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2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010

作者： Yao, Lin Lin, Chi Kong, Xiangwei Xia, Feng Wu, Guowei School of Software Dalian University of Technology Dalian 116620 China School of Electronics and Information Engineering Dalian University of Technology Dalian 116024 China

ISBN: (纸本)9780769543314

In pervasive computing environments, Location-Based Services (LBSs) are becoming increasingly important due to continuous advances in mobile networks and positioning technologies. Nevertheless, the wide deployment of LBSs can jeopardize the location privacy of mobile users. Consequently, providing safeguards for location privacy of mobile users against being attacked is an important research issue. In this paper a new scheme for safeguarding location privacy is proposed. Our approach supports location K-anonymity for a wide range of mobile users with their own desired anonymity levels by clustering. The whole area of all users is divided into clusters recursively in order to get the Minimum Bounding Rectangle (MBR). The exact location information of a user is replaced by his MBR. Privacy analysis shows that our approach can achieve high resilience to location privacy threats and provide more privacy than users expect. Complexity analysis shows clusters can be adjusted in real time as mobile users join or leave. Moreover, the clustering algorithms possess strong robustness. © 2010 IEEE.

关键词： Location based services

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Fall Detection in Elevator Cages Based on XGBoost and LSTM 26

Fall Detection in Elevator Cages Based on XGBoost and LSTM

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26th International Conference on Automation and Computing, ICAC 2021

作者： Xu, Caorong Xu, Yuanping Xu, Zhijie Guo, Benjun Zhang, Chaolong Huang, Jian Deng, Xin Chengdu University of Information Technology School of Software Engineering Chengdu China University of Hudderseld School of Computing and Engineering HudderseldQueensgate United Kingdom

ISBN: (纸本)9781860435577

Fall detection has always been challenging. There are many current works that have achieved good results in fall detections, but most of the existing works only consider single feature dimension (i.e., temporal features or spatial features). This study proposes a method that combines LSTM and XGBoost model to detect human body falls through fusing both temporal and spatial features. It starts with extracting bond points on human body in each frame of the video by using AlphaPose. In the second step, three features of the human body (i.e., vertical height, aspect ratio of the human external rectangle and angle of the knee joint) are calculated as spatial features through the extracted bone point information. Then use LSTM to learn these three kinds of features on both the temporal and spatial dimension. Furthermore, this model integrates the XGBoost to learn multiple features to improve the recognition rate. Finally, various human body fall detections in elevator cabs are applied to test the usability and validity of this study. The experimental results of the whole model reach 92.11% recognition accuracy, and 93.33% on the the F1-measure index. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Fall detection

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An Improved PointNet++ Based Method for 3D Point Cloud Geometric Features Segmentation in Mechanical Parts 18

An Improved PointNet++ Based Method for 3D Point Cloud Geome...

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18th CIRP Conference on Computer Aided Tolerancing, CAT 2024

作者： Zhang, Peng Kong, Chao Xu, Yuanping Zhang, Chaolong Jin, Jin Li, Tukun Jiang, Xiangqian Tang, Dan School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom

The extraction of geometric features such as holes, arcs, and surfaces of mechanical parts is crucial for quality control. The existing methods for geometrical feature segmentations on 3D point clouds still have limitations, especially for simultaneously extracting multiple types of geometric features from comprehensive workpieces. To this end, this study investigates segmentation methods that take 3D point cloud datasets of mechanical parts as inputs, and employs an improved PointNet++ deep learning model to solve this extraction difficulty. Firstly, the Set Abstraction module in PointNet++ was modified by incorporating Self-Attention mechanisms to increase interactivity and global correlation among data points. Then, the local feature extraction Multilayer Perceptron (MLP) from PointNet-Transformer was integrated to enhance the feature extraction accuracy. Due to the inherent class imbalance issue, the Focal Tversky Loss is employed as the loss function to ensure that geometric features with relatively lower proportions can be fully trained. Finally, the Statistical filtering algorithm is utilized to mitigate noise and attenuate subtle irregularities, such that the smoothness of geometric features can be substantially enhanced. The experimental results demonstrate that the proposed model achieves an accuracy of 86.6% on geometric feature segmentations and a mean Intersection over Union (mIoU) of 0.84. The comparison with the original PointNet++ proves that the proposed method can improve accuracy and mIoU by 3.7% and 0.03 respectively. © 2024 The Authors. Published by Elsevier B.V.

