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检索条件"任意字段=21st International Conference on Distributed Computing and Intelligent Technology"

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Abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection 1st

Abnormal Events and Behavior Detection in Crowd Scenes Based...

引用

1st international conference on Artificial Intelligence and Computer Visions, AICV 2020

作者： Almazroey, Alaa Atallah Jarraya, Salma Kammoun Computer Science Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia MIR@CL Laboratory Sfax University Sfax Tunisia

ISBN: (纸本)9783030442880

In the last few years, surveillance cameras have been massively distributed both indoors and outdoors in public, due to security concerns creating a need to monitor unexpected actions or activities in a scene. An intelligent automated approach is highly required to detect anomalies from the scene as well, to save the time and cost required by laborers to detect the anomalies manually from monitor screens. In this research, we propose a deep learning-based approach to detect abnormal events and behavior from surveillance videos in crowd scenes. Thus, by using the keyframe extractor to localize the keyframes that contain important information from video frames. The selected keyframes are used to compute the optical flow values of magnitudes, orientations, and velocities of each keyframe to generate several 2D templates. Then, the obtained 2D templates are supplied to a pre-trained model ‘AlexNet’ to extract high-level features. The Neighborhood Component Analysis (NCA) feature selection method is applied to select the appropriate features, then use these features to generate a classification model via Support Vector Machine (SVM) classifier. Results are evaluated on several public datasets, along with a new dataset that we built it that contains different videos covering abnormal events and behaviors. The obtained results proved that the proposed method outperforms other methods. © 2020, Springer Nature Switzerland AG.

关键词： Security systems

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Privstream: Enabling privacy-preserving inferences on IoT data stream at the edge 21

PrivStream: Enabling privacy-preserving inferences on IoT da...

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21st IEEE international conference on High Performance computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Wang, Dan Ren, Ju Xu, Chugui Liu, Juncheng Wang, Zhibo Zhang, Yaoxue Shen, Xuemin School of Computer Science and Engineering Central South University China School of Cyber Science and Engineering Wuhan University China Department of Electronic and Computer Engineering University of Waterloo Canada

ISBN: (纸本)9781728120584

Differential privacy (DP) has been recognized as a useful technique to achieve the trade-off between privacy guarantee and data utility. However, applying DP on the continuous data stream to protect sensitive inferred information faces two significant challenges. Firstly, directly adding noise into the continuous data stream may significantly reduce the data utility. Secondly, it is difficult to effectively and efficiently sample the data stream since the distribution of the data stream is usually unknown in advance. In this paper, we propose Privstream, a privacy-preserving IoT data stream analytical framework based on edge computing, to address the two challenges. Specifically, Privstream is split into two parts distributed at the IoT device and edge server, respectively. The first part, deployed at the device side, adaptively samples the data stream, leverages a tailored autoencoder to conduct data minimization and obfuscates the learnt features by injecting Laplace noise. The second part, deployed at the edge server side, reconstructs the perturbed features into obfuscated data stream for useful inferences without privacy disclosure. Moreover, we present the implementation details of Privstream on a practical IoT system and theoretically analyze that Privstream can provide provable privacy guarantee. The experimental results based on realistic IoT data stream demonstrate that Privstream can preserve the data utility of useful inferences and mitigate the sensitive inferences simultaneously with efficient system overheads. © 2019 IEEE.

关键词： Edge computing

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CFADefense: A security solution to detect and mitigate crossfire attacks in software-defined iot-edge infrastructure 21

CFADefense: A security solution to detect and mitigate cross...

