arXiv subjects

Guanqun Song

Publications and source records attributed to Guanqun Song.

24 records · Page 2Linked to original sources

arXiv2023

Beyond the Frame: Single and mutilple video summarization method with user-defined length

Video smmarization is a crucial method to reduce the time of videos which reduces the spent time to watch/review a long video. This apporach has became more important as the amount of publisehed video is increasing everyday. A single or multiple videos can be summarized into a relatively short video using various of techniques from multimodal audio-visual techniques, to natural language processing approaches. Audiovisual techniques may be used to recognize significant visual events and pick the most important parts, while NLP techniques can be used to evaluate the audio transcript and extract the main sentences (timestamps) and corresponding video frames from the original video. Another approach is to use the best of both domain. Meaning that we can use audio-visual cues as well as video transcript to extract and summarize the video. In this paper, we combine a variety of NLP techniques (extractive and contect-based summarizers) with video processing techniques to convert a long video into a single relatively short video. We design this toll in a way that user can specify the relative length of the summarized video. We have also explored ways of summarizing and concatenating multiple videos into a single short video which will help having most important concepts from the same subject in a single short video. Out approach shows that video summarizing is a difficult but significant work, with substantial potential for further research and development, and it is possible thanks to the development of NLP models.

cs.CV↗

arXiv2023

Efficient Semantic Segmentation on Edge Devices

Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.

cs.CV↗

arXiv2022

Heterogeneous Computing Systems

This survey of heterogeneous computing systems will help in analyzing the technological trends that will be at the basis of heterogeneous computing systems, highlighting the major opportunities and challenges such technologies will bring with them. This will help to understand the importance of heterogeneous computing systems, which are becoming common architectural elements of not only the modern data centers but also highly integrated devices (IoT). Identify problems related to it, such as the resource allocation problem, middleware, processing architectures, programming challenges, etc. from the perspective of heterogeneous resources.

eess.SY↗

arXiv2022

Security, Privacy and Challenges in Microservices Architecture and Cloud Computing- Survey

Security issues in processor architectures remain really critical since users and devices continue to share computing as well as networking resources. So, preserving data privacy in such an environment is really a critical concern. We know that there is a continuous growth in security and privacy issues that need to be addressed. Here, we have chosen a microservice architecture, which is a small or even an independent microprocess that interacts, acts, and responds to messages via lightweight technologies such as Thrift, HTTP, or REST API.

cs.CR↗

arXiv2022

ML-based Secure Low-Power Communication in Adversarial Contexts

As wireless network technology becomes more and more popular, mutual interference between various signals has become more and more severe and common. Therefore, there is often a situation in which the transmission of its own signal is interfered with by occupying the channel. Especially in a confrontational environment, Jamming has caused great harm to the security of information transmission. So I propose ML-based secure ultra-low power communication, which is an approach to use machine learning to predict future wireless traffic by capturing patterns of past wireless traffic to ensure ultra-low-power transmission of signals via backscatters. In order to be more suitable for the adversarial environment, we use backscatter to achieve ultra-low power signal transmission, and use frequency-hopping technology to achieve successful confrontation with Jamming information. In the end, we achieved a prediction success rate of 96.19%.

cs.CR↗

arXiv2022

A Miniscule Survey on Blockchain Scalability

With the rise of cryptocurrency and NFTs in the past decade, blockchain technology has been an area of increasing interest to both industry and academic experts. In this paper, we discuss the feasibility of such systems through the lens of scalability. We also briefly dive into the security issues of such systems, as well as some applications, including healthcare, supply chain, and government applications.

cs.CR↗