Improvements to the complex question answering models

x, 128 leaves : ill. ; 29 cmIn recent years the amount of information on the web has increased dramatically. As a result, it has become a challenge for the researchers to find effective ways that can help us query and extract meaning from these large repositories. Standard document search engines try to address the problem by presenting the users a ranked list of relevant documents. In most cases, this is not enough as the end-user has to go through the entire document to find out the answer he is looking for. Question answering, which is the retrieving of answers to natural language questions from a document collection, tries to remove the onus on the end-user by providing direct access to relevant information. This thesis is concerned with open-domain complex question answering. Unlike simple questions, complex questions cannot be answered easily as they often require inferencing and synthesizing information from multiple documents. Hence, we considered the task of complex question answering as query-focused multi-document summarization. In this thesis, to improve complex question answering we experimented with both empirical and machine learning approaches. We extracted several features of different types (i.e. lexical, lexical semantic, syntactic and semantic) for each of the sentences in the document collection in order to measure its relevancy to the user query. We have formulated the task of complex question answering using reinforcement framework, which to our best knowledge has not been applied for this task before and has the potential to improve itself by fine-tuning the feature weights from user feedback. We have also used unsupervised machine learning techniques (random walk, manifold ranking) and augmented semantic and syntactic information to improve them. Finally we experimented with question decomposition where instead of trying to find the answer of the complex question directly, we decomposed the complex question into a set of simple questions and synthesized the answers to get our final result

Cyber attacks detection from smart city applications using artificial neural network

Recently, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities, which utilise smart applications to maximize operational efficiency, and thereby the quality of services and the wellbeing of people. In this paper, we propose an attack and anomaly detection technique based on machine learning algorithms to mitigate IoT cybersecurity threats in a smart city. Notably, while there are many different machine learning (ML) algorithms, including computationally expensive deep learning network, we opted for using artificial neural network (ANN) since an ANN can provide a simple and computationally faster architecture as needed for smart city operations. A widely used performance metrics, namely, accuracy, precision, recall, and F1 score are utilized to evaluate the model. Experiment results with the recent attack dataset demonstrate that the proposed technique can effectively identify the cyber attacks and outperform results reported in an existing wor