Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement disease detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT

Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild

Automatic image-based pavement distress detection and recognition are vital for pavement maintenance and management. However, existing deep learning-based methods largely omit the specific characteristics of pavement images, such as high image resolution and low distress area ratio, and are not end-to-end trainable. In this paper, we present a series of simple yet effective end-to-end deep learning approaches named Weakly Supervised Patch Label Inference Networks (WSPLIN) for efficiently addressing these tasks under various application settings. To fully exploit the resolution and scale information, WSPLIN first divides the pavement image under different scales into patches with different collection strategies and then employs a Patch Label Inference Network (PLIN) to infer the labels of these patches. Notably, we design a patch label sparsity constraint based on the prior knowledge of distress distribution, and leverage the Comprehensive Decision Network (CDN) to guide the training of PLIN in a weakly supervised way. Therefore, the patch labels produced by PLIN provide interpretable intermediate information, such as the rough location and the type of distress. We evaluate our method on a large-scale bituminous pavement distress dataset named CQU-BPDD. Extensive results demonstrate the superiority of our method over baselines in both performance and efficiency.Comment: Extension of ICASSP 2021 Paper entitled "Weakly Supervised Patch Label Inference Network with Image Pyramid for Pavement Diseases Recognition in the Wild", Submitted to IEEE T-IT

Beyond Sentiment Polarity: Cognitive Theory-Based Emotion Type Analysis of Social Media Text for Business Applications

The emotion types expressed in social media have a strong influence over the perspectives and judgments of readers and their decision making in commercial activities. Developing the capability to automatically and unobtrusively detect and evaluate the expression of emotions in social media is of great interest to consumers, firms, and researchers. In this dissertation, we present a novel framework and methodology for the automated analysis of the emotions expressed in online social media content. Its design is informed by a leading cognitive theory of emotions. Our approach addresses two of the major technical challenges faced by established methods for opinion and emotion analyses. It utilizes semi-supervised learning to bootstrap knowledge on the expression of emotions in a target domain of application, eliminating the requirement for annotated training data; and rule-based classification inspired by the leading cognitive theory of emotions, analysis easily interpreted and relied upon by an end-user. To evaluate our approach to emotion analysis, we conduct a series of empirical experiments on several datasets in emotion type classification and key business applications of emotion analysis predicting several social media measures of interest. The results indicate that our proposed approach accurately identified expressions of emotions in social media and outperformed the established methods in emotion type classification. Emotion information extracted by our proposed approach was more effective in key business applications than sentiment polarity information in predicting social media measures of interest. This study offers novel contributions to the research on emotion analysis and into emotion theory, as well as insights for practitioners on evaluating expressions of emotions in social media and business applications of emotion analysis.Dissertation not available (per author’s request

CURVE4COVID: Comprehensive Understanding via Representative Variable Exploration for COVID-19

Coronavirus disease 2019 (COVID-19) has been one of the most severe global pandemics in the 21st century. By the end of 2020, more than 83 million cases were confirmed, and more than 1.6 million deaths were reported globally. Understanding how COVID-19 spreads across diverse communities is key to public health surveillance and management for such a devastating and highly infectious disease. Current studies mainly analyze either non-human factors or human factors, focusing on a limited number of variables that can influence COVID-19 transmission. However, in a real-world context, these factors interact with each other and collectively shape the infection rate curve. Therefore, a comprehensive study based on both non-human and human factors on a large scale is required to fully understand disease transmission. Here, we propose a research framework named Comprehensive Understanding via Representative Variable Exploration for COVID-19 (CURVE4COVID). With the accessibility of various data online, including COVID-19-related Google Trends (e.g., human search behavior) and government-managed data (e.g., weather, air pollution, economic indicators), we conduct a large-scale and multi-variable analysis of the critical factors for COVID19 transmission, which can shed light on the complexity of infectious disease management. The results demonstrate that combining non-human and human factors provides better predictive power for infection rates than non-human factors or human factors alone. This study’s findings can provide new insights into disease transmission and help policymakers enhance preventative measures and healthcare management, thus having a far-reaching impact on society

Social Media-based Overweight Prediction Using Deep Learning

Overweight is epidemic in the United States and elsewhere in the world, causing major health concerns. Based on self-disclosure theory, i.e., people have the tendency to disclose information concerning their feelings, intentions, and acts (e.g., food consumption) online, we aim to leverage social media platforms to develop an unobtrusive approach to predicting overweight. However, traditional statistical and machine learning-based approaches either deliver unsatisfactory performance or demand a large number of features. In this paper, we present a novel social media-based overweight prediction approach based on deep learning as applied in the context of Natural Language Processing (NLP). The input to this approach is food-related Twitter posts. Our computational results show the effectiveness of our method, with remarkable improvement in terms of accuracy over a set of benchmark methods

Exploratory Study for Readmission in Cancer Patients

Cancer, a major threat to public health worldwide, causes substantial financial burdens. Cancer readmission time interval, or Out-of-Hospital Days (OHD) between two consecutive hospital admissions, has been widely adopted as an important measure of healthcare service quality. However, there is a paucity of models focusing on OHD and associated risk factors due to limited access to cancer patients’ data and absence of important factors (e.g., geographic factors) in the data. We utilize OHD (\u3e30) as the result of cancer patient’s conditions (e.g., personal, medical) and treatment costs. We analyze a sample of 22,231 admissions extracted from 635,261 cancer patient Electronic Health Records (EHR) from 190 hospitals in China. We apply text mining on free-form address fields to extract patients’ home address and hospitals’ location information. Using hierarchical linear regression, we find various types of factors significantly influence OHD: age, marital status, number of admissions and whether the treating hospital is in the same province as the patient

Exploratory Analysis of Out-of-Hospital Days Based on Cancer Patients in China

Cancer (re)admission time interval, or Out-of-Hospital Days (OHD) between two consecutive hospital (re)admissions, is commonly considered as an indicator of health service quality. Despite its importance, the risk factors of OHD are largely unknown because of limited access to cancer patients’ data and the lack of relevant characteristics (e.g., geographic factors) in the data. To explore the association between patients’ conditions and readmission events, we analyze a sample of 22,231 admissions (OHD\u3e30), consisting of demographic, medical, and financial factors, extracted from Electronic Health Records (EHR) of 635,261 cancer patients from 190 hospitals in China. Geographic factors are also included by applying text mining to the free-form address fields of patients’ homes and hospitals. Using hierarchical linear regression, we find that various factors significantly influence OHD: age, marital status, number of admissions, and whether the treating hospital is in the same province as the patient’s home address. Available at: https://aisel.aisnet.org/pajais/vol10/iss4/6