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ÐArcher: Detecting on-chain-off-chain synchronization bugs in decentralized applications

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arXiv 2021年

作者： Zhang, Wuqi Wei, Lili Li, Shuqing Liu, Yepang Cheung, Shing-Chi Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Guangdong Shenzhen China

Since the emergence of Ethereum, blockchain-based decentralized applications (DApps) have become increasingly popular and important. To balance the security, performance, and costs, a DApp typically consists of two layers: an on-chain layer to execute transactions and store crucial data on the blockchain and an off-chain layer to interact with users. A DApp needs to synchronize its off-chain layer with the on-chain layer proactively. Otherwise, the inconsistent data in the off-chain layer could mislead users and cause undesirable consequences, e.g., loss of transaction fees. However, transactions sent to the blockchain are not guaranteed to be executed and could even be reversed after execution due to chain reorganization. Such non-determinism in the transaction execution is unique to blockchain. DApp developers may fail to perform the on-chain-off-chain synchronization accurately due to their lack of familiarity with the complex transaction lifecycle. In this work, we investigate the challenges of synchronizing on-chain and off-chain data in Ethereum-based DApps. We present two types of bugs that could result in inconsistencies between the on-chain and off-chain layers. To help detect such on-chain-off-chain synchronization bugs, we introduce a state transition model to guide the testing of DApps and propose two effective oracles to facilitate the automatic identification of bugs. We build the first testing framework, ÐArcher, to detect on-chain-off-chain synchronization bugs in DApps. We have evaluated ÐArcher on 11 popular real-world DApps. ÐArcher achieves high precision (99.3%), recall (87.6%), and accuracy (89.4%) in bug detection and significantly outperforms the baseline methods. It has found 15 real bugs in the 11 DApps. So far, six of the 15 bugs have been confirmed by the developers, and three have been fixed. These promising results demonstrate the usefulness of ÐArcher. © 2021, CC BY.

关键词： Software testing

Effects of Initialization Methods on the Performance of Multi-Objective Evolutionary Algorithms

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Effects of Initialization Methods on the Performance of Mult...

IEEE International Conference on Systems, Man and Cybernetics

作者： Cheng Gong Lie Meng Pang Yang Nan Hisao Ishibuchi Qingfu Zhang Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Population initialization is always needed in evolutionary multi-objective optimization (EMO) algorithms. Intuitively, a well-designed initialization method can help facilitate the evolutionary process and improve the performance of EMO algorithms. However, very few studies have investigated the effects of initialization methods on the performance of EMO algorithms. Many existing EMO algorithms randomly generate an initial population to start the evolutionary process. To fill this research gap and attract more attention from EMO researchers to this important yet under-explored issue, in this paper, we examine the effects of various initialization methods that may become promising alternatives to the commonly-used random initialization method. Each initialization method is evaluated through computational experiments on test problems of various sizes with 5–1000 decision variables. Experimental results clearly demonstrate the advantage of well-designed initialization methods over the random initialization method. This study provides useful insights into EMO algorithm design and motivates further research on population initialization.

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Evolutionary Multiparty Distance Minimization

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arXiv 2022年

作者： She, Zeneng Luo, Wenjian Lin, Xin Chang, Yatong Shi, Yuhui School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China School of Computer Science and Technology The University of Science and Technology of China Anhui Hefei230027 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives. In the real-world applications, there usually exist more than one DM and each DM concerns parts of these objectives. Multiparty multiobjective optimization problems (MPMOPs) are proposed to depict the MOP with multiple decision makers involved, where each party concerns about certain some objectives of all. However, in the evolutionary computation field, there is not much attention paid on MPMOPs. This paper constructs a series of MPMOPs based on distance minimization problems (DMPs), whose Pareto optimal solutions can be vividly visualized. To address MPMOPs, the new proposed algorithm OptMPNDS3 uses the multiparty initializing method to initialize the population and takes JADE2 operator to generate the offsprings. OptMPNDS3 is compared with OptAll, OptMPNDS and OptMPNDS2 on the problem suite. The result shows that OptMPNDS3 is strongly comparable to other algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

Neural network based bilingual language model growing for statistical machine translation

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Neural network based bilingual language model growing for st...

2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014

作者： Wang, Rui Zhao, Hai Lu, Bao-Liang Utiyama, Masao Sumita, Eiichro Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong University Shanghai200240 China Multilingual Translation Laboratory MASTAR Project National Institute of Information and Communications Technology 3-5 Hikaridai Keihanna Science City Kyoto619-0289 Japan Key Laboratory Shanghai Education Commission Intelligent Interaction Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781937284961

Since larger n-gram Language Model (LM) usually performs better in Statistical Machine Translation (SMT), how to construct efficient large LM is an important topic in SMT. However, most of the existing LM growing methods need an extra monolingual corpus, where additional LM adaption technology is necessary. In this paper, we propose a novel neural network based bilingual LM growing method, only using the bilingual parallel corpus in SMT. The results show that our method can improve both the perplexity score for LM evaluation and BLEU score for SMT, and significantly outperforms the existing LM growing methods without extra corpus. © 2014 Association for Computational Linguistics.

