Video summaries come in many forms, from traditional single-image thumbnails, animated thumbnails, storyboards, to trailer-like video summaries. Content creators use the summaries to display the most attractive portion of their videos; the users use them to quickly evaluate if a video is worth watching. All forms of summaries are essential to video viewers, content creators, and advertisers. Often video content management systems have to generate multiple versions of summaries that vary in duration and presentational forms. We present a framework ReconstSum that utilizes LSTM-based autoencoder architecture to extract and select a sparse subset of video frames or keyshots that optimally represent the input video in an unsupervised manner. The encoder selects a subset from the input video while the decoder seeks to reconstruct the video from the selection. The goal is to minimize the difference between the original input video and the reconstructed video. Our method is easily extendable to generate a variety of applications including static video thumbnails, animated thumbnails, storyboards and "trailer-like" highlights. We specifically study and evaluate two most popular use cases: thumbnail generation and storyboard generation. We demonstrate that our methods generate better results than the state-of-the-art techniques in both use cases.

Secure group-oriented communication is crucial to a wide range of applications in Internet of Things (IoT). Security problems related to group-oriented communications in IoTbased applications placed in a privacy-sensitive environment have become a major concern along with the development of the technology. Unfortunately, many IoT devices are designed to be portable and light-weight; thus, their functionalities, including security modules, are heavily constrained by the limited energy resources (e.g., battery capacity). To address these problems, we propose a group key management scheme based on a novel physically unclonable function (PUF) design: multistage interconnected PUF (MIPUF) to secure group communications in an energy-constrained environment. Our design is capable of performing key management tasks such as key distribution, key storage and rekeying securely and efficiently. We show that our design is secure against multiple attack methods and our experimental results show that our design saves 47.33% of energy globally comparing to state-of-the-art Elliptic-curve cryptography (ECC)-based key management scheme on average.

Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.

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