Search results for: RANDOM FIELD
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Bounds on the Cover Time of Parallel Rotor Walks
PublicationThe rotor-router mechanism was introduced as a deterministic alternative to the random walk in undirected graphs. In this model, a set of k identical walkers is deployed in parallel, starting from a chosen subset of nodes, and moving around the graph in synchronous steps. During the process, each node maintains a cyclic ordering of its outgoing arcs, and successively propagates walkers which visit it along its outgoing arcs in...
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Amplifying the Randomness of Weak Sources Correlated With Devices
PublicationThe problem of device-independent randomness amplification against no-signaling adversaries has so far been studied under the assumption that the weak source of randomness is uncorrelated with the (quantum) devices used in the amplification procedure. In this paper, we relax this assumption, and reconsider the original protocol of Colbeck and Renner using a Santha-Vazirani (SV) source. To do so, we introduce an SV-like condition...
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Compressive Sensing Approach to Harmonics Detection in the Ship Electrical Network
PublicationThe contribution of this paper is to show the opportunities for using the compressive sensing (CS) technique for detecting harmonics in a frequency sparse signal. The signal in a ship’s electrical network, polluted by harmonic distortions, can be modeled as a superposition of a small number of sinusoids and the discrete Fourier transform (DFT) basis forms its sparse domain. According to the theory of CS, a signal may be reconstructed...
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A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study
PublicationThis study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective...
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Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm
PublicationIntroduction: Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. Methods: To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological...
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Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection
PublicationDue to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for detecting malware...
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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor
PublicationNitrous oxide (N2O) is a key parameter for evaluating the greenhouse gas emissions from wastewater treatment plants. In this study, a new method for predicting liquid N2O production during nitrification was developed based on a mechanistic model and machine learning (ML) algorithm. The mechanistic model was first used for simulation of two 15-day experimental trials in a nitrifying sequencing batch reactor. Then, model predictions...
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Instructor Presence in Video Lectures: Preliminary Findings From an Online Experiment
PublicationMotivation. Despite the widespread use of video lectures in online and blended learning environments, there is still debate whether the presence of an instructor in the video helps or hinders learning. According to social agency theory, seeing the instructor makes learners believe that s/he is personally teaching them, which leads to deeper cognitive processing and, in turn, better learning outcomes. Conversely, according to cognitive...
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A New Adaptive Method for the Extraction of Steel Design Structures from an Integrated Point Cloud
PublicationThe continuous and intensive development of measurement technologies for reality modelling with appropriate data processing algorithms is currently being observed. The most popular methods include remote sensing techniques based on reflected-light digital cameras, and on active methods in which the device emits a beam. This research paper presents the process of data integration from terrestrial laser scanning (TLS) and image data...
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Low-Cost Modeling of Microwave Components by Means of Two-Stage Inverse/Forward Surrogates and Domain Confinement
PublicationFull-wave electromagnetic (EM) analysis is one of the most important tools in the design of modern microwave components and systems. EM simulation permits reliable evaluation of circuits at the presence of cross-coupling effects or substrate anisotropy, as well as for accounting for interactions with the immediate environment. However, repetitive analyses required by EM-driven procedures, such as parametric optimization or statistical...
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Probabilistyczna ocena wrażliwości stanów granicznych konstrukcji inżynierskich na imperfekcje geometryczne i materiałowe
PublicationTematem pracy jest analiza probabilistyczna wrażliwości mechanicznej odpowiedzi konstrukcji inżynierskich na imperfekcje geometryczne oraz zmianę własności materiałów. Z tematem tym ściśle związany jest problem estymacji niezawodności konstrukcji, również szeroko opisany w rozprawie. W pracy dokonano przeglądu metod wykorzystywanych w analizie probabilistycznej oraz zaproponowano procedury wykorzystujące te metody w analizie wrażliwości...
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Comparative study on total nitrogen prediction in wastewater treatment 1 plant and effect of various feature selection methods on machine learning algorithms performance
PublicationWastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods are used in the pre-processing section for enhancing the model performance. This study aims to evaluate the effect of seven different FS methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen (TN) in the WWTP...
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Employing Subjective Tests and Deep Learning for Discovering the Relationship between Personality Types and Preferred Music Genres
PublicationThe purpose of this research is two-fold: (a) to explore the relationship between the listeners’ personality trait, i.e., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms. We assume that this may help match songs according to the listener’s personality in social music networks....
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Investigation of Weigh-in-Motion Measurement Accuracy on the Basis of Steering Axle Load Spectra
PublicationWeigh-in-motion systems are installed in pavements or on bridges to identify and reduce the number of overloaded vehicles and minimise their adverse eect on road infrastructure. Moreover, the collected trac data are used to obtain axle load characteristics, which are very useful in road infrastructure design. Practical application of data from weigh-in-motion has become more common recently, which calls for adequate attention to...
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Implementation of Non-Probabilistic Methods for Stability Analysis of Nonlocal Beam with Structural Uncertainties
PublicationIn this study, a non-probabilistic approach based Navier’s Method (NM) and Galerkin Weighted Residual Method (GWRM) in term of double parametric form has been proposed to investigate the buckling behavior of Euler-Bernoulli nonlocal beam under the framework of the Eringen's nonlocal elasticity theory, considering the structural parameters as imprecise or uncertain. The uncertainties in Young’s modulus and diameter of the beam are...
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Enriching the Context: Methods of Improving the Non-contextual Assessment of Sentence Credibility
PublicationThis paper presents several methods of automatic context enrichment of sentences that need to be evaluated, tagged or fact-checked by human judges. We have created a corpus of medical Web articles. Sentences from this corpus have been fact-checked by medical experts in two modes: contextually (reading the entire article and evaluating sentence by sentence) and without context (evaluating sentences from all articles in random order)....
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Spatio-temporal filtering for determination of common mode error in regional GNSS networks
PublicationThe spatial correlation between different stations for individual components in the regional GNSS networks seems to be significant. The mismodelling in satellite orbits, the Earth orientation parameters (EOP), largescale atmospheric effects or satellite antenna phase centre corrections can all cause the regionally correlated errors. This kind of GPS time series errors are referred to as common mode errors (CMEs). They are usually...
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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
PublicationWetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral...
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RAGE as a Novel Biomarker for Prostate Cancer: A Systematic Review and Meta-Analysis
PublicationThe receptor for advanced glycation end-products (RAGE) has been implicated in driving prostate cancer (PCa) growth, aggression, and metastasis through the fueling of chronic inflammation in the tumor microenvironment. This systematic review and meta-analysis summarizes and analyzes the current clinical and preclinical data to provide insight into the relationships among RAGE levels and PCa, cancer grade, and molecular effects....
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Intelligent Decision Forest Models for Customer Churn Prediction
PublicationCustomer churn is a critical issue impacting enterprises and organizations, particularly in the emerging and highly competitive telecommunications industry. It is important to researchers and industry analysts interested in projecting customer behavior to separate churn from non‐churn consumers. The fundamental incentive is a firm’s intent desire to keep current consumers, along with the exorbitant expense of gaining new ones....