作者机构：
[Qin, Jiaohua; Tu, Guangming] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Peoples R China.;[Xiong, Neal N.] Sul Ross State Univ, Dept Comp Sci & Math, Alpine, TX 79832 USA.
通讯机构：
[Jiaohua Qin] C;College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
deep learning;YOLO;composite attention;computer mainboard quality detection;real-time detection
摘要：
<jats:p>Automated industrial quality detection (QD) boosts quality-detection efficiency and reduces costs. However, current quality-detection algorithms have drawbacks such as low efficiency, easily missed detections, and false detections. We propose QD-YOLO, an attention-based method to enhance quality-detection efficiency on computer mainboards. Firstly, we propose a composite attention module for the network’s backbone to highlight appropriate feature channels and improve the feature fusion structure, allowing the network to concentrate on the crucial information in the feature map. Secondly, we employ the Meta-ACON activation function to dynamically learn whether the activation function is linear or non-linear for various input data and adapt it to varied input scenarios with varying linearity. Additionally, we adopt Ghost convolution instead of ordinary convolution, using linear operations as possible to reduce the number of parameters and speed up detection. Experimental results show that our method can achieve improved real-time performance and accuracy on the self-created mainboard quality defect dataset, with a mean average precision (mAP) of 98.85% and a detection speed of 31.25 Frames Per Second (FPS). Compared with the original YOLOv5s model, the improved method improves mAP@0.5 by 2.09% and detection speed by 2.67 FPS.</jats:p>
作者机构：
[谭云; 彭海阔; 秦姣华; 薛有元] College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China
通讯机构：
College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, China
摘要：
<jats:p>As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images.</jats:p>
关键词：
coverless information hiding;text big data;web text;web spider;location information
摘要：
Coverless information hiding has become a hot topic because it can hide secret information (SI) into carriers without any modification. Aiming at the problems of the low hiding capacity (HC) and mismatch in text big data, a novel method of coverless information hiding by retrieving the massive amount of web text on the Internet. First, the proposed method uses a web spider technology to capture web texts associated with SI to construct a web-text library. Second, some texts containing SI are searched and the optimal web text is selected from them. Then, the location of the SI in the selected web text is described by using a 2-D coordinate system. Finally, the URL of the web text is combined with the obtained location information and then sent to the recipient. The experimental results and analysis show that the performances are improved in terms of HC, hiding success rate, and security.
摘要：
The encrypted image retrieval in cloud computing is a key technology to realize the massive images of storage and management and images safety. In this paper, a novel feature extraction method for encrypted image retrieval is proposed. First, the improved Harris algorithm is used to extract the image features. Next, the Speeded-Up Robust Features algorithm and the Bag of Words model are applied to generate the feature vectors of each image. Then, Local Sensitive Hash algorithm is applied to construct the searchable index for the feature vectors. The chaotic encryption scheme is utilized to protect images and indexes security. Finally, secure similarity search is executed on the cloud server. The experimental results show that compared with the existing encryption retrieval schemes, the proposed retrieval scheme not only reduces the time consumption but also improves the image retrieval accuracy.
期刊：
International Journal of Embedded Systems,2018年10(2):113-119 ISSN：1741-1068
通讯作者：
Pan, Lili(lily_pan@163.com)
作者机构：
[Tiane Wang] The Commission Institute, Hunan Electric Power Transmission and Substation Construction Company, 410017 Changsha, Hunan, China;[Lili Pan; Jiaohua Qin; Xuyu Xiang] College of Computer Science and Information Technology, Central South University of Forestry and Technology, 410004 Changsha, Hunan, China
通讯机构：
College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, Hunan, China
关键词：
Fault detection;Information use;Testing;APFD;Average of the percentage of faults detected;Class method;DU-chain coverage;Regression testing;Test case;Software testing
摘要：
Test case prioritisation schedules the test cases for execution in an order that attempts to maximise (an) objective(s) or expose faults earlier in testing. In the past, many test case prioritisation techniques prioritised test cases based on mainly test-requirement coverage and ignored many other testing factors. In view of the DU-chain importance in programs, this paper presents a test case prioritisation approach of method-based DU-chain coverage. The technique combines the DU-chain coverage and fault detection rate as test-case quantitative factors. Different from existing techniques, the novel approach makes use of information from executed testing and module coupling, and dynamically calculates a priority quantitative value for every test case. The experiments performed show that the dynamic prioritisation approach is fault-detection effective, and the APFD of the test suites constructed by the dynamic prioritisation approach is higher than that of the test suites constructed by the static prioritisation technique.
摘要：
Deep learning has brought a series of breakthroughs in image processing. Specifically, there are significant improvements in the application of food image classification using deep learning techniques. However, very little work has been studied for the classification of food ingredients. Therefore, this paper proposes a new framework, called DeepFood which not only extracts rich and effective features from a dataset of food ingredient images using deep learning but also improves the average accuracy of multi-class classification by applying advanced machine learning techniques. First, a set of transfer learning algorithms based on Convolutional Neural Networks (CNNs) are leveraged for deep feature extraction. Then, a multi-class classification algorithm is exploited based on the performance of the classifiers on each deep feature set. The DeepFood framework is evaluated on a multi-class dataset that includes 41 classes of food ingredients and 100 images for each class. Experimental results illustrate the effectiveness of the DeepFood framework for multi-class classification of food ingredients. This model that integrates ResNet deep feature sets, Information Gain (IG) feature selection, and the SMO classifier has shown its supremacy for food-ingredients recognition compared to several existing work in this area.
