Abstract:Ethereum is one of the classic applications of blockchain technology which is valuable and has important prospects in many fields. With the development of blockchain technology, its supervision has become more inportant. Although there is currently no suitable supervision method, the measurement of blockchain provides support for the blockchian supervision. When measuring the spread of Ethereum transactions, it is necessary to deploy as many probe nodes as possible, which will bring the consumption of storage resources that cannot be underestimated. Thereby the consumption of storage resources reducing the feasibility of the measurement of spread of transaction on Ethereum. This paper proposes a method for Ethereum node shared storage. This method designs and modifies the storage mode of Ethereum ndoes so that enabling multiple nodes to be deployed on the same server at the same time, and this mode guarantees the independence and function completeness of shared storage nodes on Ethereum at the same time. According to the experimental results, the data sharing rate of shared storage between nodes could reach 74% which effectively reduces the storage occupation of the probe nodes during deployment.

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	1. C/S Mode Based Ethereum Node Shared Storage Method
	GAO Jiachen,WU Zhigang
	Computer Science and Technology 28 February 2020
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	Abstract:Ethereum is one of the classic applications of blockchain technology which is valuable and has important prospects in many fields. With the development of blockchain technology, its supervision has become more inportant. Although there is currently no suitable supervision method, the measurement of blockchain provides support for the blockchian supervision. When measuring the spread of Ethereum transactions, it is necessary to deploy as many probe nodes as possible, which will bring the consumption of storage resources that cannot be underestimated. Thereby the consumption of storage resources reducing the feasibility of the measurement of spread of transaction on Ethereum. This paper proposes a method for Ethereum node shared storage. This method designs and modifies the storage mode of Ethereum ndoes so that enabling multiple nodes to be deployed on the same server at the same time, and this mode guarantees the independence and function completeness of shared storage nodes on Ethereum at the same time. According to the experimental results, the data sharing rate of shared storage between nodes could reach 74% which effectively reduces the storage occupation of the probe nodes during deployment.
	TO cite this article:GAO Jiachen,WU Zhigang. C/S Mode Based Ethereum Node Shared Storage Method[OL].[28 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750956


	2. Class-balanced and Local Median Loss Jointly Supervised for Wild Facial Expression Recognition
	Shi Cong-Cong,TIAN,TIAN Mei
	Computer Science and Technology 07 February 2020
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	Abstract:Over the past few years, Convolutional Neural Networks (CNNs) have shown effective performance on facial expression recognition. However, it is still a challenge problem for facial expression in the wild. The facial expression recognition dataset in the wild usually has the problem that imbalanced distribution of facial expression data and large intra-class differences caused by factors such as pose, lighting and gender. In order to solve this problem, this paper presents a novel loss function -- CALM Loss (Class-balanced and Local Median Loss). CALM Loss contains two parts. The first part is the class-balanced softmax loss function, which is uesd to solve the problem of data imbalance. The data set is divided into two classes, with two expressions with less data as one class and the other five as one class. During the network training process, the weight of the class with less data is adaptively increased. The second part is the local median loss function, which uses the median of serveral neatest neighbors in the same class as the center of class, weakens the influence of difficult samples on the selection of class center. Finally, the CALM Loss training network was adopted in this paper. The average recognition accuracy on the RAF dataset reaches 77.34$\%$, which proves the effectiveness of the proposed method.
	TO cite this article:Shi Cong-Cong,TIAN,TIAN Mei. Class-balanced and Local Median Loss Jointly Supervised for Wild Facial Expression Recognition[OL].[ 7 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750634

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Saved Papers


	3. A Practical Concolic Execution Technique for Large Scale Software Systems
	Yu Wenqing,Liang Hongliang
	Computer Science and Technology 06 February 2020
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	Abstract:The state explosion problem faced by concolic execution is serious when detecting vulnerabilities in large scale software systems. To mitigate the issue, we propose a practical concolic execution-based approach to detect vulnerabilities in this paper. First, the correlation between symbolic memory and control flow is determined by static analysis, and the critical symbolic memory and ordinary symbolic memory are distinguished according to the correlation. Then, different state generation strategies are designed for two kinds of symbolic memory. We implement the above approach as a tool, Pracolic. Experimental results show that Pracolic can not only mitigate the problem of state explosion, but also outperform S2E, a state-of-the-art analysis tool, on vulnerability detection and code coverage for large scale software systems.
	TO cite this article:Yu Wenqing,Liang Hongliang. A Practical Concolic Execution Technique for Large Scale Software Systems[OL].[ 6 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750654


