Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Spatio-Temporal Graph Contrastive Learning

Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlations with graph neural networks. Despite their effectiveness, several key challenges have not been well addressed in existing methods: i) Data noise and missing are ubiquitous in many spatio-temporal scenarios due to a variety of factors. ii) Input spatio-temporal data (e.g., mobility traces) usually exhibits distribution heterogeneity across space and time. In such cases, current methods are vulnerable to the quality of the generated region graphs, which may lead to suboptimal performance. In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources. Our AutoST framework is built upon a heterogeneous graph neural architecture to capture the multi-view region dependencies with respect to POI semantics, mobility flow patterns and geographical positions. To improve the robustness of our GNN encoder against data noise and distribution issues, we design an automated spatio-temporal augmentation scheme with a parameterized contrastive view generator. AutoST can adapt to the spatio-temporal heterogeneous graph with multi-view semantics well preserved. Extensive experiments for three downstream spatio-temporal mining tasks on several real-world datasets demonstrate the significant performance gain achieved by our AutoST over a variety of baselines. The code is publicly available at https://github.com/HKUDS/AutoST.
HTTP Steady Connections for Robust Web Acceleration

HTTP’s intrinsic request-and-response traffic pattern makes most web servers often idle, leaving a potential to accelerate page loads. We present the notion of HTTP steady connections, which fully utilizes the server’s available network bandwidth during a page load using the promising HTTP/3 server push, transforming the intermittent workload of loading a page into a more steady one. To construct a proper server push policy to achieve this, we separate the structure of a page, which is a relatively static factor, from the page load environments including client and network characteristics, which are generally dynamic and unknown to servers. We formulate a deadline-based sequencing problem using a page load model with dependency graphs and design a feedback-based reprioritization mechanism within HTTP server push to reactively match client progress robustly. Experiments with a prototype and a wide range of real-world pages show that HTTP steady connections significantly improve web page loads compared with state-of-the-art accelerators, even under packet losses and without any prior knowledge of network environments.
Learning to Simulate Crowd Trajectories with Graph Networks

Crowd stampede disasters often occur, such as recent ones in Indonesia and South Korea, and crowd simulation is particularly important to prevent and avoid such disasters. Most traditional models for crowd simulation, such as the social force model, are hand-designed formulas, which use Newtonian forces to model the interactions between pedestrians. However, such formula-based methods may not be flexible enough to capture the complex interaction patterns in diverse crowd scenarios. Recently, due to the development of the Internet, a large amount of pedestrian movement data has been collected, allowing us to study crowd simulation in a data-driven way. Inspired by the recent success of graph network-based simulation (GNS), we propose a novel method under the framework of GNS, which simulates the crowd in a data-driven way. Specifically, we propose to model the interactions among people and the environment using a heterogeneous graph. Then, we design a heterogeneous gated message-passing network to learn the interaction pattern that depends on the visual field. Finally, the randomness is introduced by modeling the context’s different influences on pedestrians with a probabilistic emission function. Extensive experiments on synthetic data, controlled-environment data and real-world data are performed. Extensive results show that our model can generally capture the three main factors which contribute to crowd trajectories while adapting to the data characteristics beyond the strong assumption of formulas-based methods. As a result, the proposed method outperforms existing methods by a large margin.
Chemo‐mechanics of cemented granular solids subjected to precipitation and dissolution of mineral species

This paper studies the chemo‐mechanics of cemented granular solids in the context of continuum thermodynamics for fluid‐saturated porous media. For this purpose, an existing constitutive model formulated in the frame of the Breakage Mechanics theory is augmented to cope with reactive processes. Chemical state variables accounting for the reactions between the solid constituents and the solutes in the pore fluid are introduced to enrich the interactions among the microstructural units simulated by the model (i.e., grains and cement bonds). Two different reactive processes are studied (i.e., grain dissolution and cement precipitation), using the chemical variables to describe the progression of the reactions and track changes in the size of grains and bonds. Finally, a homogenization strategy is used to derive the energy potentials of the solid mixture, adopting probability density functions that depend on both mechanical and chemical indices. It is shown that the connection between the statistics of the micro‐scale attributes and the continuum properties of the solid enables the mathematical capture of numerous mechanical effects of lithification and chemical deterioration, such as changes in stiffness, expansion/contraction of the elastic domain, and development of inelastic strains during reaction. In particular, the model offers an interpretation of the plastic strains generated by aggressive environments, which are here interpreted as an outcome of chemically driven debonding and comminution. As a result, the model explains widely observed macroscopic signatures of geomaterial degradation by reconciling the energetics of the deformation/reaction processes with the evolving geometry of the microstructural attributes. Copyright © 2015 John Wiley & Sons, Ltd.
Semi-Supervised Embedding of Attributed Multiplex Networks

