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共 462 条记录，以下是171-180 订阅

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Knowledge-Enabled Intent-Driven Network Configuration Generation for 5G Core Networks 24

Knowledge-Enabled Intent-Driven Network Configuration Genera...

引用

24th IEEE International Conference on Communication Technology, ICCT 2024

作者： Li, Qiuying Cai, Jiajun Xu, Ji'ang Liu, Ruosi Yuan, Caixia Gao, Hui Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Moe Beijing China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Bantian Industrial Base Huawei Technoligies Shenzhen China

ISBN: (纸本)9798350363760

Automating the configuration of 5G core networks (CNs) is crucial for meeting increasingly diverse and specialized application requirements. In this paper, we propose an intent-driven automation model for configuring 5G CNs. Our approach leverages existing 5G CN equipment manuals and extracts the underlying configuration knowledge to enable intent translation. Unlike traditional methods, the proposed model employs a knowledge fusion approach that computes the semantic correlation between user intents and equipment configuration commands. Specifically, the proposed model integrates graph neural networks and pre-trained language models, exhibiting strong reasoning capabilities. Moreover, by converting complex texts into structured knowledge graphs, the proposed model provides precise command suggestions, significantly enhancing the degree of automation in 5G CN configurations. Simulation results show that the generated commands achieve high accuracy across various scenarios, validating the effectiveness of our approach. © 2024 IEEE.

关键词： Graph neural networks

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Experimental Demonstration of 2D-Trellis Coded Modulation PAM8 for 50G PON Based on 10G-class O-Band DML 27

Experimental Demonstration of 2D-Trellis Coded Modulation PA...

引用

27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and computing, OECC/PSC 2022

作者： Cui, Han Liu, Shuangyue Lu, Yueming Qiao, Yaojun School of Information and Communication Engineering State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China School of Cyberspace Security Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing100876 China

We present the first experimental demonstration of using 2D-TCM-PAM8 for a 50Gb/s PON system based on 10G-class O-band DML. Compared to PAM6, the receiver sensitivity of 2D-TCM-PAM8 is improved by 1.1 dB over 20-km tr... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Viterbi algorithm

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Chinese open relation extraction with pointer-generator networks 5

Chinese open relation extraction with pointer-generator netw...

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

作者： Cheng, Ziheng Wu, Xu Xie, Xiaqing Wu, Jingchen Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Library Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728195582

Most of the traditional Chinese open relation extraction (Open RE) system exploit the syntactic, lexical and other language structure information obtained by natural language processing (NLP) tools from sentences to build hand-crafted patterns for extraction, which is easy to cause error propagation and affect the accuracy of extraction. In this paper, we propose an end-to-end abstract Chinese Open RE model based on the Pointer-Generator network, PGCORE. We employ the results extracted by the state-of-the-art pattern-based Chinese Open RE system as the training set of the model. Experimental results show that our method is outperforms the pattern-based several baselines system, which proves the feasibility and effectiveness of using deep learning models for Chinese Open RE. © 2020 IEEE.

关键词： Extraction

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Robust Text Clustering with Graph and Textual Adversarial Learning

Robust Text Clustering with Graph and Textual Adversarial Le...

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IEEE International Conference on Data Science in Cyberspace (DSC)

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

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

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.

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Cross-Lingual Propaganda Detection

Cross-Lingual Propaganda Detection

引用

IEEE International Conference on Big Data

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

ISBN: (纸本)9781665480468

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.

关键词： Social networking (online) Fuses Semantics Syntactics Big Data Transformers Information filters

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Communication Encryption Scheme of 5G Power Trading Private Network Based on Dynamic key 2

Communication Encryption Scheme of 5G Power Trading Private ...

引用

2nd Asia Conference on Electrical, Power and Computer Engineering, EPCE 2023

作者： Zhou, Rui Song, Yiyang Xu, Ying Harbin Institute of Technology Electrical Engineering and Automation School Harbin China Guangdong Power Exchange Center Information Data Department Guangzhou China Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350310818

Security has been the primary concern for the 5G power trading private network, which involves a large amount of power trading information. The communication must be encrypted to prevent the leakage of important information. The communication encryption scheme for 5G power trading private network should meet the characteristics of small communication overhead, low storage space requirement and strong resistance to attack. Based on these characteristics, the dynamic key-based encryption scheme is designed. To ensure the security and reduce the computation overhead, the optimized symmetric encryption algorithm is used to update the shared key. And the dynamic key generation method is improved by adding random numbers to solve the problem that the correct transmission of the data link does not have true randomness. The encryption mode used can generate dynamic keys directly and synchronously at both ends of the communication, reducing the overhead of key exchange and eliminating the need for trusted third parties. The algorithm is also analyzed for security and efficiency and compared with other algorithms. The experimental results show that the improved algorithm enhances security with guaranteed efficiency. © 2023 IEEE.

