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Combined with the real-time and robust requirements of paper detection in the common paper-pen interaction, a fast paper detection method based on edge detection is proposed. In the edge detection stage, a fast paper edge detection method based on cross-layer feature fusion is advanced. The linear bottleneck inverted residual blocks and efficient channel B-ECA blocks are added to the HED backbone, which greatly reduce the numbers of parameters and increase the weight of salient features. The features of all stages and all layers are fused in order to retain the more edge features. The high-level features are upsampled and cross-layer fused with the low-level features to solve the problem of edge blur. Training and testing on the self-made MPDS data set shows that, compared with the original HED method, the proposal increases the ODS and OIS by 8.1% and 6.6% respectively, and improves the detection speed from 22.08 FPs to 39.02 FPS. In the paper extraction stage, a paper extraction method based on the paper structure is proposed. After thinning the paper edge based on non-maximum suppression, detection and filtering the line, and extraction paper vertex based on structural constraints, the image containing only paper is extracted. The experimental results show that the paper extraction method can quickly and accurately extract the entire paper image in various complex desktop environments and occlusion situations, which provides an interaction basis for the common paper-pen interaction method. [ABSTRACT FROM AUTHOR]

Aiming at low success rate and efficiency of exam paper generation for small exam data- base, the paper proposed a prediction calculation based intelligent exam paper generation algorithm without backtracking. Based on prediction calculation, according to the constraints, this algorithm firstly figures out the allocation matrix that meets the requirements of the exam database , and then accesses the exam database. This can improve the success rate of exam paper generation and there is no backtracking when the exam database is accessed. Besides , according some strategy , a batch extraction scheme is used to speed up exam paper generation. This algorithm has a certain universality and high research value for constructing intelligent exam system. [ABSTRACT FROM AUTHOR]

In order to improve the fitness of RMB paper currency number recognition algorithm, based on structural feature of characters, a novel RMB paper currency number recognition algorithm is proposed, which is based on multiple mutual compensation structure characteristics. And the image pre process of RMB paper currency number is studied including image positioning, image binarization, single character segmentation, character size normalization, etc. Algorithms of English letter arrangement, Arabia digital arrangement and their mixed arrangement of RMB paper currency number are designed. These algorithms are introduced by taking partial flow chain of mixed arrangement recognition program as an example. An ARM embedded system is used to validate three recogntion programs. Some suggestions are proposed to solve the issues such as font diference of characters, and character interference in the experiment. The statistical results of accuracy of three recognition programs show that the algorithm has high precision and meets the practical needs. [ABSTRACT FROM AUTHOR]

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Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of small chaotic space and weak chaotic ability of existing chaotic mapping models, a new three-dimensional chaotic mapping model is designed by coupling two-dimensional Henon chaotic mapping model and Sine chaotic mapping model. Aiming at the problem that the security of encryption system will be reduced if the key is reused, this paper designs a new key generation method using Chebyshev mapping and plaintext image. Using the 3D chaotic mapping and key generation method designed in this paper, a color image encryption method is proposed. The results show that the method can encrypt images safely and effectively and protect the security of image information. [ABSTRACT FROM AUTHOR]

