Transformer-Based Multi-Task Learning for Crisis Actionability Extraction

Social media has become a valuable information source for crisis informatics. While various methods were proposed to extract relevant information during a crisis, their adoption by field practitioners remains low. In recent fieldwork, actionable information was identified as the primary information need for crisis responders and a key component in bridging the significant gap in existing crisis management tools. In this paper, we proposed a Crisis Actionability Extraction System for filtering, classification, phrase extraction, severity estimation, localization, and aggregation of actionable information altogether. We examined the effectiveness of transformer-based LSTM-CRF architecture in Twitter-related sequence tagging tasks and simultaneously extracted actionable information such as situational details and crisis impact via Multi-Task Learning. We demonstrated the system’s practical value in a case study of a real-world crisis and showed its effectiveness in aiding crisis responders with making well-informed decisions, mitigating risks, and navigating the complexities of the crisis

Use of Machine Learning and Natural Language Processing to Enhance Traffic Safety Analysis

Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies within and outside traffic safety domain.The primary source for traffic safety analysis is the structure (also called tabular) data collected from crash reports. However, structure data may be insufficient because of missing information, incomplete sequence of events, misclassified crash types, among many issues. Crash narratives, a form of free text recorded by police officers to describe the unique aspects and circumstances of a crash, are commonly used by safety professionals to supplement structure data fields. Due to its unstructured nature, engineers have to manually review every crash narrative. Thanks to the rapid development in natural language processing (NLP) and machine learning (ML) techniques, text mining and analytics has become a popular tool to accelerate information extraction and analysis for unstructured text data. The primary objective of this dissertation is to discover and develop necessary tools, techniques, and algorithms to facilitate traffic safety analysis using crash narratives. The objectives are accomplished in three areas: enhancing data quality by recovering missed crashes through text classification, uncovering complex characteristics of collision generation through information extraction and pattern recognition, and facilitating crash narrative analysis by developing a web-based tool. At first, a variety of NoisyOR classifiers were developed to identify and investigate work zone (WZ), distracted (DD), and inattentive (ID) crashes. In addition, various machine learning (ML) models, including multinomial naive bayes (MNB), logistic regression (LGR), support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and gated recurrent unit (GRU), were developed and compared with NoisyOR. The comparison shows that NoisyOR is simple, computationally efficient, theoretically sound, and has one of the best model performances. Furthermore, a novel neural network architecture named Sentence-based Hierarchical Attention Network (SHAN) was developed to classify crashes and its performance exceeds that of NoisyOR, GRU, Hierarchical Attention Network (HAN), and other ML models. SHAN handled noisy or irrelevant parts of narratives effectively and the model results can be visualized by attention weight. Because a crash often comprises a series of actions and events, breaking the chain of events could prevent a crash from reaching its most dangerous stage. With the objectives of creating crash sequences, discovering pattern of crash events, and finding missing events, the Part-of-Speech tagging (PT), Pattern Matching with POS Tagging (PMPT), Dependency Parser (DP), and Hybrid Generalized (HGEN) algorithms were developed and thoroughly tested using crash narratives. The top performer, HGEN, uses predefined events and event-related action words from crash narratives to find new events not captured in the data fields. Besides, the association analysis unravels the complex interrelations between events within a crash. Finally, the crash information extraction, analysis, and classification tool (CIEACT), a simple and flexible online web tool, was developed to analyze crash narratives using text mining techniques. The tool uses a Python-based Django Web Framework, HTML, and a relational database (PostgreSQL) that enables concurrent model development and analysis. The tool has built-in classifiers by default or can train a model in real time given the data. The interface is user friendly and the results can be displayed in a tabular format or on an interactive map. The tool also provides an option for users to download the word with their probability scores and the results in csv files. The advantages and limitations of each proposed methodology were discussed, and several future research directions were outlined. In summary, the methodologies and tools developed as part of the dissertation can assist transportation engineers and safety professionals in extracting valuable information from narratives, recovering missed crashes, classifying a new crash, and expediting their review process on a large scale. Thus, this research can be used by transportation agencies to analyze crash records, identify appropriate safety solutions, and inform policy making to improve highway safety of our transportation system

Intelligent Data Analytics using Deep Learning for Data Science

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization

Efficient resource allocation for automotive active vision systems

Individual mobility on roads has a noticeable impact upon peoples' lives, including traffic accidents resulting in severe, or even lethal injuries. Therefore the main goal when operating a vehicle is to safely participate in road-traffic while minimising the adverse effects on our environment. This goal is pursued by road safety measures ranging from safety-oriented road design to driver assistance systems. The latter require exteroceptive sensors to acquire information about the vehicle's current environment. In this thesis an efficient resource allocation for automotive vision systems is proposed. The notion of allocating resources implies the presence of processes that observe the whole environment and that are able to effeciently direct attentive processes. Directing attention constitutes a decision making process dependent upon the environment it operates in, the goal it pursues, and the sensor resources and computational resources it allocates. The sensor resources considered in this thesis are a subset of the multi-modal sensor system on a test vehicle provided by Audi AG, which is also used to evaluate our proposed resource allocation system. This thesis presents an original contribution in three respects. First, a system architecture designed to efficiently allocate both high-resolution sensor resources and computational expensive processes based upon low-resolution sensor data is proposed. Second, a novel method to estimate 3-D range motion, e cient scan-patterns for spin image based classifiers, and an evaluation of track-to-track fusion algorithms present contributions in the field of data processing methods. Third, a Pareto efficient multi-objective resource allocation method is formalised, implemented, and evaluated using road traffic test sequences