Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer Learning-Based Collaborative Multiview Clustering

Collaborative multiview clustering methods can efficiently realize the view fusion by exploring complementary and consistent information among multiple views. However, these studies ignore all the differences between multiple views in fusion. In fact, in the multiview clustering, the data are diverse from view to view. The larger the difference between any two views is, the more the fusion of these views is required. Moreover, a global tradeoff parameter is generally adopted to restrain the penalty related to the disagreement of all views, which is often defined empirically. Inspired by the idea of transfer learning, a series of novel collaborative multiview clustering algorithms are proposed to tackle these challenges. In the most basic one, each view performs clustering independently and learns from others to improve its own clustering performance, in which a global learning factor is defined to control the interaction between multiple views. The fuzzy memberships are regarded as the important knowledge to provide guidance between views, and the consensus constraint is defined to ensure the consistent partitions of all views. In addition, the local adaptive learning factors between any two views instead of a global fixed one are adopted in an improved version to emphasize the difference between views, and the adjustment strategy for the learning factor is further designed to guarantee the stability of multiview clustering without the influence of initial values. Finally, to identify the significance of different views to the clustering, the extended versions are excavated with the assignment of view weights and the maximum entropy regularization technique is employed to optimize the weights. Experiments on various real-world multiview datasets verify the superiority of the presented approaches.
Enhancing weak input modes for improved NLMS convergence

A technique is introduced to whiten the inputs of an adaptive filter in such a way as to improve the convergence of the normalized least mean-squares (NLMS) adaptation algorithm. This approach, based on the orthogonalization of successive input vectors, is shown to provide a better conditioned input while introducing some added misadjustment. It is shown, however, that in some applications the gains achieved are considerably more than the losses incurred.
Analisis Topik Informasi Publik Media Sosial di Surabaya Menggunakan Pemodelan Latent Dirichlet Allocation (LDA)

Radio Suara Surabaya mengembangkan siaran interaktif berbasis jurnalistik masyarakat, dimana gagasan ini melibatkan partisipasi warga dalam melaporkan peristiwa kepada penyiar radio yang sedang bertugas. Laporan masyarakat yang masuk kemudian disebarluaskan kembali baik melalui siaran radio maupun media social untuk memberikan informasi kepada masyarakat.Tingginya jumlah laporan perhari yang masuk melalui media social dan beragamnya topik dari laporan tersebut menimbulkan kesulitan dalam mengidentifikasi suatu topik dari kumpulan laporan media social masyarakat dan menghabiskan banyak waktu jika dilakukan secara manual oleh manusia.Dengan kondisi demikian, dibutuhkan suatu pemodelan topik yang mampu secara otomatis mengklasifikasikan pesan media social ke dalam topik-topik yang muncul dari hasil pemodelan. Pemodelan topik dilakukan dengan metode Latent Dirichlet Allocation (LDA) , sebuah metode text mining untuk menemukan pola tertentu pada sebuah dokumen dengan menghasilkan beberapa macam topik yang berbeda. Eksperimen pemodelan topic dengan metode LDA menyimpulkan bahwa jumlah topik yang terdapat dalam pesan media social adalah 4 topik. Hasil eksperimen ini telah diuji secara mesin dan diuji kemudahannya untuk diinterpretasi oleh manusia melalui uji koherensi topik. Kesimpulan dari uji koherensi topik menyatakan bahwa model yang dihasilkan dengan metode LDA pada studi kasus ini dapat diinterpretasi manusia dengan baik.
Patient Factors Predicting Success in Lateral Graft Tympanoplasty

