocene (6.30–0.49 Mya, plastid DNA; 1.45–0.03 Mya, ribosomal DNA), which discards a putative human-mediated origin. In fact, the haplotype network and the low gene flow estimated between disjunct areas suggest long-term isolation. Dispersal is the most likely explanation for the disjunction as interpreted from the highly fragmented distribution projected to the past. Particularly, a northward dispersal from Southeast is proposed since C. navasii haplotype network is connected to the sister-group through the southern haplotype. Although the reassessment of C. navasii conservation status is more optimistic under the new extent of occurrence, its long-term survival may be compromised due to the: (1) natural fragmentation and rarity of the species habitat, (2) genetic isolation between the two disjunct areas, and (3) northward shift of suitable areas under future climate change scenarios. Several ex-situ and in-situ conservation measures are proposed for integrating Central East Spanish populations into the on-going recovery plan, which still only contemplates Southeast populations and therefore does not preserve the genetic structure and diversity of the species.
Lunar Ejecta Pattern Detection and Analysis Using Faster R-CNN and GAN Augmentation

Gaining understanding of the Moon's geological and impact history requires a thorough analysis and inspection of its surface. Understanding the history of impact events, planetary development, crater formation mechanisms, geological composition, lunar regolith, planetary defense strategies, space weathering effects, and the broader field of comparative planetary science depend greatly on the analysis of ejecta patterns. This study examines the automated recognition and interpretation of complex lunar patterns from high-resolution lunar pictures using region-based convolutional neural networks. The model is customized and fine-tuned to identify a wide range of craters with prominent ejecta patterns. An essential component for enhancing the model's effectiveness and reliability is the integration of Generative Adversarial Networks. GAN s, a class of generative models, are employed to generate additional lunar images that closely resemble real lunar surface features. This augmentation strategy serves multiple technical purposes, such as improving resistance to noise and artifacts, expanding the training dataset, and enhancing overall robustness. The initial step encompasses essential tasks such as orthorectification and radiometric calibration, aimed at optimizing image quality and ensuring accurate geometric and radiometric characteristics. Subsequently, this study trained the model for feature extraction and region proposal generation. This is followed by region classification and bounding box regression to accurately pinpoint and categorize lunar crater patterns. The analysis encompasses characterizing ejecta patterns based on their size, shape, distribution, and their relationship with other lunar surface features. Proposed model has achieved an outstanding accuracy rate of 85.35% in the automatic recognition and interpretation of intricate lunar patterns.
Electromagnetic tunneling through an inhomogeneous plasma sheath with negative-μ metamaterials

The plasma sheath wrapped outside the high-speed aircraft during reentry can cause the attenuation of communication signals and threaten the safety of aircraft, which is called radio frequency blackout. In this paper, the electromagnetic tunneling through inhomogeneous plasma sheath with negative-$\mu$ metamaterials is investigated by mean of the scattering matrix method. We find complete tunneling occurs with zero phase delay in the composite structure by properly choosing the parameters of negative-$\mu$ (MNG) metamaterials. Besides, we note resonance occurs at the interface of inhomogeneous plasma sheath and MNG. Furthermore, for thicker plasma sheath, results demonstrate the high transmittance band shifts and becomes narrower. Therefore, the paired bilayer may be an attemptable solution to mitigate radio frequency blackout, for its complete tunneling and zero phase delay.
The Stop Smoking Before Surgery Program

Objective: This study aimed to examine the impact of a Stop Smoking Before Surgery (SSBS) program in a health authority where responsibility for surgical services is shared by health professionals in regional centers and outlying communities. Methods: A between-subjects, pre-post mixed method program evaluation was conducted. Elective surgery patients at 2 Northern Canadian hospitals were recruited and surveyed at 2 time points: pre-SSBS implementation (n = 150) and 1 year post-SSBS implementation (n = 90). In addition, semistructured interviews were conducted with a purposeful sample of participants (n = 18). Results: Participants who received information about stopping smoking before surgery post-SSBS implementation were more likely than expected to have reduced their smoking, χ2(1, 89) = 10.62, P = . 001, and had a significantly higher Awareness of Smoking-Related Perioperative Complications score than those that were advised to quit smoking prior to SSBS implementation (U = 1288.0, P < .001). Being advised by a health care professional was the second strongest predictor of whether or not participants reduced their smoking before surgery post-SSBS implementation. However, there was no significant change in the number of participants who reported being advised to quit smoking before surgery between groups. Conclusion: Providing surgery-specific resources to increase awareness of and support for surgery-specific smoking cessation had limited success in this rural context. Additional strategies are needed to ensure that every surgical patient who smokes receives information about the benefits of quitting for surgery and is aware of available cessation resources.
Broadband Substrate Integrated Coaxial Leaky-Wave Antenna with Composite Wide Slots

In this paper, a broadband substrate integrated coaxial line (SICL) leaky-wave antenna (LWA) with open stopband (OSB) suppression is presented. Periodic composite wide slots are employed for OSB suppression, and the -1th spatial harmonic is excited efficiently. The antenna can realize a continuous beam scanning from backward to forward directions including broadside direction. The simulated beam scanning range is from - 76° to + 14° when the frequency sweeps from 3.9 to 7.3 GHz. An impedance bandwidth over 60% can be obtained, and the peak gain can reach 13.9 dBi.
Internet of Medical Things (IoMT) for Premature Estimate of Epileptic Seizures