	4. A Telecom Anti-Fraud Approach Based on Graph
	Li Zhengzheng,Wang Chun
	Computer Science and Technology 05 February 2020
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	Abstract:Currently, telecommunications fraud is on the rise. Traditional anti-fraud techniques are basically unable to capture various new telecommunication fraud methods. After studying the case of fraud, we proposes a telecom fraud detection method based on graph embedding, which is used to mine the potential features of the call correlation graph between fraudsters and users. First, the proposed approach is based on the embedding of attribute network, which not only saves the network structure information, but also incorporates the statistical features of the nodes extracted for the call behavior, in order to represent the nodes as low-dimensional vectors. Secondly, the proposed approach belongs to semi-supervised learning. In the supervised part, the corresponding labels are predicted. In the unsupervised part, the node context vector is predicted. Both share parameters and optimize the model at the same time. When experimenting on the telecommunications fraud dataset, compared with other baseline approaches., the experimental results prove the effectiveness of the proposed method in countering telecommunications fraud.
	TO cite this article:Li Zhengzheng,Wang Chun. A Telecom Anti-Fraud Approach Based on Graph[OL].[ 5 February 2020] http://en.paper.edu.cn/en_releasepaper/content/4750616


	5. An Improved Visual-Inertial Odometry Based on Self-Adaptive Attention-Anticipation Feature Selector
	Ruan Wenlong,Wang Jing
	Computer Science and Technology 22 November 2019
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	Abstract:Visual inertia odometers have achieved great success with the development of robot vision. However, it remains a challenging problem to achieve robust and efficient pose estimation on low-power platforms such as smartphones. This paper proposes a new visual inertial odometer scheme for low-power platforms, named visual inertial odometer based on adaptive attention-anticipation mechanism, which adds visual information to the VINS-based visual inertial odometer. The attention distribution module and the motion information forward anticipation module are controlled by the adaptive adjustment module to reduce the system operation load and improve the system tracking accuracy. We contribute in the following three aspects: 1) A attention mechanism for visual inertia history is proposed, which provides visual attention distribution for system radical motion in complex space environment, and extracts vision with high weight on system influence. Feature tracking; 2) A visual feature screening mechanism based on motion prediction is proposed to filter the visual features that will escape the camera perspective in advance; 3) use the adaptive adjustment module for front-end control and efficiently allocate restricted computing resources. Our approach achieves advanced estimation performance on the Euroc MAV datasets.
	TO cite this article:Ruan Wenlong,Wang Jing. An Improved Visual-Inertial Odometry Based on Self-Adaptive Attention-Anticipation Feature Selector[OL].[22 November 2019] http://en.paper.edu.cn/en_releasepaper/content/4750006


	6. Separable Robust Reversible Watermarking in Encrypted 2D Vector Graphics
	Peng Fei,Qi Ying,Lin Zi-xing,Long Min
	Computer Science and Technology 26 April 2019
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	Abstract:With the popular use of cloud computing, much attention has been paid to reversible watermarking in encryption domain. However, most existing algorithms are designed for redundant carriers, and they are difficult to resist common attacks. Furthermore, watermark can only be extracted in the plaintext domain or the ciphertext domain. In this paper, a separable robust reversible watermarking in encrypted 2D vector graphics is proposed. Firstly, a content owner uses an encryption key to scramble the polar angle of the vertices to encrypt the graphics in the polar coordinate system. After that, a watermark embedder maps the encoded watermark bits to different vertices under the control of an embedded key and a hash function, and then the polar angle of the vertex is slightly adjusted to embed a watermark. Since the decryption operation does not affect the embedded watermark, the watermark can be extracted both in the plaintext and ciphertext domain. Experimental results and analysis show that the proposed algorithm can achieve good invisibility, and it can effectively resist common operations (such as rotation, translation, scaling (RST)and entity reordering) and malicious attacks (such as the addition and deletion of vertices or entities).
	TO cite this article:Peng Fei,Qi Ying,Lin Zi-xing, et al. Separable Robust Reversible Watermarking in Encrypted 2D Vector Graphics[OL].[26 April 2019] http://en.paper.edu.cn/en_releasepaper/content/4748714


