检索结果-内蒙古大学图书馆

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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Maximum Likelihood Estimation based on Random Subspace EDA: Application to extrasolar planet detection

arXiv

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arXiv 2017年

作者： Liu, Bin Chen, Ke-Jia School of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu210023 China

This paper addresses maximum likelihood (ML) estimation based model fitting in the context of extrasolar planet detection. This problem is featured by the following properties: 1) the candidate models under consideration are highly nonlinear;2) the likelihood surface has a huge number of peaks;3) the parameter space ranges in size from a few to dozens of dimensions. These properties make the ML search a very challenging problem, as it lacks any analytical or gradient based searching solution to explore the parameter space. A population based searching method, called estimation of distribution algorithm (EDA), is adopted to explore the model parameter space starting from a batch of random locations. EDA is featured by its ability to reveal and utilize problem structures. This property is desirable for characterizing the detections. However, it is well recognized that EDAs can not scale well to large scale problems, as it consists of iterative random sampling and model fitting procedures, which results in the well-known dilemma curse of dimensionality. A novel mechanism to perform EDAs in interactive random subspaces spanned by correlated variables is proposed and the hope is to alleviate the curse of dimensionality for EDAs by performing the operations of sampling and model fitting in lower dimensional subspaces. The effectiveness of the proposed algorithm is verified via both benchmark numerical studies and real data analysis. Copyright © 2017, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Implementation and performance op timization of dynamic random forest

Implementation and performance op timization of dynamic rand...

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第九届网络分布式计算与知识发现国际会议( 2017 International Conference on Cyber-enabled distributed computing and knowledge discovery)

作者： Xiaolong Xu Wen Chen School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and

Bernard combines the weight updating of the boosting algorithm with the Random Forest(RF),and proposes a new RF induction algorithm called Dynamic Random Forest(DRF).The idea with DRF is to grow only trees that would fit the sub-forest already built,use the existing forest to update the weight of each randomly selected training instance,force the next tree to pay attention to those samples that can not be classified well by the current forest,thus improving the RF ***,this weight updating method is still flawed,which does not make a good distinction between the samples classified correctly and the samples classified wrongly by the current *** this paper,we implement the DRF algorithm,and propose a new weight update method,that is,giving higher weight to the samples classified wrongly by the current forest,giving lower weight to the samples classified correctly by the current forest,so that the next tree will be more concerned with those misclassified *** results show that our method is better than DRF algorithm and traditional RF algorithm.

关键词： Random forest Boosting Dynamic random forest Weight updating

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SCALABLE ATTRIBUTION OF ADVERSARIAL ATTACKS VIA MULTI-TASK LEARNING

arXiv

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arXiv 2023年

作者： Guo, Zhongyi Han, Keji Ge, Yao Ji, Wei Li, Yun Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

Deep neural networks (DNNs) can be easily fooled by adversarial attacks during inference phase when attackers add imperceptible perturbations to original examples, i.e., adversarial examples. Many works focus on adversarial detection and adversarial training to defend against adversarial attacks. However, few works explore the tool-chains behind adversarial examples, which can help defenders to seize the clues about the originator of the attack, their goals, and provide insight into the most effective defense algorithm against corresponding attacks. With such a gap, it is necessary to develop techniques that can recognize tool-chains that are leveraged to generate the adversarial examples, which is called Adversarial Attribution Problem (AAP). In this paper, AAP is defined as the recognition of three signatures, i.e., attack algorithm, victim model and hyperparameter. Current works transfer AAP into single label classification task and ignore the relationship between these signatures. The former will meet combination explosion problem as the number of signatures is increasing. The latter dictates that we cannot treat AAP simply as a single task problem. We first conduct some experiments to validate the attributability of adversarial examples. Furthermore, we propose a multi-task learning framework named Multi-Task Adversarial Attribution (MTAA) to recognize the three signatures simultaneously. MTAA contains perturbation extraction module, adversarial-only extraction module and classification and regression module. It takes the relationship between attack algorithm and corresponding hyperparameter into account and uses the uncertainty weighted loss to adjust the weights of three recognition tasks. The experimental results on MNIST and ImageNet show the feasibility and scalability of the proposed framework as well as its effectiveness in dealing with false alarms. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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On Link Formation in Heterogeneous Information Networks: A View Based on Multi-Label Learning

