OpenRaiser/Intern-Atlas huggingface dataset:
Epidemic Forecasting with a Data-Centric Lens
Authors: Alexander Rodríguez, Harshavardhan Kamarthi, B. Aditya Prakash
Venue: KDD
Year: 2022

The recent COVID-19 pandemic has reinforced the importance of epidemic forecasting to equip decision makers in multiple domains, ranging from public health to economics. However, forecasting the epidemic progression remains a non-trivial task as the spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics and environmental conditions, etc. Research interest has been fueled by the increased availability of rich data sources capturing previously unseen facets of the epidemic spread and initiatives from government public health and funding agencies like forecasting challenges and funding calls. This has resulted in recent works covering many aspects of epidemic forecasting. Data-centered solutions have specifically shown potential by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This tutorial will explore various data-driven methodological and practical advancements. First, we will enumerate epidemiological datasets and novel data streams capturing various factors like symptomatic online surveys, retail and commerce, mobility and genomics data. Next, we discuss methods and modeling paradigms with a focus on the recent data-driven statistical and deep-learning based methods as well as novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some open problems found across the forecasting pipeline.
A Generalized Backward Compatibility Metric
Authors: Tomoya Sakai 0001
Venue: KDD
Year: 2022

Retraining a classifier with new data is inseparable from ML/AI applications, but most of the existing ML methods do not take into account the backward compatibility of predictions. That is, although the overall performance of a new classifier is improved, users will be confused by the wrong predictions of the new classifier, especially when the predictions of the old classifier are correct for the same samples. To this end, several metrics and learning methods for backward compatibility have been actively studied recently. Despite significant interest in backward compatibility, the metrics and methods are not well known from a theoretical perspective. In this paper, we first analyze the existing backward compatibility metrics and reveal that these metrics essentially assess the same quantity between old and new models. In addition, to obtain a unified view of backward compatibility metrics, we propose a generalized backward compatibility (GBC) metric that can represent the existing backward compatibility metrics. We formulate a learning objective based on the GBC metric and derive the estimation error bound, and the result is applied to one of the existing methods. Through further analysis, we reveal that the existing backward compatibility metrics are not suitable for imbalanced classification. We then design a backward compatibility metric for imbalanced classification on the basis of the GBC metric and empirically demonstrate the practicality of the proposed metric.
ACM KDD AI4Cyber/MLHat: Workshop on AI-enabled Cybersecurity Analytics and Deployable Defense
Authors: Sagar Samtani, Gang Wang 0011, Ali Ahmadzadeh, Arridhana Ciptadi, Shanchieh Yang, Hsinchun Chen
Venue: KDD
Year: 2022

Federal funding agencies and industry entities are seeking innovative approaches to address the ever-growing cybersecurity crisis. Increasingly, numerous cybersecurity thought leaders are indicating that Artificial Intelligence (AI)-enabled analytics can help tackle key cybersecurity tasks and deploy defenses. This half-day workshop, co-located with ACM KDD, sought to attain significant research contributions to various aspects of AI-enabled analytics for cybersecurity applications and deployable defense solutions from academics and practitioners. This workshop was a joint workshop of the 2021 AI-enabled Cybersecurity Analytics and 2021 International Workshop on Deployable Machine Learning for Security Defense. As such, we developed an interdisciplinary Program Committee with significant experience in various aspects of AI, cybersecurity, and/or deployable defense.
Machine Learning for Materials Science (MLMS)
Authors: Avadhut Sardeshmukh, Sreedhar Reddy, Gautham B. P., Ankit Agrawal 0001
Venue: KDD
Year: 2022

Artificial intelligence and machine learning are being increasingly used in scientific domains such as computational fluid dynamics and chemistry. Particularly notable is a recently renewed interest in solving partial differential equations using machine learning models, especially deep neural networks, as partial differential equations arise in many scientific problems of interest. Within materials science literature, there has been a surge in publications on AI-enabled materials discovery, in the last five years. Despite this, the interaction between machine learning researchers and materials scientists (especially, scientists working on structural materials, their microstructures, textures and so on) has been very sparse. On the other hand, AI/ML techniques can clearly be integrated into materials design frameworks (e.g., MGI efforts) to support accelerated materials development, novel simulation methodologies and advanced data analytics. Hence there is an immediate need for exchange of ideas and collaborations between machine learning and materials science communities. We believe a workshop dedicated to this theme would be well- suited to foster such collaborations. The aim of this workshop is to bring together the computer science and materials science communities and foster deeper collaborations between the two to accelerate the adoption of AI/ML in materials science. We hope and envision this workshop to facilitate in building a community of researchers in this interdisciplinar area in the years ahead.
Semantic Aware Answer Sentence Selection Using Self-Learning Based Domain Adaptation
Authors: Rajdeep Sarkar, Sourav Dutta 0001, Haytham Assem, Mihael Arcan, John P. McCrae
Venue: KDD
Year: 2022