	7. Detecting double compression for JPEG images of low quality factor
	YUAN Huiling,OU Bo,Tian Huawei
	Computer Science and Technology 11 April 2019
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	Abstract:Double JPEG compression is often used to conceal some illegal operations on the source image. Since there exist some traces when an image undergoes double JPEG compression, the detection of double JPEG compression can be used as a tool of digital forensics. In the past decade, the researchers have focused on detecting double compression with the same quality factor (QF), but cannot provide reliable results when the QF is approximately below 70. To remedy this, we propose to analyze the low-frequency components and the error image, and extract two types of features to improve the detection accuracy. Since the high-frequency components are easily lost in the JPEG compression, the low-frequency components are utilized as the reliable feature. With this in mind, our method first analyzes low-frequency discrete cosine transform (DCT) coefficients and the lost information. Then, the features are extracted to characterize the difference between the two sequential compressions. Finally, the feature set is fed to a support vector machine for classification. Experimental results on two standard image databases verify that our method could improve the detection accuracy of the low QF double compressed JPEG images with the same QF. The average classification accuracy of the different QFs (the range of QF is usually set from 75 to 20) was obtained as 88.22%.
	TO cite this article:YUAN Huiling,OU Bo,Tian Huawei. Detecting double compression for JPEG images of low quality factor[OL].[11 April 2019] http://en.paper.edu.cn/en_releasepaper/content/4748366


	8. Multi-scale Feature Fusion CNN For Scene Recognition
	HanLing Zhang,Yi Zheng
	Computer Science and Technology 09 April 2019
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	Abstract:In recent years, convolutional neural network (CNN) has performed well in a number of image classification tasks, but it hit a bottleneck on scene recognition task, due to the multilevel semantic information in a scene. This paper is dedicated to studying the deep learning methods in scene recognition task, and making contributes to improving the classification performance in the field of scene recognition, and an effective method that captures and fuses multi-level semantic information is proposed. First of all, we compare the differences between object classification task and scene recognition task in order to apply the successful replication of CNN in object classification task to scene recognition task after resolving the differences. Then we use a multi-scale learning method to capture different scale visual features at multiple levels. In addition, on the basis of multi-scale learning, we propose a method of feature fusion at the level of category, aiming to effectively combine different scale features. The experimental results show that the success of the object classification task can be applied to the scene recognition task by our method.
	TO cite this article:HanLing Zhang,Yi Zheng. Multi-scale Feature Fusion CNN For Scene Recognition[OL].[ 9 April 2019] http://en.paper.edu.cn/en_releasepaper/content/4748310


	9. Super-resolution-based traffic prohibitory sign recognition
	XIAO Degui,LIU Liang
	Computer Science and Technology 28 March 2019
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	Abstract:Traffic signs in actual traffic scenarios should be recognized when they are far from the vehicle itself. Traffic signs occupy a small proportion in the image, which makes feature extraction difficult. This study focuses on small prohibitory signs. First, we introduce super resolution (SR), which can improve the quality of the image, to solve the recognition problem of small prohibitory signs. Second, we propose a new model that can detect and classify various prohibitory signs compared with a traditional joint model. The proposed model utilizes the color and shape of prohibitory signs in generating proposals and filters the negative samples. The retained proposals are divided into small, medium, and large objects based on the size of prohibitory signs, and the small and medium objects are reconstructed through SR. Finally, the proposals are classified by using a support vector machine (SVM) algorithm. Experiments on Tsinghua-Tencent 100K and German Traffic Sign Recognition Benchmark (GTSRB) datasets demonstrate that the proposed method is feasible. The proposed method achieves recognition accuracies of 78% and 67% on the small objects of Tsinghua-Tencent 100K and GTSRB datasets, respectively, which exceed the recognition accuracy without SR
	TO cite this article:XIAO Degui,LIU Liang. Super-resolution-based traffic prohibitory sign recognition[OL].[28 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4748143


	10. Design and Implementation of Hardware Accelerator for Recommendation System Based on Heterogeneous Platform
	LI Yang,DAI Zhitao
	Computer Science and Technology 28 March 2019
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	Abstract:This study takes advantage of the combination of heterogeneous platform control and computing power, and optimizes the parallelization of the popular collaborative filtering recommendation algorithm. Compared with the traditional algorithm, the speedup has a certain degree of improvement and power consumption has also declined as well.
	TO cite this article:LI Yang,DAI Zhitao. Design and Implementation of Hardware Accelerator for Recommendation System Based on Heterogeneous Platform[OL].[28 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4748110

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