13251 research outputs found
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Evaluation of Different Machine Learning, Deep Learning and Text Processing Techniques for Hate Speech Detection
No full textSocial media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social media posts. To gauge the optimal approach, I employ Term Frequency-Inverse Document Frequency (TF-IDF), unigrams, bigrams, trigrams, a combination of unigrams and bigrams, and a combination of unigrams, bigrams, and trigrams for the machine learning models to analyze the text corpus. Given the imbalanced nature of the datasets, I implement both under-sampling and over-sampling techniques to investigate their impact on the results. I also investigated the performance of different deep learning algorithms on the three datasets. The results show that the Biderctional Encoders Representations from Transformers (BERT) model gives the best performance among all the models on imbalanced datasets by achieving an F1-score of 90.6% on one of the datasets, and F1-scores of 89.7% and 88.2% on the other two datasets. Comparative analysis reveals that BERT and Robustly Optimized BERT Pretraining Approach (RoBERTa) outperform traditional Machine Learning (ML) algorithms, with F1-scores approximately 20% higher. The investigation indicates that RoBERTa, with its enhanced training strategies, comes remarkably close to the performance of BERT. The outcomes show the transformative impact of deep learning and pretrained models on hate speech detection, with larger, more diverse datasets further enhancing model performance
Optimal Cloud Datacenter Selection Using Evolutionary Algorithms
No full textCloud performance relies on carefully selecting datacenters, load-balancing algorithms, and resource utilization. The datacenter selection policy is crucial in ensuring the choice of an efficient and effective datacenter. Researchers found that using evolutionary algorithms can provide better datacenter selection policies for cloud computing environments. In this thesis, I have started with a genetic algorithm to find the most suitable datacenter for specific userbases. I aimed to improve overall response and data processing times within the cloud environment. The genetic algorithm is a well-known nature-inspired evolutionary algorithm that emulates nature\u27s selection, crossover, and mutation mechanisms across generations to discover optimal problem solutions. Results showed that the genetic algorithm provides better results than other algorithms, yet consistency in the results is missing. For this reason, I explored the swarm intelligence algorithms for the datacenter selection policy. Particle swarm optimization algorithms utilize collective knowledge of the swarm to navigate toward optimal solutions, leveraging insights from neighboring particles. However, the swarm intelligence algorithm\u27s performance was subpar compared to the genetic algorithm. Meanwhile, differential evolution relies on a mutation-driven evolutionary process, enabling comprehensive search space exploration to discover the most suited datacenter. Because of this reason, I worked with the differential evolution algorithm to examine performance in datacenter selection. The outcomes of my thesis unequivocally demonstrate the superiority of the proposed evolutionary algorithms over existing baseline datacenter selection methods, like the closest datacenter selection policy and optimized response time policy. The results also indicate that the evolutionary algorithms can match or outperform the baseline datacenter selection policies when the number of datacenters is reduced. Moreover, the results show significant improvements in response and data processing times for my proposed algorithms compared with existing evolutionary-based datacenter selection policies. Furthermore, in this thesis research, I show that differential evolution consistently delivers better response and data processing times among the three evolutionary algorithms
The Rhetorical Use of the Other: An Analysis of Symbolic Disability in Contemporary Horror Films
No full textIn this research, I examine the concept of the Other in horror films. I use Kenneth Burke’s identification, Jean-Francois Lyotard’s metanarrative concept, and Lennard Davis’s bell curve of normalcy to describe the Other and how otherness relates to disability. First, I discuss how horror films have portrayed the Other historically in a negative context and slowly transition to the virtuous Other, the final girl. Next, I discuss the trend of portraying disability or otherness as an asset or tool in contemporary films like A Quiet Place, Birdbox, and Don’t Breathe. Then, I examine how current horror films explore the implications of donning otherness for personal gain as seen in Jordan Peele’s Get Out and Us. My analysis leads to a discussion on how the practice of adopting otherness or imitating the Other may be reflected in current identity politics, the struggle for clout, or protection from cancel culture
Tissue and Sex-Dependent Regulation of Innate Immunity and RNA Editing in Mice
No full textInflammation occurs as a result of insult or infection within the body. Individual cells respond to inflammation by upregulating genes that help mediate the immune response, such as ADAR1. ADAR1 helps regulate the immune response but also catalyzes a process called RNA editing. RNA editing alters the sequence of select mRNAs to alter the encoded proteins. The result is altered function of the encoded protein, which is often beneficial for the cell. Our goal was to determine how inflammation affects the function of ADAR1. Since we know that the effects of inflammation vary between different organs and sexes, we examined ADAR1 function in heart, brain, and muscle in male and female mice after the introduction of LPS, an inflammation-inducing agent. We found that editing in the heart and brain was unaffected. However, RNA editing of FLNB in skeletal muscle was increased by LPS in males but was unaffected in females. Another RNA editing target, FLNA was unaffected by the treatment of LPS, but showed a sex-dependent difference in editing. These results show that the effects of inflammation may selectively affect the function of FLNB in muscle. Furthermore, expression of inflammatory factors ADAR1, TNFα, and MDA5 was induced by LPS, as expected, but TNFα and MDA5 expression was induced more in females. Our work suggests that the impact of sex on inflammatory factors may also indirectly affect the rate of RNA editing of select transcripts in select tissues
A Study of Synthesis and Characterization of Naxmno2-δ as a Cathode Material for Sodium-Ion Battery
