Deep Neural Networks (DNNs) have achieved tremendous success in various tasks. However, DNNs exhibit uncertainty and unreliability when faced with well-designed adversarial examples, leading to misclassification. To address this, a variety of methods have been proposed to improve the robustness of DNNs by detecting adversarial attacks. In this paper, we combine model explanation techniques with adversarial models to enhance adversarial detection in real-world scenarios. Specifically, we develop a novel adversary-resistant detection framework called EXPLAINER, which utilizes explanation results extracted from explainable learning models. The explanation model in EXPLAINER generates an explanation map that identifies the relevance of input variables to the model’s classification result. Consequently, adversarial examples can be effectively detected by comparing the explanation results of a given sample with its denoised version, without relying on any prior knowledge of attacks. The proposed framework is thoroughly evaluated against different adversarial attacks, and experimental results demonstrate that our approach achieves promising results in white-box attack scenarios.

Deep learning models have achieved exceptional predictive performance in a wide variety of tasks, ranging from computer vision, natural language processing, to graph mining. Many businesses and organizations across diverse domains are now building large-scale applications based on deep learning. However, there are growing concerns, regarding the fairness, security, and trustworthiness of these models, largely due to the opaque nature of their decision processes. Recently, there has been an increasing interest in explainable deep learning that aims to reduce the opacity of a model by explaining its behavior, its predictions, or both, thus building trust between human and complex deep learning models. A collection of explanation methods have been proposed in recent years that address the problem of low explainability and opaqueness of models. In this tutorial, we introduce recent explanation methods from a data perspective, targeting models that process image data, text data, and graph data, respectively. We will compare their strengths and limitations, and offer real-world applications.

This project built a world-wide database of coron-avirus cases, which helps to model the spread of the coronavirus disease (COVID-19), and to identify policy and social factors that impact the spread of COVID-19. Four essential tasks are implemented: 1) build a comprehensive database of coronavirus cases world-wide; 2) visualize the heatmap of confirmed cases for each country, provide detailed spreading trends for each countries and comparison among countries; 3) collect tweets about COVID-19 in real-time and extract people's daily concern flow; 4) integrate breaking news such as first confirmed/death case in each country. This demo will provide decision-makers with accurate data-driven representations in an easy to understand format that enables them to make more timely and cost-effective preparation and response plans.

Chronic alcohol consumption has become one of the major concerns of the society in the sector of public health. The death toll due to excessive consumption of alcohol is increasing exponentially. Face-to-face interaction to create awareness and to stymie the consumption of alcohol is a quixotic solution. Alcohol relapse remains a challenging problem in disorders associated with alcohol addiction which is related to spatial-temporal factors like periods and specific places. In this paper, a new generation of relapse prevention mobile application called “BeSober” is proposed. It assists users to develop abstemious habits in drinking, providing support within the intervention period, tracking risky alcoholic spots, presenting community-based support from alternative users or alcohol addiction therapists, monitoring addict's behaviour and offer personalized recommendations to assist the addict in staying sober.

Ahstract-A crucial and time-sensitive task when any disaster occurs is to rescue victims and distribute resources to the right groups and locations. This task is challenging in populated urban areas, due to a huge burst of help requests made in a very short period. To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule a rapid deployment of volunteers to rescue victims in dynamic settings. The core concept is to quickly identify victims and volunteers from social network data and then schedule rescue parties with an adaptive learning algorithm. This framework performs two key functions: 1) identify trapped victims and volunteers, and 2) optimize the volunteers' rescue strategy in a complex time-sensitive environment. The proposed ResQ algorithm can speed up the training processes through a heuristic function which reduces the state-action space by identifying a set of particular actions over others. Experimental results showed that the proposed heuristic multi-agent reinforcement learning based scheduling outperforms several state-of-art methods, in terms of both reward rate and response times.

The Coronavirus Disease 2019 (COVID-19) has now become a pandemic, inflicting millions of people and causing tens of thousands of deaths. To better understand the dynamics of COVID-19, we present a comprehensive COVID-19 tracking and visualization platform that pinpoints the dynamics of the COVID-19 worldwide. Four essential components are implemented: 1) presenting the visualization map of COVID-19 confirmed cases and total counts all over the world; 2) showing the worldwide trends of COVID-19 at multi-grained levels; 3) provide multiview comparisons, including confirmed cases per million people, mortality rate and accumulative cure rate; 4) integrating a multi-grained view of the disease spreading dynamics in China and showing how the epidemic is taken under control in China. This demo will spur further disease spreading modeling for researchers, support decision-maker, and enrich the public awareness of the spreading situations of COVID-19 worldwide. This demo offers decision-makers with accurate data-driven representations in an easy to understand format that informs them the pandemic dynamics and enables them to make more timely and cost-effective preparation and response plans.

Automating pneumonia diagnosis from X-ray images could significantly improve patient diagnosing outcomes. A major challenge is that disease information (features) must be extracted directly from the image backgrounds. Motivated by recent advances in Convolutional Neural Network (CNN), we propose a hierarchical weighting deep learning model, ChestWNet, that combines DenseNet and transfer learning to detect and localize thoracic diseases from chest x-rays. Hierarchical weighting networks are designed to assign scores reflecting the importance of specific pixels (regions), and learning weights at pixel-, region-, and image-levels, jointly learning these hierarchical weighting networks and the image classification network in an end-to-end manner. Chest X-ray datasets are customized to solve the unbalancing label problem in these datasets. Extensive experiments show that ChestWNet significantly outperforms other established prediction methods, and can also be applied to similar scenarios with fixed point-of-interest regions in images.

Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from Reddit.com demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions 'joy' and 'negative'. This work is one of the first attempts to predict relapses using massive social media (Reddit.com) data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment.

It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods. Current studies suggest that those factors can be utilized for better relapse interventions, however, there is no relapse prevention application that makes use of those factors. In this paper, we introduce a mobile app called "Addict Free", which records user profiles, tracks relapse history and summarizes recovering statistics to help users better understand their recovering situations. Also, this app builds a relapse recovering community, which allows users to ask for advice and encouragement, and share relapse prevention experience. Moreover, machine learning algorithms that ingest spatial and temporal factors are utilized to predict relapse, based on which helpful addiction diversion activities are recommended by a recovering recommendation algorithm. By interacting with users, this app targets at providing smart suggestions that aim to stop relapse, especially for alcohol and tobacco addiction users.

Social media provided a significant communication platform for rescuing people when Harvey hit Houston area. In this devastating flooding crisis, the overloaded official emergency institutes were not able to respond quickly due to the burst of call for help in a very short period of time. In this circumstance, many volunteers and people who needed help often post their information on social media such as Twitter and Facebook. How to organize volunteers smartly and efficiently to help people is an extremely challenging and significant problem considering the constraints of volunteer's time slots, urgent priorities, etc. In this paper, we propose three rescue scheduling algorithms to explain how to provide victims timely help by the volunteers on social media.