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2021 International Conference on Robotics Automation and intelligent Control, ICRAIC 2021

作者： Wu, Yong Shen, Lingfeng Li, Ping Jiangsu Power Transmission and Transformation Co. Ltd. Nanjing211106 China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

Point cloud registration is one of the key issues in fields that need 3D scenes with global vision, including 3D scene reconstruction in robot technology, high-accuracy 3D map reconstruction in automatic driving, 3D reconstruction of real-time monitoring underground tunnel. Recently, point cloud registration methods based on deep neural networks have made great progress by learning from a large amount of well-aligned point cloud pairs. However, these large-scale labelled samples are usually difficult to obtain in the real-world applications such as visual simultaneous location and mapping of mobile robots which obtains unlabelled scene images in real time. To solve this problem, we propose a deep point cloud registration method based on the semi-supervised learning. Specifically, for the problem of less supervised data, we use Iterative Closest Point (ICP) algorithm to generate pseudo labels for the point cloud samples with unknown rigid transformations, effectively increasing the number of supervised data;in order to amend the pseudo labels, we design an alternating optimization algorithm to jointly learn the pseudo labels and deep point cloud registration model. The deep point cloud registration model and ICP are used alternately to continuously improve the precision of pseudo labels and the performance of the deep model. Experimental results show that this method can still obtain a reliable point cloud registration model even when just observing a small amount of point cloud samples with ground truth labels. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Surface measurement

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Decomposing Source Codes by Program Slicing for Bug Localization

Decomposing Source Codes by Program Slicing for Bug Localiza...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jian Yong Ziye Zhu Yun Li Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications

Bug localization, which aims to automatically locate buggy source code files based on the given bug report, is a critical yet time-consuming task in the software engineering field. Existing advanced bug localization methods have successfully leveraged deep learning to bridge the lexical gap between bug reports and source code files at the semantic level. These methods usually first build the entire source code file semantic representation and then match it with the bug report. However, the bug described in the bug report may be related to only part of the source code file semantics. Directly constructing a semantic representation of the entire source code file would increase the difficulty of semantic matching between bug reports and source code files. In this paper, we propose a novel model named S-BugLocator, which decomposes source code file with the help of program slicing. Especially, our proposed S-BugLocator incorporates two distinctly structured slice feature extraction components in processing source code files to cope with the significant discrepancy between multi-row slices and single-row slices. For each multi-row slice, a CNN and Bi-LSTM network is firstly employed to extract its semantics and then a keywords supervised attention mechanism is designed to build its semantic representation by focusing on slices that have strong relevance with the bug report. For each single-row slice, the semantic representation is obtained by fusing word embeddings in single-row slices. The experimental results on four real-world large-scale projects indicate that our proposed model outperforms existing state-of-the-art bug localization methods.

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Dlr: Adversarial Examples Detection and Label Recovery for Deep Neural Networks

SSRN

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SSRN 2023年

作者： Han, Keji Ge, Yao Wang, Ruchuan Li, Yun Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

Deep neural networks (DNNs) are demonstrated to be vulnerable to the adversarial example crafted by the adversary to fool the target model. Adversarial training and adversarial example detection are two popular methods to mitigate the adversarial issue. The adversarial training makes the target model recognize the adversarial example correctly for the image classification task, while it does not generalize well. The adversarial detection generalizes well but cannot help the target model recognize the adversarial example. We first define the label recovery task to address the adversarial issue for DNNs. Then we propose a new generative classifier and apply it to the adversarial example label recovery task. The proposed method is named as Detection and Label Recovery (DLR) defense method, which consists of Detector and Recover. Detector feeds the legitimate and adversarial examples to the target model and Recover, respectively. The Recover explores the ground-truth label of the detected adversarial example. DLR combines the merits of adversarial training and adversarial example detection. Experimental results demonstrate that our method outperforms some state-of-the-art adversarial training methods. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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CROA: A content-based recommendation optimization algorithm for personalized knowledge services 21

CROA: A content-based recommendation optimization algorithm ...

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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： Sun, Xiao Xu, Xiaolong Xia, Fei Jiangsu Key Laboratory Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Information and communication Branch State Grid Jiangsu Electric Power Company Nanjing China

ISBN: (纸本)9781728120584

Information explosion is a typical feature of the big data. Learners can easily find a wide variety of knowledge information online. However, the expansion of information also makes it difficult for learners to retrieve the information they want and fall into the problem of knowledge overload. In order to solve this problem effectively, we propose a content-based recommendation optimization algorithm for personalized knowledge services (CROA). First, the feature vector model is established, where each item information is represented by specific words and key information is highlighted by weighting. Second, a dynamic user vector model is then established to capture the latest preference information of the user. Finally, the feature vector model is combined with the multi-user comparison results thereby obtaining a list of recommendations. Experimental results indicate that CROA outstands in knowledge recommendation mechanism and can also alleviate knowledge overload. © 2019 IEEE.

关键词： Optimization

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An optimal profit route planning scheme based on ACO-OPP 21

An optimal profit route planning scheme based on ACO-OPP

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作者： Qiu, Yuhua Xu, Xiaolong Xia, Fei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Information and communication Branch State Grid Jiangsu Electric Power Company Nanjing China

ISBN: (纸本)9781728120584

Taxi plays an important role of urban public transportation system. However, without appropriate route planning scheme, taxi drivers can only choose to wait or seek passengers in the absence of orders, leading to wasting a lot of time and fuel. In this paper, an optimal profit route planning scheme for taxi drivers with Ant Colony Optimization Algorithm based on Optimal Profit Points (ACO-OPP) is proposed. First, the taxi trajectory data are preprocessed and the pick-up and drop-off points are extracted to obtain high-quality data;second, the data spatiotemporal distribution is analyzed to plan a more appropriate and rational route;the clustering algorithm is used to obtain the optimal profit points to increase the occupancy rate of taxis;then, ACO-OPP is utilized to plan the profit routes for each taxi. A series of experiments are implemented based on the real taxi trajectory dataset to test and compare the performance of the simulated annealing algorithm, the genetic algorithm, the ant colony algorithm and ACO-OPP on the optimal route length and the profitability with the unit time profit function. The experimental results show that ACO-OPP has the shortest optimal route length and the best profit per unit time. © 2019 IEEE.

