Traffic Danger Recognition With Surveillance Cameras Without Training Data

We propose a traffic danger recognition model that works with arbitrary traffic surveillance cameras to identify and predict car crashes. There are too many cameras to monitor manually. Therefore, we developed a model to predict and identify car crashes from surveillance cameras based on a 3D reconstruction of the road plane and prediction of trajectories. For normal traffic, it supports real-time proactive safety checks of speeds and distances between vehicles to provide insights about possible high-risk areas. We achieve good prediction and recognition of car crashes without using any labeled training data of crashes. Experiments on the BrnoCompSpeed dataset show that our model can accurately monitor the road, with mean errors of 1.80% for distance measurement, 2.77 km/h for speed measurement, 0.24 m for car position prediction, and 2.53 km/h for speed prediction.Comment: To be published in proceedings of Advanced Video and Signal-based Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 378-383, IEE

TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning

The increasing of pre-trained models has significantly facilitated the performance on limited data tasks with transfer learning. However, progress on transfer learning mainly focuses on optimizing the weights of pre-trained models, which ignores the structure mismatch between the model and the target task. This paper aims to improve the transfer performance from another angle - in addition to tuning the weights, we tune the structure of pre-trained models, in order to better match the target task. To this end, we propose TransTailor, targeting at pruning the pre-trained model for improved transfer learning. Different from traditional pruning pipelines, we prune and fine-tune the pre-trained model according to the target-aware weight importance, generating an optimal sub-model tailored for a specific target task. In this way, we transfer a more suitable sub-structure that can be applied during fine-tuning to benefit the final performance. Extensive experiments on multiple pre-trained models and datasets demonstrate that TransTailor outperforms the traditional pruning methods and achieves competitive or even better performance than other state-of-the-art transfer learning methods while using a smaller model. Notably, on the Stanford Dogs dataset, TransTailor can achieve 2.7% accuracy improvement over other transfer methods with 20% fewer FLOPs.Comment: This paper has been accepted by AAAI202

A Survey on EOSIO Systems Security: Vulnerability, Attack, and Mitigation

EOSIO, as one of the most representative blockchain 3.0 platforms, involves lots of new features, e.g., delegated proof of stake consensus algorithm and updatable smart contracts, enabling a much higher transaction per second and the prosperous decentralized applications (DApps) ecosystem. According to the statistics, it has reached nearly 18 billion USD, taking the third place of the whole cryptocurrency market, following Bitcoin and Ethereum. Loopholes, however, are hiding in the shadows. EOSBet, a famous gambling DApp, was attacked twice within a month and lost more than 1 million USD. No existing work has surveyed the EOSIO from a security researcher perspective. To fill this gap, in this paper, we collected all occurred attack events against EOSIO, and systematically studied their root causes, i.e., vulnerabilities lurked in all relying components for EOSIO, as well as the corresponding attacks and mitigations. We also summarized some best practices for DApp developers, EOSIO official team, and security researchers for future directions.Comment: 34 pages, 12 figure

Automated Aspect Recommendation through Clustering-Based Fan-in Analysis

Identifying code implementing a crosscutting concern (CCC) automatically can benefit the maintainability and evolvability of the application. Although many approaches have been proposed to identify potential aspects, a lot of manual work is typically required before these candidates can be converted into refactorable aspects. In this paper, we propose a new aspect mining approach, called Clustering-Based Fan-in Analysis (CBFA), to rec-ommend aspect candidates in the form of method clusters, instead of single methods. CBFA uses a new lexical based clustering approach to identify method clusters and rank the clusters using a new ranking metric called cluster fan-in. Experiments on Linux and JHotDraw show that CBFA can provide accurate recommendations while improving aspect mining coverage significantly compared to other state-of-the-art mining approaches. 1

Fluorescent gold nanoparticles-based fluorescence sensor for Cu2+ ions

A new fluorescence sensor for the highly selective detection of Cu2+ ion with a detection limit of 3.6 nM based on the aggregation-induced fluorescence quenching of the highly fluorescent glutathione-capped gold nanoparticles is reported.National Natural Science Foundation of China [20675068, 20835005

The Vulnerability Is in the Details: Locating Fine-grained Information of Vulnerable Code Identified by Graph-based Detectors

Vulnerability detection is a crucial component in the software development lifecycle. Existing vulnerability detectors, especially those based on deep learning (DL) models, have achieved high effectiveness. Despite their capability of detecting vulnerable code snippets from given code fragments, the detectors are typically unable to further locate the fine-grained information pertaining to the vulnerability, such as the precise vulnerability triggering locations.In this paper, we propose VULEXPLAINER, a tool for automatically locating vulnerability-critical code lines from coarse-level vulnerable code snippets reported by DL-based detectors.Our approach takes advantage of the code structure and the semantics of the vulnerabilities. Specifically, we leverage program slicing to get a set of critical program paths containing vulnerability-triggering and vulnerability-dependent statements and rank them to pinpoint the most important one (i.e., sub-graph) as the data flow associated with the vulnerability. We demonstrate that VULEXPLAINER performs consistently well on four state-of-the-art graph-representation(GP)-based vulnerability detectors, i.e., it can flag the vulnerability-triggering code statements with an accuracy of around 90% against eight common C/C++ vulnerabilities, outperforming five widely used GNN-based explanation approaches. The experimental results demonstrate the effectiveness of VULEXPLAINER, which provides insights into a promising research line: integrating program slicing and deep learning for the interpretation of vulnerable code fragments

Advances, Mechanisms and Applications in Oxygen Evolution Electrocatalysis of Gold- driven

© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the Creative Commons Attribution-Non Commercial-No Derivatives CC BY-NC-ND licence, https://creativecommons.org/licenses/by-nc-nd/4.0/The oxygen evolution reaction (OER) plays a crucial role in electrochemical energy storage and conversion. Among different metal elements, gold (Au) stands out due to its high electronegativity and remarkable catalytic properties, especially in nanoscale size. In this review, we aim to comprehensively analyze the oxygen electrocatalytic performance of nanosized Au, including the influence of the crystal surface, morphology, substrate materials of Au nanoparticles, size and ligands of Au nanoclusters, and Au single atoms on oxygen electrocatalysis. By exploring the catalytic performance of noble metals, non-noble metals, oxides, hydroxides/oxyhydroxides/layered double hydroxides, sulfides, phosphides, nitrides, and selenides through the integration of nanosized Au, which offers valuable insights for enhancing the OER efficiency. These effects can be attributed to two mechanisms: i) adsorbate evolution mechanism (AEM) and ii) lattice oxygen mechanism (LOM), where the nanosized Au changed the electronic structure of the catalysts and improved the adsorption of reaction intermediates to accelerate electron transfer process or exerts the synergistic effect between metallic Au and oxygen vacancies. For instance, Au-driven OER catalysts can be widely used in zinc-air batteries and water splitting in the future.Peer reviewe