Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly detection for cellular networks using big data analytics

Broadband connectivity and mobile technology have been widely applied in the world. With these advanced technologies, the proliferation of smart devices and their applications by accessing mobile internet have come up with a giant leap forward, leading to the ever-increasing scale and complexity of cellular networks. This presents imminent challenges to anomaly detection in cellular networks. In this study, the authors discuss challenges and current literature of anomaly detection for cellular networks to embrace the ‘big data’ era. First, they review the state-of-the-art techniques in the area of anomaly detection in cellular networks. Then, the challenges are pinpointed for anomaly detection due to the cellular network big data. Finally, they introduce a big data analytic-based anomaly detection method for cellular networks.
Uma Comparação entre os Sistemas de Detecção de Ameaças Distribuídas de Rede Baseado no Processamento de Dados em Fluxo e em Lotes

With the advancement of technology, allowing a greater massification of devices connected to the Internet of Things, there is a huge increase in the communication that circulates through the network, resulting in a growing number of vulnerability exploitations detected every year. Thus, faster and more accurate systems are needed to efficiently detect distributed denial of service attacks and port scans. This paper proposes a system for on-line detection of distributed network threats using data stream processing. The results obtained by the proposed system are compared with the results obtained by a system using batch processing, both operating on the same database, widely known by the scientific community. The proposed system is evaluated through two metrics: accuracy and number of false-positive and false-negative. The results show that using data stream processing improved detection accuracy by up to 17,50%, reducing the number of false-positives and false-negatives by up to 66,61%.
Existence Results for Nonlinear Quasi-hemivariational Inequality Systems

This paper aims at establishing the existence solutions of a non-standard quasi-hemivariational inequality‚ whose solutions are discussed in a subset of a reflexive Banach space for every . In addition, we introduce two applications. The first one shows the relationship between our inequality problems and equilibrium problems. The second one applies the extend some results concerning nonlinear quasi-hemivariational inequalities.
Comparing variational methods for the hinged Kirchhoff plate with corners

The hinged Kirchhoff plate model contains a fourth order elliptic differential equation complemented with a zeroeth and a second order boundary condition. On domains with boundaries having corners the strong setting is not well‐defined. We here allow boundaries consisting of piecewise C2, 1‐curves connecting at corners. For such domains different variational settings will be discussed and compared. As was observed in the so‐called Saponzhyan–Babushka paradox, domains with reentrant corners need special care. In that case, a variational setting that corresponds to a second order system needs an augmented solution space in order to find a solution in the appropriate Sobolev‐type space.
Optimization of an artificial neural network to estimate laser ablation efficiency

The use of fiber laser in surgery has many advantages due to its small size, efficiency, operation in continuous and pulse mode and easy coupling to fiber optics for various applications. Laser surgery requires very precise control on laser parameters, and imprecision can cause unacceptable damages during operation, particularly in neurosurgical applications. There have been many experimental studies looking to optimize laser parameters to control laser thermal therapy. In this study, we propose an artificial neural network method (ANN) to predict laser ablation damage as a function of laser parameters and temperature. The purpose of this study was to investigate the performance of artificial neural networks to predict the ablation efficiency of a 1940 nm thulium fiber laser on ovine brain tissue. Twelve experimental data were used to train an ANN model. Tissue type (cortical/subcortical), laser mode, laser power, laser energy, time, temperature and temperature change were used as inputs for the ANN and the ablation efficiency (ablated area/total thermally altered area) was the output of the model. Four untrained data were used to validate the ANN prediction ability after finding correlation between the ANN inputs and output. Experimental and predicted data were compared to find the accuracy of the model. Moreover, optimum laser mode (continuous/pulse) selection was also studied. Five different machine learning methods were used for laser mode selection, and the results were compared. Our results showed that prediction for the ablation efficiency of an ANN is lower than 15% and 87.5% classification accuracy was obtained for optimum laser mode selection.
Prospects of photonic crystal fiber for analyte sensing applications: an overview

The detection and monitoring of physical, chemical and biomedical parameters are increasingly reliant on fiber-optic sensing technology. Of applicable optical methods, photonic crystal fiber (PCF) sensors show its potential as a sensitive technique in environmental, industrial, food preservation and medical applications. Such a system incorporates the fabrication of a particular PCF, the interaction of wave propagation with the measured field, signal processing to offer automated real-time measurement in terms of the amplitude, phase, polarization and wavelength of spectrum. This article is an endeavour towards giving a brief overview of the development of analyte sensors using PCFs in the last few years. Different kinds of PCF analyte sensors are discussed based on the measuring entity and reported works. This discussion integrates a variety in the nature of the core, metal coating on the PCF and liquid infiltration in the holes. It is also considered to present the phenomena of its internal structure and interference techniques for several applications. Advances in this technology, particularly in the areas of gas sensing, chemical spices and bio analytes, will be discussed in this article with some applications.
Generalized Mirror Prox for Monotone Variational Inequalities: Universality and Inexact Oracle

