Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning for Anomaly Detection: A Systematic Review

Anomaly detection has been used to detect and analyze anomalous elements from data for years. Various techniques have been developed to detect anomalies. However, the most convenient one is Machine learning which is performing well but still has limitations for large-scale unlabeled datasets. Deep Reinforcement Learning (DRL) based techniques outperform the existing supervised or unsupervised and other alternative techniques for anomaly detection. This study presents a Systematic Literature Review (SLR), which analyzes DRL models that detect anomalies in their application. This SLR aims to analyze the DRL frameworks for anomaly detection applications, proposed DRL methods, and their performance comparisons against alternative methods. In this review, we have identified 32 research articles published from 2017–2022 that discuss DRL techniques for various anomaly detection applications. After analyzing the selected research articles, this paper presents 13 different applications of anomaly detection found in the selected research articles. We identified 50 different datasets applied in experiments on anomaly detection and demonstrated 17 distinct DRL models used in the selected papers to detect anomalies. Finally, we analyzed the performance of these DRL models and reviewed them. Additionally, we observed that detecting anomalies using DRL frameworks is a promising area of research and showed that DRL had shown better performance for anomaly detection where other models lack. Therefore, we provide researchers with recommendations and guidelines based on this review.
Desıgn and Utılızatıon of Televısıon and Radıo Lessons Amıd Covıd-19: Opportunıtıes, Challenges and Inıtıatıves

In light of the COVID-19 pandemic, the Division of Cagayan de Oro City encourages stakeholders to use TV and radio as supplementary learning resources to address literacy needs. The study examined the lesson design, opportunities, challenges, and school-based initiatives promoting supplementary learning resources. R&D and qualitative research methods were employed. Experts validated the created audio and video lessons by the division creative teams. The validation phases incorporated learning resource standards such as content and technical design. Document analysis and interviews yielded data. Findings revealed that partner stations could air the supplemental learning resources. The content and technical design requirements were met satisfactorily. The lessons were widely accepted, applicable, and useful by stakeholders and learners. With the help of the supplemental learning resources, learners could continue to improve their knowledge and skills even while learning remotely. The radio and television-based experiences of students, parents, and teachers are evident. Visual learners can watch television while auditory learners can listen to the radio. Communicating with stakeholders shows a commitment to the students' welfare, which is central to basic education.
Single-step genomic BLUP with many metafounders

Single-step genomic BLUP (ssGBLUP) model for routine genomic prediction of breeding values is developed intensively for many dairy cattle populations. Compatibility between the genomic (G) and the pedigree (A) relationship matrices remains an important challenge required in ssGBLUP. The compatibility relates to the amount of missing pedigree information. There are two prevailing approaches to account for the incomplete pedigree information: unknown parent groups (UPG) and metafounders (MF). unknown parent groups have been used routinely in pedigree-based evaluations to account for the differences in genetic level between groups of animals with missing parents. The MF approach is an extension of the UPG approach. The MF approach defines MF which are related pseudo-individuals. The MF approach needs a Γ matrix of the size number of MF to describe relationships between MF. The UPG and MF can be the same. However, the challenge in the MF approach is the estimation of Γ having many MF, typically needed in dairy cattle. In our study, we present an approach to fit the same amount of MF as UPG in ssGBLUP with Woodbury matrix identity (ssGTBLUP). We used 305-day milk, protein, and fat yield data from the DFS (Denmark, Finland, Sweden) Red Dairy cattle population. The pedigree had more than 6 million animals of which 207,475 were genotyped. We constructed the preliminary gamma matrix (Γ pre ) with 29 MF which was expanded to 148 MF by a covariance function (Γ 148). The quality of the extrapolation of the Γ pre matrix was studied by comparing average off-diagonal elements between breed groups. On average relationships among MF in Γ 148 were 1.8% higher than in Γ pre . The use of Γ 148 increased the correlation between the G and A matrices by 0.13 and 0.11 for the diagonal and off-diagonal elements, respectively. [G]EBV were predicted using the ssGTBLUP and Pedigree-BLUP models with the MF and UPG. The prediction reliabilities were slightly higher for the ssGTBLUP model using MF than UPG. The ssGBLUP MF model showed less overprediction compared to other models.
A Patient-Specific Algorithm for Lung Segmentation in Chest Radiographs

Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on a case-by-case basis, it selects which outputs are fused to form the final segmentation for that patient. Our candidate lung segmentation networks include U-Net, with five different encoder depths, and DeepLabv3+, with two different backbone networks (ResNet50 and ResNet18). Our selector network is a ResNet18 image classifier. We perform all training using the publicly available Shenzhen CR dataset. Performance testing is carried out with two independent publicly available CR datasets, namely, Montgomery County (MC) and Japanese Society of Radiological Technology (JSRT). Intersection-over-Union scores for the proposed approach are 13% higher than the standard averaging ensemble method on MC and 5% better on JSRT.
COVID-19 preventive practices and associated factors among high school and preparatory school students in Dessie City, Ethiopia

Background As the COVID-19 pandemic continues to ravage the world, the most pretentious sector besides the economy is the education system. Ethiopia is not equipped with the infrastructure and facilities to provide online classes for students at all levels. Hence, all institutions were re-opened with mandatory infection prevention and control (IPC) protocols such as the use of face masks, physical distancing, shifts in classes, and routine hand washing practices with soap and water to restrict the spread of the virus. Nevertheless, there has been no monitoring and follow- up and there is no data on IPC compliance among school children in the country. The purpose of this study was to examine the COVID-19 preventive practices and their associated factors among high and preparatory school students in Dessie City, Ethiopia. Methods A cross-sectional study was carried out by using a pre-tested face-to-face applied structured questionnaire and direct observations from March 8 to March 20, 2021, in five high and preparatory schools in Dessie City. The sample size was proportionally allocated in each school based on the students' total number registered in the first academic semester, then stratified by grade level, and proportionally allocated to each grade and section. Finally, a simple random sampling method was used to select study participants. Variables with p-values < 0.25 in the bivariate logistic regression analysis were entered into the multivariate logistic regression model. Results This study involved 422 students with a response rate of 98.8%. The level of good preventive practices was 43.7%. After adjusting for covariates, female, positive attitudes, received IPC training and clear accessible sharing of information and feedback with parents, students and teachers were identified as predictors of good precaution practices. Conclusion The prevalence of good prevention practices for COVID-19 among students was relatively very low. Therefore, the Department of Health and Education of Dessie City and each school should implement environmental health programs and promote prevention practices in high schools and preparatory schools.
Design and Comparison of Different Types of Dual-Frequency Matching Networks Used in Double-Tuned Coils for Multinuclear Magnetic Resonance Imaging and Spectroscopy

Multi-resonant RF coils are often used in multinuclear MR imaging and/or spectroscopy experiments, and a large variety of strategies for multi-tuning coils exist. However, designing a multi-tuned coil with good performance is challenging, and improvements in sensitivity are always desirable - particularly on the X-nucleus coil due to the intrinsically lower MR sensitivity of non-proton nuclei. In this work, various dual-frequency matching networks in double-tuned coils are compared, and their effect on the coil performance is investigated. Four different dual-frequency matching networks were designed and constructed with frequency-splitting or -blocking traps, which enable exploration of both proton-1 (1H) and sodium-23 (23Na) nuclei. Two single-frequency matching networks were also built without any additional lossy components as a reference, and their matchings were set to either the 1H or the 23Na frequency. The initial evaluation was conducted on the bench using a network analyser to examine the scattering (S)-parameters and quality factors of the connected RF coils. The performance of the attached matching networks was then further evaluated by measuring the corresponding signal-to-noise ratio (SNR) based on images obtained at a 7 T clinical MRI scanner. It was found that even though the tuning and matching conditions were nearly impeccable in the S-parameters, the actual 1H SNR decreased significantly due to the inserted traps, whereas the SNRs of the 23Na frequency were nearly maintained with the added traps and the losses were much less. The dual-frequency matching networks create additional sensitivity loss, which is dependent on the actual implementation of the matching circuit. This is in agreement with previously reported results associated with the evaluation of inserted double-tuning traps for RF coils.
Persistent-Homology-Based Machine Learning and Its Applications -- A Survey

A suitable feature representation that can both preserve the data intrinsic information and reduce data complexity and dimensionality is key to the performance of machine learning models. Deeply rooted in algebraic topology, persistent homology (PH) provides a delicate balance between data simplification and intrinsic structure characterization, and has been applied to various areas successfully. However, the combination of PH and machine learning has been hindered greatly by three challenges, namely topological representation of data, PH-based distance measurements or metrics, and PH-based feature representation. With the development of topological data analysis, progresses have been made on all these three problems, but widely scattered in different literatures. In this paper, we provide a systematical review of PH and PH-based supervised and unsupervised models from a computational perspective. Our emphasis is the recent development of mathematical models and tools, including PH softwares and PH-based functions, feature representations, kernels, and similarity models. Essentially, this paper can work as a roadmap for the practical application of PH-based machine learning tools. Further, we consider different topological feature representations in different machine learning models, and investigate their impacts on the protein secondary structure classification.
Strong negative effects of recent wildfires on two endemic Macaronesian bush‐crickets