Objective: To compare the effects of preoperative medical comorbidities and operative findings on the success of lateral graft tympanoplasty. Study Design: Retrospective chart review. Setting: Tertiary medical center. Patients: Ninety-six patients undergoing lateral graft tympanoplasty from December 2008 to November 2020 with at least 2 months follow-up were included. Patient demographics, comorbidities including smoking status, intraoperative findings, and healing, and hearing outcomes were recorded. Interventions: Lateral graft tympanoplasty. Main Outcome Measures: The primary outcome was perforation closure. Secondary outcomes were postoperative complications and change in air-bone gap (ABG). Results: Ninety-nine ears (mean age 40.94 ± 18.44 years) were included. Tympanic membrane perforation closure was achieved in 92 (92.9%) ears. Perforation closure was not associated with diabetes (P > 0.99), smoking (P > 0.99), or the presence of cholesteatoma at the time of lateral graft tympanoplasty (P = 0.10). Increased age (odds ratio [OR] = 1.04, P = 0.31) was also not correlated with tympanic membrane closure rate. An absent malleus resulted in a higher rate of lateralization (31.3% versus 2.1%; OR = 18.41, 95% confidence interval [CI] = 3.09-95.95, P = 0.001) but not blunting (12.5% versus 4.8%; OR = 0.24, 95% CI = 0.49-12.93, P = 0.24). The mean ABG improved 6.82 ± 11.33 dB (P < 0.01). History of prior tympanoplasty was associated with smaller ABG improvement following surgery (ß = 4.038, R2 = 0.262, P = 0.04) but not perforation closure (OR = 3.25, 95% CI = 0.63-16.81, P = 0.24). Conclusions: Diabetes, active smoking, and advancing age were not associated with adverse healing in patients undergoing lateral graft tympanoplasty. Lateralization was more common with an absent malleus.
The Role of Dual Bronchodilation and the Conscientious Use of Inhaled Corticosteroid in COPD

Chronic obstructive pulmonary disease (COPD) is a global public health burden that stands out as a leading cause of mortality around the world, especially in less developed countries. COPD treatment should be centered on the individual necessities of each patient, integrating pharmacological therapy, immunization, nutritional support, pulmonary rehabilitation and control of comorbidities. Combining a long-acting antimuscarinic (LAMA) with a long-acting beta2-agonist (LABA) bronchodilator has proven to be more effective than monotherapy with LABA or LAMA, in terms of the functional improvement of forced expiratory volume (FEV1), symptoms and life quality and may be helpful in patients with progressive dyspnea. The eosinophil blood count is a simple biomarker that could guide personalized treatment strategies by identifying patients with greater clinical benefits of inhaled corticosteroid (ICS) treatment. However, one critical concern is the increased risk of pneumonia in individuals treated with ICS.
An Efficient Parallel Algorithm for Clustering Big Data based on the Spark Framework

—The principal objective of this paper is to provide a parallel implementation focused on the main steps of the parameter-free clustering algorithm based on K-means (PFK- means) using the Spark framework and a machine learning-based model to process Big Data. Thus, the process consists of parallelizing the main tasks of the first stage of the PFK-means clustering algorithm using successive RDD functions. Then, the parallel K-means provided by Spark MLlib is invoked by setting the cluster centers and the number of clusters determined in the previous step as input parameters of the parallel K-means. Furthermore, a comparison between the parallel designed algorithm and the parallel K-means was conducted using UCI data sets in terms of the sum of squared errors and the processing time. The experimental results, performed locally using the Spark framework, demonstrate the efficiency of the proposed solution.
HEALTH HAZARDS ASSOCIATED WITH RAW MILK IN EGYPTIAN MARKETS

Bacteriological quality of 160 raw cow's milk collected from 6 different governorates in Egypt was evaluated. Milk samples were obtained from retail markets in winter and summer seasons. Total Bacterial Count, Total Coliform Count, Faecal Coliform Count and Staphylococcus aureus count were estimated in the examined samples. 100% of the examined samples failed the legal standards for Total Bacterial count, Total Coliform Count and Staphylococcus aureus count which are 104, 102and 102cfu/ml respectively. Bacillus cereus , Salmonella sp . , E.coli O157:H7 and Listeria monocytogenes could be detected in the examined samples with ratios: 26.25%, 0%, 5% and 5% in winter season samples and 32.50%, 3.75%, 6.25% and 6.25% in summer season samples respectively. The findings of this study raised the need for educational programs for dairy producers and consumers about the risk associated with the consumption of raw milk.
The Effect of Ambient Temperature On Device Classification Based On Radio Frequency Fingerprint Recognition