No full textThe proliferation of renewable energy sources and the promising market for net-scale battery applications immediately increases the need for electrochemical energy storage technology. Sodium (Na) components are more accessible and less expensive than lithium. Being a sodium-based material with a high-power density provided by Na-ion diffusion, NaxMnO2- δ is a strong contender for large-scale Sodium-ion-battery (SIB) applications. In the current study, NaxMnO2- δ is created using a solid-state reaction technique, and investigated structural, electrical, and electrochemical properties of materials were investigated using X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), UV-VIS spectroscopy, and X-ray photoelectron spectroscopy (XPS). The Rietveld refinement on the respective XRD pattern led to a hexagonal structure with space group P63/mmc. Raman spectroscopy provides information about the structural fingerprint of the prepared powders by identifying the vibrational modes. XPS analysis was carried out to investigate the Mn valence of NaxMnO2- δ. Electrochemical charge-discharge cycling was performed from 2.0- 4.2 V vs Na+/Na for C/10 where the initial discharge capacity 102 mA h/g-1 and 90% capacity retention after 20 cycles for NaxMnO2- δ. An ideal layered P2-type NaxMnO2-δ cathode calcined at 400°C has the highest specific discharge capacity of 130 mAh/g at 0.1C with capacity retention of approximately 99% and average coulombic efficiency of 98% after 20 cycles compared to 300°C in between 2.0-4.2V range (Na+/Na). These findings will encourage more investigation into SIB
Mrs. Blackbird and the Visiting Chair
No full textThe following thesis is a middle grade novel exploring the events of one summer in the lives of two siblings, Susannah and Sawyer. The siblings are grieving the recent death of their mother and, at the same time, attempting to navigate the emotional withdrawal of their father. During the summer, the siblings get to know their eccentric neighbor, Mrs. Blackbird, who communicates with the spirit of her dead husband through an old armchair which is rumored to have magical powers. The novel deals primarily with the theme of grief and its pervasive nature in people’s lives. The story looks at how community and the practice of storytelling can help people in processing the pain of devastating loss
Thawing Interests: The Arctic in U.S. Grand Strategy
No full textThe thawing Arctic is subject to increasing activity, attention, and a renewal of interests in the region from around the globe. National interests have compelled strategic planning in the Arctic region and are connected to global geopolitics. A concept of grand strategy is distilled from theories of past authors, understood within the modern context. That concept includes a terminological framework consisting of interests and threats to inform an ends, ways, and means design of strategy, composed of all instruments of state power, blending policy with strategy, and across the peace-war continuum. Then fundamental precepts of existing U.S. grand strategy are presented within that grand strategic framework as derived from Congressional and Executive understandings. Next, contemporary Arctic strategy is similarly studied, and qualitative connections between Arctic and U.S. grand strategy are discussed. I conclude that the U.S.’s Arctic strategy is conceptually inseparable from and instrumental towards its grand strategy. The Arctic regional strategy must be subordinately connected to objectives outside the region. Existing U.S. Arctic pronouncements demonstrate ambition for high levels of attention, loosely understand the connection between regional interests and grand strategic objectives, are disjointed and uncoordinated from each other, fail to utilize a coherent set of terminology of strategic theory, and risk falling prey to distractions and diversions. Even so, current programs regarding the region are mostly appropriate
Renewable Fuels: Molecular Dynamics Investigations Into Pyrolysis of Methyl Linoleate
No full textWith the rapid depletion of the world’s supply of fossil fuels, especially petroleum products, petroleum prices have risen by approximately 800% between the 1970s and now and are projected to continue rising. It is also expected that the world’s consumption of energy will increase commensurate with its growing population. Although biodiesel is a good renewable alternative, it has its limitations including high production costs and poor low-temperature performance. We seek to improve conventional biodiesel with pyrolysis to produce low molecular-weight compounds with high energy densities. Understanding the pyrolysis path on the atomic scale is key as it will allow us to determine and engineer adequate reactants that maximize yield of desired energy producing molecules. An in-house generated database of 100 ab initio trajectories of methyl linoleate were examined for significant bond-breaking and bond-forming events. The times of the events and the position in the molecule were logged. These are tested against an in-house computer-automated analysis method, and comparison of the results will be presented. Quantum chemical techniques were also used to compute thermodynamic properties of the resulting fragments
Growth and Characterization of SM3HFBI5
No full textFirst found experimentally in 2015, topological Weyl materials are desirable compounds that have garnered much interest due to their ability to conduct electricity via their surface states even though the bulk material is a semimetal. Such a candidate, Sm3HfBi5, was discovered with a flux crystal growth method, following an extensive amount of reaction syntheses. This thesis reports on the discovery, growth, structural characterization via x-ray diffraction, and magnetization measurements on Sm3HfBi5
Exploration and Synthesis of Half-Heusler and Disordered Ternary Intermetallic Single Crystals
No full textDue to their distinct physical and chemical characteristics, half-Heusler compounds are intermetallic materials that have undergone substantial research. The general formula for these compounds is XYZ, where X and Y are transition metals and Z is a p-block element. Thermoelectricity is among the half-Heusler compounds\u27 most exciting potential uses, but additional possible applications include spintronics, magnetic refrigeration, and superconductivity, among others. Recently, half-Heuslers (and Heuslers) have emerged as candidates for catalysts for hydrogenation. The CoNbSn half-Heusler compound, as well as another unique intermetallic ternary compound, the disordered Mn4-xCrxAl11, were successfully grown as single crystals for the first time using Sn and Al self-flux techniques, respectively. Structural and compositional studies have been performed with x-ray diffraction and EDS, followed by magnetization measurements. Future plans include in-situ observation of catalysis in Raman spectroscopy