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

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

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

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

Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries

Although existing techniques have proposed automated approaches to alleviate the path explosion problem of symbolic execution, users still need to optimize symbolic execution by applying various searching strategies carefully. As existing approaches mainly support only coarse-grained global searching strategies, they cannot efficiently traverse through complex code structures. In this paper, we propose Eunomia, a symbolic execution technique that allows users to specify local domain knowledge to enable fine-grained search. In Eunomia, we design an expressive DSL, Aes, that lets users precisely pinpoint local searching strategies to different parts of the target program. To further optimize local searching strategies, we design an interval-based algorithm that automatically isolates the context of variables for different local searching strategies, avoiding conflicts between local searching strategies for the same variable. We implement Eunomia as a symbolic execution platform targeting WebAssembly, which enables us to analyze applications written in various languages (like C and Go) but can be compiled into WebAssembly. To the best of our knowledge, Eunomia is the first symbolic execution engine that supports the full features of the WebAssembly runtime. We evaluate Eunomia with a dedicated microbenchmark suite for symbolic execution and six real-world applications. Our evaluation shows that Eunomia accelerates bug detection in real-world applications by up to three orders of magnitude. According to the results of a comprehensive user study, users can significantly improve the efficiency and effectiveness of symbolic execution by writing a simple and intuitive Aes script. Besides verifying six known real-world bugs, Eunomia also detected two new zero-day bugs in a popular open-source project, Collections-C.Comment: Accepted by ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 202

Evaluation of recombinant baculovirus clearance during rAAV production in Sf9 cells using a newly developed fluorescent-TCID50 assay

IntroductionRecombinant adeno-associated virus (rAAV) vectors provide a safe and efficient means for in vivo gene delivery, although its large-scale production remains challenging. Featuring high manufacturing speed, flexible product design, and inherent safety and scalability, the baculovirus/Sf9 cell system offers a practical solution to the production of rAAV vectors in large quantities and high purity. Nonetheless, removal and inactivation of recombinant baculoviruses during downstream purification of rAAV vectors remain critical prior to clinical application.MethodsThe present study utilized a newly developed fluorescent-TCID50 (F-TCID50) assay to determine the infectious titer of recombinant baculovirus (rBV) stock after baculovirus removal and inactivation, and to evaluate the impact of various reagents and solutions on rBV infectivity.Results and discussionThe results showed that a combination of sodium lauryl sulfate (SLS) and Triton X-100 lysis, AAVx affinity chromatography, low pH hold (pH3.0), CsCl ultracentrifugation, and NFR filtration led to effective removal and/or inactivation of recombinant baculoviruses, and achieved a log reduction value (LRV) of more than 18.9 for the entire AAV purification process. In summary, this study establishes a standard protocol for downstream baculovirus removal and inactivation and a reliable F-TCID50 assay to detect rBV infectivity, which can be widely applied in AAV manufacturing using the baculovirus system