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作者： Rafique, Wajid He, Xin Liu, Zifan Sun, Yuhu Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9781728120584

Managing the Internet of Things (IoT) infrastructure has become a critical challenge due to an enormous increase in the connected devices and the lack of available security solutions. Software-Defined Networking (SDN) has been extensively involved in network infrastructure management. Moreover, numerous recent studies demonstrate the use of SDN for managing IoT networks. In SDN, policy consistency and security of the data plane is maintained through Waypoint Enforcement (WPE) which ensures that the traffic traverses policy nodes/switches to implement high-level network requirements. Previous studies on SDN primarily secure SDN infrastructure against traditional distributed Denial of Service (DDoS) attacks. However, we investigate Crossfire Attack (CFA), which is a novel DDoS attack capable of interrupting communication of data plane switches using low-rate legitimate traffic. CFA has the potential to isolate policy switch from the rest of the data plane devices which introduces many security anomalies and routing inconsistencies. We first demonstrate how CFA is lethal on policy switch attacks and then present the design and implementation of a novel CFA countermeasure called CFADefense, which employs link selection, attack detection, and malicious flows interception modules. CFADefense has been developed as an application at the application layer of the open-source Floodlight controller. Our evaluation demonstrates that CFADefense accurately detects and efficiently mitigates CFA and poses minimal overhead on the controller in dealing with this attack. © 2019 IEEE.

关键词： Internet of things

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Fast convergence average consensus-based time synchronization with innovative implementation 21

Fast convergence average consensus-based time synchronizatio...

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作者： Shi, Fanrong Tuo, Xianguo Ran, Lili Ren, Zhenwen Yang, Simon X. School of Information Engineering University of Science and Technology Mianyang China Sichuan University of Science and Engineering Zigong China School of Engineering University of Guelph Guelph Canada

ISBN: (纸本)9781728120584

The robust average consistency theory is ideal for building time synchronization in the dynamic and distributed networks, however it remains the major challenge due to the iteration. Existing consensus-based approaches converge slowly, although they are reliable even in dynamic networks. It is thus unreasonable to implement these algorithms in a large-scale wireless network. Motivated by this consideration, we note that the graph theory-based theory suggests that the greater the algebraic connectivity, the faster the convergence, thereby leading to the proposal of multi-hop average consensus time synchronization (MACTS) with innovative implementation in consideration of the features of wireless sensor network. To accelerate the synchronization convergence without changing the network topology, we present a packet fast forwarding model to generate virtual communication links among multi-hop nodes, meanwhile, an attenuation methods for the forwarding model is designed to balance the accuracy and cost of synchronization. We compensate relative clock offset estimates by transmitting delay estimates to achieve accurate synchronization. Moreover, based on the popular one-way broadcast model, the proposed MACTS is easy to implement. The experimental results indicate that MACTS is more accurate and converges much faster than the typical average consistency synchronization algorithm. © 2019 IEEE.

关键词： Synchronization

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A skeleton pattern representation method for anomaly detection in wireless sensor networks 21

A skeleton pattern representation method for anomaly detecti...

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作者： Chen, Yanping Yang, Ping Gao, Cong Wang, Zhongmin Yu, Zhong School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an China School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781728120584

Anomaly detection of time series in wireless sensor networks has gained significant attention. Researchers employ representation techniques to reduce the dimensionality of time series. The Piecewise Aggregate Pattern Representation (PAPR) method vertically divides the time series and represents as patterns. However, the computational cost of the PAPR method for anomaly detection is high. To overcome this issue, we propose a time series representation method for anomaly detection called Skeleton Pattern Representation for anomaly detection (SPR-AD). An improved Perceptually Important Point (iPIP) identification algorithm is devised to transform the subsequence into a skeleton with PIPs. Then the skeleton is divided into several equal-sized subspaces in the amplitude domain. statistical information of the PIPs located in subspaces is constructed to represent the skeleton as a pattern. Based on the two-phase representation scheme, anomaly is determined by anomaly scores which are based on the of similarity among pattern. The experimental results demonstrate the performance of the SPR-AD method is better in terms of computational cost and detection accuracy than the PAPR method. © 2019 IEEE.

关键词： Anomaly detection

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Response generation reflecting the personality of a chat dialogue system

Response generation reflecting the personality of a chat dia...

