Project

F2-M GraphBTM: Graph Enhanced VAE for Biterm Topic Model

June 29, 2022 in 2018-2019, Project

GraphBTM is a topic model which is an unsupervised algorithm to understand documents. It learns to discover the latent representation of documents and produce meaning clustering of words in the same topic. The goal of GraphBTM is to overcome the limitations of the Latent Dirichlet Allocation (LDA) which suffers from the data sparsity problem in short text and Biterm Topic Model (BTM) which claims an insufficient whole-corpus topic distribution.

F3-M TPR Learning: a Symbolic Neural Approach for Vision Language Intelligence

June 29, 2022 in 2018-2019, Project

Deep learning (DL) has in recent years been widely used in computer vision and natural language processing (NLP) applications due to its superior performance. However, while images and natural languages are known to be rich in structures expressed, for example, by grammar rules, DL has so far not been capable of explicitly representing and enforcing such structures [Huang18]. In this project, we propose an approach to bridging this gap by exploiting tensor product representations (TPR) [Smolensky90a, Smolensky90b], a structured neural-symbolic model developed in cognitive science, aiming to integrate DL with explicit language rules, logical rules, or rules that summarize the human knowledge about the subject.

F4-M Adaptive Manifold Learning for Multi-Sensor Translation and Fusion given Missing Data

June 29, 2022 in 2018-2019, Project

The goal of this work is to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes investigation of computational topology for manifold learning, data summarization, and intrinsic dimensionality estimation. In practice, for a given application, processing chains are generally developed for a particular sensor or set of sensors. 

F6-V Machine-Learning-Enabled Video Coding Strategy for Object Detection

June 29, 2022 in 2018-2019, Project

The goal of this project is to develop a machine-learning-enabled video coding strategy for object detection.  Most existing video encoders minimizes distortion under a rate constraint. However, for surveillance video, it is desired for a video encoder to maximize detection probability under a rate constraint. To address this, we will design a new video coding strategy that maximizes object detection probability under a rate constraint. We will locate the information important to object detector, develop Rate-Detection-Optimized framework for mode selection, and design optimized bit rate allocation method.

M2-S Smart-ARM: Smart Streaming Telemetry for Agile and Resilient Management of Wireless and Mobile Software Defined Networking

June 28, 2022 in 2018-2019, Project

Traditional application-driven (i.e., fault, SLA, or DoS attack detection) “pull” model (SNMP) based network management is expensive and slow for network problem detection, isolation, and root cause analysis. Especially, the recent federation of novel softwareisation and virtualization architectures, as well as Internet of Things (IoT) technologies, require better management over the heterogeneous systems and services. The project adopts “push” based open source forwarding (streaming) network management technologies by using P4 (Programming Protocol-Independent Packet Processors) Inband Network Telemetry (INT).

M3-T Mobile Edge Point Cloud Services for Auto Driving

June 28, 2022 in 2018-2019, Project

Provide Realtime live 3D map service from mobile edge using distributed sensing and low latency point cloud aggregation and multicasting. We will use high efficiency scalable point cloud source coding. We will provide real time point cloud LiveMaps aggregated through mobile edge computing. We propose a joint source-channel coding for V2V and V2I communication.