Physical layer authentication is an important technique for cybersecurity, especially in military scenarios. Device classification using radio frequency fingerprinting, which is based on recognizing device-unique characteristics of the transient waveform observed at the beginning of a transmission from a radio device, is a promising method in this context. In this study, the effect of the ambient temperature on the performance of radio device classification based on RF fingerprinting is investigated. The radio devices used in the study belong to the same brand, model, and production date, making the problem more difficult than classifying radio devices of different brands or models. Our results show that high levels of accuracy can be attained using convolutional neural network models such as ResNet50 when the test data and the training data are collected at the same temperature, whereas performance suffers when the test data and the training data belong to different temperature values. We also provide the performance figures of a blended training model that uses training data taken at various temperature values.
Feynman's Path Integrals as Evolutionary Semigroups

We show that, for a class of systems described by a Lagrangian L(x,\dot{x},t) = 1/2\dot{x}^{2} - V(x,t) the propagator can be reduced via Noether's Theorem to a standard path integral multiplied by a phase factor. Using Henstock's integration technique, this path integral is given a firm mathematical basis. Finally, we recast the propagator as an evolutionary semigroup.
Dynamic reactive power reserve optimisation in wind power integration areas

To prevent the wind units from cascading trip faults, the concept of an autonomous voltage security region (AVSR) has been discussed in previous studies. However, the AVSR is sometimes not sufficiently large due to the insufficient dynamic reactive power reserve (DRPR) under normal conditions, resulting in secure operation problems and a large amount of wind curtailment. Therefore, reservation of an appropriate capacity of dynamic reactive power is a crucial issue in wind farms, so a DRPR optimisation model to enlarge the AVSR is proposed in this work. The computation of the optimal DRPR includes six steps and involves the exchange of information between wind farms and the control centre. The computation is rapid and can be applied to real wind systems online. The proposed method demonstrated good performance in a simple test case and a real system in Northern China.
An End-to-End Named Entity Recognition Platform for Vietnamese Real Estate Advertisement Posts and Analytical Applications

The volume and complexity of publicly available real estate data have been snowballing. As a result, information extraction and processing have become increasingly challenging and essential for many PropTech (Property Technology) companies worldwide. The challenges are even more pronounced with languages other than English, such as Vietnamese, where few studies in this field have taken place. This paper presents an end-to-end framework for automatically collecting real estate advertisement posts from different data sources, extracting useful information, and storing computed data into proper data warehouses and data marts for the Vietnamese advertisement posts in real estate. After that, one can serve aggregated data for other descriptive and predictive analytics. We combine two models for constructing the most appropriate extraction step: Noise Filtering and Named Entity Recognition (NER). These models can help process initial input data and extract all helpful information. The experiment results show that using $\text{PhoBERT}_{large}$ can achieve the best performance compared to other approaches. Furthermore, we can obtain the corresponding F1 scores of the Noise filtering module and the NER module as 0.8697 and 0.8996, respectively. Finally, we utilize Superset for implementing analytic dashboards to visualize the predicted results and serve for further analysis and management processes.
Analysis of the Influence of Banking Performance and Macroeconomic on Banking Stability in Indonesia

This study aims to analyze the effect of banking and macroeconomic performance on banking stability in Indonesia. The main factor that causes fluctuations in banking stability is influenced by internal factors (fundamental factors) are factors that originate from within the company and can be controlled by company management, while external factors (non-fundamental factors) are caused by economic conditions such as interest rates and government policies. The purpose of this study is to analyze the effect of banking and macroeconomic performance on banking stability in Indonesia. The researcher uses the period of years after the issuance of OJK rules from 2014 to 2021. This study uses dynamic panel regression with Generalized Method of Moments (GMM) estimation. The result of this research is that the variables of NPL, GDP, inflation, and Bi Rate have a significant influence on banking stability.
Reconfigurable Ebg at 18 GHz using Perimeter Defects

