Hyperparameter Learning via Distributional Transfer

Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks. This results in a joint Gaussian process model on hyperparameters and data representations. Representations make use of the framework of distribution embeddings into reproducing kernel Hilbert spaces. The developed method has a faster convergence compared to existing baselines, in some cases requiring only a few evaluations of the target objective

A New Method for Analyzing Integrated Stealth Ability of Penetration Aircraft

AbstractTaking into account the limitations of existing stealth performance analysis methods, a method termed as the integrated stealth performance analysis method is proposed for evaluating the stealth ability of the penetration aircraft. Based on various target radar cross section (RCS) scattering characters, this article integrates the relevant parameters needed for building up target circumferential RCS scattering model and proposes the RCS scattering controlling parameters to control the changing trends of the relevant model RCS scattering characters. According to the radar dynamic detecting characters during the whole penetration course, a dynamic stealth performance evaluating model is proposed accompanied by a series of stealth ability estimation rules. This new analysis method can enhance the integrality and dependability of the stealth analysis conclusions and summarize the relationship between the target RCS scattering characters and their effects on stealth performance. The rules indicated by this relationship can be used as the reference for designing new type of stealth aircraft and setting up specific penetration tactics

To Healthier Ethereum: A Comprehensive and Iterative Smart Contract Weakness Enumeration

With the increasing popularity of cryptocurrencies and blockchain technology, smart contracts have become a prominent feature in developing decentralized applications. However, these smart contracts are susceptible to vulnerabilities that hackers can exploit, resulting in significant financial losses. In response to this growing concern, various initiatives have emerged. Notably, the SWC vulnerability list played an important role in raising awareness and understanding of smart contract weaknesses. However, the SWC list lacks maintenance and has not been updated with new vulnerabilities since 2020. To address this gap, this paper introduces the Smart Contract Weakness Enumeration (SWE), a comprehensive and practical vulnerability list up until 2023. We collect 273 vulnerability descriptions from 86 top conference papers and journal papers, employing open card sorting techniques to deduplicate and categorize these descriptions. This process results in the identification of 40 common contract weaknesses, which are further classified into 20 sub-research fields through thorough discussion and analysis. SWE provides a systematic and comprehensive list of smart contract vulnerabilities, covering existing and emerging vulnerabilities in the last few years. Moreover, SWE is a scalable, continuously iterative program. We propose two update mechanisms for the maintenance of SWE. Regular updates involve the inclusion of new vulnerabilities from future top papers, while irregular updates enable individuals to report new weaknesses for review and potential addition to SWE

Multi-frequency RCS Reduction Characteristics of Shape Stealth with MLFMA with Improved MMN

AbstractThree new control factors are presented for calculating the multipole mode number (MMN) efficiently and precisely. The effects of these control factors on the number of integral samples and the precision of multilevel fast multipole algorithm (MLFMA) are investigated. A new approach based on control factors which is proven to be able to improve the computational efficiency and reduce the needed memory significantly as well as ensuring the proper precision. For three aircraft models, the improved MLFMA is employed to analyze their multi-frequency scattering characteristics. It is found that aircraft shape can influence radar cross section (RCS) in different frequency zones. Both the multi-frequency RCS reduction characteristics of shape stealth aircraft and the conventional aircraft with stealth design taken into account are investigated, and the results show that shape stealth exhibits significant RCS reduction in the resonance and high-frequency zones, and with a weaker influence in the Rayleigh zone. Compared with radar absorbing material (RAM), shape stealth yields a wider multi-frequency RCS reduction. The above-mentioned results can be applied to stealth design for multiple frequencies or even for all frequencies

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation, and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from the source post is involved in each layer of GCN to enhance the influences from the roots of rumors. Encouraging empirical results on several benchmarks confirm the superiority of the proposed method over the state-of-the-art approaches.Comment: 8 pages, 4 figures, AAAI 202