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检索条件"机构=Dept of Computer Science and Applied Mathematics"

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Evaluation of the Effectiveness of Vulnerable Road User Clustering in C-V2X Systems

Evaluation of the Effectiveness of Vulnerable Road User Clus...

引用

2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Rupp, Matthias Wischhof, Lars Munich University of Applied Sciences HM Dept. of Computer Science and Mathematics Munich Germany

ISBN: (纸本)9798350346473

Vulnerable Road Users (VRUs) are at special risk in road traffic. The dissemination of information about VRUs in Intelligent Transportation Systems (ITS) can help to protect VRUs from various risks. The Vulnerable Road User Awareness Message (VAM) format has been specified by ETSI as a way of distributing VRU-related information. The standard also describes a clustering mechanism that can help to reduce the number of generated messages and therefore prevent computing overload and network congestion in certain situations. This paper presents initial results on the effectiveness of the clustering approach proposed in the ETSI standard as well as its impact on position accuracy. Therefore, the clustering schemes are implemented in coupled network-and mobility simulations, modeling traffic situations with pedestrians and vehicles. In addition, the impact of one of the most relevant parameters for forming clusters, the maxClusterVelocityDifference, on clustering is assessed. The results indicate that VRU clustering can signifi-cantly reduce the number of VAM messages but also leads to a larger position error between the actual-and received position of individual VRUs. © 2023 IEEE.

关键词： Roads and streets

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Simulating Home Appliance Behavior and Operational Condition-Action Rules for Autonomous Intelligence 3

Simulating Home Appliance Behavior and Operational Condition...

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3rd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Ehimwenma, Kennedy Efosa Ji, Haowen Jinle, Lin Sharji, Safiya Al Cheraghy, Maryam Bowen, Yu Dept of Computer Science College of Science Mathematics & Technology Wenzhou Kean University Wenzhou China Information Technology Dept University of Technology & Applied Sciences Muscat Oman

ISBN: (纸本)9798331529222

The future of home appliances in the IoT (Internet of Things) lies in self-awareness, independent condition-action rule implementation, and device autonomy. In the concept of autonomy, agent-based IoT appliances can SLEEP and WAKE-UP independently as when necessary, perceive and compute data;and engage in communication or negotiation with other agent-based devices. In the paradigm of OOP, this paper describes various home appliances' autonomous artificial intelligent (AAI) behavior. While self-awareness borders on the appliance knowing its current status and location, self-control relates to the appliance's ability to control its outgoing and incoming data and take the appropriate action based on some given environment data conditional rules. Thus, this paper presents some IoT home appliances' operational condition-action behavior (CAB). Firstly, by simulation, then followed by the actual transformation of the CAB rules into modular programs in object-oriented paradigm for sensor and appliance agents. The WindSpeedSensor, TempSensor, and HumiditySensor utilize the given environment data input to communicate with their respective WindowAppliance and CoffeeMachineAppliance agent, as demonstrated in this paper. In turn, the appliance agents take the decision reached by their sensors to perform some autonomous action. This model depicts independent AAI behavior of home appliances for the benefit of home owners. ©2024 IEEE.

关键词： Domestic appliances

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Firefly Algorithm Approach for Recovering the Mathematical Structure of One-Dimensional Chaotic Maps from Time Series Data 3

Firefly Algorithm Approach for Recovering the Mathematical S...

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3rd International Conference on computer Technologies, ICCTech 2024

作者： Galvez, Akemi Fister, Iztok Fister, Iztok Iglesias, Andres University of Cantabria Dept. of Applied Mathematics & Computational Sciences Santander Spain University of Maribor Faculty of Electrical Engineering & Computer Science Maribor Slovenia

ISBN: (纸本)9798350395419

Uncovering the mathematical structure of unknown chaotic systems from limited time series data poses a significant challenge in the field of dynamical systems, with broad applications across various domains. In this paper, we propose a swarm intelligence approach to tackle this issue, specifically focusing on one-dimensional discrete maps. Our approach leverages the firefly algorithm, a popular nature-inspired metaheuristic, known for its optimization capabilities. We assess the performance of the method by applying it to two illustrative examples of time series derived from one-dimensional maps: the Hénon map and the Burger map. The results show that this approach is promising for recovering the equations of motion of one-dimensional chaotic maps, even in situations where no additional information is available beyond the provided data points. © 2024 IEEE.

