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Front Cover: International Journal of Intelligent Systems, Volume 37 Issue 11 November 2022

International Journal of Intelligent Systems 2022年第11期37卷

作者： Xianjia Meng Yong Yang Ximeng Liu Nan Jiang School of Information Science and Technology Northwest University Xi'an People's Republic of China Fujian Province Key Laboratory of Information Security of Network System and College of Computer and Data Science Fuzhou University Fuzhou People's Republic of China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen People's Republic of China Department of Internet of Things School of Information Engineering East China Jiao Tong University Nanchang People's Republic of China

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Dense attention fluid network for salient object detection in optical remote sensing images

arXiv

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arXiv 2020年

作者： Zhang, Qijian Cong, Runmin Li, Chongyi Cheng, Ming-Ming Fang, Yuming Cao, Xiaochun Zhao, Yao Kwong, Sam The Department of Computer Science City University of Hong Kong Hong Kong The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Computer Science and Engineering Nanyang Technological University Singapore The College of Computer Science Nankai University Tianjin300071 China School of Information Technology Jiangxi University of Finance and Economics NanchangJiangxi330032 China The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains an open and challenging problem. In this paper, we propose an end-to-end Dense Attention Fluid network (DAFNet) for SOD in optical RSIs. A Global Context-aware Attention (GCA) module is proposed to adaptively capture long-range semantic context relationships, and is further embedded in a Dense Attention Fluid (DAF) structure that enables shallow attention cues flow into deep layers to guide the generation of high-level feature attention maps. Specifically, the GCA module is composed of two key components, where the global feature aggregation module achieves mutual reinforcement of salient feature embeddings from any two spatial locations, and the cascaded pyramid attention module tackles the scale variation issue by building up a cascaded pyramid framework to progressively refine the attention map in a coarse-to-fine manner. In addition, we construct a new and challenging optical RSI dataset for SOD that contains 2,000 images with pixel-wise saliency annotations, which is currently the largest publicly available benchmark. Extensive experiments demonstrate that our proposed DAFNet significantly outperforms the existing state-of-the-art SOD competitors. https://***/ rmcong/DAFNet_TIP20 © 2020, CC BY-NC-SA.

关键词： Object detection

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An Enhanced Feature Selection Approach based on Mutual Information for Breast Cancer Diagnosis

An Enhanced Feature Selection Approach based on Mutual Infor...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Nawel Zemmal Nabiha Azizi Amel Ziani Nacer Eddine Benzebouchi Monther Aldwairi Labged Laboratory Badji Mokhtar University Annaba Algeria Computer science department Lri laboratory: Information Research Laboratory Annaba Algeria Department of Network Engineering and Security Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9781728131573

Breast cancer is the most feared disease in the female population. Early detection plays an important role to improve prognosis. Mammography is the best examination for the detection of breast cancer. However, in some cases, reading mammograms is difficult for radiologists. For this reason, several researches have been conducted to develop computer Aided Diagnosis tools (CAD) for this disease which aims to interpret mammography images. This paper investigates a new CAD system based on Transductive scheme and Mutual Information for breast abnormalities diagnosis. In the proposed method, a feature vector contains a combination of two features extraction method: Grey Level Co-occurrence Matrix and local Binary Pattern. In the next step, a novel scheme combining Mutual Information and Correlation-based feature selection was applied for selecting the most relevant features. Finally, the classification was achieved using a Transductive Support Vector Machine classifier. The effectiveness of the proposed CAD is examined on the DDSM dataset using classification accuracy, recall and precision. Experimental results demonstrate that the proposed CAD system is clinically significant and can be used to classify the abnormalities of the breast.

关键词： Breast cancer computer aided diagnosis (CAD) Transductive support vector machine (TSVM) Semi supervised learning (SSL) Mutual information Correlation-based feature selection Breast Cancer computer aided design system breast neoplasm diagnosis Feature extraction Digital Mammography Eigenvector malformations Breast

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Guest editorial: Artificial intelligence (AI)-driven spectrum management

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China Communications 2020年第2期17卷 III-V页

作者： Li, Zan Ding, Zhiguo Shi, Jia Saad, Walid Yang, Lie-Liang Xidian University Xian China Queen's University Belfast Imperial College Newcastle University Lancaster University United Kingdom Laboratory of Integrated Services Networks Xidian University China Department of Electrical and Computer Engineering Virginia Tech Network SciEnce Wireless and Security (NEWS) Laboratory United States Shanghai TieDao University Shanghai China

Recent advances in communication and networking technologies are leading to a plethora of novel wireless services that range from unmanned aerial vehicle (UAV) communication to smart cognitive networks and massive Internet of Things (IoT) systems. Enabling these emerging applications over the fifth generation (5G) of wireless cellular systems requires meeting numerous challenges pertaining to spectrum sharing and management. In fact, most 5G applications will be highly reliant on intelligent spectrum management techniques, which should adapt to dynamic network environments while also guaranteeing high reliability and high quality-of-experience (QoE). In this context, the use of artificial intelligence (AI) techniques that include deep learning, convolutional neural networks, and reinforcement learning, among many others, is expected to play a very important role in paving the way towards truly AI-driven spectrum management, thus enabling tomorrow's smart city services. Therefore, it has become imperative to investigate and apply AI techniques to solve emerging spectrum management problems in various wireless networks. This includes leveraging AI to address a wide range of wireless networking challenges ranging from network management to dynamic spectrum sharing and resource management.

