I Know Your Intent: Graph-enhanced Intent-aware User Device Interaction Prediction via Contrastive Learning

Article Computer Science, Information Systems

ADRIoT: An Edge-Assisted Anomaly Detection Framework Against IoT-Based Network Attacks

Ruoyu Li et al.

Summary: Internet of Things (IoT) is undergoing rapid development and widespread deployment. However, due to the inability of low-power devices to support complex security mechanisms, they are highly vulnerable to malware attacks. This article proposes ADRIoT, an edge computing-based anomaly detection framework for IoT networks, which effectively uncovers potential threats. By utilizing LSTM autoencoders and a collaborative mechanism among multiple edge devices, ADRIoT is able to detect various IoT-based attacks and contribute to building a more secure IoT environment.

IEEE INTERNET OF THINGS JOURNAL (2022)

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Proceedings Paper Computer Science, Information Systems

Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge

Hyunsik Jeon et al.

Summary: SmartSense is an accurate action recommendation method for smart home that addresses the challenges of context correlations, queried contexts, and capricious intentions in action sequences, achieving state-of-the-art performance in the field.

PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022 (2022)

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Article Computer Science, Information Systems

Wheels Know Why You Travel: Predicting Trip Purpose via a Dual-Attention Graph Embedding Network

Chengwu Liao et al.

Summary: This paper focuses on predicting trip purposes in door-to-door ride services using only vehicle GPS trajectory and public POI check-in data. By utilizing a dual-attention graph embedding model with graph attention networks and soft-attention, the study aims to obtain comprehensive activity semantics and improve prediction accuracy. Extensive experiments on large-scale datasets in Beijing show significant improvement compared to state-of-the-arts, demonstrating the feasibility of the approach.

PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT (2022)

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Article Computer Science, Information Systems

Activities Recognition, Anomaly Detection and Next Activity Prediction Based on Neural Networks in Smart Homes

Khaled A. Alaghbari et al.

Summary: This paper proposes a unified deep learning model for monitoring elderly in their daily life activities. The model consists of three stages: activity recognition, anomaly detection, and next activity prediction. By utilizing the results of each stage, the model provides useful information to help identify activities and generate preventive and corrective measures for the elderly, caregivers, and medical teams.

IEEE ACCESS (2022)

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Article Chemistry, Analytical