Wildfires increase globally in frequency and extent as a consequence of climate change. These wildfires may have negative effects on insect populations. Macaronesia is a global biodiversity hotspot with wildfires occurring annually, but the consequences of these wildfires on endemic insects are poorly understood. Using bioacoustics monitoring, we studied the consequences of recent wildfires on two endemic silvicolous Macaronesian Bush‐Cricket species, which are listed as threatened on the IUCN Red List of Threatened Species. Our results show that wildfires had strong negative effects on populations of both species. However, when analysing the effects of fire history and size, we found that one of the species was found on older burnt sites, suggesting a capacity to recolonise smaller burnt areas from adjacent non‐burnt areas. Reducing wildfire frequency and size will be crucial to conserving the two species. Additionally, restoring native laurel forest vegetation will support the recovery of both species.
Power quality event detection and analysis in power system using wavelet transformation

By means of wavelet transform and neural network, a novel approach for power quality (PQ) event classification is proposed. The feature vectors for various PQ events are extracted using wavelet transform which can accurately localizes the characteristics of signal in time-frequency domains. The feature vectors are then applied to the neural network for training and PQ pattern classification. The neural network have demonstrated promise in pattern recognition and been considered a potential alternative approach to pattern recognition It is concluded that the proposed neural network has better data driven learning and local interconnections performance by comparing with a classic neural network. The comparison between the proposed method and the other existing method is discussed. It is proved that the proposed approach can provide accurate classification results and give a new way for detection and analysis of power quality events.
Quantum Image Segmentation Based on Grayscale Morphology

The classical image segmentation algorithm based on grayscale morphology can effectively segment images with uneven illumination, but with the increase of image data, the real-time problem will emerge. In order to solve this problem, a quantum image segmentation algorithm is proposed in this article, which can use a quantum mechanism to simultaneously perform morphological operations on all pixels in a grayscale image, and which then quickly segments the image into a binary image. In addition, several quantum circuit units, including dilation, erosion, bottom-hat transformation, top-hat transformation, etc., are designed in detail, and then, they are combined together to construct the complete quantum circuits for segmenting the novel enhanced quantum representation images. For a <inline-formula><tex-math notation="LaTeX">$2^{n} \times 2^{n}$</tex-math></inline-formula> image with <inline-formula><tex-math notation="LaTeX">$q$</tex-math></inline-formula> grayscale levels, the complexity of our algorithm can be reduced to O<inline-formula><tex-math notation="LaTeX">$(n^{2}+q)$</tex-math></inline-formula>, which is an exponential speedup than the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum era.
Combined Jasmonic Acid and Ethylene Treatment Induces Resistance Effect in Faba Bean Plants Against Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae)

Simple Summary The western flower thrips Frankliniella occidentalis (Thysanoptera: Thripidae) is a major pest of vegetable and flower crops worldwide. A feasible and environmentally friendly method to control F. occidentalis is to induce resistance in host plants. In this study, we attempted to induce resistance against F. occidentalis in faba bean plants. We first investigated the effect of different concentrations of jasmonic acid (JA) and ethylene (ET) alone on the oviposition of F. occidentalis. We then used different concentrations of the combined treatment (JA and ET) to evaluate the most suitable concentrations affecting the oviposition and feeding selectivity of F. occidentalis. Second, we examined the effects of mixed exogenous JA and ET treatments on the activities of JA and ET biosynthetic enzymes in faba bean leaves and on the expression levels of JA/ET pathway marker genes. Finally, we evaluated the effects of JA/ET treatment-related induced resistance on the life history and population parameters of F. occidentalis. Our results provide reference data for the control of thrips infestation in faba bean crops via application of exogenous phytohormones. Abstract Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae) is a serious invasive pest in China. In this study, we determined whether exogenous jasmonic acid (JA) and ethylene (ET) treatments could induce resistance against F. occidentalis in faba bean plants. First, we investigated the effects of different concentrations of JA or ET alone on F. occidentalis and then assessed the effects of optimal concentrations of JA and ET combined. Our results showed that the optimal concertation of JA was 2 mmol/L and ET was 0.5 mmol/L. JA + ET mixture showed the greatest inhibitory effect in terms of oviposition and feeding. JA with ET was found to induce changes in the activities of lipoxygenase (LOX), allene oxide synthase (AOS), polyphenol oxidase (PPO), 1-aminocyclopropane 1-carboxylic acid synthase (ACS), and trypsin inhibitor (TI). This treatment also activated or inhibited the relative expression levels of LOX1, ACO2, ACS2, and AP2/ERF. Treatment of faba bean plants with JA and ET significantly prolonged F. occidentalis development and adult preoviposition period, significantly reduced per-female oviposition, and altered male longevity and offspring demographic parameters. These results indicate that JA with ET can induce defenses against the growth and development of F. occidentalis in faba bean plants.
Stereo Visual Odometry With Automatic Brightness Adjustment and Feature Tracking Prediction

