Recent advances in large graph-based models have shown great performance in a variety of tasks, including node classification, link prediction, and influence modeling. However, these graph-based models struggle to capture high-order relations and interactions among entities effectively, leading them to underperform in many real-world scenarios. This thesis focuses on analyzing networks using hypergraphs for detection, classification, and prediction methods in social media-related problems. In particular, we study four specific applications with four proposed novel methods: detecting topic-specific influential users and tweets via hypergraphs; detecting spatiotemporal, topic-specific, influential users and tweets using hypergraphs; augmenting data in hypergraphs to mitigate class imbalance issues; and introducing a novel hypergraph convolutional network model designed for the multiclass classification of mental health advice in Arabic tweets. For the first method, existing solutions for influential user detection did not consider topics that could produce incorrect results and inadequate performance in that task. The proposed contributions of our work include: 1) Developing a hypergraph framework that detects influential users and tweets. 2) Proposing an effective topic modeling method for short texts. 3) Performing extensive experiments to demonstrate the efficacy of our proposed framework. For the second method, we extend the first method by incorporating spatiotemporal information into our solution. Existing influencer detection methods do not consider spatiotemporal influencers in social media, although influence can be greatly affected by geolocation and time. The contributions of our work for this task include: 1) Proposing a hypergraph framework that spatiotemporally detects influential users and tweets. 2) Developing an effective topic modeling method for short texts that geographically provides the topic distribution. 3) Designing a spatiotemporal topic-specific influencer user ranking algorithm. 4) Performing extensive experiments to demonstrate the efficacy of our proposed framework. For the third method, we address the challenge of bot detection on social media platform X, where there's an inherent imbalance between genuine users and bots, a key factor leading to biased classifiers. Our approach leverages the rich structure of hypergraphs to represent X users and their interactions, providing a novel foundation for effective bot detection. The contributions of our work include: 1) Introducing a hypergraph representation of the X platform, where user accounts are nodes and their interactions form hyperedges, capturing the intricate relationships between users. 2) Developing HyperSMOTE to generate synthetic bot accounts within the hypergraph, ensuring a balanced training dataset while preserving the hypergraph's structure and semantics. 3) Designing a hypergraph neural network specifically for bot detection, utilizing node and hyperedge information for accurate classification. 4) Conducting comprehensive experiments to validate the effectiveness of our methods, particularly in scenarios with pronounced class imbalances. For the fourth method, we introduce a Hypergraph Convolutional Network model for classifying mental health advice in Arabic tweets. Our model distinguishes between valid and misleading advice, leveraging high-order word relations in short texts through hypergraph structures. Our extensive experiments demonstrate its effectiveness over existing methods. The key contributions of our work include: 1) Developing a hypergraph-based model for short text multiclass classification, capturing complex word relationships through hypergraph convolution. 2) Defining four types of hyperedges to encapsulate local and global contexts and semantic similarities in our dataset. 3) Conducting comprehensive experiments in which the proposed model outperforms several baseline models in classifying Arabic tweets, demonstrating its superiority. For the fifth method, we extended our previous Hypergraph Convolutional Network (HCN) model to be tailored for sarcasm detection across multiple low-resource languages. Our model excels in interpreting the subtle and context-dependent nature of sarcasm in short texts by exploiting the power of hypergraph structures to capture complex, high-order relationships among words. Through the construction of three hyperedge types, our model navigates the intricate semantic and sentiment differences that characterize sarcastic expressions. The key contributions of our research are as follows: 1) A hypergraph-based model was adapted for the task of sarcasm detection in five short low-resource language texts, allowing the model to capture semantic relationships and contextual cues through advanced hypergraph convolution techniques. 2) Introducing a comprehensive framework for constructing hyperedges, incorporating short text, semantic similarity, and sentiment discrepancy hyperedges, which together enrich the model's ability to understand and detect sarcasm across diverse linguistic contexts. 3) The extensive evaluations reveal that the proposed hypergraph model significantly outperforms a range of established baseline methods in the domain of multilingual sarcasm detection, establishing new benchmarks for accuracy and generalizability in detecting sarcasm within low-resource languages. / Doctor of Philosophy / In the digital era, social media platforms are not just tools for communication but vast networks where billions of messages, opinions, and pieces of advice are exchanged every day. Navigating through this massive data to identify influential content, detect misleading information, or understand subtle expressions like sarcasm presents a significant challenge. Traditional methods often struggle to grasp the complex relationships and nuances embedded within the data. This dissertation introduces innovative approaches using hypergraphs—a type of network representation that captures complex interactions more effectively than traditional network models. The research presented explores six distinct applications of hypergraphs in social media analysis, each addressing a unique challenge: 1) The identification of influential users and content specific to certain topics, extending beyond general influence to understand context-driven impact. 2) The incorporation of time and location to detect influential content, recognizing that relevance can significantly vary by these factors. 3) Addressing the issue of imbalanced data in bot detection, where genuine user interactions are overwhelmed by automated accounts, through novel data augmentation techniques. 4) Classifying mental health advice in Arabic tweets to differentiate between valid and misleading information is crucial, given the subject's sensitivity. 5) Detecting sarcasm in low-resource languages is particularly challenging due to its subtle and context-dependent nature. 6) Predicting metro passenger ridership at each metro station is challenging due to the constantly evolving nature of the network and passengers going in and out of stations. This work contributes to the field by demonstrating the capability of hypergraphs to provide more fine-grained and context-aware analyses of social media content. Through extensive experimentation, it showcases the effectiveness of these methods in improving detection, classification, and prediction tasks. The findings not only advance our technical understanding and capabilities in social media analysis but also have practical implications for enhancing the reliability and usefulness of information disseminated on these platforms.