This paper proposes an Internet of Medical Things (IoMT) Seizure Detection Algorithm that uses smartphone acceleration sensors to detect early seizures. The propose algorithm used based on MATLAB Mobile, which seamlessly accesses MATLAB capabilities and uses MATLAB Drive for cloud-based data storage and analysis. Time-domain and frequency-domain analysis turn acceleration impulses into meaningful feature vectors. Then, machine learning classifiers like SVMs or deep learning models are used to train the system on a seizure and non-seizure dataset. The algorithm is calibrated to distinguish s suraabdlattif@student.usm. my eizure occurrences from regular activity. The IoMT technique uses real-time streaming to send sensor data to the cloud-based MATLAB Drive for continuous monitoring. The MATLAB Mobile app allows smartphone users to remotely monitor seizure activity using the seizure detection algorithm. The proposed IoMT Seizure Detection Algorithm is tested utilizing a broad dataset of seizure episodes and physical activities. Accuracy, sensitivity, specificity, and the ROC curve are evaluated. The algorithm’s robustness and effectiveness in detecting seizure occurrences highlight its potential as a noninvasive, cost-effective early seizure detection system. MATLAB Mobile and MATLAB Drive integration shows that IoMT-based healthcare solutions implemented in real-world circumstances, improving patient care and medical insights. The auto-correlation between the patient’s motion and the [] data helped clinicians decide if the patient’s motion was typical or not. Up to 85% consistency was found while using our proposed method for detecting epilepsy.
An Intrusion Detection Scheme Based on bidirectional FastGRNN for Edge Network Nodes

This article proposes a lightweight, fast, and accurate edge network computing model recommendation scheme based on FastGRNN address the problem that existing network intrusion detection systems are difficult to deploy on resource-constrained IoT edge network nodes. Compared to RNN, LSTM, and GRU networks, the bidirectional FastGRNN network has advantages such as low resource consumption, a small model, overcoming gradient disappearance, and supporting bidirectional propagation. The scheme preprocesses and selects features of network security datasets using the method of central mixed feature values. Moreover, it trains a single classifier using a lightweight Bi-directional gated circulation unit to correctly identify and predict normal behavior or attack behavior in network packets. According to experimental results, the model performs well even under resource constraints, maintaining close to 99% accuracy in anomaly detection. Compared to traditional GRU, it achieved a 34% reduction in memory usage and required much less testing time.
Potential responses to climate change in organisms with complex life histories: evolution and plasticity in Pacific salmon

Salmon life histories are finely tuned to local environmental conditions, which are intimately linked to climate. We summarize the likely impacts of climate change on the physical environment of salmon in the Pacific Northwest and discuss the potential evolutionary consequences of these changes, with particular reference to Columbia River Basin spring/summer Chinook (Oncorhynchus tshawytscha) and sockeye (Oncorhynchus nerka) salmon. We discuss the possible evolutionary responses in migration and spawning date egg and juvenile growth and development rates, thermal tolerance, and disease resistance. We know little about ocean migration pathways, so cannot confidently suggest the potential changes in this life stage. Climate change might produce conflicting selection pressures in different life stages, which will interact with plastic (i.e. nongenetic) changes in various ways. To clarify these interactions, we present a conceptual model of how changing environmental conditions shift phenotypic optima and, through plastic responses, phenotype distributions, affecting the force of selection. Our predictions are tentative because we lack data on the strength of selection, heritability, and ecological and genetic linkages among many of the traits discussed here. Despite the challenges involved in experimental manipulation of species with complex life histories, such research is essential for full appreciation of the biological effects of climate change.
University of Huddersfield Repository Future Trends of Virtual, Augmented Reality, and Games for Health

Serious game is now a multi-billion dollar industry and is still growing steadily in many sectors. As a major subset of serious games, de-signing and developing Virtual Reality (VR), Augmented Reality (AR), and serious games or adopting off-the-shelf games to support medical education, rehabilitation, or promote health has become a promising frontier in the healthcare sector since 2004, because games technology is inexpensive, widely available, fun and entertaining for people of all ages, with various health conditions and different sensory, motor, and cognitive capabilities. In this chapter, we provide the reader an overview of the book with a perspec-tive of future trends of VR, AR simulation and serious games for healthcare.
TransClone: A Language Agnostic Code Clone Detector

We introduce TransClone, a language-agnostic clone detection tool that leverages a source-to-source translator and an extended version of the graph matching network to detect clones. This tool accepts a source directory or directories as input and identifies code clones within it. It comes with a command line interface and reports identified clones in a CSV file format. Currently, it supports code written in Java and Python. However, TransClone's modular structure provides a flexible way to augment clone-detection support for multiple languages, as better code translators become available. Our experiment on the benchmark dataset shows that it can detect clones with high precision and recall. The TransClone tool including the datasets and trained model can be found here11https://github.com/subrotonpi/transclone.