Vision-based localization and mapping can be easily affected by unstable feature tracking and illumination variations. To address these problems, we propose a point-based stereo visual odometry (VO) system with image brightness adjustment and feature tracking prediction. The system incorporates two threads that run in parallel: front-end and back-end. The front-end thread performs brightness adjustment, feature tracking, and motion estimation between frames. When the brightness of image changes significantly, a cumulative gray-scale histogram is used to estimate the exposure of the camera and adjust the brightness of the image. Additionally, a constant acceleration motion model and stereo geometric constraint are used to predict the location of feature points in the target image, providing a reliable initial guess for the Lucas–Kanade (LK) optical flow tracker. In order to improve the accuracy and reduce computational complexity, the back-end performs a sliding window bundle adjustment (BA) to achieve optimal camera poses and landmark positions. Experiments on publicly available datasets indicate that the proposed scheme has a better performance than state-of-the-art stereo VO.
Can Smartphones be a cost-effective alternative to LENA for Early Childhood Language Intervention?

Although non-profit commercial products such as LENA can provide valuable feedback to parents and early childhood edu-cators about their children’s or student’s daily communication interactions, their cost and technology requirements put them out of reach of many families who could benefit. Over the last two decades, smartphones have become commonly used in most households irrespective of their socio-economic background. In this study, conducted during the COVID-19 pandemic, we aim to compare audio collected on LENA recorders versus smartphones available to families in an unsupervised data collection protocol. Approximately 10 hours of audio evaluated in this study was collected by three families in their homes during parent-child science book reading activities with their children. We report comparisons and found similar performance between the two audio capture devices based on their speech signal-to-noise ratio (NIST STNR) and word-error-rates calculated using automatic speech recognition (ASR) engines. Finally, we discuss implications of this study for expanding this technology to more diverse populations, limitations and future directions.
Resource Allocation in 5G Platoon Communication: Modeling, Analysis and Optimization

New features brought by 5G New Radio V2X (NR-V2X) support multiple vehicular communication types (unicast, groupcast, and broadcast) to coexist in road scenarios. However, the current standard does not specify the resource scheduling approach for groupcast to support such a new feature, which may severely degrade its packet delivery performance impacted by other communication types. In this paper, we investigate the scheduling and resource allocation approaches for a groupcast-based application, vehicle platooning, under the environment characterizing this new feature. We first analyze two baseline resource allocation approaches, i.e., Semi-persistent Scheduling (SPS) stated in the 3 rd generation partnership project (3GPP) and Random Selection (RS), based on the metric of packet collision probability. Subsequently, considering the issues from baseline approaches, we develop an Improved Random Selection (IRS) scheme to decrease the collision probability. We further propose to employ Deep Deterministic Policy Gradient (DDPG) algorithm to overcome the impact of inter-vehicle collaboration in the platoon based on local information. A Monte Carlo simulator is then used to verify the analytical models' results. The numerical results show that IRS significantly mitigates the packet collision probability compared with the baselines. Meanwhile, DDPG outperforms IRS in terms of the packet collision probability as well as average scheduled delay and is more robust to the change in the environment.
Counting the number of distinct real roots of certain polynomials by Bezoutian and the Galois groups over the rational number field

In this article, we count the number of distinct real roots of certain polynomials in terms of Bezoutian form. As an application, we construct certain irreducible polynomials over the rational number field which have given number of real roots and by the result of Oz Ben-Shimol [On Galois groups of prime degree polynomials with complex roots, Algebra Disc. Math. 2 (2009), pp. 99–107], we obtain an algorithm to construct irreducible polynomials of prime degree p whose Galois groups are isomorphic to S p or A p .
In-situ study of crystallization kinetics in ternary bulk metallic glass alloys with different glass forming abilities

In-situ transmission electron microcopy and time-resolved neutron diffraction were used to study crystallization kinetics of two ternary bulk metallic glasses during isothermal annealing in the supercooled liquid region. It is found that the crystallization of Zr56Cu36Al8, an average glass former, follows continuous nucleation and growth, while that of Zr46Cu46Al8, a better glass former, is characterized by site-saturated nucleation, followed by slow growth. Possible mechanisms for the observed differences and the relationship to the glass forming ability are discussed.