Selecting an appropriate and relevant context forms an essential component for the efficacy of several information retrieval applications like Question Answering (QA) systems. The problem of Answer Sentence Selection (AS2) refers to the task of selecting sentences, from a larger text, that are relevant and contain the answer to users' queries. While there has been a lot of success in building AS2 systems trained on open-domain data (e.g., SQuAD, NQ), they do not generalize well in closed-domain settings, since domain adaptation can be challenging due to poor availability and annotation expense of domain-specific data. This paper proposes SEDAN, an effective self-learning framework to adapt AS2 models for domain-specific applications. We leverage large pre-trained language models to automatically generate domain-specific QA pairs for domain adaptation. We further fine-tune a pre-trained Sentence-BERT architecture to capture semantic relatedness between questions and answer sentences for AS2. Extensive experiments demonstrate the effectiveness of our proposed approach (over existing state-of-the-art AS2 baselines) on different Question Answering benchmark datasets.
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
Authors: Zezhi Shao, Zhao Zhang 0011, Fei Wang 0014, Yongjun Xu 0001
Venue: KDD
Year: 2022

Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods. STGNNs jointly model the spatial and temporal patterns of MTS through graph neural networks and sequential models, significantly improving the prediction accuracy. But limited by model complexity, most STGNNs only consider short-term historical MTS data, such as data over the past one hour. However, the patterns of time series and the dependencies between them (i.e., the temporal and spatial patterns) need to be analyzed based on long-term historical MTS data. To address this issue, we propose a novel framework, in which STGNN is Enhanced by a scalable time series Pre-training model (STEP). Specifically, we design a pre-training model to efficiently learn temporal patterns from very long-term history time series (e.g., the past two weeks) and generate segment-level representations. These representations provide contextual information for short-term time series input to STGNNs and facilitate modeling dependencies between time series. Experiments on three public real-world datasets demonstrate that our framework is capable of significantly enhancing downstream STGNNs, and our pre-training model aptly captures temporal patterns.
Deep Learning for Prognosis Using Task-fMRI: A Novel Architecture and Training Scheme
Authors: Ge Shi 0007, Jason Smucny, Ian Davidson
Venue: KDD
Year: 2022

Most existing brain imaging work focuses on resting-state fMRI (rs-fMRI) data where the subject is at rest in the scanner typically for disease diagnosis problems. Here we analyze task fMRI (t-fMRI) data where the subject performs a multi-event task over multiple trials. t-fMRI data allows exploring more challenging applications such as prognosis of treatment but at the cost of being more complex to analyze. Not only do multiple types of trials exist but the trials of each type are repeated a varying number of times for each subject. This leads to a multi-view (multiple types of trials) and multi-instance (multiple trials of each type of each subject) setting. We propose a deep multi-model architecture to encode multi-view brain activities from t-fMRI data and a multi-layer perceptron ensemble model to combine these view models and make subject-wise predictions. We explore domain adaptation transfer learning between models to address unbalanced views and a novel way to make predictions out of multi-instance embeddings. We evaluate our model's performance on subject-wise cross-validations to accurately determine performance. The experimental results show the proposed method outperforms published methods on the AX-CPT fMRI data for the prognosis problem of predicting treatment improvement in recent-onset childhood schizophrenia. To our knowledge, this is the first data-driven study of the aforementioned task on voxel-wise t-fMRI data of the whole brain.
Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation
Authors: Weili Shi, Ronghang Zhu, Sheng Li 0001
Venue: KDD
Year: 2022

Unsupervised domain adaptation (UDA) has become an appealing approach for knowledge transfer from a labeled source domain to an unlabeled target domain. However, when the classes in source and target domains are imbalanced, most existing UDA methods experience significant performance drop, as the decision boundary usually favors the majority classes. Some recent class-imbalanced domain adaptation (CDA) methods aim to tackle the challenge of biased label distribution by exploiting pseudo-labeled target samples during training process. However, these methods suffer from the issues with unreliable pseudo labels and error accumulation during training. In this paper, we propose a pairwise adversarial training approach for class-imbalanced domain adaptation. Unlike conventional adversarial training in which the adversarial samples are obtained from the lp ball of the original samples, we generate adversarial samples from the interpolated line of the aligned pairwise samples from source and target domains. The pairwise adversarial training (PAT) is a novel data-augmentation method which can be integrated into existing UDA models to tackle with the CDA problem. Experimental results and ablation studies show that the UDA models integrated with our method achieve considerable improvements on benchmarks compared with the original models as well as the state-of-the-art CDA methods. Our source code is available at: https://github.com/DamoSWL/Pairwise-Adversarial-Training
Recommendation in Offline Stores: A Gamification Approach for Learning the Spatiotemporal Representation of Indoor Shopping
Authors: Jongkyung Shin, Changhun Lee, Chiehyeon Lim, Yunmo Shin, Junseok Lim
Venue: KDD
Year: 2022

