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6th International Conference on Computer information Science and Application technology, CISAT 2023

作者： Rahim, Asif Zhong, Yanru Ahmad, Tariq School of Computer and Information Security Guilin university of Electronic Technology Guilin54001 China Guangxi Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin541001 China School of Information and Communication Engineering Guilin University of Electronic Technology Guilin541001 China

ISBN: (纸本)9781510668546

The utilization and advancement of Internet of Things (IoT) gadgets have noticeably increased in recent times. Various sectors, including intelligent homes, healthcare, sports analysis, and different industries, use IoT devices. Traffic is a crucial aspect of IoT devices, and it differs from conventional device traffic in several ways. This investigation employed 41 Internet of Things (IoT) devices. A machine learning model was developed that can classify IoT devices into multiple categories based on 13 different network traffic features. The raw data that was gathered was preprocessed using techniques like standardization and scaling techniques of the dataset. Algorithms for feature selecting can be utilized to extract characteristics from textual data. Following stratification, The dataset contains 106,234 sets of features that were utilized to enhance the predictive model. This research employed various evaluation metrics to demonstrate the effectiveness of the logistic-boosted algorithms. In this research, collaborative machine learning methods were utilized as a starting point to develop an intrusion detection system (IDS) that can identify abnormal network traffic. The principal aim of this investigation is to identify attacks and abnormalities within a Smart Home IoT setting. We have introduced a new technique for constructing Logit-Boosted algorithm, namely Logi-CatBoost Classifier. Once the logitboosted algorithm was employed to classify the dataset, Logi-CBC performed the best, with an accuracy of 88.70%. We suggested Logi-CBC algorithm achieved the top precision among previously used Logit-Boosted algorithms in past studies on the same dataset. © 2023 SPIE.

关键词： Internet of things

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Ontology-Based Deep Web Synchronous-Annotation

Ontology-Based Deep Web Synchronous-Annotation

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ISECS International Colloquium on Computing, communication, Control, and Management, CCCM

作者： PengYu Hu Wei Fang ZhiMing Cui Jiangsu Provincial Key Laboratory of Computer Information Processing Technology The Institute of Intelligent Information Processing and Application Soochow University Suzhou China

An increasing number of databases have become Web accessible through HTML form-based search interfaces, which is so-called deep Web. For full utilization of deep Web resources and improving Web intelligence, which is essential for many applications such as deep Web data collection and comparison shopping, they need to be extracted out and assigned meaningful labels. In this paper, we present a synchronous-annotation approach that introduce domain ontology as a global schema ordered by Web databases to the annotation process. We combine ontology, interface schema and result schema and adopt the strategy of query ontology instance to implement annotation. In order to verify the effectiveness of the method proposed in this paper, we test on a number of different areas of Web databases. The experimental results indicate that the proposed approach is more effective than existing approaches.

关键词： Ontologies Databases Vocabulary Data mining Books Music Motion pictures

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From Wrapping to Knowledge: Domain Ontology Learning from Deep Web

From Wrapping to Knowledge: Domain Ontology Learning from De...

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International Symposiums on information processing (ISIP)

作者： Zhiming Cui Pengpeng Zhao Wei Fang Chao Lin The Institute of Intelligent Information Processing and Application China Jiangsu Key Laboratory of Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9781424430536;9780769531519

The next generation of the Web, called Semantic Web, has to improve the Web with semantic page annotations to enable knowledge-level querying and searches. However, manual construction of these ontologies is a time consuming and difficult task. In this paper, we describe an automatic extraction method that learns domain ontologies for semantic web from deep web. Our approach first learns a base ontology from deep web query interfaces, then grows the current ontology by probing the sources and discovering additional concepts and instances from the result pages. We have evaluated our approach in several real-world domains. Preliminary results indicate that the proposed extraction method discovers concepts and instances with high accuracy.

关键词： Wrapping Ontologies Semantic Web information processing Databases Knowledge engineering Chaos Application software Laboratories Machine learning

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Distributed query processing in an ad-hoc semantic web data sharing system

Distributed query processing in an ad-hoc semantic web data ...