On Link Formation in Heterogeneous Information Networks: A V...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Ke-Jia Chen Shijun Xue Yun Li Bin Liu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

ISBN: (数字)9781450349932

ISBN: (纸本)9781450349932

This paper studies the problem of relationship prediction in heterogeneous information networks. Our goal is not only to predict links/relationships more accurately but also to provide more viable paths to facilitate the formation of new links/relationships. A relationship prediction method based on multi-label learning named ML 3 P is proposed. In ML 3 P, each meta-path between nodes is regarded as a type of relationship and is given a label. Under the framework of multi-label learning, any potential relationship including the target relationship can be predicted. The results of comparative experiments in DBLP and Twitter datasets show that ML 3 P better uses heterogeneous information in supervised learning process and thus achieves better performance. Moreover, our method can output the correlation between relationships.

关键词： Twitter Task analysis Feature extraction Training Measurement Correlation

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SLBERT: An Improved FLAT-BERT for Named Entity Recognition

SSRN

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SSRN 2022年

作者： Fei, Yuefan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

Named Entity Recognition (NER) aims to identify named entities mentioned in unstructured text and classify them into predefined named entity classes. Although models using word fusion have achieved relatively high performance, the recognition of ambiguous entities in text remains challenging, and the performance of the models depends on a sufficient amount of manually labeled data;exploring models that fuse more feature dimensions and schemes that fuse existing datasets is of farreaching interest. In this work, we propose an improved scheme of FLAT-BERT, SLBERT, that can be used in the field of named entity *** scheme consists of two phases that more fully exploit the global semantics of the text. In the first stage, the model is trained based on the current dataset, another dataset is predicted for cross-dataset fusion, and the correctness of label annotation is improved by entity confidence modeling. In the second stage, the global semantics of the text is extracted using Sentence BERT and embedded into the feature matrix encoded using Lattice BERT for subsequent Token classification. We have conducted experiments on several datasets, and the experimental results demonstrate the effectiveness of the proposed scheme in this paper. In terms of F1 values, compared with FLAT-BERT, the improvement is 0.15%, 0.34%, 0.61%, and 0.58% on datasets OntoNotes, MSRA, Resume, and Weibo, respectively. © 2022, The Authors. All rights reserved.

关键词： Semantics

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LogPal: A Generic Anomaly Detection Scheme of Heterogeneous Logs for Network Systems

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security and Communication Networks 2023年第1期2023卷

作者： Sun, Lei Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

As a key resource for diagnosing and identifying problems, network syslog contains vast quantities of information. And it is the main source of data for anomaly detection of systems. Syslog presents the characteristics of large scale, diverse types and sources, data noise, and quick evolvement, which makes the detection methods not generic enough. To effectively address problem of log anomaly labelling caused by massive heterogeneous logs, we propose LogPal, a generic anomaly detection scheme of heterogeneous logs for network systems, which innovatively combines template sequences and raw log sequences to construct and generate log pattern events. By improving the self-attention mechanism of transformer, LogPal proactively synthesizes self-attention and handles log pattern events in a unique way. The model can make full use of log template and sequence semantic information, by automatically becoming aware of the pattern of logs. We implemented experiments to evaluate the performance of LogPal on publicly available datasets, and the outcome of the experiments shows that LogPal automatically adapts to log type changes and improves precision, recall, and F1 score to 99% on publicly available datasets. © 2023 Lei Sun and Xiaolong Xu.

关键词： Anomaly detection

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