关键词： Profitability

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Non-structured Pruning for Deep-learning based Steganalytic Frameworks

Non-structured Pruning for Deep-learning based Steganalytic ...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Qiushi Li Zilong Shao Shunquan Tan Jishen Zeng Bin Li Shenzhen Key Laboratory of Media Security Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Image steganalysis aims to discriminate innocent cover images and those suspected stego images embedded with secret message. Recently, increasing advanced deep neural networks have been proposed and used in image steganalysis. Though those deep learning models can gain superior performance, they also result in redundancy of computational resource and memory storage. In this paper, we apply a non-structured pruning method to prune XuNet2 and SRNet - the two state-of-the-art deep-learning framework in the field of JPEG image steganalysis. We obtain the priorities of the connections among neurons according to a certain criterion, then keep those significant weights and prune those nonsignificant ones in the meantime. We have conducted extensive experiments on BOSSBase and BOWS image dataset. The experimental results demonstrate that our proposed non-structured pruning method can significantly reduce the cost of computation and storage required by the original deep-learning frameworks without affecting their detection accuracy.

关键词： Transform coding Training Convolution Computer architecture Computational modeling Brain modeling Testing

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Evaluating Consideration Level of Responses For Mental Health Chatbots

SSRN

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SSRN 2023年

作者： Xu, Bei Zhuang, Ziyuan Wang, Yue Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China

Mental health chatbots are widely used and proven useful in the conversation of psychological treatments. The users of mental health chatbots require "considerate" responses because they are relatively sensitive or vulnerable. However, current mental health chatbots do not evaluate the consideration level of the responses. Current mental health chatbots generally use the categories of positive and negative to reflect consideration and sharp. This paper proposes a consideration evaluation model (CEM) to evaluate the consideration level of the responses from mental health chatbots. The experiments first prove that positive and negative cannot precisely reflect considerate and sharp, and then prove that CEM can effectively evaluate the consideration level of responses. We further use CEM to test five typical chatbots and the results indicates that some mental health chatbots may give sharp responses. © 2023, The Authors. All rights reserved.

关键词： Computational electromagnetics

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Hyperspectral Image Destriping and Denoising with Spectral Low Rank and Tensor Nuclear Norm

Hyperspectral Image Destriping and Denoising with Spectral L...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Pengfei Liu Lanlan Liu School of Computer Science Nanjing University of Posts and Telecommunications China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing China

In this paper, we propose a new method for simultaneous hyperspectral image (HSI) destriping and denoising with spectral low-rank and tensor nuclear norm under the tensor framework. Specifically, the tensor nuclear norm is used to model the tensor low-rank property of stripe. Moreover, the nuclear norm is used to model the low-rank property of spectral gradient of HSI. Then, the ADMM algorithm is used to effectively solve the proposed model. Experimental results on simulated HSI dataset and real HSI dataset verify the superiority of the proposed method.

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Privacy Protection for Image Sharing Using Reversible Adversarial Examples

Privacy Protection for Image Sharing Using Reversible Advers...

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IEEE International Conference on Communications (ICC)

作者： Ping Yin Wei Chen Jiaxi Zheng Yiting Zhang Lifa Wu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Online image sharing on social media platforms faces information leakage due to deep learning-aided privacy attacks. To avoid these attacks, this paper proposes a privacy protection mechanism for image sharing without changing the visual effect, which is based on reversible adversarial examples. Specifically, social media platform users can change the class activation feature to convert the original image into an adversarial image before sharing. When users want to restore the adversarial image to the original image, they can use an improved generative adversarial network model to restore it. The experimental results prove that the conversion model in this paper can effectively prevent privacy attacks from analyzing and stealing users' private information while having no visual impact. At the same time, the proposed restoration model can restore the adversarial examples with high accuracy.

关键词： Training Privacy Image recognition Social networking (online) Perturbation methods Visual effects Information leakage

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Application of C4.5 decision tree for scholarship evaluations

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Procedia Computer Science 2019年 151卷 179-184页

作者： X. Wang C. Zhou X. Xu College of Overseas Education Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China

Under the global trend of modern quality education, the set of higher educational scholarship is aimed to reward the students who work hard both inside and outside class activities and achieve great success in kinds of academic competitions. At this time, with great emphasis by governments and outstanding construction on the educational infrastructure, the number of students in universities has been increasing dramatically. Meanwhile, during the development of the education in such a long period, universities generally accumulate a huge amount of the information concerned with the students. What the administrators always do to handle such information are merely storing, querying and doing some other basic manipulations instead of making full use of these data. For example, those manual evaluations of the educational scholarship always revolve around the rank of scholarship and the number of the students who are rewarded rather than analyzing the facts which can influence the achieving of the scholarship. As a result, the evaluation is apparently lack of fairness and efficiency. In this paper, based on C4.5 decision tree, a higher educational scholarship evaluation model is built. By using such algorithm, an efficient and fair scholarship evaluation system can be realized.

关键词： C4.5 Decision Tree Scholarship Evaluation data Mining

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