Complex information can be represented as networks (graphs) characterized by a large number of nodes, multiple types of nodes, and multiple types of relationships between them, i.e. multiplex networks. Additionally, these networks are enriched with different types of node features. We propose a Semi-supervised Embedding approach for Attributed Multiplex Networks (SSAMN), to jointly embed nodes, node attributes, and node labels of multiplex networks in a low dimensional space. Network embedding techniques have garnered research attention for real-world applications. However, most existing techniques solely focus on learning the node embeddings, and only a few learn class label embeddings. Our method assumes that we have different classes of nodes and that we know the class label of some, very few nodes for every class. Guided by this type of supervision, SSAMN learns a low-dimensional representation incorporating all information in a large labeled multiplex network. SSAMN integrates techniques from Spectral Embedding and Homogeneity Analysis to improve the embedding of nodes, node attributes, and node labels. Our experiments demonstrate that we only need very few labels per class in order to have a final embedding that preservers the information of the graph. To evaluate the performance of SSAMN, we run experiments on four real-world datasets. The results show that our approach outperforms state-of-the-art methods for downstream tasks such as semi-supervised node classification and node clustering.
Hidden Indicators of Collective Intelligence in Crowdfunding

Extensive literature argues that crowds possess essential collective intelligence benefits that allow superior decision-making by untrained individuals working in low-information environments. Classic wisdom of crowds theory is based on evidence gathered from studying large groups of diverse and independent decision-makers. Yet, most human decisions are reached in online settings of interconnected like-minded people that challenge these criteria. This observation raises a key question: Are there surprising expressions of collective intelligence online? Here, we explore whether crowds furnish collective intelligence benefits in crowdfunding systems. Crowdfunding has grown and diversified quickly over the past decade, expanding from funding aspirant creative works and supplying pro-social donations to enabling large citizen-funded urban projects and providing commercial interest-based unsecured loans. Using nearly 10 million loan contributions from a market-dominant lending platform, we find evidence for collective intelligence indicators in crowdfunding. Our results, which are based on a two-stage Heckman selection model, indicate that opinion diversity and the speed at which funds are contributed predict who gets funded and who repays, even after accounting for traditional measures of creditworthiness. Moreover, crowds work consistently well in correctly assessing the outcome of high-risk projects. Finally, diversity and speed serve as early warning signals when inferring fundraising based solely on the initial part of the campaign. Our findings broaden the field of crowd-aware system design and inform discussions about the augmentation of traditional financing systems with tech innovations.
Multi-emotion Recognition Using Multi-EmoBERT and Emotion Analysis in Fake News

Emotion recognition techniques are increasingly applied in fake news veracity or stance detection. While multiple co-existing emotions tend to co-occur in a single news article, most existing fake news detection has only leveraged single-label emotion recognition mechanisms. In addition, the relationship between the emotion of an article and its stance has not been sufficiently explored. To address these research gaps, we have developed and applied a multi-label emotion recognition tool called Multi-EmoBERT in fake news datasets. The tool delivers state-of-the-art performance on SemEval2018 Task 1. We apply the tool to identify emotions in several fake news datasets and examine the relationships between veracity/stance and emotion. Our work demonstrates the potential for predicting multiple co-existing emotions for fake news and implications against fake news spread.
MLN4KB: an efficient Markov logic network engine for large-scale knowledge bases and structured logic rules

Markov logic network (MLN) is a powerful statistical modeling framework for probabilistic logic reasoning. Despite the elegancy and effectiveness of MLN, the inference of MLN is known to suffer from an efficiency issue. Even the state-of-the-art MLN engines can not scale to medium-size real-world knowledge bases in the open-world setting, i.e., all unobserved facts in the knowledge base need predictions. In this work, by focusing on a certain class of first-order logic rules that are sufficiently expressive, we develop a highly efficient MLN inference engine called MLN4KB that can leverage the sparsity of knowledge bases. MLN4KB enjoys quite strong theoretical properties; its space and time complexities can be exponentially smaller than existing MLN engines. Experiments on both synthetic and real-world knowledge bases demonstrate the effectiveness of the proposed method. MLN4KB is orders of magnitudes faster (more than 103 times faster on some datasets) than existing MLN engines in the open-world setting. Without any approximation tricks, MLN4KB can scale to real-world knowledge bases including WN-18 and YAGO3-10 and achieve decent prediction accuracy without bells and whistles. We implement MLN4KB as a Julia package called MLN4KB.jl. The package supports both maximum a posteriori (MAP) inference and learning the weights of rules. MLN4KB.jl is public available at https://github.com/baidu-research/MLN4KB .
Identifying Checkworthy CURE Claims on Twitter

