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2011 2nd International Conference on Challenges in Environmental Science and Computer Engineering(CESCE 2011)

作者： Lin Feng College of Computer Science Sichuan Normal University Sichuan Key Laboratory of Visualization Computing and Virtual Reality

For high-dimensional water treatment plant data sets and a single rough classifier’s weak classification ability for data sets with many classes,a new computing approach,termed CMBMRCS(water treatment plant Classification Model Based on Multiple Rough Classifier Systems),is ***,by combing rough sets theory,some subset of attributes is ***,each simplified data set establishes a group of rough ***,the water treatment plant data classification result is obtained according to the absolute majority voting *** experimental results illustrate the effectiveness of the proposed methods.

关键词： Rough sets theory attribute reduction decision rules water treatment plant monitoring

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Analysis of an M(λ1,λ2)/M/1/WV queue with controlled vacation interruption and variable arrival rate

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Mathematical and Computational Applications 2015年第1期20卷 39-49页

作者： Wu, Wenqing Tang, Yinghui Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University ChengduSichuan China School of Mathematics and Software Science Sichuan Normal University ChengduSichuan China

This paper studies a Markovian queue with multiple working vacations and controlled vacation interruption. If there are at least N customers waiting upon completion of a service at a lower rate, the vacation is interrupted and the server returns to the system to resume the normal working level. Otherwise, the server continues the vacation until the system is non-empty after a vacation ends or there are at least N customers after a service ends. Moreover, the variable arrival rate of the customers is taken into account. Under such assumptions, by using the quasi-birth-and-death process, the matrix-geometric method and the difference equation theory, the steady-state queue length distribution along with various performance measures are derived. Additionally, under a certain cost structure, the optimal threshold N∗ that minimizes the long-run expected cost function per unit time is numerically determined. © 2015, Association for Scientific Research. All rights reserved.

关键词： Cost functions

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Medical image blind denoising using context bilateral filter

Medical image blind denoising using context bilateral filter

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2010 International Conference on Medical Image Analysis and Clinical Application, MIACA 2010

作者： Liao, Zhiwu Hu, Shaoxiang Yu, Zhiqiang Sun, Dan Visual Computing and Virtual Reality Key Laboratory Of Sichuan Province Sichuan Normal University Chengdu China School of Computer Science Sichuan Normal University Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Aircraft Industrial Co. Ltd. Chengdu China

ISBN: (纸本)9781424480128

Edge and detail preserving is a very important requirement in blind medical denoising since the details can help doctors diagnosis diseases. Bilateral flter (BF) is a famous filter in preserving details while smoothing. However, its performance in image denoising is unsatisfied. The main reason for this situation is that the bilateral filter uses gray levels of pixels directly, which causes the propagation of noise (PoN). In this paper, we propose a method, named context bilateral filter (CBF), which conditions the bilateral filter on the context. That is, the range filter is not defined on the gray levels directly while it is defned on the context! Since context can be defined as a smoother version for the image, the PoN can be suppressed greatly. In order to demonstrate the good performance for our method, experimental results using real medical images are given. © 2010 IEEE.

关键词： Image denoising

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Water Treatment Plant Decision Making Using Rough Multiple Classifier Systems

Water Treatment Plant Decision Making Using Rough Multiple C...

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2011 International Conference on Energy and Environment(ICEE 2011)

作者： Lin Feng College of Computer Science Sichuan Normal University Sichuan Key Laboratory of Visualization Computing and Virtual Reality

For high-dimensional water treatment plant data sets and a single rough classifier's weak classification ability for data sets with many classes, a new computing approach, termed CMBMRCS(water treatment plant Classification Model Based on Multiple Rough Classifier Systems), is proposed. First, by combing rough sets theory, some subset of attributes is selected. Then, each simplified data set establishes a group of rough classifiers. Finally, the water treatment plant data classification result is obtained according to the absolute majority voting strategy. The experimental results illustrate the effectiveness of the proposed methods.

关键词： Rough sets theory attribute reduction decision rules water treatment plant monitoring

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Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Liu, Yunfei Ma, Xingjun Bailey, James Lu, Feng State Key Laboratory of Virtual Reality Technology and Systems School of CSE Beihang University Beijing China Peng Cheng Laboratory Shenzhen China School of Information Technology Deakin University Geelong Australia School of Computing and Information Systems University of Melbourne Parkville Australia

ISBN: (数字)9783030586072

ISBN: (纸本)9783030586065

Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at training time. A backdoor attack installs a backdoor into the victim model by injecting a backdoor pattern into a small proportion of the training data. At test time, the victim model behaves normally on clean test data, yet consistently predicts a specific (likely incorrect) target class whenever the backdoor pattern is present in a test example. While existing backdoor attacks are effective, they are not stealthy. The modifications made on training data or labels are often suspicious and can be easily detected by simple data filtering or human inspection. In this paper, we present a new type of backdoor attack inspired by an important natural phenomenon: reflection. Using mathematical modeling of physical reflection models, we propose reflection backdoor (Refool) to plant reflections as backdoor into a victim model. We demonstrate on 3 computer vision tasks and 5 datasets that, Refoolcan attack state-of-the-art DNNs with high success rate, and is resistant to state-of-the-art backdoor defenses. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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A Nested Edge Addition Strategy for Network Controllability Robustness Enhancement

A Nested Edge Addition Strategy for Network Controllability ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chengpei Wu Siyi Xu Zhuoran Yu Junli Li School of computer science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu Sichuan China

Edge rectification is a widely used method to enhance network robustness. However, in some networked systems, edge rectification may be challenging or even infeasible to implement. An edge addition strategy is proposed as an alternative optimization method in this paper. Nested Ring Structure (NRS), whereby each node's edges connect its nearest neighbors along the backbone direction, have exhibited robust controllability against random attacks. Therefore, The Nested Edge Addition (NEA) strategy is proposed, which enhances network controllability by building NRS through edge addition to a given initial network. With a small number of added edges, NEA can rapidly enhance network controllability, allowing the network to be controlled using just one driver node. The more nested edges are added, the stronger the NRS in a network, thus exhibiting better controllability robustness. The effectiveness of NEA is verified by simulations on both synthetic and real-world networks. Extensive experimental results demonstrate that NEA is an efficient strategy for designing network topology and optimizing real-world networks.