关键词： Time series

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On Crowdsourcing-Design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms 48

On Crowdsourcing-Design with Comparison Category Rating for ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Suarez, Angelica S. Z. Laroche, Clement Clemmensen, Line H. Das, Sneha Technical University of Denmark Dept. of Applied Mathematics and Computer Science Denmark GN Audio Research Ballerup Denmark

ISBN: (纸本)9781728163277

Speech enhancement techniques improve the quality or the intelligibility of an audio signal by removing unwanted noise. It is used as preprocessing in numerous applications such as speech recognition, hearing aids, broadcasting and telephony. The evaluation of such algorithms often relies on reference-based objective metrics that are shown to correlate poorly with human perception. In order to evaluate audio quality as perceived by human observers it is thus fundamental to resort to subjective quality assessment and in doing so we identify subgroups of users where the subjective assessments correlate better to objective metrics. In this paper, a user evaluation based on crowdsourcing (subjective) and the Comparison Category Rating (CCR) method is compared against the DNS-MOS, ViSQOL and 3QUEST (objective) metrics. The overall quality scores of three speech enhancement algorithms from real time communications (RTC) are used in the comparison using the P.808 toolkit. Results indicate that while the CCR scale allows participants to identify differences between processed and unprocessed audio samples, two groups of preferences emerge: some users rate positively by focusing on noise suppression processing, while others rate negatively by focusing mainly on speech quality. We further present results on the parameters, size considerations and speaker variations that are critical and should be considered when designing the CCR-based crowdsourcing evaluation1. © 2023 IEEE.

关键词： Speech enhancement

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Sensorless Unmanned Bicycle Robot

Sensorless Unmanned Bicycle Robot

引用

2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Efraim, Hanoch Basri, Ronen Weizmann Institute of Science Dept. of Computer Science and Applied Mathematics Rehovot Israel

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this work, we present a new approach for designing an autonomous bicycle robot. It is well known that bicycles can be laterally stable at certain velocities. This means that if the steering bar remains free to rotate, a bicycle can be designed so that it naturally steers in a way that corrects for deviations, keeping the bicycle upright. However, to the best of our knowledge this desirable property is not taken advantage of in previous works, since connecting a standard actuator to the steering bar, which is required in order to guide the bicycle to a desired direction, does not allow the steerer to be free to rotate. In this work we propose a unique actuation mechanism that allows guidance while keeping the steerer free to rotate, thus preserving the natural stability of the bicycle. Since the bicycle is mechanically stable there is no need for a low level controller, and thus, no need for sensors and sensor fusion algorithms. We demonstrate the concept with experiments performed on a 3D printed bicycle powered by a small electric motor and guided remotely. We provide all the CAD files and software in hope that it will be used by additional researchers in the field, and advance the frontiers of autonomous systems. © 2022 IEEE.

关键词： Bicycles

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The metric-aware kernel-width choice for LIME 1

The metric-aware kernel-width choice for LIME

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Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Barrera-Vicen, Aurelio Paluzo-Hidalg, Eduardo Gutiérrez-Naranjo, Miguel A. Dept. Computer Science and Artificial Intelligence University of Seville Spain Dept. Applied Mathematics I University of Seville Spain

Local Interpretable Model-Agnostic Explanations (LIME) are a well-known approach to provide local interpretability to Machine Learning models. LIME uses an exponential smoothing kernel based on the kernel width value, which defines the width of the local neighbourhood. In this paper, we study the influence of the distances for these local explanations, and we explore the choice of kernel width to guarantee a fair performance comparison between the distances. © 2023 CEUR-WS. All rights reserved.