Based on a network with appropriate geometry and frequency range, we want to create a reconfigurable EBG using microplasmas. This device has two different modes of operation: either to block an 18 GHz signal, or to split and pass the 18 GHz signal to two beams that are offset from the incident beam by +/− 45 degrees. This paper only describes the electromagnetic problem, but includes plasma constraint in terms of geometry and size. The switch between blocking and transmitting configuration is obtained by removing some rods on the perimeter of the EBG, which corresponds to creating defects in the structure.
Laboratory Assessment of the Impact of Chemical Oxidation, Mineral Dissolution, and Heating on the Nitrogen Isotopic Composition of Fossil‐Bound Organic Matter

Fossil‐bound organic material holds great potential for the reconstruction of past changes in nitrogen (N) cycling. Here, with a series of laboratory experiments, we assess the potential effect of oxidative degradation, fossil dissolution, and thermal alteration on the fossil‐bound N isotopic composition of different fossil types, including deep and shallow water scleractinian corals, foraminifera, diatoms and tooth enamel. Our experiments show that exposure to different oxidizing reagents does not significantly affect the N isotopic composition or N content of any of the fossil types analyzed, demonstrating that organic matter is well protected from changes in the surrounding environment by the mineral matrix. In addition, we show that partial dissolution (of up to 70%–90%) of fossil aragonite, calcite, opal, or enamel matrixes has a negligible effect on the N isotopic composition and N content of the fossils. These results suggest that the isotopic composition of fossil‐bound organic material is relatively uniform, and also that N exposed during dissolution is lost without significant isotopic discrimination. Finally, our heating experiments show negligible changes in the N isotopic composition and N content of all fossil types at 100°C. At 200°C and hotter, any N loss and associated nitrogen isotope changes appear to be directly linked to the sensitivity of the mineral matrix to thermal stress, which depends on the biomineral type. These results suggest that, so long as high temperature does not compromise the mineral structure, the biomineral matrix acts as a closed system with respect to N, and the N isotopic composition of the fossil remains unchanged.
Fourier optics approaches to enhanced depth-of-field applications in millimetre-wave imaging and microscopy

In the first part of this thesis millimetre-wave interferometric imagers are considered for short-range applications such as concealed weapons detection. Compared to real aperture systems, synthetic aperture imagers at these wavelengths can provide improvements in terms of size, cost, depth-of-field (DoF) and imaging flexibility via digitalrefocusing. Mechanical scanning between the scene and the array is investigated to reduce the number of antennas and correlators which drive the cost of such imagers. The tradeoffs associated with this hardware reduction are assessed before to jointly optimise the array configuration and scanning motion. To that end, a novel metric is proposed to quantify the uniformity of the Fourier domain coverage of the array and is maximised with a genetic algorithm. The resulting array demonstrates clear improvements in imaging performances compared to a conventional power-law Y-shaped array. The DoF of antenna arrays, analysed via the Strehl ratio, is shown to be limited even for infinitely small antennas, with the exception of circular arrays. In the second part of this thesis increased DoF in optical systems with Wavefront Coding (WC) is studied. Images obtained with WC are shown to exhibit artifacts that limit the benefits of this technique. An image restoration procedure employing a metric of defocus is proposed to remove these artifacts and therefore extend the DoF beyond the limit of conventional WC systems. A transmission optical microscope was designed and implemented to operate with WC. After suppression of partial coherence effects, the proposed image restoration method was successfully applied and extended DoF images are presented.
Formulation development and evaluation of novel polyherbal tablet with complete antidiabetic solution