We introduce an inexact oracle model for variational inequalities (VI) with monotone operator, propose a numerical method which solves such VI's and analyze its convergence rate. As a particular case, we consider VI's with H\"older-continuous operator and show that our algorithm is universal. This means that without knowing the H\"older parameter $\nu$ and H\"older constant $L_{\nu}$ it has the best possible complexity for this class of VI's, namely our algorithm has complexity $O\left( \inf_{\nu\in[0,1]}\left(\frac{L_{\nu}}{\varepsilon} \right)^{\frac{2}{1+\nu}}R^2 \right)$, where $R$ is the size of the feasible set and $\varepsilon$ is the desired accuracy of the solution. We also consider the case of VI's with strongly monotone operator and generalize our method for VI's with inexact oracle and our universal method for this class of problems. Finally, we show, how our method can be applied to convex-concave saddle point problems with H\"older-continuous partial subgradients.
Utilização e verificação do código GEANT4 em aplicações médicas: estudo de caso para elétrons

O aumento cada vez mais acelerado da utilizacao de radiacao ionizante nos varios setores da medicina e uma realidade. Neste sentido, varios codigos inspirados no Metodo Monte Carlo tem sido desenvolvidos e adaptados para atender a crescente demanda no uso da radiacao ionizante em aplicacoes medicas. Entre os codigos mais utilizados para transporte de radiacao atualmente podemos incluir o GEometry ANd Tracking 4 (GEANT4) disponibilizado pelo Conseil Europeen pour la Recherche Nucleaire (CERN). O GEANT4 e um conjunto de ferramentas computacionais, chamado de toolkit. O codigo utiliza programacao orientada a objeto em C++ e atualmente e utilizado em varias areas com uma ampla faixa de energia. Diversos processos fisicos e particulas tem sido incorporados no toolkit do GEANT4 permitindo seu uso em fisica medica. Este trabalho apresenta uma metodologia para instalacao e utilizacao do codigo GEANT4 bem como a verificacao do mesmo em aplicacoes com eletrons com o objetivo de aumentar a difusao e aperfeicoamento do codigo entre um numero cada vez maior de usuarios.
Attitude and Position Control Using Real-acking

A variety of sensors and positioning methods have been developed over the years. Most methods rely on active sensors (such as sonars or lasers) which have range and power restrictions, or rely on computationally complex (and often slow) methods such as recovering position from stereo or optical flow. This paper describes a system that can determine a robot’s position and orientation, in all six degrees of freedom, relative to a simple passive target. The system can determine its position relative to the target from a single image frame, and process the image and calculate the position faster than a camera’s frame rate (60Hz). It uses a standard, uncalibrated color video camera as its sensor. The system runs on an inexpensive microprocessor (a Motorola 68332) leaving many cycles left over. By careful design of the target and the vision processing hardware and software, we are able to selectively perceive only the relevant parts of the target, greatly simplifying and speeding all the necessary computations. The system is being developed for autonomous spacecraft station keeping and docking.
Utility/Privacy Trade-off through the lens of Optimal Transport

Strategic information is valuable either by remaining private (for instance if it is sensitive) or, on the other hand, by being used publicly to increase some utility. These two objectives are antagonistic and leaking this information might be more rewarding than concealing it. Unlike classical solutions that focus on the first point, we consider instead agents that optimize a natural trade-off between both objectives. We formalize this as an optimization problem where the objective mapping is regularized by the amount of information revealed to the adversary (measured as a divergence between the prior and posterior on the private knowledge). Quite surprisingly, when combined with the entropic regularization, the Sinkhorn loss naturally emerges in the optimization objective, making it efficiently solvable. We apply these techniques to preserve some privacy in online repeated auctions.
A Stereo Matching System

Determination of the depth of the image feature distinctive automation and other industries of machine vision and computer vision technology in everyday life are becoming increasingly popular. Some techniques have been proposed to extract from the current depth of a 2D image of the feature, which defines a particular object or structure of the information. In many cases, these techniques are automatic, such as a suitable carrier moving average depth identify objects placed in the 2D image. For this intensive depth cues to solve two stereo matching algorithm using a machine learning algorithm. Other methods, relative to the camera based on the motion of the object have been proposed and analyzed by estimating the optical flow calculation depth map. The method of dense and sparse three-dimensional surface of the object to provide the three-dimensional information. This paper discusses the evaluation of the depth cues, through intensive two standard fast algorithm for real-time stereo image matching algorithm.
Diffuse molecular gas in cloud envelopes and the galaxy