关键词： Lime

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Site Connectivity with Towalink - Implementing an Open Source "SD-WAN Light" 30

Site Connectivity with Towalink - Implementing an Open Sourc...

引用

30th Telecommunications Forum, TELFOR 2022

作者： Henrici, Dirk Wischhof, Lars University of Applied Sciences Dept. of Computer Science and Mathematics Munich Germany

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

Towalink is an open source solution for interconnecting multiple sites securely and flexibly. It is based on WireGuard and BGP. With central management of the site routers, it can be considered an "SD-WAN light". We explain the rationale and design decisions of the implementation and share the experiences of using the solution in a real-world setup with a complex topology. © 2022 IEEE.

关键词： Wide area networks

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An Efficient Framework for classifying Cancer diseases using Ensemble machine learning over Cancer Gene Expression and Sequence Based Protein Interactions. 2

An Efficient Framework for classifying Cancer diseases using...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Metipatil, Prabhuraj Bhuvaneshwari, P. Basha, Syed Muzamil Patil, Siddanagouda S REVA University School of CSE Bengaluru India Dept. of Computer Science & Applied Mathematics UAS Bengaluru Bengaluru India

ISBN: (纸本)9798350320923

In recent years, a significant number of deaths worldwide have been due to cancer diseases. Analysis of Microarray gene expressions and protein interaction data facilitates early cancer identification. The accurate prediction of information for thousands of genes is made possible by using DNA microarray technology. Protein-Protein Interactions (PPIs) are the crucial protein activities involved in the cell cycle that replicates the DNA and cellular signaling. Determining whether a pair of proteins interacts is crucial for diagnosing an illness in molecular biology is therefore important. In existing machine learning classifiers have two-class problem that is limited and only be used to solve binary class problems, additionally, they can be prone to overfitting, as the classification framework may also become too specialized to the training data and not generalized to the varied data. To overcome this problem, this paper proposes an ensemble machine learning technique;ensembling combines the strengths of both classifiers that allow more robust and accurate framework. The better combination of both Support Vector machine and Naïve Bayes ensemble provides better performance in terms of various performance parameters. The proposed SVM-NB Ensemble classifier outperforms the existing classifiers by 15-20% over various performance parameters like classification accuracy, time taken for classification, precision, recall, and F-measure. The results were drawn by comparing the proposed ensemble (SVM+NB) classifier with the existing most applied classifiers like Logistic Regression (LR), Support Vector Machine and Naive Bayes techniques. © 2023 IEEE.

关键词： Support vector machines

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Amortized Variational Peak Fitting For Spectroscopic Data 33

Amortized Variational Peak Fitting For Spectroscopic Data

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Hansen, David Frich Alstrom, Tommy Sonne Schmidt, Mikkel N. Technical University of Denmark Richard Petersens Dept. of Applied Mathematics and Computer Science Plads 324 Kgs. Lyngby2800 Denmark

ISBN: (纸本)9798350324112

Spectroscopic analysis relies on identifying and understanding the spectral peaks that represent unique characteristics of an analyte. In high-speed real-time settings, current peak fitting techniques, particularly Bayesian methods involving MCMC or variational approximation, can be prohibitively expensive. We propose an unsupervised method using a convolutional neural network to estimate the number of peaks and their parameters along with posterior uncertainty, by amortizing variational inference in a classical parametric peak model. In a simulated data study, our method reliably determines the number of peaks, precisely estimates parameters similar to direct variational inference, and accurately captures uncertainty comparable to MCMC methods. Our novel, fast, and precise method for Bayesian spectral analysis opens new possibilities in real-time spectral data processing for high-speed monitoring and control. © 2023 IEEE.

关键词： Spectrum analysis

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