91341 research outputs found
Sort by
A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
Get PDFMonitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment. Human-curated symbolic knowledge sources, such as lexicons for standard language and slang terms, can potentially elevate social media signals in evolving language. We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources, enhancing the detection and interpretation of mental health-related tweets relevant to COVID-19. Our method was evaluated using a corpus of large datasets (approximately 12 billion tweets, 2.5 million subreddit data, and 700k news articles) and multiple knowledge graphs. This method dynamically adapts to evolving language, outperforming purely data-driven models with an F1 score exceeding 92\%. This approach also showed faster adaptation to new data and lower computational demands than fine-tuning pre-trained large language models (LLMs). This study demonstrates the benefit of neurosymbolic methods in interpreting text in a dynamic environment for tasks such as health surveillance
Understanding Impact of Graph Structure on Knowledge Graph Embedding
Get PDFThe effectiveness of a deployed knowledge graph is commonly evaluated with defined use-cases from domain experts. This poses challenges during the development cycle in determining how to represent data. Developers of a knowledge graph can optionally include semantics into a knowledge graph by abstracting the data representation in such a way that mirrors information as it exists in the real world. Consequently, the abstraction is represented by additional layers, resulting in performant differences in knowledge graph embedding; such as, the embedded model\u27s ability to infer facts between entities through link predictions. This thesis presents a comprehensive analysis of the performance impact observed across a range of knowledge graph embedding models trained on FB15k-237, a widely recognized benchmark dataset for knowledge graph completion. Additionally, the experiment is performed with augmented versions of FB15k-237, serving to introduce semantics into the knowledge graph
ML-Assisted Side Channel Security Approaches for Hardware Trojan Detection and PUF Modeling Attacks
Get PDFHardware components are becoming prone to threats with increasing technological advances. Malicious modifications to such components are increasing and are known as hardware Trojans. Traditional approaches rely on functional assessments and are not sufficient to detect such malicious actions of Trojans. Machine learning (ML) assisted techniques play a vital role in the overall detection and improvement of Trojan. Our novel approach using various ML models brings an improvement in hardware Trojan identification with power signal side channel analysis. This study brings a paradigm shift in the improvement of Trojan detection in integrated circuits (ICs). In addition to this, our further analysis towards hardware authentication extends towards PUFs (Physical Unclonable Functions). Arbiter PUFs were chosen for this purpose. These are also Vulnerable towards ML attacks. Advanced ML assisted techniques predict the responses and perform attacks which leads to the integrity of PUFs. Our study helps improve ML-assisted hardware authentication for ML attacks. In addition, our study also focused on the defense part with the addition of noise and applying the same attack ML-assisted model. Detection of Trojan in hardware components is achieved by implementing machine learning techniques. For this Purpose, several Machine learning models were chosen. Among them, Random Forest classifier (RFC) and Deep neural network shows the highest accuracy. This analysis plays a vital role in the security aspect of all hardware components and sets a benchmark for the overall security aspects of hardware. Feature extraction process plays major role for the improvement of accuracy and reliability of hardware Trojan classification. Overall, this study brings significant improvement in the field of overall hardware security. Our study shows that RFC performs best in hardware classification with an average of 98. 33% precision of all chips, and deep learning techniques give 93. 16% precision of all chips. Moreover, on the hardware authentication side, RFC performs the best of all other models with the accuracy of 89% in the attack part and 81% in the defense part in 6000 data samples
Investigating the Impact of Stress and Irradiation Flux on Latent Track Formation in TiO2 under Swift Heavy Ion Irradiation: A Phase Field Study
Get PDFSwift Heavy Ions (SHI) irradiation, characterized by high kinetic energy ions, induces significant material/structural modification, e.g., latent track. However, the intricate interaction among various physics, i.e., mechanical stress, phase transition, and heat transfer, has been ignored in the continuum-based approaches in favor of simplicity. Here, we developed a two-dimensional coupled phase-field inelastic-thermal spike (PF-iTS) model to investigate the effect of thermal crosstalk, elastic energy, and irradiation flux on latent track formation. A particular focus is placed on investigating the influence of internal mechanical stress on latent track formation. Simulation results reveal a shift in critical stopping energy and a reduction in track radius in the presence of mechanical stress, indicating a suppressive effect on latent track formation. Furthermore, the paper explores the effects of ion flux and simultaneous versus delayed ion collisions on track morphology. Simulations demonstrate how thermal cross-talk between simultaneous incidents can alter track dimensions, with closer incidents resulting in larger track radii due to temperature field overlap. The results reveal a shift in the critical electronic stopping energy and a reduced track radius under mechanical stress. Additionally, the study demonstrates the impact of thermal crosstalk between incidents, i.e., simultaneous and delayed double ion impacts, showing potential track merging and variations in track morphology