关键词： Geometry

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Evaluating social spammer detection systems 18

Evaluating social spammer detection systems

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2018 Australasian Computer Science Week Multiconference, ACSW 2018

作者： Ho, Kenny Liesaputra, Veronica Yongchareon, Sira Mohaghegh, Mahsa Department of Computer Science Unitec Institute of Technology Auckland New Zealand Department of IT and Software Engineering Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9781450354363

The rising popularity of social network services, such as Twitter, has attracted many spammers and created a large number of fake accounts, overwhelming legitimate users with advertising, malware and unwanted and disruptive information. This not only inconveniences the users' social activities but causes financial loss and privacy issues. Identifying social spammers is challenging because spammers continually change their strategies to fool existing anti-spamming systems. Thus, many researchers have tried to propose new classification systems using various types of features extracted from the content and user's information. However, no comprehensive comparative study has been done to compare the effectiveness and the efficiency of the existing systems. At this stage, it is hard to know what the best anti spamming system is and why. This paper proposes a unified evaluation workbench that allows researchers to access various user and content-based features, implement new features, and evaluate and compare the performance of their systems against existing systems. Through our analysis, we can identify the most effective and efficient social spammer detection features and help develop a faster and more accurate classifier model that has higher true positives and lower false positives. © 2018 ACM.

关键词： Losses

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A Detection Method of Electro-bicycle in Elevators Based on Improved YOLO v4 26

A Detection Method of Electro-bicycle in Elevators Based on ...

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26th International Conference on Automation and Computing, ICAC 2021

作者： Wang, Wanting Xu, Yuanping Xu, Zhijie Zhang, Chaolong Li, Tukun Wang, Jie Jiang, Han Chengdu University of Information Technology School of Software Engineering Chengdu China University of Huddersfield School of Computing and Engineering Huddersfield Queensgate United Kingdom

ISBN: (纸本)9781860435577

Currently, electro-bicycles are one of the most convenient and affordable traveling tools. However, when an electro-bicycle enters a crowded elevator, it will not only affect the service life of the elevator, but also it has great potential safety hazards when the battery charges at home. Thus, this study focuses on detecting various electro-bicycle intelligently in elevators in an online manner through improving the YOLO (You Only Look Once) v4 network. The YOLO v4 algorithm has high detection accuracy and speed due to its single stage mode and the priori frame mechanism. Thus, this study applies YOLO v4 as the fundamental network for electro-bicycle detections. The improved YOLO v4 algorithm, named as W_YOLO v4, reconstructs the YOLO v4 feature pyramid and the corresponding backbone feature extraction network. It also integrates the attention mechanism into the residual network of the backbone network to improve the detection accuracy. The experimental results show that the mAP (mean Average Precision) of W_YOLO v4 algorithm is 91.37 with 5000 data, which is 6.5 higher than the mAP of YOLO v4 algorithm, and the network parameters are reduced, such that the model is lighter than the YOLO v4 model. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Object detection

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Cancer classification with data augmentation based on generative adversarial networks

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Frontiers of Computer Science 2022年第2期16卷 69-79页

作者： Kaimin WEI Tianqi LI Feiran HUANG Jinpeng CHEN Zefan HE College of Information Science and Technology Jinan UniversityGuangzhou 510632China Guangdong Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Software Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Accurate diagnosis is a significant step in cancer *** learning can support doctors in prognosis decision-making,and its performance is always weakened by the high dimension and small quantity of genetic ***,deep learning can effectively process the high dimensional data with ***,the problem of inadequate data remains unsolved and has lowered the performance of deep *** end it,we propose a generative adversarial model that uses non target cancer data to help target generator *** use the reconstruction loss to further stabilize model training and improve the quality of generated *** also present a cancer classification model to optimize classification *** results prove that mean absolute error of cancer gene made by our model is 19.3%lower than DC-GAN,and the classification accuracy rate of our produced data is higher than the data created by *** for the classification model,the classification accuracy of our model reaches 92.6%,which is 7.6%higher than the model without any generated data.