Medical claims on social media, if left unchecked, have the potential to directly affect the well-being of consumers of online health information. However, existing studies on claim detection do not specifically focus on medical cure aspects, neither do they address if a cure claim is “checkworthy", an indicator of whether a claim is potentially beneficial or harmful, if unchecked. In this paper, we address these limitations by compiling CW-CURE, a novel dataset of CURE tweets, namely tweets containing claims on prevention, diagnoses, risks, treatments, and cures of medical conditions. CW-CURE contains tweets on four major health conditions, namely, Alzheimer’s disease, Cancer, Diabetes, and Depression annotated for claims, their “checkworthiness", as well as the different types of claims such as quantitative claim, correlation/causation, personal experience, and future prediction. We describe our processing pipeline for compiling CW-CURE and present classification results on CURE tweets using transformer-based models. In particular, we harness claim-type information obtained with zero-shot learning to show significant improvements in checkworthiness identification. Through CW-CURE, we hope to enable research on models for effective identification and flagging of impactful CURE content, to safeguard the public’s consumption of medical content online.
Combining Worker Factors for Heterogeneous Crowd Task Assignment

Optimising the assignment of tasks to workers is an effective approach to ensure high quality in crowdsourced data - particularly in heterogeneous micro tasks. However, previous attempts at heterogeneous micro task assignment based on worker characteristics are limited to using cognitive skills, despite literature emphasising that worker performance varies based on other parameters. This study is an initial step towards understanding whether and how multiple parameters such as cognitive skills, mood, personality, alertness, comprehension skill, and social and physical context of workers can be leveraged in tandem to improve worker performance estimations in heterogeneous micro tasks. Our predictive models indicate that these parameters have varying effects on worker performance in the five task types considered – sentiment analysis, classification, transcription, named entity recognition and bounding box. Moreover, we note 0.003 - 0.018 reduction in mean absolute error of predicted worker accuracy across all tasks, when task assignment is based on models that consider all parameters vs. models that only consider workers’ cognitive skills. Our findings pave the way for the use of holistic approaches in micro task assignment that effectively quantify worker context.
Dělen: Enabling Flexible and Adaptive Model-serving for Multi-tenant Edge AI

Model-serving systems expose machine learning (ML) models to applications programmatically via a high-level API. Cloud platforms use these systems to mask the complexities of optimally managing resources and servicing inference requests across multiple applications. Model serving at the edge is now also becoming increasingly important to support inference workloads with tight latency requirements. However, edge model serving differs substantially from cloud model serving in its latency, energy, and accuracy constraints: these systems must support multiple applications with widely different latency and accuracy requirements on embedded edge accelerators with limited computational and energy resources. To address the problem, this paper presents Dělen,1 a flexible and adaptive model-serving system for multi-tenant edge AI. Dělen exposes a high-level API that enables individual edge applications to specify a bound at runtime on the latency, accuracy, or energy of their inference requests. We efficiently implement Dělen using conditional execution in multi-exit deep neural networks (DNNs), which enables granular control over inference requests, and evaluate it on a resource-constrained Jetson Nano edge accelerator. We evaluate Dělen flexibility by implementing state-of-the-art adaptation policies using Dělen’s API, and evaluate its adaptability under different workload dynamics and goals when running single and multiple applications.
Learning Faithful Attention for Interpretable Classification of Crisis-Related Microblogs under Constrained Human Budget