关键词： Efficiency

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Blockchain Storage Method Based on Erasure Code 8

Blockchain Storage Method Based on Erasure Code

引用

8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Meng, Fanyao Li, Jin Gao, Jiaqi Liu, Junjie Ru, Junpeng Lu, Yueming School of Cyberspace Security Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications The State Key Laboratory of Information Photonics and Optical Communications Beijing China

ISBN: (纸本)9798350331035

Blockchain, as an emerging distributed database, effectively addresses the issue of centralized storage in IoT data, where storage capacity cannot match the explosive growth in devices and data scale, as well as the contradictions arising from centralized data management concerning data privacy and security concerns. To alleviate the problem of excessive pressure on single-point storage and ensure data security, a blockchain data storage method based on erasure codes is proposed. This method involves constructing mathematical functions that describe the data to split the original block data into multiple fragments and add redundant slices. These fragments are then encoded and stored in different locations using a circular hash space with the addition of virtual nodes to ensure load balancing among nodes and reduce situations where a single node stores too many encoded data blocks, effectively enhancing the storage space utilization efficiency of the distributed storage database. The blockchain storage method stores encoded data digest information such as storage location, creation time, and hashes, allowing for the tracing of the origin of encoded data blocks. In case of accidental loss or malicious tampering, this enables effective recovery and ensures the integrity and availability of data in the network. Experimental results indicate that compared to traditional blockchain approaches, this method effectively reduces the storage pressure on nodes and exhibits a certain degree of disaster recovery capability. © 2023 IEEE.

关键词： Blockchain Consistent Hashing Erasure Codes Internet of Things

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CoStock: A DeepFM Model for Stock Market Prediction with Attentional Embeddings

CoStock: A DeepFM Model for Stock Market Prediction with Att...

引用

IEEE International Conference on Big Data

作者： Jieyun Huang Xi Zhang Binxing Fang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Forecasting the stock market trend is a vital component of financial systems. Traditional methods mainly rely on quantitative trading data to make predictions. With the increasing volume of Web information, researchers begin to extract effective indicators (e.g., the events and sentiments) from the Web to facilitate the prediction. It is beneficial to fuse the heterogeneous multi-sourced data to achieve superior performance. Existing solutions adopt simple data fusing methods with conventional machine learning models, which may fail to effectively model the complex interactions among the data. With the success of deep neural networks (DNN) in various fields, we propose a DNN-based model that considers the various interactions and complex correlations among the multi-sourced data into one unified framework. In such a framework, the low-and high-order feature interactions are modeled with the factorization machine and the deep neural network respectively, and stock correlations are incorporated with an attention-based feature embedding method. Evaluations on the stock data from the year 2015 to 2017 show that our model can outperform the state-of-the-art methods.

关键词： Machine-to-machine communications Big Data Conferences

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

Modification Method for Internal Network Flow Anomaly Detect...

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IEEE International Conference on Data Science in Cyberspace (DSC)

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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

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.

关键词： Hands Neural networks Cyberspace Semisupervised learning Data science Wavelet analysis Wavelet packets Security Information technology Anomaly detection

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Mining the relation between dorm arrangement and student performance

Mining the relation between dorm arrangement and student per...

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IEEE International Conference on Big Data

作者： Man Li Ruisheng Shi Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts & Telecommunications Beijing China

This paper discusses the relation between dorm arrangement and student performance. One of the unsupervised learning algorithms, k-means algorithm, is mainly used in the process of analysis. Students are clustered into several clusters according to their similarity of performance scores. This paper analyzes the result of clustering by comparing it with actual dorm arrangement. In the end, drawbacks of k-means and reliability of this student dorm-performance relation are evaluated. Finally, this paper draws a conclusion that student performances are influenced by dorm arrangement.

关键词： Clustering algorithms Algorithm design and analysis Data mining Data analysis Image color analysis Telecommunications Unsupervised learning

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