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international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Toru Sugimoto Yusuke Temmanya Faculty of Engineering Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

This paper investigates the relationship between speakers' personalities and their utterances in dialogue, and propose a response generation method that reflects the personality of a chat dialogue system. We construct a corpus of utterances by persons with specific personality features, and calculate percentages of various types of speech act. From the analysis result, features of utterances by various personalities are extracted. With these utterance features, response generation strategies for specific personality features are designed and implemented in a simple chat dialogue system. The experimental result shows that the personality features of “shyness” and “nurturing parent” are appropriately reflected in our dialogue system.

关键词： Psychology Linguistics Correlation Task analysis Generators Feature extraction Data analysis

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Application of Mathematical Evidence to Estimate the Demand for Parking Spaces for Motorways

Application of Mathematical Evidence to Estimate the Demand ...

引用

international Scientific conference Transport of the 21st Century, ISCT21 2019

作者： stawowy, Marek Perlicki, Krzysztof Czarnecki, Tomasz Wilczewski, Grzegorz Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9783030276867

This article presents the application of uncertainty modelling based on DS (Dempster and Shafer) mathematical evidence in order to estimate the factors which enable to determine the parameter defining the attractiveness of a car park. The mathematical evidence theory renders the synthesis of information from many independent sources possible. Thus it appears to be the best choice, because the attractiveness of a car park for the user depends on several independent qualities. A simulation for the chosen modelling was conducted. The presented results demonstrated a dependency between the final values and the factor values of the qualities denoting facilities applied at the parking place. © 2020, Springer Nature Switzerland AG.

关键词： Uncertainty analysis

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Frost Prediction for Vineyard Using Machine Learning

Frost Prediction for Vineyard Using Machine Learning

引用

international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Yosuke Tamura Liya Ding Kosuke Noborio Kazuki Shibuya School of Science and Technology Meiji University Kawasaki Japan School of Agriculture Meiji University Kawasaki Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

Frost damage in vineyard is severe. In this study, we use the concept of causal modeling proposed in our previous study to predict the frost in vineyards located in Hokkaido, Japan. It is possible to use a trained model for forecasting frost in the next few hours based on recent observations of temperature and vapor pressure. Several methods of input granulation are examined, and it is verified that methods with exponential moving average outperform those with simple moving average in terms of F1 measure.

关键词： Predictive models Data models Atmospheric modeling Temperature measurement Support vector machines Labeling Forecasting

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Aggregations of Subjective Probabilities Based on Logical Information Source Model and of Possibilistic CFs Using Dempster's Rule

Aggregations of Subjective Probabilities Based on Logical In...

引用

international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Koichi Yamada Nagaoka University of Technology Nagaoka Niigata JAPAN

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

The paper reviews an aggregation of multiple possibilities for a hypothesis based on a logical information source model proposed recently, and applies the approach to a new aggregation of subjective probabilities. It also recalls an old and controversial uncertainty representation called Certainty Factors (CFs), and reinterprets them as another representation of possibilities. Then, a new aggregation rule of CFs, or equivalently a new combination rule of possibilities is proposed, which employs Dempster's rule of combination as well as uncertainty transformation and approximation between possibility distributions and mass functions.

关键词： Probabilistic logic Uncertainty Mathematical model Possibility theory Risk analysis Bayes methods Analytical models

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Automatic Detection of Beginner's Welding Joint

Automatic Detection of Beginner's Welding Joint

引用

international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Shigeru Kato Takanori Hino Hironori Kumeno Tomomichi Kagawa Hajime Nobuhara Niihama College National Institute of Technology Niihama-City Ehime Japan University of Tsukuba Tsukuba-City Ibaraki Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

This paper describes the construction of a system for the automatic evaluation of stainless steel plates welded by beginners. As a subgoal for that purpose, we constructed RCNN that automatically detects welded joints. In the experiment, fifty welded plate pictures were used for training RCNN. When several pictures of welded plates not used for training were inputted to trained RCNN, it was confirmed that the welded joint part could be detected almost properly.

关键词： Welding Training Image edge detection Feature extraction Convolutional neural networks Support vector machines steel

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