The recent widespread use of social media platforms has created convenient ways to obtain and spread up-to-date information during crisis events such as disasters. Time-critical analysis of crisis data can help human organizations gain actionable information and plan for aid responses. Many existing studies have proposed methods to identify informative messages and categorize them into different humanitarian classes. Advanced neural network architectures tend to achieve state-of-the-art performance, but the model decisions are opaque. While attention heatmaps show insights into the model’s prediction, some studies found that standard attention does not provide meaningful explanations. Alternatively, recent works proposed interpretable approaches for the classification of crisis events that rely on human rationales to train and extract short snippets as explanations. However, the rationale annotations are not always available, especially in real-time situations for new tasks and events. In this paper, we propose a two-stage approach to learn the rationales under minimal human supervision and derive faithful machine attention. Extensive experiments over four crisis events show that our model is able to obtain better or comparable classification performance (∼ 86% Macro-F1) to baselines and faithful attention heatmaps using only 40-50% human-level supervision. Further, we employ a zero-shot learning setup to detect actionable tweets along with actionable word snippets as rationales.
Misinformation Detection Algorithms and Fairness across Political Ideologies: The Impact of Article Level Labeling

Multiple recent efforts have used large-scale data and computational models to automatically detect misinformation in online news articles. Given the potential impact of misinformation on democracy, many of these efforts have also used the political ideology of these articles to better model misinformation and study political bias in such algorithms. However, almost all such efforts have used source level labels for credibility and political alignment, thereby assigning the same credibility and political alignment label to all articles from the same source (e.g., the New York Times or Breitbart). Here, we report on the impact of journalistic best practices to label individual news articles for their credibility and political alignment. We found that while source level labels are decent proxies for political alignment labeling, they are very poor proxies – almost the same as flipping a coin – for credibility ratings. Next, we study the implications of such source level labeling on downstream processes such as the development of automated misinformation detection algorithms and political fairness audits therein. We find that the automated misinformation detection and fairness algorithms can be suitably revised to support their intended goals but might require different assumptions and methods than those which are appropriate using source level labeling. The results suggest caution in generalizing recent results on misinformation detection and political bias therein. On a positive note, this work shares a new dataset of journalistic quality individually labeled articles and an approach for misinformation detection and fairness audits.
Detecting Mental Disorders with Wearables: A Large Cohort Study

Depression and anxiety are among the most prevalent mental disorders, and they are usually interconnected. Although these mental disorders have drawn increasing attention due to their tremendous negative impacts on working ability and job performance, over 50% of patients are not recognized or adequately treated. Recent literature has shown the potential of using wearables for expediting the detection of mental health disorders, as physical activities are reported to be related to some mental health disorders. However, most prior studies on mental health with wearables were limited to small cohorts. The feasibility of detecting mental disorders in the community with a large and diverse population remains an open question. In this paper, we study the problem of detecting depression and anxiety disorders with commercial wearable activity trackers based on a public dataset including 8,996 participants and 1,247 diagnosed with mental disorders. The large cohort is highly diverse, spanning a wide spectrum of age, race, ethnicity, and education levels. While prior studies were usually limited to shallow machine learning models and feature engineering to accommodate the small sample sizes, we develop an end-to-end deep model combining a transformer encoder and convolutional neural network to directly learn from daily wearable features and detect mental disorders. WearNet achieves an area Under the Receiver Operating Characteristic curve (AUROC) of 0.717 (S.D. 0.009) and an AUPRC of 0.487 (S.D. 0.008) in detecting mental disorders while outperforming traditional and state-of-the-art machine learning models. This work demonstrates the feasibility and promise of using wearables to detect mental disorders in a large and diverse community.
AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation

The Embarrassingly Shallow Autoencoders (EASE and SLIM) are strong recommendation methods based on implicit feedback, compared to competing methods like iALS and VAE-CF. However, EASE suffers from several major shortcomings. First, the training and inference of EASE can not scale with the increasing number of items since it requires storing and inverting a large dense matrix; Second, though its optimization objective – the square loss– can yield a closed-form solution, it is not consistent with recommendation goal – predicting a personalized ranking on a set of items, so that its performance is far from optimal w.r. t ranking-oriented recommendation metrics. Finally, the regularization coefficients are sensitive w.r. t recommendation accuracy and vary a lot across different datasets, so the fine-tuning of these parameters is important yet time-consuming. To improve training and inference efficiency, we propose a Similarity-Structure Aware Shallow Autoencoder on top of three similarity structures, including Co-Occurrence, KNN and NSW. We then optimize the model with a weighted square loss, which is proven effective for ranking-based recommendation but still capable of deriving closed-form solutions. However, the weight in the loss can not be learned in the training set and is similarly sensitive w.r. t the accuracy to regularization coefficients. To automatically tune the hyperparameters, we design two validation losses on the validation set for guidance, and update the hyperparameters with the gradient of the validation losses. We finally evaluate the proposed method on multiple real-world datasets and show that it outperforms seven competing baselines remarkably, and verify the effectiveness of each part in the proposed method.