Traditional measurements of molecular gas via rotational and hyperfine transitions of diatomic species such as CO, CH, and OH are only able to detect the cores and adjacent low-density molecular regions of interstellar molecular clouds. Longer than usual integration times of the CO(J = 1-0) line at 115 GHz and the OH 1667 MHz ground state, hyperfine, main line transition have revealed the presence of low-density, diffuse gas surrounding the molecular core MBM40. Using the Schlegel, Finkbeiner and Davis (1998) dust maps in conjunction with new CO and OH observations, the amount of molecular gas in the diffuse outer regions of MBM40 has been determined to be equal if not greater than the mass in the core region. This much mass was not expected to be found in the outer most regions of the cloud. In addition, a re-analysis of high-latitude CO survey data reveals that this low-density molecular gas may be widespread in the Galaxy. The relationship between the low-density, diffuse molecular component studied in this thesis and the recently identified “dark” molecular gas is discussed. Index words: ISM: clouds, ISM: molecules, radio, MBM40, CO, OH, CH Diffuse Molecular Gas in Cloud Envelopes and the Galaxy
Two-tailed asymptotic inferences for the odds ratio in prospective and retrospective studies: evaluation of methods of inference

ABSTRACT Various asymptotic methods have been proposed for obtaining a two-tailed confidence interval (CI) for the odds ratio (OR) based on two independent samples. This paper evaluates 22 different methods, including 14 new methods or modifications of old methods. Because the CI is obtained by inversion in θ of the two-tailed test for H0: OR = θ, this paper evaluates the tests for various values of θ, rather than the CIs that are obtained. The paper concludes that none of the classic methods were selected as a top method, although the Agresti logit method is an acceptable option when the sample imbalance and the true value of OR are moderate; however the CI so obtained is very wide. The best methods are based on the likelihood-ratio test and on the inverse sine transformation – which is a new method – after adding 0.5 to all the data; the likelihood-ratio test method has one disadvantage, which is that the CI is obtained iteratively. The paper also selects the best methods for ensuring compatibility between the conclusions for the CI and the independence test.
Supervised Functional Data Discriminant Analysis for Hyperspectral Image Classification

This article proposes a functional data discriminant analysis (FDDA) method for hyperspectral image (HSI) classification. This method analyzes and processes the HSI data from a functional point of view, which is a novel perspective in HSI processing. The classical methods achieve dimensionality reduction by directly eliminating the redundancy of the HSI data. However, the proposed method extracts the functional features by utilizing the redundancy of the HSI data. Functional features can effectively reveal inherent characteristics of the HSI data with the change in the wavelengths. Based on this, a regularized weighted fitting model is first built for converting a spectral vector into a spectral curve. Second, an FDDA method defined in the function field is presented for extracting the functional features of the spectral curves. Finally, a novel spectral–spatial framework is designed for classification tasks of HSI data sets. Experimental results in three commonly used HSI data sets indicate that the proposed method is effective and leads to promising classification results compared with some benchmarking methods. More importantly, the work tries to diversify and develop the existing theory and methods of HSI classification from discrete (vector) data learning methods to continuous (functional) data learning methods.
A Variable-Correlation Model to Characterize Asymmetric Dependence for Postprocessing Short-Term Precipitation Forecasts

Statistical postprocessing methods can be used to correct bias and dispersion error in raw ensemble forecasts from numerical weather prediction models. Existing postprocessing models generally perform well when they are assessed on all events, but their performance for extreme events still needs to be investigated. Commonly used joint probability postprocessing models are based on the correlation between forecasts and observations. Because the correlation may be lower for extreme events as a result of larger forecast uncertainty, the dependence between forecasts and observations can be asymmetric with respect to the magnitude of the precipitation. However, the constant correlation coefficient in the traditional joint probability model lacks the flexibility to model asymmetric dependence. In this study, we formulated a new postprocessing model with a decreasing correlation coefficient to characterize asymmetric dependence. We carried out experiments using Global Ensemble Forecast System reforecasts for daily precipitation in the Huai River basin in China. The results show that, although it performs well in terms of continuous ranked probability score or reliability for all events, the traditional joint probability model suffers from overestimation for extreme events defined by the largest 2.5% or 5% of raw forecasts. On the contrary, the proposed variable-correlation model is able to alleviate the overestimation and achieves better reliability for extreme events than the traditional model. The proposed variable-correlation model can be seen as a flexible extension of the traditional joint probability model to improve the performance for extreme events.
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs

Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-dimensional inputs, with little capability of incorporating prior knowledge. This results in prohibitively long training times for use on real-world robotic tasks. Existing algorithms capable of extracting task-level representations from high-dimensional inputs, e.g. object detection, often produce outputs of varying lengths, restricting their use in RL methods due to the need for neural networks to have fixed length inputs. In this work, we propose a framework that combines deep sets encoding, which allows for variable-length abstract representations, with modular RL that utilizes these representations, decoupling high-level decision making from low-level control. We successfully demonstrate our approach on the robot manipulation task of object sorting, showing that this method can learn effective policies within mere minutes of highly simplified simulation. The learned policies can be directly deployed on a robot without further training, and generalize to variations of the task unseen during training.
Convex Optimization for Bundle Size Pricing Problem

We study the bundle size pricing (BSP) problem where a monopolist sells bundles of products to customers, and the price of each bundle depends only on the size (number of items) of the bundle. Although this pricing mechanism is attractive in practice, finding optimal bundle prices is difficult since it involves characterizing distributions of the maximum partial sums of order statistics. In this paper, we propose to solve the BSP problem under a class of choice model using only the first and second moments of customer valuations. We show that the BSP problem under this model is convex and can be efficiently solved using off-the-shelf solvers. Our approach is flexible in optimizing any given bundle sizes, and numerical results show that it performs very well compared with state-of-the-art heuristics.
Emerging modes of temporal coordination : Mandarin and non-native consonant clusters

This dissertation investigates the perception and production of non-native consonant clusters (CCs) by native speakers of Mandarin Chinese, a language with relatively simple syllable structure. We focus on the different modifications (‘errors’) that emerge in perception and production, in light of the role played by phonological knowledge and by sensitivity to phonetic details.We hypothesized that if native phonotactic knowledge is primarily affecting non-native adaptation, Mandarin speakers will perceive and produce a vowel systematically in consonant clusters. Alternatively, if sensitivity to phonetic details primarily contributes to adaptation, Mandarin speakers will show various modifications depending on phonetic properties of the actual clusters presented. These hypotheses were tested through a series of experiments—an ABX discrimination experiment, a transcription experiment, and a prompted production experiment.In the ABX discrimination experiment, Mandarin speakers were highly sensitive to the CC-CVC contrast, showing that native phonotactics does not impede their perception of non-native clusters. Participants relied instead on the phonetic details of the clusters. The weaker the C1 burst in stop-stop clusters, the less vowel epenthesis was perceived.In the follow-up transcription experiment, results showed that correct transcription was absent from the data. Contrary to the discrimination results, Mandarin speakers transcribed non-native CCs with a high percentage of epenthetic vowels. However, vowel transcription may be biased by Pinyin orthography.Therefore, we conducted a production experiment, where speakers heard the stimuli with clusters and produced them aloud. The results of this prompted production experiment showed that Mandarin speakers produce a vocoid (a ‘vowel’ in a purely phonetic sense, see Pike, 1943) within CCs, which is similar to a reduced vowel in Mandarin, with short duration and schwa-like quality. The acoustic measures of the production indicated that the native gestural timing pattern was maintained in the production with vocoids, even though speakers were able to compress the vocoid acoustically. Interestingly, despite the absence of clusters in Mandarin, speakers sometimes ‘correctly’ produced non-native CC sequences, or produced them with a period of voicing, just relying on auditory inputs. We thus learned that the production of non-native clusters by Mandarin speakers is highly affected by their phonological knowledge, while their perception of the same clusters is primarily influenced by their sensitivity to phonetic details.
Architecture and Devices for Silicon Photonic Switching in Wavelength, Polarization and Mode

Switching can be performed with multiple physical dimensions of an optical signal. Previously optical switching was mainly focused in the wavelength domain. In this paper we discuss the general architecture of integrated silicon photonic switches by exploiting multi-dimensions in wavelength, polarization, and mode. To route a data channel from one input port to an arbitrary output port in a network node, three basic functions are required: de-multiplexing, switching, and multiplexing. The multiplexing and de-multiplexing processes can be realized in any one physical dimension. The capacity of a switch can be effectively scaled by using joint physical dimensions. As two examples, we first present a wavelength switch based on dual-nanobeam cavities with high quality factors, a low power consumption, and a compact footprint. We then propose a design of a mode-polarization-wavelength selective switch by leveraging three physical dimensions, and experimentally demonstrate the building blocks and key functionalities.