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Building an efficient intrusion detection system based on feature selection and ensemble classifier

arXiv

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arXiv 2019年

作者： Zhou, Yuyang Cheng, Guang Jiang, Shanqing Dai, Mian School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Jiangsu Provincial Key Laboratory of Computer Network Technology Southeast University Nanjing China National Key Laboratory of Science and Technology on Information System Security Beijing China

Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Data mining

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A Hole Repairing Method Based on Slicing 12th

A Hole Repairing Method Based on Slicing

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Zhao, Yanni Wang, Yinghui Wang, Ningna Ning, Xiaojuan Shi, Zhenghao Zhao, Minghua Lv, Ke Huang, Liangyi Institute of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an China Department of Computer Science Shannxi Vocational and Technical College Xi’an China Booking B.V. Herengracht 597 Amsterdam1017 CE Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington D.C. United States

ISBN: (纸本)9783030237110

The repairing of 3D point cloud holes has an important meaning to ensure the integrity of cloud data. We present a slice-based repairing method for 3D point cloud in this paper. Firstly, the model is horizontal sliced and each slice is projected on a two-dimensional plane, then the band-shaped points obtained during projection are clustered to select boundary points of the hole. Combined with optimal fitting points, the hole repairing point sets in the projection layer are re-sampled based on the cubic B-spline curve to fit boundary points. Finally, all hole repairing point sets in the projection layer are combined in 3D space to finish the entire hole recovery. The experimental results show that the proposed method can effectively repair complex holes for various point cloud models. © 2019, Springer Nature Switzerland AG.

关键词： Curve fitting

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A Hole Repairing Method Based on Edge-Preserving Projection 12th

A Hole Repairing Method Based on Edge-Preserving Projection

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Wang, Yinghui Zhao, Yanni Wang, Ningna Ning, Xiaojuan Shi, Zhenghao Zhao, Minghua Lv, Ke Huang, Liangyi Institute of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an China Department of Computer Science Shannxi Vocational and Technical College Xi’an China Booking B.V. Herengracht 597 Amsterdam1017 CE Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington D.C. United States

ISBN: (纸本)9783030237110

A point cloud hole repairing method based on edge-preserving projection is proposed in order to maintain sharp features of holes. First, the hole boundary points are acquired by quadrants and angles, then connections among 3D hole boundary points are projected onto the two-dimensional plane using the edge-preserving projection. Secondly, the two-dimensional region obtained from projection is subjected to point collectivization for obtaining filling points, at the same time, the radial-basis interpolation mapping technique is used to construct the hole surface according to the 3D hole boundary point. Finally, the two-dimensional filling points are reflected onto the surface of the constructed hole to complete hole repairing. Experimental results show that our method can effectively repair the hole of the point cloud and restore the sharp features of the hole. © 2019, Springer Nature Switzerland AG.

关键词： Repair

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Correlation-Aware Virtual Machine Placement in Data Center networks 7th

Correlation-Aware Virtual Machine Placement in Data Center N...

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7th EAI International Conference on Cloud Computing, CloudComp 2016 and 1st EAI International Conference on security and Privacy in New Computing Environments, SPNCE 2016

作者： Chen, Tao Zhu, Yaoming Gao, Xiaofeng Kong, Linghe Chen, Guihai Wang, Yongjian Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China

ISBN: (纸本)9783319696041

The resource utilization (CPU, memory) is a key performance metric in data center networks. The goal of the cloud platform supported by data center networks is achieving high average resource utilization while guaranteeing the quality of cloud services. Previous work focus on increasing the time-average resource utilization and decreasing the overload ratio of servers by designing various efficient virtual machine placement schemes. Unfortunately, most of virtual machine placement schemes did not involve the service level agreements and statistical methods. In this paper, we propose a correlation-aware virtual machine placement scheme that effectively places virtual machines on physical machines. First, we employ Neural networks model to forecast the resource utilization trend according to the historical resource utilization data. Second, we design correlation-aware placement algorithms to enhance resource utilization while meeting the user-defined service level agreements. The results show that the efficiency of our virtual machine placement algorithms outperform the previous work by about 15%. © 2018, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Virtual machine

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Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model

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Journal of Physics: Conference Series 2020年第1期1682卷

作者： Wei Wei Zhuo Wang Changlong Fu Robertas Damaševičius Rafal Scherer Marcin Wožniak School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 Shaanxi Key Laboratory for Network Computing and Security Technology China.

Nowadays, data plays a unique role in various fields. Based on the background of the era of big data, this paper collects user evaluations of certain commodities and labels the evaluations into positive emotions and negative emotions. We propose an intelligent recommendation algorithm based on Naive Bayes algorithm, which determines the user's preference for a product according to the user's opinion on a product, then uses the collaborative filtering algorithm to recommend other similar products according to the similarity between the product and other products, so as to realize the recommendation of other similar products. This can not only help users choose more goods in the environment, but also maximize the value of goods. In this paper, bayesian classifier was used for prediction, and the accuracy reached about 97%.

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A novel Image security Protection Method Based on DCT Compression Theory and Hyper-chaotic Mapping

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Journal of Physics: Conference Series 2021年第1期2066卷

作者： Xianglian Xue Haiyan Jin School of Computer Science and Engineering Xi'an University of Technology Xi'an 710077Shaanxi China Shaanxi University of Chinese Medicine Xianyang712046Shaanxi China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an710077Shaanxi China

This paper studies the current situation of image compression encryption and analyzes the influence of low frequency (DC coefficient) and high frequency (AC coefficient) on image structure in DCT transformation. Based on this, a novel image security protection method based on DCT compression theory and hyper-chaotic mapping is proposed. First, the position of the pixel of the original image is disturbed, and converts the image from spatial domain into frequency domain by the two-dimensional DCT transformation and quantization. Second, change the pixel values by modifying the values of the sign bit of AC coefficient and DC coefficient. At last, the encrypted image is obtained by carrying out inverse quantization, inverse transformation and reverse operation by bit.

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