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Designing a proper scoring function is the key to ensuring the excellent performance of knowledge base (KB) embedding. Recently, the scoring function search method introduces the automated machine learning technique to design the data-aware scoring ...

Dense high dimensional vectors are becoming increasingly vital in fields such as computer vision, machine learning, and large language models (LLMs), serving as standard representations for multimodal data. Now the dimensionality of these vector ...${\mathcal{}}_{\mathrm{}}$${\mathcal{}}_{\mathrm{}}$

Travel planning plays an increasingly important role in our society. The travel plans, which consist of the paths each vehicle is suggested to follow and its corresponding departure time, influence the traffic conditions naturally. However, existing ...

Making accurate and safe clinical decisions for patients has long been a challenging task. With the proliferation of electronic health records and the rapid advancement of technology, drug recommender systems have emerged as invaluable aids for healthcare ...

Anomaly detection is one of the most significant tasks in multivariate time series analysis, while it remains challenging to model complex patterns for improving detection accuracy and to interpret the root causes of anomalies. However, existing studies ...

Embodied AI is one of the most popular studies in artificial intelligence and robotics, which can effectively improve the intelligence of real-world agents (i.e. robots) serving human beings. Scene knowledge is important for an agent to understand the ...

Shortest path computation is a basic operation for many graph-based applications and has been extensively studied. However, most existing works only consider the optimal path of a single criterion but ignore real-world situations involving multiple ...

The prediction of stock prices is a highly sought-after topic in the data mining field. In recent decades, many promising methods have been proposed and widely adopted for stock price prediction. However, these methods have inherent limitations, such as ...

Lane-level traffic prediction is crucial for refined smart city applications, yet the scarcity and quality issues of datasets hinder its development. To overcome these challenges, this study introduces a novel <u> M </u>ulti-<u> c </u>hannel <u> g </u>...

Spatial trajectory prediction is a fundamental problem for diverse location-based applications. However, existing methods fall short in learning and generalization, and cannot sufficiently capture users&#x2019; spatiotemporal preferences, especially for ...

Recent years have witnessed a revolution in Spatial Crowdsourcing (SC), in which people with mobile connectivity can perform spatio-temporal tasks that involve traveling to specified locations. In this paper, we identify and study in depth a new multi-...

In this paper, we address the challenges of data augmentation in Multi-Modal Knowledge Graphs (MMKGs), a relatively under-explored area. We propose a novel diffusion-based generative model, the Simple Denoising Probabilistic Latent Diffusion Model (...

Sequential Recommendation (SR) methods play a crucial role in recommender systems, which aims to capture users&#x2019; dynamic interest from their historical interactions. Recently, Contrastive Learning (CL), which has emerged as a successful method for ...

Forecasting of Irregular Multivariate Time Series (IMTS) is critical for numerous areas, such as healthcare, biomechanics, climate science, and astronomy. Despite existing research addressing irregularities in time series through ordinary differential ...

The rapid proliferation of location-based services (LBSs) has facilitated the collection of extensive location data by potentially untrustworthy servers, raising privacy concerns. Conventional solutions provide location privacy but often fail to ...

The protocols that satisfy Local Differential Privacy (LDP) enable untrusted third parties to collect aggregate information about a population without disclosing each user&#x0027;s privacy. In particular, each user locally encodes and perturbs his private ...

Automation in road vehicles is an emerging technology that has developed rapidly over the last decade. There have been many inter-disciplinary challenges posed on existing transportation infrastructure by autonomous vehicles. In this paper, we conduct an ...

The uniqueness of trajectory data for user re-identification has received unprecedented attention as the increasing popularity of location-based services boosts the excessive collection of daily trajectories with sufficient spatiotemporal coverage. ...

With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs (e.g., chemical compounds). Although subgraph ...

Uncertainty is an essential consideration for time series forecasting tasks. In this work, we focus on quantifying the uncertainty of traffic forecasting from a unified perspective. We develop a novel traffic forecasting framework, namely Deep Spatio-...