The object of this paper is to present the properties and some of the applications of the Elliptic Integrals.

No description available.

Ce mémoire présente le développement de systèmes de traitement de surface au laser qui seront utilisés pour des applications de robotique. Ce mémoire vise à résoudre un problème présent dans l'entreprise Laserax, une entreprise qui implante des solutions de traitement laser dans des industries. Le principal problème est que leur système laser montés sur le robot n'est pas optimal pour répondre au besoin de Laserax car il limite les zones accessibles pour effectuer un travail laser. En effet, l'entreprise veut une solution laser plus versatile pour leur bras robotisé afin de pouvoir mieux répondre aux besoins de leurs clients. L'objectif de ce projet de maîtrise est de développer deux nouveaux systèmes lasers monté sur le bras robotisé pour maximiser l'espace atteignable du robot. Le mémoire débute avec une revue des technologies existantes dans l'industrie. Ensuite on présente une description des logiciels utilisés pour la réalisation du projet. Le laboratoire robot laser présent chez Laserax est ensuite présenté. Enfin, on présente le développement de deux systèmes lasers dans la longueur d'onde 1064nm, l'un monomode et l'autre multimode. Le système laser monomode est un laser 3D qui va permettre d'être plus versatile sur le traitement des pièces comparativement au son prédécesseur qui lui était seulement un laser 2D. Pour ce qui est du système laser multimode, c'est aussi un laser 2D, mais celui-ci possède une très grande profondeur de champ ainsi qu'une très haute puissance qui va permettre de mieux répondre au besoin grandissant du marché. Il est donc nécessaire de concevoir deux nouveaux boitiers de têtes laser à poser sur le bras robotisé. / This thesis presents the development of two fiber optic laser systems that will be used for robotic laser surface treatment applications. This thesis aims to solve a problem present in the company Laserax, a company that implements laser treatment solutions in the industry. The main problem is that their laser system mounted on the robot is not optimized to meet their needs because it restricts the areas available for performing laser work. In fact, the company is looking for a more versatile laser solution on its robotic arm to better meet its customers' needs. The objective of this project is therefore to develop two new fiber optic laser systems to be mounted on the robotic arm. This thesis begins with an analysis of what is available in the industry. It continues with a description of the software used for the realization of the project. A presentation of the laser robot laboratory present at Laserax follows. Finally, it presents the development of two fiber optic laser systems in the 1064 wavelength, one single-mode and the other multi-mode. The single-mode laser system is a 3D laser that is more versatile to process parts compared to its predecessor which was only a 2D laser. As for the multimode laser system, it is also a 2D laser, but it has a very large depth of field as well as a very high power which will make it possible to better meet the growing need of the market. It is therefore necessary to design two new housings for the laser heads than can be mounted to the robotic arm.

No / A description of the ab initio quantum chemistry package GAMESS-UK is presented. The package offers a wide range of quantum mechanical wavefunctions, capable of treating systems ranging from closed-shell molecules through to the species involved in complex reaction mechanisms. The availability of a wide variety of correlation methods provides the necessary functionality to tackle a number of chemically important tasks, ranging from geometry optimization and transition-state location to the treatment of solvation effects and the prediction of excited state spectra. With the availability of relativistic ECPs and the development of ZORA, such calculations may be performed on the entire Periodic Table, including the lanthanides. Emphasis is given to the DFT module, which has been extensively developed in recent years, and a number of other, novel features of the program. The parallelization strategy used in the program is outlined, and detailed speedup results are given. Applications of the code in the areas of enzyme and zeolite catalysis and in spectroscopy are described.

Yes / The web environment has been developing remarkably, and much work has been done towards improving web based notification systems, where servers act smartly by notifying and feeding clients with subscribed data. In this paper we have reviewed some of the problems with current solutions to real-time updates of multi user web applications; we introduce a new concept “adaptive polling” based on one AJAX technique “Polling” to reduce the high volume of redundant server connections with reasonable latency, we demonstrated a prototype implementation of the new concept which is then evaluated against the existing one; the positive results clearly indicated more efficiency in terms of client-server bandwidth.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California / This paper describes the design and implementation of a test instrumentation network configuration and verification system. Given a multivendor instrument part catalog that contains sensor, actuator, transducer and other instrument data; user requirements (including desired measurement functions) and technical specifications; the instrumentation network configurator will select and connect instruments from the catalog that meet the requirements and technical specifications. The instrumentation network configurator will enable the goal of mixing and matching hardware from multiple vendors to develop robust solutions and to reduce the total cost of ownership for creating and maintaining test instrumentation networks.