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Predicting Robustness Performance with Noises in Network Representation

Predicting Robustness Performance with Noises in Network Rep...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chengpei Wu Siyi Xu Junli Li School of computer science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu Sichuan China

The connectivity and controllability of complex networks play an important role in ensuring the proper functioning of network systems. Robustness of connectivity and controllability is the ability of a network to maintain its basic functions against various malicious attacks. Convolutional neural network (CNN)-based approaches provide an efficient framework to approximate the network robustness, which significantly reduces computation time compared to attack simulations. In this paper, the performance of CNN-based prediction for connectivity and controllability robustness is investigated, when there are noises in the network input representation. Two CNN-based predictors are compared and investigated, 1) convolutional neural network-based robustness predictor (CNN-RP), and 2) spatial pyramid pooling-based convolutional neural network (CNN-SPP). Two aspects of network information noises are considered and investigated, 1) the random node information noises (RNIN), and 2) the random edge information noises (REIN). The following main conclusions are obtained from extensive experimental studies on synthetic networks: 1) CNN-RP is more tolerant than CNN-SPP to network noises, 2) The characteristics of small-world and scale-free networks make them have a favorable anti-noise ability, and 3) RNIN has less impact on CNN-based prediction performance than REIN, RNIN and REIN show opposite effects on prediction performance when the size of the predicted network is out of the training network size range.

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Automatic image annotation with real-world community contributed data set

Automatic image annotation with real-world community contrib...

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作者： Tian, Feng Shen, Xukun Shang, Fuhua School of Computer and Information Technology Northeast Petroleum University DaQing163318 China School of Computing National University of Singapore Singapore119077 Singapore State Key Laboratory of Virtual Reality Technology and Systems BeiHang University Beijing100191 China

With the massive explosion of social multimedia community, social images have become very popular in our daily life. The image-associated labels are a valuable resource for automatic image annotation, but they tend to be unreliable. In this paper, we exploit the problem of image annotation from real-world community contributed images and their associated incorrect, insufficient, and personalized labels. We present SNTag, a novel semantic neighborhood learning method, on which image annotation task can be efficiently carried out in real-world scenario. First, we propose to use image-associated labels as the supervising information to guide the replenishment of training images, which enable the labels for training image not only more sufficient, but also more correct. Then, the "semantic balanced neighborhood" for image is generated, thus enabling the presence of more rare labels in image label list. Furthermore, we generate "semantic consistent neighborhood" within corresponding "semantic balanced neighborhood". The retrieved neighbor images are not only visually alike but also semantically related. Contrary to earlier work, these neighbors are retrieved from the same subspace by the integration of metric learning embedded in multiple labels and sparse reconstruction. Based on the neighbor set, we propose a novel algorithm to assign the optimal labels to the image, which is more robust to noise. We conduct extensive experiments on several standard real-world benchmark data sets downloaded from community websites. The experimental results demonstrate that it outperforms the current state-of-the-art methods. © 2017, Springer-Verlag Berlin Heidelberg.

关键词： Image annotation

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Fast Collection of Target Tags Information for Large-Scale RFID Systems 20

Fast Collection of Target Tags Information for Large-Scale R...

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20th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chu, Chu Fu, Ning Xu, Rui College of Computer Science Sichuan Normal University Chengdu610101 China The Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610101 China School of Network and Communication Engineering Chengdu Technological University Chengdu611730 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, making efficient collection of tag information an important task. However, most works focus on collecting all tags but lack effective mechanisms to avoid interfering from irrelevant tags when the application only cares about a part of the tags in some cases. Besides, in more common scenarios with multiple RFID readers, existing works fail to effectively determine the surrounding tags for each reader, increasing the execution time. To conquer these defects, we propose a new solution called the efficient Vector-based target tag Information Collection (VIC) protocol to accelerate the collection rate. First, we introduce a composite vector to silence irrelevant tags and determine the local tags. During this process, the expected empty slots are skipped and the useless information transmitted by tags is reduced. Second, we use an allocation vector to collect tag information by resolving tag collision, thus improving the time efficiency. Finally, we provide theoretical analyses to optimize the parameter settings. Extensive experiments prove that VIC outperforms existing solutions by up to 32% increase in time efficiency. © 2023 IEEE.

关键词： Efficiency

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Water Treatment Plant Decision Making Using Rough Multiple Classifier Systems

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Procedia Environmental Sciences 2011年 11卷 1419-1423页

作者： Lin Feng College of Computer Science Sichuan Normal UniversityChengdu Sichuan China Sichuan Key Laboratory of Visualization Computing and Virtual Reality Chengdu Sichuan China

For high-dimensional water treatment plant data sets and a single rough classifier's weak classification ability for data sets with many classes, a new computing approach, termed CMBMRCS (water treatment plant Classification Model Based on Multiple Rough Classifier Systems), is proposed. First, by combing rough sets theory, some subset of attributes is selected. Then, each simplified data set establishes a group of rough classifiers. Finally, the water treatment plant data classification result is obtained according to the absolute majority voting strategy. The experimental results illustrate the effectiveness of the proposed methods.

关键词： Rough sets theory attribute reduction decision rules water treatment plant monitoring

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