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关键词： Telescopes   Star & galaxy formation   Algorithms   Light   Candidates   Crowdsourcing   Sun   Pipelines   Astronomy   Astrophysics   Computer science   Optics   Physics  

摘要： The Kepler mission has been the catalyst for discovery of nearly 5,000 confirmed and candidate exoplanets. The majority of these candidates orbit Sun-like stars, and have orbital periods comparable to or shorter than that of the Earth, due to the selection bias inherent in the transit method and the limitations of automated transit search algorithms. We aim to develop a richer understanding of the population of exoplanets around the lowest-mass stars, the M spectral type. We are particularly interested in exoplanets with long orbital periods, which are difficult or impossible to find using standard transit search algorithms. In our study, we develop a unique methodology using the novel Planet Hunters citizen science project in conjunction with a new searching/scoring pipeline, SATCHEL, constructed from scratch to handle crowd-sourced time-series photometry. With the results from SATCHEL, we perform a rigorous completeness and reliability study to estimate the true number of planets orbiting our sample of 3,262 M-dwarf stars. After correcting for the geometric transit probability and the window function of the Kepler mission, we calculate occurrence rates for planets with radii 2 − 43 R⊕ and orbital periods 1 − 10, 000 days, obtaining a total rate of about 10.0 ± 5.11 planets per M-dwarf star. The vast majority of these planets likely lie in bins of small radius and long periods, implying highly efficient planet formation out to at least 5 AU from the host star. For planets with periods 1−1, 000 days, our results of 1.77±0.55 planets per M-dwarf are in good agreement with contemporary values, and qualitative agreement with predictions from other detection methods.

关键词： Physics   Applied Mathematics   Computer science   Fluid mechanics   Mathematics   Mechanics   Nuclear engineering   Thermodynamics  

摘要： In the past, majority of tools for modeling and simulation of nuclear reactors were focused towards simulating light water reactors, due to the large operational experience. Only recently has there been a lot of development with regards to tools for modeling Liquid Fuel Molten Salt Reactors (LFMSRs). The analysis of LFMSRs during steady state, operational transients and accident scenarios require addressing unique reactor multi-physics challenges with coupling between thermal hydraulics, neutronics, inventory control and species distribution phenomena. To further the development of LFMSR concepts, researchers need advanced modeling approaches to obtain results with a good trade-off between accuracy and computational efficiency. The key is to identify and model the multi-physics phenomena that impose the potential challenges to modeling and simulation of these reactors. This work utilizes the General Nuclear Field Operation and Manipulation (GeN-Foam) and System Analysis Module (SAM) to perform coupled thermal-hydraulics and neutronics calculation of the reactor. A framework is proposed as part of this study to perform modeling, design optimization and uncertainty quantification. The framework aims to establish a protocol for the studies and analyses of LFMSR which can later be expanded to other advanced reactor concepts too. Keeping this in mind, certain code developments followed by verification studies have been done to attain confidence with the codes. A new species transport model has been implemented in GeN-Foam along with the capability to model heat conduction to calculate the associated thermal stresses. As part of the framework, the uncertainty quantification and sensitivity analysis tool Dakota was successfully coupled with GeN-Foam and SAM to perform uncertainty quantification studies. The uncertainties were propagated through the input design parameters and the output uncertainties were characterized using statistical analysis and Spearman rank correlati

关键词： Computer science   Data collection   Data pipeline   Data science   Mathematics  

摘要： Temporal sentiment labels are used in various multimedia studies. They are useful for numerous classification and detection tasks such as video tagging, segmentation, and labeling. However, generating a large-scale sentiment dataset through manual labeling is usually expensive and challenging. Some recent studies explored the possibility of using online Time-Sync Comments (TSCs) as the primary source of their sentiment maps. Although the approach has positive results, existing TSCs datasets are limited in scale and content categories. Guidelines for generating such data within a constrained budget are yet to be developed and discussed. This dissertation tries to address the above issues by leveraging existing live comments from a popular video distributed platform, YouTube, as a primary time-synchronized data source and exploring efficient strategies for generating TSCs with a constrained budget. An automatic data mining system was first developed and deployed across multiple platforms. Then, long-period experiments were conducted to test the efficiency of the framework. Additionally, two large-scale TSCs datasets were created through the proposed data framework and analyzed for their characteristics. Finally, the outcomes were tested against the original temporal Automatic Speech Recognition (ASR) sentiment labeling to validate their accuracy. The experiment shows the potential of automatically generating temporal sentiment datasets through the proposed mapping system. This project also provides valuable tools for future multimedia research.