关键词： Neural networks

The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection

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arXiv 2021年

作者： Zou, Zhikang Ye, Xiaoqing Du, Liang Cheng, Xianhui Tan, Xiao Zhang, Li Feng, Jianfeng Xue, Xiangyang Ding, Errui Baidu Inc. China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University China School of Data Science Fudan University China School of Computer Science Fudan University China

Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. In this paper, we dig into the 3D object detection task and reformulate it as the sub-tasks of object localization and appearance perception, which benefits to a deep excavation of reciprocal information underlying the entire task. We introduce a Dynamic Feature Reflecting Network, named DFR-Net, which contains two novel standalone modules: (i) the Appearance-Localization Feature Reflecting module (ALFR) that first separates task-specific features and then self-mutually reflects the reciprocal features;(ii) the Dynamic Intra-Trading module (DIT) that adaptively realigns the training processes of various sub-tasks via a self-learning manner. Extensive experiments on the challenging KITTI dataset demonstrate the effectiveness and generalization of DFR-Net. We rank 1st among all the monocular 3D object detectors in the KITTI test set (till March 16th, 2021). The proposed method is also easy to be plug-and-play in many cutting-edge 3D detection frameworks at negligible cost to boost performance. The code will be made publicly available. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

Garbage Classification Method Based on YOLOv3

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Garbage Classification Method Based on YOLOv3

International Conference on Computational Intelligence and Security

作者： Rencao Jin Hongfei Huang Shihao Jiang Hua Zhang Yifan Wu Guojun Dai School of Computer Science and Technology Hangzhou Dianzi University Hangzhou Zhejiang Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province Hangzhou Dianzi University Hangzhou Zhejiang

ISBN: (纸本)9781665494908

computer vision has been widely used in various fields, but garbage classification based on object detection has received little attention. This paper has carried out the practice on the garbage classification in dataset of the real scene by using the network structure of object detection of YOLOv3 (you only look once version 3). First of all, two types of datasets are established, one contains only bottles, the other contains multiple categories, such as plastic, paper, etc., which are composed of pictures with complex backgrounds in real scene. Then, the principle and architecture of YOLOv3 network are described in detail, and the practice is carried out on the garbage classification dataset in the real scene. Finally, the experimental results show that the method has high accuracy in the dataset containing bottles, the recognition rate reaches 95.33%, and the recognition speed reaches 50 FPS. In addition, the yolov4 network is also applied to the dataset containing multiple garbage.

关键词： Training computer vision Shape Object detection computer architecture Security Plastics

Small Object Detection of Non-standard THT Solder Joints Based on Improved YOLOv3

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Small Object Detection of Non-standard THT Solder Joints Bas...

International Conference on Computational Intelligence and Security

作者： Jinlai Huang Mingkai Yang Haoshi Zhi Leilei Xiang Hua Zhang Yifan Wu School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781665494908

Object detection on non-standard THT (Through Hole technology) solder joints is a difficult task, due to the reason that solder joints are small targets, which are hard to detect on PCB (Printed Circuit Board). Moreover, there is a lack of datasets and benchmarks for non-standard THT solder joints detection. To solve the two issues mentioned above, we propose a new object detector (YOLOv3_5L) and establish a new dataset of non-standard THT solder joints. Based on YOLOv3, the YOLOv3_5L adopts five scales of feature maps instead of three scales of feature maps to predict bounding boxes of objects, which can obtain more texture and contour information to detect small objects. Extensive experiments on our dataset show that YOLOv3_5L have better performance than YOLOv3 while ensuring that the speed is almost unchanged. On our test set of $1024\times 1024$ pixels, the mAP@0.5 result of YOLOv3_5L is 97.2%, which improves about 1.2% compared with baseline model (YOLOv3).

关键词： Printed circuits Object detection Detectors Benchmark testing Feature extraction Security Soldering

Differential-Critic GAN: Generating What You Want by a Cue of Preferences

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arXiv 2021年

作者： Yao, Yinghua Pan, Yuangang Tsang, Ivor W. Yao, Xin Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Australian Artificial Intelligence Institute University of Technology Sydney Australia A*STAR Center for Frontier AI Research Singapore. Australian Artificial Intelligence Institute University of Technology Sydney Australia Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Computer Science University of Birmingham United Kingdom

This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property. DiCGAN generates desired data that meets the user’s expectations and can assist in designing biological products with desired properties. Existing approaches select the desired samples first and train regular GANs on the selected samples to derive the user-desired data distribution. However, the selection of the desired data relies on global knowledge and supervision over the entire dataset. DiCGAN introduces a differential critic that learns from pairwise preferences, which are local knowledge and can be defined on a part of training data. The critic is built by defining an additional ranking loss over the Wasserstein GAN’s critic. It endows the difference of critic values between each pair of samples with the user preference and guides the generation of the desired data instead of the whole data. For a more efficient solution to ensure data quality, we further reformulate DiCGAN as a constrained optimization problem, based on which we theoretically prove the convergence of our DiCGAN. Extensive experiments on a diverse set of datasets with various applications demonstrate that our DiCGAN achieves state-of-the-art performance in learning the user-desired data distributions, especially in the cases of insufficient desired data and limited supervision. The code is available in https://***/EvaFlower/Differential-Critic-GAN. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

A Framework based on Deep Neural Network for Ranking-oriented Software Defect Prediction

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A Framework based on Deep Neural Network for Ranking-oriente...

IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Jiapeng Dai Xiaoxing Yang Bingding Huang Xiaofen Lu College of Big Data and Internet Shenzhen Technology University Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Software systems are getting larger and more complex than ever before. In order to improve software reliability, software defect prediction is applied to assist developers in bug discovery. The ranking-oriented software defect prediction aims to rank software modules according to the predicted defect counts. However, existing ranking-oriented defect prediction models are constructed based on traditional hand-crafted features, which might overlook the rich syntactic information buried inside the source codes. In this paper, we propose a universal deep learning-based framework called United Deep Network for ranking-oriented software defect prediction. This framework utilizes deep neural networks to automatically generate features from source code with the syntactic and structural information preserved, and it can combine extracted features with traditional hand-crafted features in order to take advantage of both kinds of features to construct prediction models. Experimental results over 29 sets of data show the good performance of the proposed framework for building ranking-oriented defect prediction models.

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