Integrating Knowledge Graphs with Large Language Models for Natural Language Querying
Get PDFThis research explores the integration of knowledge graphs with large language models that have already been trained on a vast pool of unstructured text data. Large language models trained on this type of data have a tendency to hallucinate and produce factually inaccurate results. This behavior is primarily due to the data being trained is unstructured and huge text corpus, and large language model uses predictive text analysis methods to obtain a response. These issues can be addressed by applying Retrieval Augmented Generation and Fine-tuning to large language models, employing an underlying domainspecific knowledge graph. Integrating knowledge graph and large language models aids in learning the underlying semantics with the contextual understanding provided by large language models, resulting in an efficient system capable of interpreting and responding to user queries in a more contextually aware manner while reducing hallucinations. In this thesis, we conducted experiments to improve the efficiency of large language models using knowledge graphs, Retrieval Augmented Generation, and fine-tuning strategies, where the large language model responds to user query with the help of either Retrieval Augmented Generation or fine-tuning techniques. The results of this study show that Retrieval Augmented Generation outperformed fine-tuning in terms of generating precise responses to user queries
Attentional Demands of Smooth Pursuit
Get PDFThe current study investigates whether maintenance of smooth pursuit eye movements requires attention or if the traditional small-spot pursuit stimulus has propagated this long-held belief by imposing the attentional demands of the saccadic system (through foveation) onto the pursuit task. Using a within-subjects design, we compare single- and dual-task performance for two difficulty levels of an auditory tone discrimination task and a pursuit eye movement task with both small and large pursuit targets (N = 10). Pursuit performance improved when target size increased (for catch-up saccade rate and steady-state gain) and when auditory task difficulty increased (for catch-up saccade rate). Importantly, we did not observe decrements in pursuit performance under dual-task conditions, which would be expected if pursuit competed for attentional resources with the auditory task. Practical implications for clinical diagnosis and eye-tracking applications as well theoretical implications for understanding the pursuit system are discussed
HyperCell: Advancing Cell Type Classification with Hyperdimensional Computing
No full textSingle-cell RNA sequencing (scRNA-seq) has revolutionized genomics, enabling the exploration of cellular heterogeneity at an unprecedented resolution. However, scRNA-seq data poses challenges, including high dimensionality, inherent noise, and sparse gene expression. In this paper, we propose a novel approach, utilizing hyperdimensional computing, to enhance cell type classification accuracy in scRNA-seq datasets. We use the QuantHD method for high-dimensional hypervector encoding and iterative training. Experiments on diverse datasets subjected to both split by batch and random split settings demonstrate the superiority of our proposed model in handling noise and outperforming established classification methods such as XGBoost, Seurat reference mapping, and scANVI. Our findings highlight the potential of hyperdimensional computing to advance single-cell data analysis, yielding deep insights into cellular dynamics, tissue functions, and disease mechanisms. This work paves the way for more accurate cell type annotation and brings new opportunities for biomedical research and personalized medicine
Uquery: Static Security Analysis of PHP-Based Web Programs Using Graph Models
No full textThis paper introduces UQuery, a novel framework designed for agile security analysis of server-side web applications. UQuery employs a dependency graph, an innovative program representation that concurrently models data and control dependencies. This graph model facilitates efficient data- and control-flow analyses by converting them into intuitive graph queries. Currently, UQuery includes two security applications: one for detecting unrestricted file upload vulnerabilities and another for identifying information leakage through exploitable race conditions. It has detected vulnerabilities in 16 applications, resulting in the discovery of 6 previously unreported CVEs
Undergraduate and Graduate Course Descriptions, Summer 2024
Get PDFWright State University undergraduate and graduate course descriptions from Summer 202
3D Flow Field Quantification in an Artery Model Using Optical Flow Method Based on Simulated Multi-Angle Angiography
Get PDF3D flow field in a numerically simulated arterial model is quantified using the three-dimensional optical flow method based on simulated simultaneous multi-angle X-ray angiography of the blood flow with contrast agent perfusions