Miracle-dz/UniFAR-FTUs huggingface dataset:
Classifying Brain Tumors using Convolutional Neural Networks ‎on MRI Scans

Brain tumors are a significant cause of morbidity and mortality ‎worldwide, with a high demand for accurate and robust ‎diagnosis. The brain classification tumors using convolutional ‎neural networks are a rapidly growing field in medical imaging. ‎Brain tumors are a serious condition that can cause significant ‎morbidity and mortality. Deep neural networks have been used in ‎recent years to identify and classify images, with good results in ‎areas such as medical image analysis. This work aims to use deep ‎learning methods and CNNs to identify brain tumors in magnetic ‎resonance imaging scans. Our work is motivated by the ‎challenges that physicians face when trying to recognize brain ‎tumors, particularly in the early stages, when mistakes can be ‎made. Through various MRI experiments, we developed and ‎evaluated two pre-trained convolutional neural networks which ‎are InceptionV3 and ResNet50. Normal, Glioma, Meningioma, ‎and Pituitary are the four distinct classes of brain images ‎categorized by these models. The ResNet50 model was able to ‎classify brain tumors using MRI scans with an overall accuracy ‎of 96%. ‎
Enhancing Malware Classification with Vision Transformers: A Comparative Study with Traditional CNN Models

Malware classification is an important task in cybersecurity, where machine learning techniques have been widely used to automate the process of identifying and categorizing malicious software. In this paper, we propose a new approach to malware classification using Vision Transformers (ViT), a state-of-the-art deep learning architecture that has shown good results in computer vision field. Our proposed ViT-based model outperforms traditional Convolutional Neural Networks (CNNs) in terms of accuracy and robustness, as it can capture long-range dependencies in the input data without relying on hand-crafted features. We evaluate our proposed model on Malimg database, and demonstrate that it achieves state-of-the-art performance, outperforming the existing traditional approaches. Furthermore, we investigate the impact of different input representations, model configurations, and training strategies on the ViT-based model's performance. Our results show that the proposed ViT-based model offers several advantages over traditional CNN models, such as better performance on large-scale and complex datasets, higher interpretability, and scalability. However, the ViT-based model requires significantly more computational resources and longer training time. Our proposed approach offers a promising direction for malware classification using ViT-based models, which can be further improved by exploring different architectures, optimization techniques, and transfer learning strategies.
Networks of international knowledge flows

Innovation systems, being dynamic structures tend to become progressively more globalized, reflecting the presence of a greater intensity of cross-border flows. This process is related to the emergence of a Global Innovation System (GIS). Our research investigates this structural transformation by focusing on international knowledge flows (IKFs). To integrate this investigation with the previous literature on national innovation systems (NISs), we examine international knowledge flows connecting institutions (firms, universities, research institutes, hospitals) from different NISs. Among possible IKFs types, we focus on those created by patent citations - of other patents or scientific articles - and scientific co-authorships. We represent the institutions and the IKFs they create as networks where the nodes are the institutions and the links connecting these nodes are the IKFs. We organize the network in three layers according to the type of IKF that connects the institutions: scientific collaboration, patent citation or article citation in patents. We have divided this paper into six sections. The first presents our theoretical background. The second discusses the characteristics and properties of complex networks and complex systems. The third discusses some characteristics of multilayer networks, a concept used as an analytical tool to develop the empirical analysis. The fourth deals with data and methodology. The fifth analyses the structure of the three network layers, their entanglement and multiplex properties, as well as the institutions connecting them. The sixth and conclusive section discusses how those findings improve the understanding of an emerging GIS.
The genus Ipomoea (Convolvulaceae) in the state of Rio Grande do Norte, Brazil

Ipomoea has a cosmopolitan distribution, being more diverse in the tropics. It is the largest genus of Convolvulaceae, containing about 700 species, with more than a half occurring in the Americas. Approximately 150 species occur in Brazil, of which 80 are found in the Northeast region. The state of Rio Grande do Norte is considered one of the least floristically known in Brazil. This study was based on field expeditions and analysis of specimens from the herbaria. Thirty-tree species were found, with eight new records for the study area: Ipomoea chiquitensis , I. cynanchifolia , I. goyazensis , I. grandifolia , I. magna , I. queirozii , I. ramosissima and I. syringifolia. An identification key, genus and species descriptions, illustrations and distribution maps are presented.
Slowdown as a Metric for Congestion Control Fairness

