DroneSig: Lightweight Digital Signature Protocol for Micro Aerial Vehicles

Micro aerial vehicles a.k.a. drones, have become an integral part of a variety of civilian and military application domains, including but not limited to aerial surveying and mapping, aerial surveillance and security, aerial inspection of infrastructure, and aerial delivery. Meanwhile, the cybersecurity of drones is gaining significant attention due to both financial and strategic information and value involved in aerial applications. As a result of the lack of security features in the communication protocol, an adversary can easily interfere with on-going communications or even seize control of the drone. In this thesis, we propose a lightweight digital signature protocol, also referred to as DroneSig, to protect drones from a man-in-the-middle attack, where an adversary eavesdrops the communication between Ground Control Station (GCS) and drone, and impersonates the GCS and sends fake commands to terminate the on-going mission or even take control over the drone. The basic idea of the DroneSig is that the drone will only execute the new command after validating the received digital signature from the GCS, proving that the new command message is coming from the authenticated GCS. If the validation of the digital signature fails, the new command is rejected immediately, and the Return-to-Launch (RTL) mode is initiated and forces the drone to return to the take-off position. We conduct extensive simulation experiments for performance evaluation and comparison using OMNeT++, and simulation results show that the proposed lightweight digital signature protocol achieves better performance in terms of energy consumption and computation time compared to the standard Advanced Encryption Standard (AES) cryptographic technique

Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn

This paper presents an image classification based approach for skeleton-based video action recognition problem. Firstly, A dataset independent translation-scale invariant image mapping method is proposed, which transformes the skeleton videos to colour images, named skeleton-images. Secondly, A multi-scale deep convolutional neural network (CNN) architecture is proposed which could be built and fine-tuned on the powerful pre-trained CNNs, e.g., AlexNet, VGGNet, ResNet etal.. Even though the skeleton-images are very different from natural images, the fine-tune strategy still works well. At last, we prove that our method could also work well on 2D skeleton video data. We achieve the state-of-the-art results on the popular benchmard datasets e.g. NTU RGB+D, UTD-MHAD, MSRC-12, and G3D. Especially on the largest and challenge NTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methods by a large margion, which proves the efficacy of the proposed method

Disentangled and Robust Representation Learning for Bragging Classification in Social Media

Researching bragging behavior on social media arouses interest of computational (socio) linguists. However, existing bragging classification datasets suffer from a serious data imbalance issue. Because labeling a data-balance dataset is expensive, most methods introduce external knowledge to improve model learning. Nevertheless, such methods inevitably introduce noise and non-relevance information from external knowledge. To overcome the drawback, we propose a novel bragging classification method with disentangle-based representation augmentation and domain-aware adversarial strategy. Specifically, model learns to disentangle and reconstruct representation and generate augmented features via disentangle-based representation augmentation. Moreover, domain-aware adversarial strategy aims to constrain domain of augmented features to improve their robustness. Experimental results demonstrate that our method achieves state-of-the-art performance compared to other methods

Boosted Hybrid Method for Solving Chemical Reaction Systems with Multiple Scales in Time and Population Size

A new algorithm, called boosted hybrid method, is proposed for the simulation of chemical reaction systems with scale-separation in time and disparity in species population. For such stiff systems, the algorithm can automatically identify scale-separation in time and slow down the fast reactions while maintaining a good approximation to the original effective dynamics. This technique is called boosting. As disparity in species population may still exist in the boosted system, we propose a hybrid strategy based on coarse-graining methods, such as the tau-leaping method, to accelerate the reactions among large population species. The combination of the boosting strategy and the hybrid method allow for an efficient and adaptive simulation of complex chemical reactions. The new method does not need a priori knowledge of the system and can also be used for systems with hierarchical multiple time scales. Numerical experiments illustrate the versatility and efficiency of the metho

On the Poisson Approximation to Photon Distribution for Faint Lasers

It is proved, that for a certain kind of input distribution, the strongly binomially attenuated photon number distribution can well be approximated by a Poisson distribution. This explains why we can adopt poissonian distribution as the photon number statistics for faint lasers. The error of such an approximation is quantitatively estimated. Numerical tests are carried out, which coincide with our theoretical estimations. This work lays a sound mathematical foundation for the well-known intuitive idea which has been widely used in quantum cryptography

Compressing Context to Enhance Inference Efficiency of Large Language Models

Large language models (LLMs) achieved remarkable performance across various tasks. However, they face challenges in managing long documents and extended conversations, due to significantly increased computational requirements, both in memory and inference time, and potential context truncation when the input exceeds the LLM's fixed context length. This paper proposes a method called Selective Context that enhances the inference efficiency of LLMs by identifying and pruning redundancy in the input context to make the input more compact. We test our approach using common data sources requiring long context processing: arXiv papers, news articles, and long conversations, on tasks of summarisation, question answering, and response generation. Experimental results show that Selective Context significantly reduces memory cost and decreases generation latency while maintaining comparable performance compared to that achieved when full context is used. Specifically, we achieve a 50\% reduction in context cost, resulting in a 36\% reduction in inference memory usage and a 32\% reduction in inference time, while observing only a minor drop of .023 in BERTscore and .038 in faithfulness on four downstream applications, indicating that our method strikes a good balance between efficiency and performance.Comment: EMNLP 2023. arXiv admin note: substantial text overlap with arXiv:2304.12102; text overlap with arXiv:2303.11076 by other author