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5th IEEE International Conference on Data Science in Cyberspace, DSC 2020

作者： Liang, Yongming Tian, Tian Jin, Kaifu Yang, Xiaoyu Lv, Yuefei Zhang, Xi Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9781728195582

Text clustering is a fundamental task that finds groups of similar texts in the corpus. Deep learning based models can capture the semantic and syntactic information in local word sequences to represent the texts but may ignore global text structures in the corpus. Recently, Graph Convolutional Networks (GCNs) have achieved the state-of-the-art performance in many applications, which may facilitate the text clustering task by exploiting the graph structures between texts via words and phrases. However, GCNs are vulnerable to adversarial attacks, e.g., small perturbations in graph structures and node attributes lead to poor performance. To address this issue, we propose a Robust Text Clustering (RTC) approach which consists of both autoencoder and GCN modules to exploit the local text features and global text structures respectively in one unified framework. We generate two types of adversarial perturbations targeting text structure and graph structure, and develop an adversarial training method to improve the robustness and generalization of the RTC framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our proposal in text clustering. © 2020 IEEE.

关键词： Semantics

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Graph Neural Network Based User Group Comments Interaction Behavior Prediction on Microblog Websites 6

Graph Neural Network Based User Group Comments Interaction B...

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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Chen, Yisen Wu, Xu Xie, Xiaqing Wu, Jingchen Qiu, Lirong Sun, Lijuan Beijing University Of Posts And Telecommunications Ministry Of Education Key Laboratory Of Trustworthy Distributed Computing And Service Beijing China

ISBN: (纸本)9781665418157

The interactive behavior of comments within the user group on microblog website is bidirectional and dynamic, reflecting the level of familiarity among users. Predicting the future comment interaction behavior within the user group is of great significance for commercial recommendation and crime fighting. Related research groups regard it as a temporal link prediction problem, assume the evolution is smooth or there is no evolution at all. Meanwhile, the feature in those studies is too unitary, leading to low link prediction performance. In this paper, we propose a method called User Group Comment Interaction Behaviour Prediction (UGCIBP), which combines structure extraction layer and evolution extraction layer to do dynamic graph representation learning to acheive the modeling of user group historical comment interaction graphs, and build the features of communication weight, interest similarity, and common degree of activity between users on microblog website. Then, we combine dynamic graph representation learning and the constructed features to do link prediction, finally, the purpose of predicting comment interaction is acheived. Experiments are conducted on the public datasets Enron and Twitter datasets to evaluate the method. The results show that the AUC scores of this method are 4.74% and 9.95% higher than existing methods, respectively, which proves the effectiveness of the UGCIBP method. © 2021 IEEE.

关键词： Websites

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Cross-Lingual Propaganda Detection

Cross-Lingual Propaganda Detection

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Zhang, Wenshan Zhang, Xi Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9781665480451

Propaganda information that aims at influencing people's opinions is becoming more and more common around people, especially through social media. For the detection of propaganda content, researchers have conducted studies from multiple perspectives, including document level, sentence level and fragment level. However, most existing methods have mainly focused on propaganda detection in a single language because of the lack of multilingual datasets. In this article, we first propose a Chinese and English propaganda detection dataset. Then we propose a cross-lingual propaganda detection method, using semantic and syntactic information between different languages. Extensive experiments on our multilingual dataset demonstrate the effectiveness of our proposal in cross-lingual propaganda detection. © 2022 IEEE.

关键词： Semantics

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QoS-aware precautionary performance monitoring for PCE-based coherent optical OFDM networks

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Chinese Optics Letters 2012年第4期10卷 8-10页

作者：陆月明侯连兴 Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and Telecommunications

A quality-of-service（QoS）aware scheme,called precautionary performance monitoring,is proposed to solve the optical impairments and congestion control in coherent optical orthogonal frequency division multiplexed（CO-OFDM）*** centralized path computation element（PCE）extensions based on the QoS level are applied to optical performance monitoring in this letter.

关键词： Optical fiber communication Quality of service

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Leveraging Multi-aspect Semantics for Conversation Structure Modeling 7

Leveraging Multi-aspect Semantics for Conversation Structure...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Guo, Tuwen Zhang, Xi Qiu, Lirong Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

With the advent of online chatting systems, conversation structure modeling has attracted increasing attention and can be beneficial to many downstream tasks such as topic summarization and dialog generation. Existing studies mainly focus on modeling the pairwise or thread-level text semantics in multiple interleaved conversations, and don't fully utilize the rich semantics such as the dialogue actions and speaker relationships. For this purpose, we propose to leverage broader semantics to better disentangle the conversations. Concretely, we devise a novel deep neural networks to capture the text contents, discourse structures and speaker attention in one framework, to better identify the utterance pairs with the 'reply-to' relationships. We evaluate our framework on two datasets, the publicly available Ubuntu IRC dataset and our proposed WeChat & QQ dataset. The results show that our model significantly outperforms the state-of-the-art baselines. © 2022 IEEE.