The conventional definition of fairness in congestion control is flow rate fairness. However, Internet users typically care about flow completion times (FCTs) and flow rate fairness does not lead to equitable FCTs for different users. Therefore, we reconsider what it means for congestion control to be fair and posit a novel stance on fairness: it is fair when no flow unnecessarily prolongs another flow. Based on this stance, we propose an evaluation framework for congestion control fairness that uses slowdown (normalized FCT) as the metric. We demonstrate the usefulness of our framework through surprising experiment results: in theory, prioritizing short flows should outperform fair queueing, but we show that this is not the case due to slow start dominating short flows. The framework can also analyze traditional flow rate fairness; we do so and verify well-known "fairness" issues, but additionally, we show that flow rate unfairness does not induce slowdown and is thus not a problem per se.
Drainage Network Planning and Analysis Based on Image Segmentation - an Application in the Bandeirinha Stream - Formosa, State of Goiás, Brazil

The present research aimed to demonstrate the feasibility of using image segmentation for better drainage extraction, automated drainage network hierarchization and main channel definition based on Horton (1945). The test area for the applications was the Bandeirinha Stream Hydrographic Basin (BSHB), which is located in the municipality of Formosa, in the state of Goiás, Brazil. The study used the Shuttle Radar Topography Mission (SRTM), with a spatial resolution of 1" x 1" (one arc second). The SRTM segmentation was performed using the Python language ODR_Obia algorithm, inserted with a QGIS plugin. Through the QGIS platform, the ODR_Hidro plugin was used to perform the delimitation of the BSHB, the extraction of the drainages and the definition of the main channel. The results were validated in the field and evaluated from the manual definition of the main channel. That was done using both the data produced by the application and the drainage data contained in the vector base of the topographic maps of the Army of Geographic Service (AGS), at the scale of 1:100,000 and 1:25,000. From the results obtained, it can be inferred that the ODR
Identifying and understanding the main challenges for sustainable water resource management in China

This article explores China’s water resource management through the application of a 10 building-block governance assessment method, which, from a holistic and integral perspective, identifies the main strengths and weaknesses of the system. It aims to provide readers with a comprehen- sive view of the current status of water management in China by identifying the main failings that should be addressed in its transition process towards sustainable water resource management.
Real-time realistic rendering of sunrise and sunset on the ocean

Title of Thesis: Real-time Realistic Rendering of Sunrise and Sunset on the Ocean Yuping Zhang, Master of Science, 2015 Thesis directed by: Dr. Marc Olano, Associate Professor Department of Computer Science and Electrical Engineering The aim of this thesis is to study advanced real-time realistic rendering techniques for outdoor natural scenes. Much research has been done for rendering realistic outdoor scenes, such as sky, ocean, terrain, etc. But sunrise and sunset are rarely discussed. Interesting phenomena like the sun mirages during sunset and sunrise could create splendid visual effects. This thesis presents a method to render sun mirages. It uses precomputed atmospheric refraction profiles for different atmosphere condition and performes ray tracing to render the sun deformation. Combined with other methods, it renders sunrise and sunset on the ocean in real time. It simulates realistic sun mirages, sky color, ocean waves and lighting, which can be useful for realistic scenes in movies, video games or for scientific simulators. Real-time Realistic Rendering of Sunrise and Sunset on the Ocean
A new Motzkin class for joint RNA secondary structures

In general RNA prediction problem includes genetic mapping, physical mapping and structure prediction. The ultimate goal of structure prediction is to obtain the three dimensional structure of bimolecules through computation. The key concept for solving the above mentioned problem is the appropriate representation of the biological structures. Even though, the problems that concern representations of certain biological structures like secondary structures either are characterized as NP-complete or with high complexity, few approximation algorithms and techniques had been constructed, mainly with polynomial complexity, concerning the prediction of RNA secondary structures. In this paper, a new class of Motzkin paths is introduced, the so-called semi-elevated inverse Motzkin peakless paths for the representation of two interacting RNA molecules. The basic combinatorial interpretations on single RNA secondary structures are extended via these new Motzkin paths on two RNA molecules and can be applied to the prediction methods of joint structures formed by interacting RNAs.
A hydrologic model for the uppermost oceanic crust constrained by temperature estimates from carbonate minerals

Hydrologic models of the oceanic crust commonly assume that the uppermost igneous extrusive layer of the aquifer is thermally well mixed, although this assumption has not been rigorously tested. Here, the assumption of a thermally well‐mixed aquifer is tested against the geological record using O isotope‐derived crystallization temperatures of carbonates in the lavas as a record of the temperatures experienced by the aquifer. It is found that carbonate formation temperatures are higher than can be explained by a model of outcrop‐to‐outcrop flow in a well‐mixed aquifer at four of the seven drilling locations analyzed. A poorly mixed aquifer is developed to further explore the crustal hydrology at these locations. Relative to a well‐mixed aquifer, a poorly mixed aquifer can achieve higher average temperatures, develops larger lateral pressure gradients driving flow, and requires a lower permeability to achieve a given lateral fluid flux. O isotope data from most of the carbonate samples analyzed are consistent with temperatures achievable in a poorly mixed aquifer; those samples which are not consistent can be explained by plausible special circumstances (such as formation at a discharge zone, where ascending fluid may warm the uppermost aquifer).
A DME Interference Signal Identification and Mitigation Approach based on Gaussian Mixture Model used for BDS B2a Receivers

