Multi-document Summarization System Using Rhetorical Information

Over the past 20 years, research in automated text summarization has grown significantly in the field of natural language processing. The massive availability of scientific and technical information on the Internet, including journals, conferences, and news articles has attracted the interest of various groups of researchers working in text summarization. These researchers include linguistics, biologists, database researchers, and information retrieval experts. However, because the information available on the web is ever expanding, reading the sheer volume of information is a significant challenge. To deal with this volume of information, users need appropriate summaries to help them more efficiently manage their information needs. Although many automated text summarization systems have been proposed in the past twenty years, none of these systems have incorporated the use of rhetoric. To date, most automated text summarization systems have relied only on statistical approaches. These approaches do not take into account other features of language such as antimetabole and epanalepsis. Our hypothesis is that rhetoric can provide this type of additional information. This thesis addresses these issues by investigating the role of rhetorical figuration in detecting the salient information in texts. We show that automated multi-document summarization can be improved using metrics based on rhetorical figuration. A corpus of presidential speeches, which is for different U.S. presidents speeches, has been created. It includes campaign, state of union, and inaugural speeches to test our proposed multi-document summarization system. Various evaluation metrics have been used to test and compare the performance of the produced summaries of both our proposed system and other system. Our proposed multi-document summarization system using rhetorical figures improves the produced summaries, and achieves better performance over MEAD system in most of the cases especially in antimetabole, polyptoton, and isocolon. Overall, the results of our system are promising and leads to future progress on this research

Procedurally Rhetorical Verb-Centric Frame Semantics as a Knowledge Representation for Argumentation Analysis of Biochemistry Articles

The central focus of this thesis is rhetorical moves in biochemistry articles. Kanoksilapatham has provided a descriptive theory of rhetorical moves that extends Swales' CARS model to the complete biochemistry article. The thesis begins the construction of a computational model of this descriptive theory. Attention is placed on the Methods section of the articles. We hypothesize that because authors' argumentation closely follows their experimental procedure, procedural verbs may be the guide to understanding the rhetorical moves. Our work proposes an extension to the normal (i.e., VerbNet) semantic roles especially tuned to this domain. A major contribution is a corpus of Method sections that have been marked up for rhetorical moves and semantic roles. The writing style of this genre tends to occasionally omit semantic roles, so another important contribution is a prototype ontology that provides experimental procedure knowledge for the biochemistry domain. Our computational model employs machine learning to build its models for the semantic roles and rhetorical moves, validated against a gold standard reflecting the annotation of these texts by human experts. We provide significant insights into how to derive these annotations, and as such have contributions as well to the general challenge of producing markups in the domain of biomedical science documents, where specialized knowledge is required

5G Networks and IoT Devices: Mitigating DDoS Attacks with Deep Learning Techniques

The development and implementation of Internet of Things (IoT) devices have been accelerated dramatically in recent years. As a result, a super-network is required to handle the massive volumes of data collected and transmitted to these devices. Fifth generation (5G) technology is a new, comprehensive wireless technology that has the potential to be the primary enabling technology for the IoT. The rapid spread of IoT devices can encounter many security limits and concerns. As a result, new and serious security and privacy risks have emerged. Attackers use IoT devices to launch massive attacks; one of the most famous is the Distributed Denial of Service (DDoS) attack. Deep Learning techniques have proven their effectiveness in detecting and mitigating DDoS attacks. In this paper, we applied two Deep Learning algorithms Convolutional Neural Network (CNN) and Feed Forward Neural Network (FNN) in dataset was specifically designed for IoT devices within 5G networks. We constructed the 5G network infrastructure using OMNeT++ with the INET and Simu5G frameworks. The dataset encompasses both normal network traffic and DDoS attacks. The Deep Learning algorithms, CNN and FNN, showed impressive accuracy levels, both reaching 99%. These results underscore the potential of Deep Learning to enhance the security of IoT devices within 5G networks