Although cross-domain recommendation systems play a crucial role in solving the data sparseness and cold start challenges in recommendation systems, current algorithms primarily rely on the user-item rating matrix for...
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The use of social media platforms has been gradually increasing and fake news spreading is becoming an alarming issue nowadays. The spreading of fake news means disseminating false, confusing, and spurious information...
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The Border Gateway Protocol (BGP) is a policy-based protocol, which enables Autonomous systems (ASes) to independently define their routing policies with little or no global coordination. AS-level topology and AS-leve...
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ISBN:
(纸本)9781450392594
The Border Gateway Protocol (BGP) is a policy-based protocol, which enables Autonomous systems (ASes) to independently define their routing policies with little or no global coordination. AS-level topology and AS-level paths inference have been long-standing problems for the past two decades, yet, an important question remains open: "which elements of Internet routing affect the AS-path inference accuracy and how much do they contribute to the error?". In this work, we: (1) identify the confounding factors behind Internet routing modeling, and (2) quantify their contribution on the inference error. Our results indicate that by solving the first-hop inference problem, we can increase the exact-path score from 33.6% to 84.1%, and, by taking geolocation into consideration, we can refine the accuracy up to 94.6%.
With the increasing penetration of distributed photovoltaic power stations in the power system, a hybrid optimization method, PSO-CNN-GRU, is proposed to ensure the secure and stable operation of the power grid. Utili...
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When employing renewable energy within a smart micro grid (SMG), the management of distributed energy resources (DER) plays a crucial role in optimizing practical objectives of SMG. This study utilizes the Shuffled fr...
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Because reliable data-driven modeling methods require quantifying model uncertainty, an estimation of the parameter uncertainty is important. Usually maximum-likelihood methods with a known probability distribution of...
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ISBN:
(数字)9781665467100
ISBN:
(纸本)9781665467100
Because reliable data-driven modeling methods require quantifying model uncertainty, an estimation of the parameter uncertainty is important. Usually maximum-likelihood methods with a known probability distribution of the noise are used. In practice, for many problems the stochastic properties of the errors are unknown and cannot be determined. In such cases, bounded error parameter estimation methods can be beneficial. These assume that the error lies within prior specified bounds. However, parameter estimation in the bounded error setting can be difficult and computationally expensive for data-driven modeling as a high number of parameters results. In this paper a new method for an approximate estimation with reduced effort of the set of feasible parameters for nonlinear data-driven modeling with Takagi-Sugeno fuzzy models is introduced. This permits to tackle large real-world problems. For this, a sampling based ray shooting method is proposed that guarantees an inner approximation of the feasible parameter set. The capability of the proposed method is demonstrated in two case studies, including one with data from an industrial hard turning process.
This paper explores the integration of machine learning (ML) techniques with magnesium-based biomedical applications, focusing on predictive modeling and personalized treatment strategies. Magnesium's biocompatibi...
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Parallel robots are versatile but challenging to control due to their complex dynamics and the need for precise modeling. This research presents a novel approach to modular control of parallel robots, utilizing reinfo...
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Various methods for acoustic impulse event detection and identification are available. They are usually based on time or frequency domain algorithms. Both these domains have their limitations and disadvantages. This a...
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ISBN:
(纸本)9783031713965;9783031713972
Various methods for acoustic impulse event detection and identification are available. They are usually based on time or frequency domain algorithms. Both these domains have their limitations and disadvantages. This article presents acoustic impulse events (such as gunshots) identification based on the Cepstral domain, combining the advantages of both frequency and time domains. It compares the efficiency of classification based on four different frequency Cepstral coefficients, namely Mel-frequency Cepstral Coefficients (MFCC), Inverse Mel-frequency Cepstral Coefficients (IMFCC), Linear-frequency Cepstral Coefficients (LFCC) and Gammatone-frequency Cepstral Coefficients (GFCC). These, originally speech features, showed to be promising in the other applications with good results. This work compares the classification accuracy of gunshots from several short and rifle guns and multiple impulse acoustic events (various types of slams, slaps, etc.) to represent false alarms. In total, more than four hundred acoustic event records have been acquired, where approx. 70% has been used for training, and the rest for validation. For a classification, a Support Vector Machine (SVM) classifier with 26 frequency Cepstral Coefficients from each MFCC, IMFCC, LFCC, and GFCC served as features are used. Accuracy and Matthew's correlation coefficient measure the classification success rate. The results confirm the superiority of GFCC to other analyzed methods.
To prevent the network from becoming a potential performance bottleneck, two approaches commonly adopted in recent High-Performance Computing systems are offloading the network stack and Remote Direct Memory Access. N...
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