E-commerce customers frequently seek detailed product information for purchase decisions, commonly contacting sellers directly with extended queries. This manual response requirement imposes additional costs and disrupts buyer’s shopping ...

Indoor self-localization has become a highly desirable system function for smartphones. The existing systems based on imaging, radio frequency, and geomagnetic sensing may have sub-optimal performance when their limiting factors prevail. In this paper, we ...

HD map is a key enabling technology towards fully autonomous driving. We propose VI-Map, the first system that leverages roadside infrastructure to enhance real-time HD mapping for autonomous driving. The core concept of VI-Map is to exploit the unique ...

Driver Behavior Modeling (DBM) aims to predict and model human driving behaviors, which is typically incorporated into the Advanced Driver Assistance System to enhance transportation safety and improve driving experience. Inverse reinforcement learning (...

Pansharpening technology is used to acquire a multispectral image with high spatial resolution from a panchromatic (PAN) image and a multispectral (MS) image. The detail injection model is popular for its flexibility. However, the accuracy of the ...

Runtime domain shifts from the training phase caused by sensor characteristic variation incur performance drops of the deep learning-based sensing systems. To address this problem, existing transfer learning techniques require substantial target-domain ...

Adversarial example attack endangers the mobile edge systems such as vehicles and drones that adopt deep neural networks for visual sensing. This paper presents Sardino, an active and dynamic defense approach that renews the inference ensemble at run ...

Mobile cloud offloading is indispensable for inference tasks based on large-scale deep models. However, transmitting privacy-rich inference data to the cloud incurs concerns. This paper presents the design of a system called PriMask, in which the mobile ...

Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded performance when their limiting factors take effect. In this paper, we ...

Massive fraudulent and phishing robocalls present threats to societies. The integration of artificial intelligence technologies, including dialogue and voice generation systems, renders the robocalls more deceptive. Existing countermeasures such as ...

Voltage stability assessment is essential for maintaining reliable power grid operations. Stability assessment approaches using deep learning address the shortfalls of the traditional time-domain simulation-based approaches caused by increased system ...

Online loan has achieved great development in recent years, which benefit to both sides of lending. In this paper, we analysis the risk of lenders, using machine learning approach to predict the performances of borrower. We play the role of clearing ...

Most swarm intelligence algorithms are stochastic metaheuristic algorithms in nature, and thus they may not solve all optimisation problems perfectly. Different algorithms may have different advantages, and the different real cases should be ...

• The historical stage division of air quality information via network science is investigated.

Urban air quality is related to human health in modern life. The statistical features of urban air quality highly depend on the division of historical stages. Conventional division methods that use a fixed period (e.g., month) can ...

Smartphone indoor location awareness is increasingly demanded by a variety of mobile applications. The existing solutions for accurate smartphone indoor localization rely on additional devices or pre-installed infrastructure (e.g., dense WiFi access ...

Deep learning-based visual sensing has achieved attractive accuracy but is shown vulnerable to adversarial attacks. Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model to yield wrong ...

Stability assessment is an important task for maintaining reliable operations of power grids. With increased system complexity, deep learning-based stability assessment approaches are promising to address the shortfalls of the traditional time-domain ...

Run-time domain shifts from training-phase domains are common in sensing systems designed with deep learning. The shifts can be caused by sensor characteristic variations and/or discrepancies between the design-phase model and the actual model of the ...

Numerous supply-chain combines with internet of things (IoT) applications have been proposed, and many methods and algorithms enhance the convenience of supply chains. However, new businesses still find it challenging to enter a supply chain, because ...

Nowadays swarm intelligence algorithms are becoming increasingly popular in solving many optimization problems. But different algorithms have their own strengths and weaknesses according to their composite designs. Wolf Search Algorithm (WSA)is a ...