Distance Measuring Equipment (DME) signal is a typical pulse interference to civil B2a signal of the BeiDou Navigation Satellite System (BDS). To guarantee the robustness of the receivers under DME interference, it is necessary to apply interference mitigation algorithms. A DME interference signal identification and mitigation approach based on Gaussian Mixture Model (GMM) is proposed in this paper. The Expectation-Maximization (EM) algorithm is used to estimate the GMM parameters, which mainly includes the variances and mixture weights of each Gaussian distribution. An outdoor experiment is conducted to collect B2a signals in a real DME interference environment around a DME station close to the Beijing Capital International Airport. The results show that the probability density function of the collected data is well fitted by the GMM. By applying the proposed GMM-based approach, the interference samples are accurately identified and effectively mitigated. By comparison, when the proposed approach is performed, the carrier-to-noise ratio of the software-defined receiver achieves 1.95 and 0.22 dB-Hz improvements compared with the non-mitigation and hard threshold pulse blanking cases, respectively, indicating that the proposed approach provides strong robustness for the B2a receiver in the presence of DME pulse interference. The analysis in this study benefits the development of pulse interference mitigation methods for the BDS receivers in aviation navigation.
Nonconvex Valid Uncertainty Modelling Approach for Robust Control Synthesis

This paper proposes an alternative nonconvex approach to fit single-input and single-output transfer function models on magnitude frequency-based response measurements, with additional constraints such as upper boundness, stability, minimum phase and validity with respect to provided data. The proposed solution presents an improvement over the established Log-Chebyshev convex fit which is used as a well-placed starting point and refines the optimization to become less conservative and feasible in cases where (near) singularities are present and limit its sole application. As such, our contribution provides advantages in cases such as in $\mu$-synthesis where the uncertainty model should have a low order to guarantee no conservativeness of the structured singular value approximation. Two case studies are described and discussed which show brought improvements.
Femtosecond Laser Pulse Picking Method Based on Mach–Zehnder Modulator by Keeping Phase of the Picked-Up Pulse Steady

In the femtosecond laser pulse picking based on the Mach–Zehnder modulator (MZM), the operating point drift of MZM and the timing mismatch between the impulse gate signal and the laser pulse limit the performance of picked-up pulses. In this article, an adaptive femtosecond laser pulse picking scheme by keeping the phase of the picked-up pulse steady is proposed. For the first time, we have established a one-to-one correspondence between the phase of the picked-up pulse and the operating point of MZM. Therefore, the operating point of MZM can be locked at the null point by maintaining the phase of the pick-up pulses at the value corresponding to the initial null point with the closed-loop bias control. By maximizing the amplitude of the picked-up pulses, the automatic matching of the impulse gate signal and the laser pulses can be realized. The principle and realization of the impulse signal generation and bias control are presented in detail. Experimental results verify that the proposed method has achieved flexible single/multiple pulse-picking with repetition rates from kHz to tens of MHz with high amplitude stability, high extinction ratio, and low temporal broadening and profile distortion.
Pitch and Heading Angles Estimation by Airplane Trajectory Data Available in ADS-B

A number of Automatic Dependent Surveillance- Broadcast (ADS-B) applications is increasing every year. Nowadays each airspace user should be equipped with a transponder of mode 1090ES to identify itself in the air traffic. Vary of settings of on-board transponders provides a different list of parameters for sharing. The most common parameters include a unique identification code of user and a position report including latitude, longitude, and pressure altitude. Missing airplane orientation angles in ADS-B data set could be a problem for tasks of air traffic surveillance and airplane visualization in flight simulator software. Also, orientation angles could improve performance of traj ectory filtering and prediction of airplane position in real-time data processing. In the paper, we consider calculation of pitch and heading angles based on trajectory data obtained by ADS-B. Also, analytical formulas for airplane orientation angles calculation based on ADS-B data are proposed in the paper. ADS-B trajectory data usually are not synchronized and include multiple gaps. A linear regression model with B-splines functions is used for trajectory data interpolation for a given time series. Also, surveillance data are used for vertical and ground speed calculation. Calculated orientation angles and interpolated position data form an array of six Degrees of Freedom (6DoF), which is widely used in different visualization tools.
A Hybrid Quantum-Classical Machine Learning Approach to Vision Sensor Data Analysis in Aerospace Applications