With the current advancements in mobile and sensing technologies used to collect real-time data in offline stores, retailers and wholesalers have attempted to develop recommender systems to enhance sales and customer experience. However, existing studies on recommender systems have primarily focused on e-commerce platforms and other online services. They did not consider the unique features of indoor shopping in real stores such as the physical environments and objects, which significantly affect the movement and purchase behaviors of customers, thereby representing the "spatiotemporal contexts" that are critical to identifying recommendable items. In this study, we propose a gamification approach wherein a real store is emulated in a pixel world and a recurrent convolutional network is trained to learn the spatiotemporal representation of offline shopping. The superiority and advantages of our method over existing sequential recommender systems are demonstrated through a real-world application in a hypermarket. We believe that our work can significantly contribute to promoting the practice of providing recommendations in offline stores and services.
Septor: Seismic Depth Estimation Using Hierarchical Neural Networks
Authors: M. Ashraf Siddiquee, Vinícius M. A. de Souza, Glenn Eli Baker, Abdullah Mueen
Venue: KDD
Year: 2022

The depth of a seismic event is an essential feature to discriminate natural earthquakes from events induced or created by humans. However, estimating the depth of a seismic event with a sparse set of seismic stations is a daunting task, and there is no globally usable method. This paper focuses on developing a machine learning model to accurately estimate the depth of arbitrary seismic events directly from seismograms. Our proposed deep learning architecture is not-so-deep compared to commonly found models in the literature for related tasks, consisting of two loosely connected levels of neural networks, associated with the seismic stations at the higher level and the individual channels of a station at the lower level. Thus, the model has significant advantages, including a reduced number of parameters for tuning and better interpretability to geophysicists. We evaluate our solution on seismic data collected from the SCEDC (Southern California Earthquake Data Center) catalog for regional events in California. The model can learn waveform features specific to a set of stations, while it struggles to generalize to completely novel sets of event sources and stations. In a simplified setting of separating shallow events from deep ones, the model achieved an 86.5% F1-score using the Southern California stations.
Robust and Informative Text Augmentation (RITA) via Constrained Worst-Case Transformations for Low-Resource Named Entity Recognition
Authors: Hyunwoo Sohn, Baekkwan Park
Venue: KDD
Year: 2022

Recent advances in deep learning have brought remarkable performance improvements in named entity recognition (NER), specifically in token-level classification problems. However, deep learning models often require a large amount of annotated data to achieve satisfactory performance, and NER annotation is significantly time-consuming and labor-intensive due to the fine-grained labels. To address this issue, we propose a textual data augmentation method that can automatically generate informative synthetic samples, which contribute to the development of a robust classifier. The proposed method generates additional training data by estimating the optimal level of worst-case transformation of training data while preserving the original annotation, and includes them into training to construct a robust decision boundary. Extensive experiments conducted on two benchmark datasets in a low-resource environment reveal that the proposed method outperforms two baseline augmentation methods including human annotation, which is typically considered to provide a decent amount of performance boost. To elucidate the processes, we also present in-depth analyses of the generated samples and estimated model parameters.
GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks
Authors: Weihao Song, Yushun Dong, Ninghao Liu 0001, Jundong Li
Venue: KDD
Year: 2022

Graph Neural Networks (GNNs) are playing increasingly important roles in critical decision-making scenarios due to their exceptional performance and end-to-end design. However, concerns have been raised that GNNs could make biased decisions against underprivileged groups or individuals. To remedy this issue, researchers have proposed various fairness notions including individual fairness that gives similar predictions to similar individuals. However, existing methods in individual fairness rely on Lipschitz condition: they only optimize overall individual fairness and disregard equality of individual fairness between groups. This leads to drastically different levels of individual fairness among groups. We tackle this problem by proposing a novel GNN framework GUIDE to achieve group equality informed individual fairness in GNNs. We aim to not only achieve individual fairness but also equalize the levels of individual fairness among groups. Specifically, our framework operates on the similarity matrix of individuals to learn personalized attention to achieve individual fairness without group level disparity. Comprehensive experiments on real-world datasets demonstrate that GUIDE obtains good balance of group equality informed individual fairness and model utility. The open-source implementation of GUIDE can be found here: https://github.com/mikesong724/GUIDE.
Automatic Phenotyping by a Seed-guided Topic Model
Authors: Ziyang Song, Yuanyi Hu, Aman Verma, David L. Buckeridge, Yue Li 0017
Venue: KDD
Year: 2022