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2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013

作者： Zhou, Jing Bochmann, Gregor V. Shi, Zhongzhi School of Computer Science Communication University of China Beijing China School of Electrical Engineering and Computer Science University of Ottawa ON Canada Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769549798

Sharing the Semantic Web data in proprietary datasets in which data is encoded in RDF triples in a decentralized environment calls for efficient support from distributed computing technologies. The highly dynamic ad-hoc settings that would be pervasive for Semantic Web data sharing among personal users in the future, however, pose even more demanding challenges for the enabling technologies. We extend previous work on a hybrid P2P architecture for an ad-hoc Semantic Web data sharing system which better models the data sharing scenario by allowing data to be maintained by its own providers and exhibits satisfactory scalability owing to the adoption of a two-level distributed index and hashing techniques. Additionally, we propose efficient distributed processing of SPARQL queries in such a context and explore optimization techniques that build upon distributed query processing for database systems and relational algebra optimization. We anticipate that our work will become an indispensable, complementary approach to making the Semantic Web a reality by delivering efficient data sharing and reusing in an ad-hoc environment. © 2013 IEEE.

关键词： Query processing

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The Application of the Alternate Descent Conditional Gradient Method in Tomographic SAR Off-grid Imaging

The Application of the Alternate Descent Conditional Gradien...

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IET International Radar Conference 2023, IRC 2023

作者： Shao, Mingxiao Su, Cikang Zhang, Zhe Zhang, Bingchen The Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China Aerospace Information Research Institute Chinese Academy of Sciences Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

Compressed sensing (CS) technology has been applied to topographic synthetic aperture radar (TomoSAR) imaging due to the sparsity of elevation signals. The traditional CS algorithms discretize the elevation into many grids and assume that the scatterers are located on grids, therefore, the accuracy of the traditional CS algorithms is limited by the off-grid effect. Although there are currently methods to address the off-grid effect, such as atomic norm minimization (ANM), its application is limited by the baseline distribution form of TomoSAR, which only applies to uniformly observed scenarios. Alternating Descent Conditional Gradient (ADCG) has been applied in many fields as a gridless imaging algorithm. In this paper, we apply ADCG to TomoSAR imaging for the first time, achieving the reconstruction of non-uniform baseline observation scenes. The effectiveness of ADCG in TomoSAR imaging was verified by comparing it with iterative soft thresholding (IST) and orthogonal matching pursuit (OMP) through simulation experiments. © The Institution of Engineering & technology 2023.

关键词： Compressed sensing

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A parallel k-means clustering algorithm based on redundance elimination and extreme points optimization employing MapReduce

A parallel k-means clustering algorithm based on redundance ...

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作者： Tang, Zhuo Liu, Kunkun Xiao, Jinbo Yang, Li Xiao, Zheng College of Information Science and Engineering Hunan University Hunan410082 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410004 China

When facing massive statistical data, the k-means algorithm is very difficult to satisfy the need of data processing as it lacks an effective parallel mechanism. This paper proposes an improved k-means algorithm (IMR-KCA) to conduct clustering analysis based on medical data employing MapReduce computing framework. Through analyzing the defects of vast redundancy in the traditional k-means algorithms, a selection model is firstly proposed to simplify the computations with multiple clustering centers. Based on several proposed theorems, we prove the correctness of this selection model. Second, this paper provides a method to calculate the distances from extreme points to central points, and the original Euclidean distance is replaced with Manhattan distance. For this simplification, a group of theorems are proposed to prove the correctness. Next, we provide a group of implementation algorithms to complete the parallelism of the clustering computation employing the MapReduce framework. Finally, the experimental results illustrate that IMR-KCA is more reliable and efficient than the direct parallelization of the traditional clustering algorithms based on MapReduce. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： MapReduce

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Joint multi-task cascade for instance segmentation

Joint multi-task cascade for instance segmentation

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作者： Wen, Yaole Hu, Fuyuan Ren, Jinchang Shang, Xinru Li, Linyan Xi, Xuefeng School of Electronic & Information Engineering Suzhou University of Science and Technology SuzhouJiangsu215009 China Centre for Signal and Image Processing University of Strathclyde Glasgow United Kingdom Suzhou Institute of Trade & Commerce SuzhouJiangsu215009 China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou University of Science and Technology SuzhouJiangsu215009 China Suzhou Key Laboratory for Big Data and Information Service Suzhou University of Science and Technology SuzhouJiangsu215009 China

