Related papers: JANUS: Benchmarking Commercial and Open-Source Clo…

With the growing volume of Internet of Things (IoT) network traffic, machine learning (ML)-based anomaly detection is more relevant than ever. Traditional batch learning models face challenges such as high maintenance and poor adaptability…

Real-time city-scale traffic analytics requires processing 100s-1000s of CCTV streams under strict latency, bandwidth, and compute limits. We present a scalable AI-driven Intelligent Transportation System (AIITS) designed to address…

3D object detection based on LiDAR point clouds is a crucial module in autonomous driving particularly for long range sensing. Most of the research is focused on achieving higher accuracy and these models are not optimized for deployment on…

As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices is, however, by no means trivial, since it incurs high performance…

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in…

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in…

The integration of the Industrial Internet of Things (IIoT) with Artificial Intelligence-Generated Content (AIGC) offers new opportunities for smart manufacturing, but it also introduces challenges related to computation-intensive tasks and…

The main goal of this research project is to evaluate the effectiveness and speed of open-source forensic tools for digital evidence collecting from various Internet-of-Things (IoT) devices. The project will create and configure many IoT…

While object detection is a common problem in computer vision, it is even more challenging when dealing with aerial satellite images. The variety in object scales and orientations can make them difficult to identify. In addition, there can…

The rapid growth of the Internet of Things (IoT) has given rise to highly diverse and interconnected ecosystems that are increasingly susceptible to sophisticated cyber threats. Conventional anomaly detection schemes often prioritize…

This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost by adapting and combining recent technical innovations. Following the common pipeline of…

As users migrate their analytical workloads to cloud databases, it is becoming just as important to reduce monetary costs as it is to optimize query runtime. In the cloud, a query is billed based on either its compute time or the amount of…

With the advancement of information retrieval, recommendation systems, and Retrieval-Augmented Generation (RAG), Approximate Nearest Neighbor Search (ANNS) gains widespread applications due to its higher performance and accuracy. While…

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the…

The rapid growth of data generated from Internet of Things (IoTs) such as smart phones and smart home devices presents new challenges to cloud computing in transferring, storing, and processing the data. With increasingly more powerful edge…

Object detectors are usually equipped with backbone networks designed for image classification. It might be sub-optimal because of the gap between the tasks of image classification and object detection. In this work, we present DetNAS to…

Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence. Existing UVOS methods either lack robustness when there are visually similar…

Drones have proven to be useful in many industry segments such as security and surveillance, where e.g. on-board real-time object tracking is a necessity for autonomous flying guards. Tracking and following suspicious objects is therefore…

Collaborative inference enables resource-constrained edge devices to make inferences by uploading inputs (e.g., images) to a server (i.e., cloud) where the heavy deep learning models run. While this setup works cost-effectively for…

Audio/visual recognition and retrieval applications have recently garnered significant attention within Internet-of-Things (IoT) oriented services, given that video cameras and audio processing chipsets are now ubiquitous even in low-end…