Convolutional neural networks have achieved great success in the field of image analysis. With the development of deep learning, deep learning models are becoming more and more complex, and he amount of deep learning calculations is increasing rapidly. The sparse algorithm can effectively reduce the amount of deep learning calculations without reducing the accuracy. This paper uses three different pruning strategies under the ResNet18 model and GoogleNet model to reduce the calculation amount of the deep learning model. The results show that the global unstructured pruning strategy has a sparsity of 94% and 90% without reducing the accuracy respectively, the level unstructured pruning strategy has an average sparsity of 83% and 56% without basically reducing the accuracy respectively, and the level structured strategy has an average sparsity of 34% and 22% without basically reducing the accuracy respectively. Under the three pruning strategies, the delay and power consumption results obtained by running the sparse deep learning model in the Eyeriss deep learning accelerator shows that, compared with the unpruned strategy, under the ResNet model, the global unstructured pruning strategy has a 66.0% reduction in latency and a 60.7% reduction in power consumption, the level unstructured pruning strategy has a 66.0% reduction in delay and a 80.6% reduction in power consumption, and the level structured pruning strategy has a 65.6% reduction in latency and a 33.5% reduction in power consumption. Under the GoogleNet model, the global unstructured pruning strategy has a 4.5% reduction in latency and a 63.2% reduction in power consumption, the level unstructured pruning strategy has a 73.6% reduction in delay and a 55.0% reduction in power consumption, and the level structured pruning strategy has a 26.8% reduction in latency and a 5.8% reduction in power consumption. Therefore, this paper concludes that the global unstructured pruning strategy can greatly reduce he delay and energy consumption without reducing the accuracy. Under the level unstructured pruning strategy, the delay and energy consumption can be greatly reduced under the premise of slightly reducing the accuracy. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, digital images have been widely used in various fields. However, due to the appearance of image editing software, a large number of images have been tampered with maliciously, which seriously affects the authenticity of image content. Different from the general object detection, the study of image tampering detection needs to pay more attention to the tamper information of the image itself, which is often manifested in a weak form. Therefore, the detection model needs to focus on learning more abundant tamper features. This paper proposes a dual-stream Faster R-CNN model that combines gradient edge information and attention mechanism, and the model can realize detection and location of regions with different tampering types. One of the two streams is the color stream, which uses the attention mechanism to extract the surface features of the image, such as brightness contrast, visual difference of tampering with the boundary, etc. The second of the two streams is a gradient stream. A Gradient high-pass filter is used to enhance the anomaly edge features between the real area and the tampered area, making it easier for the model to find faint tampered traces in the tampered image. Finally, the features of color stream and gradient stream are fused by means of compact bilinear pooling. Due to the relatively small size of publicly available image tampering data sets, the Pascal VOC 2012 is used to create an image tampering detection data set which containing 10 010 images for model pre-training. The experimental results on COVER, Columbia, and CASIA data sets show that the model proposed in this paper improves the detection accuracy by 7.1% to 9.6% compared to the latest models, and exhibits higher robustness under JPEG compression and image blur attacks. [ABSTRACT FROM AUTHOR]

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Currently, mainstream quantum computing simulators are generally based on numerical calculations, which suffer from precision loss, lack of intuitive representation of quantum states, and difficulty in supporting parameterized quantum circuits. Although symbolic quantum computing simulators have been proposed, they are limited in describing parameterized quantum states, constructing custom quantum gates, and integrating with quantum programming environments. To address this issue, this paper proposes a symbolic quantum computing simulator, SymQC. SymQC can use either full amplitude vectors or Dirac symbols to represent quantum states, calculate the equivalent matrix of symbolically parameterized quantum circuits, simulate the evolution of quantum states under quantum circuits, and output the execution results of quantum algorithms in different forms. This paper describes the software structure of SymQC and provides a mathematical proof of a commonly used quantum state update algorithm. Algorithm instances including variational quantum eigensolver (VQE) algorithm are used to verify the ability of SymQC. [ABSTRACT FROM AUTHOR]

In response to the overall trend and practical application of multi-threshold points in electromechanical equipment fault data detection, this paper proposes a cloud-edge collaborative electromechanical equipment fault detection method based on a variational autoencoder with gated recurrent unit (VAE-GRU). A cloud-edge collaborative electromechanical equipment fault detection architecture is structed, including a terminal equipment layer, an edge node layer, and a cloud center layer, in which electromechanical equipment is detected for faults through collaboration between the cloud center and edge nodes. The VAE-GRU model is design, where the input data is sampled by VAE, and GRU is used to capture the long-term correlation of the timing data. A dynamic threshold selection algorithm is used to calculate the fault detection threshold, that can automatically select the optimal threshold for different data sets to improve fault detection accuracy. Experimental results show that the proposed method improves the accuracy of electromechanical equipment fault detection while reducing latency, enduring the normal and stable operation of electromechanical equipment. [ABSTRACT FROM AUTHOR]