Instance segmentation requires both pixel-level classification accuracy and high-level semantic features at the target instance level, which is very challenging, and the cascade structure can effectively improve both of these problems. To make full use of the relationship between detection and segmentation, this paper proposes a joint multi-tasking cascade structure, which is not simply to cascade the two tasks of detection and segmentation, but to unitedly put them into multi-stage processing, and especially to integrate the information at different stages of the mask branch. The entire structure can effectively utilize the superior characteristics of each stage in the matter of detection and segmentation, thus improving the quality of mask prediction. The feature fusion process is introduced in the full convolution networks (FCN) branch, and the high-level and low-level features are effectively fused to enhance the contextual information of the picture semantic features. The experiments demonstrate the better results on the COCO dataset. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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Multi-kernel Co-SVM with a new strategy using unlabeled data for image retrieval

Multi-kernel Co-SVM with a new strategy using unlabeled data...

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2011 6th International Conference on Pervasive Computing and Applications, ICPCA 2011

作者： Zongmin, Li Yang, Liu Hua, Li School of Computer and Communication Engineering China University 0f Petroleum Shandong 257061 China Key Laboratory Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing l0080 China

ISBN: (纸本)9781457702082

Relevance feedback based on SVM classifier shows a good performance recently but the finite feedback counts limited by user's patience and the small sample size problem are not solved well, Co-SVM does a good job in solving these problems but still has some flaws. We propose three strategies to try to improve this algorithm: (1) different kernel functions are used to characterize the color and texture visual similarities;(2) a new method is proposed to caculate the confident scores of the contention samples;(3) a bunch of the most irrelevant images with the highest confident score are added into the labeled images to extend the size of labeled data while choosing a bunch of images for user labeling. Experimental results verify the superiority of our method over Co-SVM. © 2011 IEEE.

关键词： Image enhancement

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A Method to Verify Internal Parameters of Camera Calibration in World Coordinate System 6

A Method to Verify Internal Parameters of Camera Calibration...

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6th IEEE International Conference on Electronic information and communication technology, ICEICT 2023

作者： Xing, Zheng Wang, Qingchun Li, Yingsong Qi, Junwei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (纸本)9798350399059

Aiming at the problem that reprojection error (RE) cannot reflect the camera calibration accuracy intuitively, this paper proposes a method to verify the camera calibration results in world coordinate system. Firstly, the checkerboard calibration target is photographed with the camera under test, and the internal parameters and distortion parameters of the camera are calculated by using the pinhole model and the Kannala-Brandt (KB) fisheye model, respectively. Then, the same camera is used to shoot two targets in the field of view (FOV), and the pixel coordinates of the two target points are detected from the image. Thirdly, the inverse projection model is used to calculate the distance between the two objects and the angle between the camera and the two objects, and the real distance and angle are compared. The experimental results show that the average distance errors of the pinhole model and the KB fisheye model are 0.34% and 0.35%, respectively, and the angle errors are 0.40% and 0.12%, respectively, which proves that the verification method can effectively characterize the calibration accuracy of the camera. © 2023 IEEE.

关键词： Calibration

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A comparative study on artificial neural networks for environmental quality assessment

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Computer Modelling and New Technologies 2014年第7期18卷 41-47页

作者： Liu, Yijun He, Sheng Wang, Yao Wang, Xiumei Key laboratory of cloud computing and intelligent information processing of Changzhou City Jiangsu University of Technology China

The aim of this study is to use neural network tools as an environmental decision support in assessing environmental quality. A three-layer feedforward neural network using three learning approaches of BP, LM and GA-BP has been applied in non-linear modelling for the problem of environmental quality assessment. The case study shows that the well designed and trained neural networks are effective and form a useful tool for the prediction of environmental quality. Furthermore, the LM network has the fastest convergence speed and the GA-BP network outperforms the other two networks in both predictive and final classification accuracies of environmental quality.

关键词： Neural network models

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