关键词： Semantics

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Mask Neural Network for Predicting Flight Ticket Price 6th

Mask Neural Network for Predicting Flight Ticket Price

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6th Conference on Signal and Information Processing, Networking and Computers, ICSINC 2019

作者： Zhang, Qianqian He, Yuan Jing, Xiaojun Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811541629

Recently, the global air traffic has increased rapidly, most passengers choose to buy tickets in their own experiences. So accurately predicting flight ticket price is of great significance. Recently there exist some works on flight ticket price prediction. However, these works pay attention to solve a single flight price prediction. However, it’s unrealistic to build a lot of models for different flights. In this paper, we consider a lot of factors, including additional service data, flight data and time data. Firstly, we analyze data and extract flight information and time features. Furthermore, we propose a model: mask neural network, to solve flight ticket price prediction, which divides the features into two parts: individual information and general information. General information represents the portrait of a certain flight and individual information represents features of a certain flight. Since every flight has its own distribution, we cannot put individual information into the network directly. So we introduce mask concept and use general information as a mask to filter individual information. Experiments on a competition dataset with 3 months datasets introduce the effectiveness of our approaches, that can achieve the state-of-the-art in ticket price prediction. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Big data

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Insider Threat Detection Based on User Historical Behavior and Attention Mechanism 6

Insider Threat Detection Based on User Historical Behavior a...

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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： He, Weiyu Wu, Xu Wu, Jingchen Xie, Xiaqing Qiu, Lirong Sun, Lijuan Beijing University Of Posts And Telecommunications Ministry Of Education Key Laboratory Of Trustworthy Distributed Computing And Service Beijing China

ISBN: (纸本)9781665418157

Insider threat makes enterprises or organizations suffer from the loss of property and the negative influence of reputation. User behavior analysis is the mainstream method of insider threat detection, but due to the lack of fine-grained detection and the inability to effectively capture the behavior patterns of individual users, the accuracy and precision of detection are insufficient. To solve this problem, this paper designs an insider threat detection method based on user historical behavior and attention mechanism, including using Long Short Term Memory (LSTM) to extract user behavior sequence information, using Attention-based on user history behavior (ABUHB) learns the differences between different user behaviors, uses Bidirectional-LSTM (Bi-LSTM) to learn the evolution of different user behavior patterns, and finally realizes fine-grained user abnormal behavior detection. To evaluate the effectiveness of this method, experiments are conducted on the CMU-CERT Insider Threat Dataset. The experimental results show that the effectiveness of this method is 3.1% to 6.3% higher than that of other comparative model methods, and it can detect insider threats in different user behaviors with fine granularity. © 2021 IEEE.

关键词： Anomaly detection

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Tree pruning for MIMO sphere detection based on MMSE detection

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The Journal of China Universities of Posts and Telecommunications 2012年第4期19卷 64-72页

作者： WANG Shi-liang SUN Song-lin J1NG Xiao-jun LU Yue-ming School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China

Tree pruning is an effective algorithm to reduce the complexity of sphere detection （SD） for multiple-input multiple-output （MIMO） communication systems. How to determine the tree pruning rule, as well as by what the tradeoff between the performance and the complexity can be achieved, is still an open problem. In this paper, a tree pruning algorithm is proposed based on minimum mean square error （MMSE） detection. The proposed algorithm first preforms MMSE detection since the complexity of MMSE detection is very low. Then the pruning constraints will be set according to the scaled path metrics of the MMSE solution. The choice of the scale factors and their influences on the complexity and performance are also discussed. Through analysis and simulations, it is shown that the complexity is reduced significantly with negligible performance degradation and additional computations.

关键词： MIMO sphere detection tree pruning MMSE detection

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PA-GAN: Graph Attention Network for Preference-Aware Social Recommendation 4

PA-GAN: Graph Attention Network for Preference-Aware Social ...

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2021 4th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Hou, Liyang Kong, Wenping Gao, Yali Chen, Yang Li, Xiaoyong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China

Social recommendation has been recently proposed by incorporating trust relationship to alleviate data-sparsity and cold-start problems. However, most of existing works only focus on friends different contribution to model user representation. They ignore users have different preference on items, and share different preference with friends. To address these problems, in this paper, we propose a novel Preference-Aware Graph Attention Network (PA-GAN) for trust recommendation. And we design three modules: item aggregation for user, friend-preference aggregation for user and user aggregation for item to model users' local and global preference. Experiments on two publicly available datasets shows the proposed model PA-GAN outperforms the state-of-the-art recommendation models, and improves performance greatly. © Published under licence by IOP Publishing Ltd.

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Modification Method for Internal Network Flow Anomaly Detection Based on Multi-timescale Analysis 9

Modification Method for Internal Network Flow Anomaly Detect...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zhang, Xinyu Ding, Junmei Lu, Yueming Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350391367

With the continuous development of information technology, security issues from internal networks are becoming more and more important. Many anomaly detection algorithms are designed to identify anomalies, but these algorithms usually do not care about temporal information, or only care about fine-grained temporal information. On the other hand, deep-learning-based algorithms must rely on complex neural networks to capture temporal information, which is difficult to deploy and maintain in actual internal network environments. Addressing the aforementioned problems, this paper proposes a multi-timescale analyzer using wavelet packet transform and semi-supervised learning, and a method that uses the analyzer to modify the original anomaly scores called Multi-timescale Modification (MTM) method. The method is validated using the network flow dataset CIC-IDS 2017 and a real-world dataset. Experimental evaluations show that using the proposed method can obtain better performance than the original algorithms. © 2024 IEEE.

关键词： Self-supervised learning

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