Aerospace vehicle detection, localization, and subsequent parametric computations such as range and altitude in frame-based data analysis acquired through vision sensors such as cameras can be a challenging problem due to a variety of factors including but not limited to the size and shape of the vehicle, the distance and position of the vehicle, lighting conditions, and frame resolution. A combination of machine learning and computer vision techniques are generally used to address these situations. In this paper, we demonstrate a workflow using quantum-enhanced, transfer and classical machine learning to detect and localize aerospace vehicles and compute parameters such as range and altitude from color histograms. Additionally, we describe a data synthesis approach for training machine learning algorithms against aerospace imagery. The purpose of our study is not to present the superiority of a technique but rather the feasibility and practicality of incorporating quantum enhancement in aerospace data analytics, data synthesis through simulation programs, and parametric computations through color histograms.
The Accuracy of Speech Transmission Index Estimation under Conditions of Joint Action of Noise and Reverberation

In this paper, the full modulation method of speech intelligibility index (STI) estimation and its modification in the form of the full formant-modulation (FM) method are compared in terms of measurement accuracy in conditions where the speech signal is masked by noise and reverberation. Dependences of STI estimation errors on signal-to-noise ratios and on the duration of test signals for the reverberation time typical for university auditoriums of 0.8 s were obtained. It is shown that the accuracy of STI estimation in the presence of reverberation practically does not depend on the choice of estimation method. The obtained results indicate that an acceptable for practical use error of 0.01-0.02 of STI estimation in the conditions of joint action of noise and reverberation can be ensured when using test signals lasting 8–16 s.
Porous Layers as a Buffer for Synthesizing CdO/por-CdS/CdS Heterostructures

This study presents the synthesis of the CdO/por-CdS/CdS heterostructure, where a porous CdS layer is employed as a buffer layer. The elemental composition of the structure was investigated using EDX surface mapping, where cadmium and sulfur exhibited uniform distribution, and cadmium oxide was predominantly detected on spheroidal crystallites. Raman spectroscopic analysis confirmed the presence of CdS and CdO crystalline phases, indicating the material's partial amorphousness. X-ray diffraction analysis revealed characteristic peaks for CdS and amorphous features in the structure, which might be associated with ultrafine crystallites and lattice deformations. The gathered data underscore the significance of the proposed method in synthesizing CdO/por-CdS/CdS heterostructures with porous layers serving as a buffer, paving the way for future research and the fabrication of materials for photocatalysis and magnetoelectronics.
A Trading Strategy Based on BitCoin High and Low Prices: The Role of an Evolving Fuzzy Model for Interval-Valued Time Series Forecasting

This paper evaluates the predictability of high and low BitCoin prices, and the profitability of a trading strategy based on these forecasts. As high and low prices can be seen as interval-value time series, an evolving fuzzy system to model and forecast interval data is suggested to capture time varying, nonlinear and uncertain dynamics of the cryptocurrency market. The model is composed of fuzzy functional rules in which its structure and functionality are updated as data are input. It is also designed to process interval-valued data, where high and low prices are used to represent the corresponding interval bounds. Antecedents of the rules are updated using participatory learning to cluster interval-valued time series, and consequents are computed using weighted recursive least squares based on intervals center and range. In addition to the evaluation of predictability through accuracy metrics, a simple trading strategy is constructed based on high and low prices forecasts to determine entry and exit signals, composing an economic criteria. Empirical results indicated that the fuzzy model is able to produce accurate forecasts of high and low prices of BitCoin, and a higher level of return adjusted by risk is achieved when these forecasts are used to perform a trading strategy in comparison with the competitive approaches.
Reinforcement learning-based pilot assistance system for management of failures

The occurrence of abnormal or emergencies in flight is usually managed by aircraft crews which also can have remote assistance from controllers like the ATM (Air Traﬃc Management) or ANS (Air Navigation System) assisting with flight plans and routes. However, during certain scenarios, the pilot is responsible and must validate information, conditions, and protocols from the Quick Reference Handbook (QRH), which is a manual that contains step-by-step processes applicable to certain critical scenarios. Using the QRH works well where one abnormal situation happens at a time, but, in situations with multiple failures, the decision-making and steps to follow may be unclear and very dependent on pilot experience. We propose herein a learning process for an Intelligent Assistant to assist pilots to mitigate system failures based on the use of Reinforcement Learning. More specifically, the system under study was the Air Management System (AMS), responsible for controlling the air supply to the cabins keeping proper temperature and pressure, an essential function to make the interior livable and comfortable for passengers during flight. We then defined a Deep Q-learning neural network with an experience replay strategy, importance sampling, and an updated reward function that is based on a fault tree for the system, returning negative rewards as weighted sums of consequences such as critical failure in the auxiliary power unit, critical failure in the wing anti-icing, etc. For single-failure situations, the learned model was able to produce an appropriate action policy concerning QRH indications. We produced additional insights on the treatment of multiple failures, an aspect that is not fully tackled in QRHs, with results that suggest a particular sensitivity to failure probabilities and reward function adjustments. The main potential implication of our study is the proposal and assessment of a decision-making support system for pilots in situations that are not fully considered in QRHs.