Electronic health records (EHRs) provide rich clinical information and the opportunities to extract epidemiological patterns to understand and predict patient disease risks with suitable machine learning methods such as topic models. However, existing topic models do not generate identifiable topics each predicting a unique phenotype. One promising direction is to use known phenotype concepts to guide topic inference. We present a seed-guided Bayesian topic model called MixEHR-Seed with 3 contributions: (1) for each phenotype, we infer a dual-form of topic distribution: a seed-topic distribution over a small set of key EHR codes and a regular topic distribution over the entire EHR vocabulary; (2) we model age-dependent disease progression as Markovian dynamic topic priors; (3) we infer seed-guided multi-modal topics over distinct EHR data types. For inference, we developed a variational inference algorithm. Using MixEHR-Seed, we inferred 1569 PheCode-guided phenotype topics from an EHR database in Quebec, Canada covering 1.3 million patients for up to 20-year follow-up with 122 million records for 8539 and 1126 unique diagnostic and drug codes, respectively. We observed (1) accurate phenotype prediction by the guided topics, (2) clinically relevant PheCode-guided disease topics, (3) meaningful age-dependent disease prevalence. Source code is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-Seed.
Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification
Authors: Zixing Song, Yifei Zhang, Irwin King
Venue: KDD
Year: 2022

Graph Neural Networks (GNNs) have demonstrated great power for the semi-supervised node classification task. However, most GNN methods are sensitive to the noise of graph structures. Graph structure learning (GSL) is then introduced for robustification, which contains two major parts: recovering the optimal graph and fine-tuning the GNN parameters on this generated graph for the downstream task. Nonetheless, most of the existing GSL solutions merely focus on the node features during the first module for graph generation and exploit label information only by back-propagation during the second module for GNN training. They neglect the different roles that labeled and unlabeled nodes could play in GSL for the semi-supervised task, leading to a sub-optimal graph under this setting. In this paper, we give a precise definition on the optimality of the refined graph and provide the exact form of an optimal asymmetric graph structure designed explicitly for the semi-supervised node classification by distinguishing the different roles of labeled and unlabeled nodes through theoretical analysis. We propose a probabilistic model to infer the edge weights in this graph, which can be jointly trained with the subsequent node classification component. Extensive experimental results demonstrate the effectiveness of our method and the rationality of the optimal graph.
concept2code: Deep Reinforcement Learning for Conversational AI
Authors: Omprakash Sonie, Abir Chakraborty, Ankan Mullick
Venue: KDD
Year: 2022

Deep Reinforcement Learning uses best of both Reinforcement Learning and Deep Learning for solving problems which cannot be addressed by them individually. Deep Reinforcement Learning has been used widely for games, robotics etc. Limited work has been done for applying Deep Reinforcement Learning for Conversational AI. Hence, in this tutorial cover application of Deep Reinforcement Learning for Conversational AI.
Few-shot Learning for Trajectory-based Mobile Game Cheating Detection
Authors: Yueyang Su, Di Yao 0001, Xiaokai Chu, Wenbin Li 0012, Jingping Bi, Shiwei Zhao, Runze Wu 0001, Shize Zhang, Jianrong Tao, Hao Deng
Venue: KDD
Year: 2022

With the emerging of smartphones, mobile games have attracted billions of players and occupied most of the share for game companies. On the other hand, mobile game cheating, aiming to gain improper advantages by using programs that simulate the players' inputs, severely damages the game's fairness and harms the user experience. Therefore, detecting mobile game cheating is of great importance for mobile game companies. Many PC game-oriented cheating detection methods have been proposed in the past decades, however, they can not be directly adopted in mobile games due to the concern of privacy, power, and memory limitations of mobile devices. Even worse, in practice, the cheating programs are quickly updated, leading to the label scarcity for novel cheating patterns. To handle such issues, we in this paper introduce a mobile game cheating detection framework, namely FCDGame, to detect the cheats under the few-shot learning framework. FCDGame only consumes the screen sensor data, recording users' touch trajectories, which is less sensitive and more general for almost all mobile games. Moreover, a Hierarchical Trajectory Encoder and a Cross-pattern Meta Learner are designed in FCDGame to capture the intrinsic characters of mobile games and solve the label scarcity problem, respectively. Extensive experiments on two real online games show that FCDGame achieves almost 10% improvements in detection accuracy with only few fine-tuned samples.