Four herbal drugs with the complete antidiabetic solution were selected based on documented properties. Poly-herbal combinations containing 50 % hydro-alcoholic extracts, having the most significant overall antidiabetic activity were selected and polyherbal uncoated tablet (PHUT) was developed. 500 mg PHUT were developed, by homogeneously mixing 250 mg/tablet, dried and finely powdered extracts of four herbs in the most effective ratio, i.e. H. sabdariffa (100): A. marmelos (50): F. religiosa (75): A. squamosa (25), with excipients (250 mg/tablet) by direct compression method. For formulation optimization, 3 different formulations were developed by using the same excipients in different ratios and were evaluated for good flow properties and good packing ability. After pre-compression studies, powdered blends were directly compressed and were evaluated using post-compression parameters. Developed PHUTs were dark reddish-brown and devoid of any rough surface. Post compression parameters showed that all parameters are within the limit for all PHUT formulations. Out of all, PHUT 3 showed a significant average disintegration time of 9.40 minutes; hence it was considered as an optimized formulation and was subjected to 3 months accelerated stability studies. Where no significant changes were observed, PHUT 3 was considered a standard formulation with a complete antidiabetic solution.
Role of MRCP and Diffusion Weighted Imaging in Diagnosis of Extrahepatic Biliary Stricture

Background: A non-invasive and secure alternative to existing unreliable diagnostic techniques is magnetic resonance cholangiopancreatography (MRCP) and diffusion weighted imaging (DWI), which may be used to examine biliary strictures and image the biliary tree. Objectives: This study's primary goal was to assess how well DWI to MRCP in detection of level and differentiation of causes of extra-hepatic biliary stricture ( benign or malignant) . Patients and methods : South Valley University's Qena University Hospital served as the site of this cross-sectional investigation. This research comprised 30 individuals who were healthy enough for an MRI test between January 2021 and January 2022 and who had obstructive jaundice that was either known to exist or was clinically believed to exist. Results : In the studied patients, MRCP correctly diagnosed 16 cases out of 19 cases as benign stricture according to histopathology with three false negative malignant strictures. DWI correctly diagnosed 18 cases out of 19 cases as benign stricture according to histopathology with one false negative malignant stricture. Sensitivity, specificity, PPV, NPV and accuracy of MRCP in comparison with histopathology was 100%, 84.21%, 78.57%, 100% and 90% respectively. Sensitivity, specificity, PPV, NPV and accuracy of DWI in comparison with histopathology was100% ,94.74% , 91.67% , 100% and 96.6% respectively. Conclusion : DWI greatly enhances the diagnosis accuracy for identifying benign and malignant biliary strictures and is superior to MRCP in the identification and characterization of biliary obstructing disorders.
The 9th Graders’ Relation of Futuristic Thinking Factors Study

The objectives of this study are to study the relation of futuristic thinking and to create the prediction equation of the 9th graders’ relation of futuristic thinking. The subjects of this study are 860, 9th graders, studying in their second term in 2019 in Srisaket province, Thailand. The sujects of this study are from 12 schools from Multi-Stage Random Sampling. The study uses 216 students in especially big-size school, 160 students in big-size school, 302 students in medium-size school and 182 students in small-size school. The research instrument used are Futuristic Thinking form, Emotional Intelligence (EQ), Motivation (MO), Self-Directed Learning (SD) and Attitude towards learning (AT) with Likert Scale measurement (IOC), Item Total Correlation and Cronbach’s Alpha Coefficient. Statistics used are Mean, Standard Deviation, Pearson’s Product Moment Correlation (rxy), and Stepwise Multiple Regression Analysis. The study found that: All the five factors has relational statistics of . 05 with Coefficient of Correlation from . 673-. 791 Prediction equation both in the form of unstandardized scores and standardized scores are FT¢ = . 180 + . 483EQ + . 179MO + . 172SD + . 148AT ZFT¢ = . 441ZEQ + . 172ZMO + . 179ZSD + . 148ZAT All five prediction factors and variance factors can be used to explain 70.7% of futuristic thinking.
Golden-Mean and Secret Sharing Matroids

Maximum-sized results are an important part of matroid theory, and results currently exist for various classes of matroids. Archer conjectured that the maximum-sized golden-mean matroids fall into three distinct classes, as op- posed to the one class of all current results. We will prove a partial result that we hope will lead to a full proof. In the second part of this thesis, we look at secret sharing matroids, with a particular emphasis on the class of group-induced p-representable matroids, as introduced by Matúš. We give new proofs for results of Matúš', relating to M(K₄), F₇ and F⁻₇. We show that the techniques used do not extend in some natural ways, and pose some unanswered questions relating to the structure of secret sharing matroids.