132 research outputs found
Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review
(c) The Author/sfals
Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments
To fully exploit the benefits of the fog environment, efficient management of
data locality is crucial. Blind or reactive data replication falls short in
harnessing the potential of fog computing, necessitating more advanced
techniques for predicting where and when clients will connect. While spatial
prediction has received considerable attention, temporal prediction remains
understudied.
Our paper addresses this gap by examining the advantages of incorporating
temporal prediction into existing spatial prediction models. We also provide a
comprehensive analysis of spatio-temporal prediction models, such as Deep
Neural Networks and Markov models, in the context of predictive replication. We
propose a novel model using Holt-Winter's Exponential Smoothing for temporal
prediction, leveraging sequential and periodical user movement patterns. In a
fog network simulation with real user trajectories our model achieves a 15%
reduction in excess data with a marginal 1% decrease in data availability
A Novel Algorithmic Approach using Little Theorem of Fermat For Generating Primes and Poulet Numbers in Order
Computer encryption are based mostly on primes, which are also vital for communications. The aim of this paper is to present a new explicit strategy for creating all primes and Poulet numbers in order up to a certain number by using the Fermats little theorem. For this purpose, we construct a set C of odd composite numbers and transform Fermats little theorem from primality test of a number to a generating set Q of odd primes and Poulet numbers. The set Q is sieved to separate the odd primes and the Poulet numbers. By this method, we can obtain all primes and Poulet numbers in order up to a certain number. Also, we obtain a closed form expression which precisely gives the number of primes up to a specific number. The pseudo-code of the proposed method is presented
Predicting the Transportation Activities of Construction Waste Hauling Trucks: An Input-Output Hidden Markov Approach
Construction waste hauling trucks (CWHTs), as one of the most commonly seen
heavy-duty vehicles in major cities around the globe, are usually subject to a
series of regulations and spatial-temporal access restrictions because they not
only produce significant NOx and PM emissions but also causes on-road fugitive
dust. The timely and accurate prediction of CWHTs' destinations and dwell times
play a key role in effective environmental management. To address this
challenge, we propose a prediction method based on an interpretable
activity-based model, input-output hidden Markov model (IOHMM), and validate it
on 300 CWHTs in Chengdu, China. Contextual factors are considered in the model
to improve its prediction power. Results show that the IOHMM outperforms
several baseline models, including Markov chains, linear regression, and long
short-term memory. Factors influencing the predictability of CWHTs'
transportation activities are also explored using linear regression models.
Results suggest the proposed model holds promise in assisting authorities by
predicting the upcoming transportation activities of CWHTs and administering
intervention in a timely and effective manner.Comment: 21 pages, 8 figure
On Training Traffic Predictors via Broad Learning Structures:A Benchmark Study
A fast architecture for real-time (i.e., minute-based) training of a traffic predictor is studied, based on the so-called broad learning system (BLS) paradigm. The study uses various traffic datasets by the California Department of Transportation, and employs a variety of standard algorithms (LASSO regression, shallow and deep neural networks, stacked autoencoders, convolutional, and recurrent neural networks) for comparison purposes: all algorithms are implemented in MATLAB on the same computing platform. The study demonstrates a BLS training process two-three orders of magnitude faster (tens of seconds against tens-hundreds of thousands of seconds), allowing unprecedented real-time capabilities. Additional comparisons with the extreme learning machine architecture, a learning algorithm sharing some features with BLS, confirm the fast training of least-square training as compared to gradient training
RV4JaCa - Runtime Verification for Multi-Agent Systems
This paper presents a Runtime Verification (RV) approach for Multi-Agent Systems (MAS) using the JaCaMo framework. Our objective is to bring a layer of security to the MAS. This layer is capable of controlling events during the execution of the system without needing a specific implementation in the behaviour of each agent to recognise the events. MAS have been used in the context of hybrid intelligence. This use requires communication between software agents and human beings. In some cases, communication takes place via natural language dialogues. However, this kind of communication brings us to a concern related to controlling the flow of dialogue so that agents can prevent any change in the topic of discussion that could impair their reasoning. We demonstrate the implementation of a monitor that aims to control this dialogue flow in a MAS that communicates with the user through natural language to aid decision-making in hospital bed allocation
An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico
This paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabitanThis paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabita
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