关键词： Chatbots   COVID-19   Artificial intelligence   Medical services   Machine learning   Computer science   Analytical models   chatbot   LSTM algorithm   machine learning   natural language processing   query processing  

摘要： The purpose of this paper is to show concisely how we can promote chatbots in the medical sector and cure infectious diseases. We can create awareness through the users and the users can get proper medical solutions to prevent disease. We created a preliminary training model and a study report to improve human interaction in databases in 2021. Through natural language processing, we describe the human behaviors and characteristics of the chatbot. In this paper, we propose an AI Chatbot interaction and prediction model using a deep feedforward multilayer perceptron. Our analysis discovered a gap in knowledge about theoretical guidelines and practical recommendations for creating AI chatbots for lifestyle improvement programs. A brief comparison of our proposed model concerning the time complexity and accuracy of testing is also discussed in this paper. In our work, the loss is a minimum of 0.1232 and the highest accuracy is 94.32%. This study describes the functionalities and possible applications of medical chatbots and explores the accompanying challenges posed by the use of these emerging technologies during such health crises mainly posed by pandemics. We believe that our findings will help researchers get a better understanding of the layout and applications of these revolutionary technologies, which will be required for continuous improvement in medical chatbot functionality and will be useful in avoiding COVID-19.

关键词： COVID-19  

摘要： The global pandemic has forced lecturers and academics to transform how computer science is taught. Discover how innovative technology and techniques are being used during lectures that can now reach an international audience. The COVID-19 pandemic has affected both staff and students within higher education.

关键词： Computer science   Artificial intelligence  

关键词： Wireless sensor networks   Security   Sensors   Routing   Communication system security   Jamming   Computer science   Connectivity   coverage   holes problem   wireless underground sensor networks  

摘要： The paper aims to put in the forefront a survey on the problem of the existing holes in the Wireless Underground Sensor Networks (WUSNs). Anomalies in wireless sensor networks can weaken the network by impeding its functionalities. These existing anomalies commonly known as holes may cause irreversible devastating problems and may prove fatal to humans. There can be different holes in the network, such as coverage hole, routing hole, jamming hole, sink/ worm hole, etc. This paper intends to discuss various holes problems in WUSNs, reasons behind their occurrence, and different techniques to mitigate the issues. An efficient hole detection technique shall increase network lifetime, use lesser resources and give accurate results with zero false positives. To put a light on the present scenario, methods/ techniques used to cope with different types of holes have been discussed in detail presenting their analysis, constraints, and advantages. Besides, it would also touch upon the comparative analysis in terms of soundness and limitations of these techniques. The study is concluded with future research directions.

关键词： Computer science   Artificial intelligence   Transportation  

关键词： Software   Computers   Internet   Threats   Computer science   Computer networks   Computer security   Feature selection   Design   Stochastic models   Malware   Clustering   Artificial intelligence  

摘要： The statistical approach to cyber security has become an active and important area of research due to the growth in number and threat of cyber attacks perpetrated nowadays. In this thesis, we centre our attention on the Bayesian approach to cyber security, which provides several modelling advantages such as the flexibility achieved through the probabilistic quantification of uncertainty. In particular, we have found that Bayesian models have been mainly used to detect volume-traffic anomalies, network anomalies and malicious software. To provide a unifying view of these ideas, we first present a thorough review on Bayesian methods applied to cyber security. Bayesian models applied to detecting malware and classifying them into known malicious classes is one of the cyber security areas discussed in our review. However, and contrary to detecting traffic and network anomalies, this area has not been widely developed from a Bayesian perspective. That is why we have centred our attention on developing novel supervised learning Bayesian nonparametric models to detect and classify malware using binary features built directly from the executables’ binary code. For these methods, important theoretical properties and simulation techniques are fully developed and for real malware data, we have compared their performance against well-known machine learning models which have been widely applied in this area. With respect to our methodologies, we first present a new discrete nonparametric prior specifically designed for binary data that builds on an elegant nonparametric hierarchical structure, which allows us to study the importance of each individual feature across the groups found in the data. Moreover, and due to the large, and possibly redundant, number of features, we have developed a generalised version of the model that allows the introduction of a feature selection step within the inferential learning. Finally, for a more complex modelling where there is a need to introd

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