To meet the fast iteration speed and high computing power requirements of deep learning models, mainstream hardware vendors are increasingly inclined towards heterogeneous systems consisting of general-purpose processors and AI-specific accelerator cores. However, AI-specific accelerator ores only support certain core operators and do not have general programming capabilities. Therefore, how to efficiently deploy deep learning tasks on such heterogeneous architectures is worth further research. Based on the domestically developed FT-Matrix DSP+MatrixZone heterogeneous system platform, this paper designs and implements a deep learning programming framework, called KaiSa. KaiSa analyzes the input parameters of the deep learning model, identifies the operator type, and assigns it to the corresponding computing core. For complex operators, KaiSa automatically completes the optimal search for the block size based on a performance model, improving the performance of dual-core parallel computing. At the same time, KaiSa shields all low-level hardware details to provide users with a friendly programming environment for efficient program development. Experimental results show that KaiSa can achieve performance improvements of up to 39.0%. [ABSTRACT FROM AUTHOR]

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Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Depending on the large-scale parallel corpus, neural machine translation has achieved great success in some language pairs. Subsequently, unsupervised neural machine translation (UNMT) has partly solved the problem that high quality corpus is difficult to obtain. Recent studies show that crosslingual language model pretraining can significantly improve the translation performance of UNMT. This method models deep context information in cross-lingual language scenarios by using a large-scale monolingual corpus, and obtains significant results. This paper further explores UNMT based on crosslingual language pretraining, proposes several improved methods of training model, and compares the performance between UNMT and baseline system on different language pairs. Aiming at the issue of unbalanced initialization of unsupervised NMT parameters when using pre-trained models, this paper proposes a secondary pre-training stage to continue pre-training, and propose to initialize the Cross attention sub-layer with the self-attention sub-layer in unsupervised NMT model. Meanwhile, as backtranslation plays a critical role in unsupervised NMT, we propose to use Teacher-Student framework to guide back-translation.Experimental results show that, compared with the baseline system, these methods improve BLEU by 0.8-2.08 percentages at most. [ABSTRACT FROM AUTHOR]

Consensus algorithms are widely adopted in the distributed storage systems to ensure the correctness of the I/O operations. Since consensus algorithms typically use a complex protocol to ensure the correctness of the I/O operations between multiple storage nodes, it incurs high network transmission overhead and I/O delay. Due to the large differences in the implementation mechanisms of various consensus algorithms, specific consensus algorithms often need to be deployed in specific storage application scenarios in order to efficiently perform I/O operations and ensure the quality of service of the applications on them. Therefore, in the actual storage system development process, developers often need to select consensus algorithms according to storage application scenarios, thereby reducing the system overhead caused by I/O operations. In order to clarify the suitable application scenarios of various consensus algorithms, this paper introduces the consensus problems existing in distributed storage systems, and summarizes the implementation mechanism of current consensus algorithms. This paper summarizes the mainstream consensus algorithms under the replica-based storage systems and the erasure-coded storage systems, and compares the characteristics of these consensus algorithms in terms of implementation mechanism, network overhead, and data storage overhead. On this basis, this paper combines two classic application scenarios of a single data center distributed storage system and a cross-data center cloud-to-cloud storage system, and summarizes the main points that developers need to pay attention to when deploying consensus algorithms in actual storage systems. The problems that need to be solved urgently and the possible ways to improve the performance of I/O operations are analyzed, and the future development direction of the consensus algorithm is prospected. [ABSTRACT FROM AUTHOR]

Big data storage plays an important role in the whole big data application framework system in view of the increasing amount of data. Performance evaluation for big data storage can guide big data application developers to analyze performance bottlenecks and optimize the performance of big data systems. In the past, benchmarking was usually used to evaluate the performance of different big data frameworks, or the performance analysis of distributed file systems was carried out by piling and analyzing track files. These two methods adopt different analytical perspectives, but there has not been a reasonable evaluation system to evaluate the distributed storage system of big data. This paper proposes the architecture and specific implementation of the big data storage performance evaluation method combining active and passive methods. In the active evaluation method, this paper provides benchmark test programs of more than 20 applications in 6 fields to initiate performance tests on big data storage systems, and analyzes the benchmark performance indicators of big data storage systems. In the passive performance evaluation method, this paper provides analysis and positioning methods for inefficient tasks, inefficient operators and inefficient functions. By analyzing the big data applications running on the big data storage systems, we can find out the reasons for the inefficiency of big data applications. Experiments show that the architecture of the proposed big data performance evaluation method can comprehensively evaluate the performance of big data storage. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)