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Knowledge tracing, the goal of which is predicting students’ future performance given their past question response sequences to trace their knowledge states, is pivotal for computer-aided education and intelligent tutoring systems. Although many technical ...

Everyday our living city produces a tremendous amount of spatial-temporal data, involved with multiple sources from the individual scale to the city scale. Undoubtedly, such massive urban data can be explored for a better city and better life, as what the ...

Autonomous driving systems need to undergo rigorous testing in complex scenarios including a variety of extreme operating conditions before they can be put into use. In this process, digital twin technology can migrate the scenes in the physical world ...

A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of ...

The automotive and transportation industry is going through a tectonic shift in the next decade with the advent of Connectivity, Automation, Sharing, and Electrification (CASE). Autonomous driving presents a historical opportunity to transform the ...

We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the ...

In real-world express systems, couriers need to satisfy not only the delivery demands but also the pick-up demands of customers. Delivery and pickup tasks are usually mixed together within integrated routing plans. Such a mixed routing problem can be ...

The recent outbreak of COVID-19 poses a serious threat to people’s lives. Epidemic control strategies have also caused damage to the economy by cutting off humans’ daily commute. In this article, we develop an Individual-based Reinforcement Learning ...

Data quantization is an effective method to accelerate neural network training and reduce power consumption. However, it is challenging to perform low-bit quantized training: the conventional equal-precision quantization will lead to either high ...

Unlike the grid-paced RGB images, network compression, i.e.pruning and quantization, for the irregular and sparse 3D point cloud face more challenges. Traditional quantization ignores the unbalanced semantic distribution in 3D point cloud. In this work, ...

Graph Attention Network (GAT) has demonstrated better performance in many graph tasks than previous Graph Neural Networks (GNN). However, it involves graph attention operations with extra computing complexity. While a large amount of existing literature ...

Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared model without exposing their raw data. However, heterogeneous devices usually have limited and different available resources, i.e., system heterogeneity, for ...

Due to the inter-subject variability of Electroencephalogram(EEG) signals, a long calibration time is required to collect a large number of labeled trials to calibrate classifier parameters before using the Brain-computer Interface(BCI). This challenge ...

Emergency rescue scenarios are considered to be high-risk scenarios. Using a micro air vehicle (MAV) swarm to explore the environment can provide valuable environmental information. However, due to the absence of localization infrastructure and the ...

Fine-grained air pollution data is essential for smart living and efficient city management. However, it is arduous to obtain accurate air pollution data with high spatial and temporal resolutions via mobile crowdsensing (MCS) under limited budgets. ...

Motivated by reducing the data transfer activities in data-intensive neural network computing, SRAM-based compute-in-memory (CiM) has made significant progress. Unfortunately, SRAM has low density and limited on-chip capacity. This makes the deployment ...

Many anomaly detection techniques have been adopted by Industrial Internet of Things (IIoT) for improving self-diagnosing efficiency and infrastructures security. However, they are usually associated with the issues of computational-hungry and “black box.”...

Contextual knowledge is essential for reducing speech recognition errors on high-valued long-tail words. This paper proposes a novel tree-constrained pointer generator (TCPGen) component that enables end-to-end ASR models to bias towards a list of long-...

Dialogue state tracking (DST) is often used to track the system's understanding of the user goal in task-oriented dialogue systems. Existing DST methods mainly fall into two categories according to their adopted model structure: non-hierarchical ...

Existing studies on neural architecture search (NAS) mainly focus on efficiently and effectively searching for network architectures with better performance. Little progress has been made to systematically understand if the NAS-searched architectures ...