Perspectives on subnational carbon and climate footprints: A case study of Southampton, UK

Sub-national governments are increasingly interested in local-level climate change management. Carbon- (CO2 and CH4) and climate-footprints—(Kyoto Basket GHGs) (effectively single impact category LCA metrics, for global warming potential) provide an opportunity to develop models to facilitate effective mitigation. Three approaches are available for the footprinting of sub-national communities. Territorial-based approaches, which focus on production emissions within the geo-political boundaries, are useful for highlighting local emission sources but do not reflect the transboundary nature of sub-national community infrastructures. Transboundary approaches, which extend territorial footprints through the inclusion of key cross boundary flows of materials and energy, are more representative of community structures and processes but there are concerns regarding comparability between studies. The third option, consumption-based, considers global GHG emissions that result from final consumption (households, governments, and investment). Using a case study of Southampton, UK, this chapter develops the data and methods required for a sub-national territorial, transboundary, and consumption-based carbon and climate footprints. The results and implication of each footprinting perspective are discussed in the context of emerging international standards. The study clearly shows that the carbon footprint (CO2 and CH4 only) offers a low-cost, low-data, universal metric of anthropogenic GHG emission and subsequent management

Community-Based Behavioral Understanding of Mobility Trends and Public Attitude through Transportation User and Agency Interactions on Social Media in the Emergence of Covid-19

The increased availability of technology-enabled transportation options and modern communication devices (smartphones, in particular) is transforming travel-related decision-making in the population differently at different places, points in time, modes of transportation, and socio-economic groups. The emergence of COVID-19 made the dynamics of passenger travel behavior more complex, forcing a worldwide, unparalleled change in human travel behavior and introducing a new normal into their existence. This dissertation explores the potential of social media platforms (SMPs) as a viable alternative to traditional approaches (e.g., travel surveys) to understand the complex dynamics of people’s mobility patterns in the emergence of COVID-19. In this dissertation, we focus on three objectives. First, a novel approach to developing comparative infographics of emerging transportation trends is introduced by natural language processing and data-driven techniques using large-scale social media data. Second, a methodology has been developed to model community-based travel behavior under different socioeconomic and demographic factors at the community level in the emergence of COVID-19 on Twitter, inferring users’ demographics to overcome sampling bias. Third, the communication patterns of different transportation agencies on Twitter regarding message kinds, communication sufficiency, consistency, and coordination were examined by applying text mining techniques and dynamic network analysis. The methodologies and findings of the dissertation will allow real-time monitoring of transportation trends by agencies, researchers, and professionals. Potential applications of the work may include: (1) identifying spatial diversity of public mobility needs and concerns through social media platforms; (2) developing new policies that would satisfy the diverse needs at different locations; (3) introducing new plans to support and celebrate equity, diversity, and inclusion in the transportation sector that would improve the efficient flow of goods and services; (4) designing new methods to model community-based travel behavior at different scales (e.g., census block, zip code, etc.) using social media data inferring users’ socio-economic and demographic properties; and (5) implementing efficient policies to improve existing communication plans, critical information dissemination efficacy, and coordination of different transportation actors to raise awareness among passengers in general and during unprecedented health crises in the fragmented communication world

Fraud Dataset Benchmark and Applications

Standardized datasets and benchmarks have spurred innovations in computer vision, natural language processing, multi-modal and tabular settings. We note that, as compared to other well researched fields, fraud detection has unique challenges: high-class imbalance, diverse feature types, frequently changing fraud patterns, and adversarial nature of the problem. Due to these, the modeling approaches evaluated on datasets from other research fields may not work well for the fraud detection. In this paper, we introduce Fraud Dataset Benchmark (FDB), a compilation of publicly available datasets catered to fraud detection FDB comprises variety of fraud related tasks, ranging from identifying fraudulent card-not-present transactions, detecting bot attacks, classifying malicious URLs, estimating risk of loan default to content moderation. The Python based library for FDB provides a consistent API for data loading with standardized training and testing splits. We demonstrate several applications of FDB that are of broad interest for fraud detection, including feature engineering, comparison of supervised learning algorithms, label noise removal, class-imbalance treatment and semi-supervised learning. We hope that FDB provides a common playground for researchers and practitioners in the fraud detection domain to develop robust and customized machine learning techniques targeting various fraud use cases

Predicting final user satisfaction based on user experience data using machine learning

東京都立大学Tokyo Metropolitan University博士（工学）doctoral thesi

Class-Imbalanced Learning on Graphs: A Survey

The rapid advancement in data-driven research has increased the demand for effective graph data analysis. However, real-world data often exhibits class imbalance, leading to poor performance of machine learning models. To overcome this challenge, class-imbalanced learning on graphs (CILG) has emerged as a promising solution that combines the strengths of graph representation learning and class-imbalanced learning. In recent years, significant progress has been made in CILG. Anticipating that such a trend will continue, this survey aims to offer a comprehensive understanding of the current state-of-the-art in CILG and provide insights for future research directions. Concerning the former, we introduce the first taxonomy of existing work and its connection to existing imbalanced learning literature. Concerning the latter, we critically analyze recent work in CILG and discuss urgent lines of inquiry within the topic. Moreover, we provide a continuously maintained reading list of papers and code at https://github.com/yihongma/CILG-Papers.Comment: submitted to ACM Computing Survey (CSUR

A Multiple Classifier System for Predicting Best-Selling Amazon Products

In this work, I examine a dataset of Amazon product metadata and propose a heterogeneous multiple classifier system for the task of identifying best-selling products in multiple categories. This system of classifiers consumes the product description and the featured product image as input and feeds them through binary classifiers of the following types: Convolutional Neural Network, Na¨ıve Bayes, Random Forest, Ridge Regression, and Support Vector Machine. While each individual model is largely successful in identifying best-selling products from non best-selling products and from worst-selling products, the multiple classifier system is shown to be stronger than any individual model in the majority of cases of identifying best-selling products from non best-selling products, and achieves up to 83.3% accuracy, depending on the product category. To my best knowledge, this research is the first application of ensemble learning to Amazon product data of this type and the first use of product images and Convolutional Neural Networks to predict product success