关键词： data mining cancer data analysis deep learning generative adversarial networks

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Automatic audio genre classification based on support vector machine

Automatic audio genre classification based on support vector...

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3rd International Conference on Natural Computation, ICNC 2007

作者： Yingying, Zhu Zhong, Ming Qiang, Huang Faculty of Information Engineering Shenzhen University Shenzhen 518060 China Software Engineering Ltd. Harbin Institute of Technology Haerbin 150001 China

ISBN: (纸本)0769528759

Audio classification is very important in audio indexing, analysis and content-based video retrieval. In this paper, we have proposed a clip-based support vector machine (SVM) approach to classify audio signals into six classes, which are pure speech, music, silence, environmental sound, speech with music and speech with environmental sound. The classification results are then used to partition a video into homogeneous audio segments, which is used to analyze and retrieve its highlevel content. The experimental results show that the proposed system not only improves classification accuracy, but also performs better than the other classification systems using the decision tree (DT), K Nearest Neighbor (K-NN) and Neural Network (NN). © 2007 IEEE.

关键词： Audio acoustics

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A cross-language music retrieval method by using misheard lyrics 9

A cross-language music retrieval method by using misheard ly...

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9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Ye, Hongliang Zhu, Wanning Yu, Yue Hong, Lei Jinling Institute of Technology Department of Software Engineering Nanjing211100 China Hebei GEO University Deparrtment of Information Engineering Shijiazhuang050031 China

With the explosion of music data. Traditional music retrieval methods can no longer meet people's needs. The current music retrieval method is mainly based on the title of the music, the singer, or based on the waveform of the music listened to. But at present, there are often cases of searching for music only by misheard lyrics, especially cross language searching. This greatly increases the difficulty of music retrieval. Therefore, this article first proposes a TextToHumming framework, through which this article converts the misheard lyrics into corresponding music humming records. Then a floating coefficient is added to the DTW algorithm, and the database is retrieved according to the characteristics of the audio. So as to solve this problem. In our datasets, the music retrieval accuracy rate of Top-10 Music reached 88%. © 2020 IEEE.

关键词： Music

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Automatic Generation of CEP Rules using Data Analysis Techniques and Model-Driven engineering 7

Automatic Generation of CEP Rules using Data Analysis Techni...

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7th International Conference on Internet of Things and Applications, IoT 2023

作者： Gholami, Mohsen Zamani, Bahman Ghahfarokhi, Behrouz Shahgholi University of Isfahan Mdse Research Group Department of Software Engineering Isfahan Iran University of Isfahan Department of Information Technology Engineering Isfahan Iran

ISBN: (纸本)9798350369410

The widespread use of Internet of Things (IoT) systems results in a large volume of data that needs to be analyzed in real time for efficient usage. Complex Event Processing (CEP) is a technique that enables organizations to analyze data and identify complex and meaningful events. CEP uses patterns written in an Event Pattern Language (EPL) to describe an event. However, producing patterns and rules using EPL remains a significant *** research benefits from data analysis and model-driven engineering to automatically produce CEP rules that allow domain experts to extract and define patterns automatically, even if unfamiliar with data analysis or CEP rule design. At the end of the paper, a case problem is solved, resulting in a coverage coefficient of 60%, an average accuracy of 82.5%, and an average precision of 82%. The results show that this combined approach is beneficial in providing a more appropriate response in less time. © 2023 IEEE.

关键词： Internet of things

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The Construction of Applicative Strengthening Buffer Operator Based on the Monotone Function

The Construction of Applicative Strengthening Buffer Operato...

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2011 International Conference on Fuzzy Systems and Neural Computing(FSNC 2011)

作者： Gan Jian-Hong He Tong-Li Software Engineering College of CUIT Chengdu University of Information Technology Department of Intelligent Robotics Chengdu University of Information Technology

ISBN: (纸本)9781424492169

Under the axioms system of grey buffer operator,the paper contracts the buffer operator with monotone function,and constructs three types of strengthening operator,to compare with enhanced operator in textbook,to looking for their innovation,it has significantly extended the scope of buffer operator application and opened a new direction.

关键词： Grey system.Strengthening buffer operator.Monotone function

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