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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Di Chen Hui Gao Na Chen Ruohan Cao Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan

Reconfigurable intelligent surface (RIS) has gained wide attention recently as a promising solution to address the blockage issue in millimeter wave (mmWave) communication networks. In this paper, we consider a RIS-assisted mmWave communication system consisting of multiple RISs and users. A novel integrated scheme based on deep reinforcement learning (DRL) is proposed to jointly optimize the hybrid beamforming, phase shifts, and power allocation. Specifically, we design a multi-variable action output to achieve integrated optimization in both beam and power domains. We also design a weighted sum reward to balance the influence of beam and power domain optimizing variables in the training process. Experimental results demonstrate that the proposed DRL-based method can achieve at least 37% higher sum rates than the baseline algorithm.

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Toward Wisdom-Evolutionary and Primitive-Concise 6G:A New Paradigm of Semantic Communication Networks

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Engineering 2022年第1期8卷 60-73页

作者： Ping Zhang Wenjun Xu Hui Gao Kai Niu Xiaodong Xu Xiaoqi Qin Caixia Yuan Zhijin Qin Haitao Zhao Jibo Wei Fangwei Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Peng Cheng Laboratory Shenzhen 518066China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China The School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China School of Humanities Beijing University of Posts and TelecommunicationsBeijing 100876China

The sixth generation(6G)mobile networks will reshape the world by offering instant,efficient,and intelligent hyper-connectivity,as envisioned by the previously proposed Ubiquitous-X 6G *** hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks,calling for revolutionary theories and technological *** this end,we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network(WePCN)vision for the Ubiquitous-X 6G *** particular,we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework,namely semantic base,and then establishing an intelligent and efficient semantic communication(IE-SC)network *** the IE-SC architecture,a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer,network protocol layer,and application-intent layer via semantic information *** proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in *** features a lower bandwidth requirement,less redundancy,and more accurate intent *** also present a brief review of recent advances in semantic communications and highlight potential use cases,complemented by a range of open challenges for 6G.

关键词： 6G Semantic information Semantic communication Intelligent communication

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A Method for Generating Medical Text Data by Integrating Spatiotemporal Attributes and Implicit State Transitions

A Method for Generating Medical Text Data by Integrating Spa...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Xu Yang Lijuan Sun Yutong Gao Sanchuan Guo Xu Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Beijing China Intellectual Property Information Service Center Beihang University Beijing China School of Information Engineering Minzu University of China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

This study aims to generate medical text data that integrates spatiotemporal attributes to solve the problems of small size and low availability of open source text datasets due to the privacy of medical data. This paper optimizes the LeakGAN model by using a multi-layer LSTM structure and increasing the number of hidden and embedding layers to help generate networks that capture long-distance dependencies in data. In addition, we designed a spatiotemporal data generation method based on random algorithms and Markov chains to generate the spatiotemporal attributes of electronic medical records. In order to comprehensively evaluate the generation effect of the model, this paper evaluates from similarity-based indicators and likelihood-based indicators. The results show that the method designed in this article is 2.038 better than other models on NLL-oracle, 0.016 on NLL-test, and 0.0069 on EmbSim. The method proposed in this article has good ability to fit real data and can avoid the high cost and privacy leakage problems faced when manually constructing electronic medical record data sets by generating realistic medical electronic medical record synthetic data. This method also provides new solutions to the problem of insufficient data sets faced by more downstream applications (such as data auditing, anomaly detection, etc.).

关键词： Data privacy Cyberspace Data collection Data science Data models Spatiotemporal phenomena Electronic medical records Usability Long short term memory Synthetic data

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Robust text clustering with graph and textual adversarial learning 5

Robust text clustering with graph and textual adversarial le...

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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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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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Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

Not All Image Regions Matter: Masked Vector Quantization for...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Mengqi Huang Zhendong Mao Quan Wang Yongdong Zhang University of Science and Technology of China Hefei China Institute of Artificial intelligence Hefei Comprehensive National Science Center Hefei China MOE Key Laboratory of Trustworthy Beijing University of Posts and Telecommunications Distributed Computing and Service Beijing China

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage's autoregressive model's ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQVAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. Code will be released at https://***/CrossmodalGroup/MaskedVectorQuantization.

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Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

arXiv

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arXiv 2023年

作者： Huang, Mengqi Mao, Zhendong Wang, Quan Zhang, Yongdong University of Science and Technology of China Hefei China MOE Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Institute of Artificial intelligence Hefei Comprehensive National Science Center Hefei China

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage’s autoregressive model’s ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQ-VAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. Code will be released at https: / / github. com / CrossmodalGroup / MaskedVectorQuantization. Copyright © 2023, The Authors. All rights reserved.

关键词： Redundancy

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An End-to-End Raw Bytes Based Malware Classifier via Self-Attention Residual Convolutional Network

An End-to-End Raw Bytes Based Malware Classifier via Self-At...

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International Conference on Computer and Communications (ICCC)

作者： Zihan Hou Xiaoyong Li Linghui Li Jie Yuan Kaiyang Deng Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education College of Cyberspace Security Beijing University of Posts and Telecommunications Beijing P.R.China

Malware classification has always been a fundamental and cutting-edge problem in the field of cyber security. The rapidly evolution of malware variant makes the malware recognition challenging. Most of existing methods are based on symbolic execution, fingerprints, behavior sequences, manually designed feature, etc., which increase the workload of security experts and slow down the security response speed. In this paper, we propose an end-to-end raw byte based method for malware classification. Our proposed model mainly consists of a convolutional module, self-attention residual module and MLP based classifier. The convolutional module is directly used to process the byte stream to extract the high-level feature of the malware. The self-attention residual module is designed to capture the relevant information scattered in different blocks of the malware. The MLP based classifier is to calculate the category probability of the malware. We conduct extensive experiments on the well-known BODMAS dataset which contains 300GB of malwares. Our proposed method achieves 90.0% macro-F1 and is proved to be rational and effective for malware classification.

关键词： Computational modeling Manuals Probability Fingerprint recognition Feature extraction Malware Behavioral sciences

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PMU-Leaker: Performance Monitor Unit-based Realization of Cache Side-Channel Attacks

PMU-Leaker: Performance Monitor Unit-based Realization of Ca...

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Asia and South Pacific Design Automation Conference

作者： Pengfei Qiu Qiang Gao Dongsheng Wang Yongqiang Lyu Chunlu Wang Chang Liu Rihui Sun Gang Qu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education & Tsinghua University Beijing China Tsinghua University Beijing China Harbin Institute of Technology Harbin Heilongjiang China University of Maryland College Park Maryland USA

Performance Monitor Unit (PMU) is a special hardware module in processors that contains a set of counters to record various architectural and micro-architectural events. In this paper, we propose PMU-Leaker, a novel realization of all existing cache side-channel attacks where accurate execution time measurements are replaced by information leaked through PMU. The efficacy of PMU-Leaker is demonstrated by (1) leaking the secret data stored in Intel Software Guard Extensions (SGX) with the transient execution vulnerabilities including Spectre and ZombieLoad and (2) extracting the encryption key of a victim AES performed in SGX. We perform thorough experiments on a DELL Inspiron 15–7560 laptop that has an Intel® Core™ i5-7200U processor with the Kaby Lake architecture and the results show that, among the 176 PMU counters, 24 of them are vulnerable and can be used to launch the PMU-Leaker attack.

关键词： Program processors Portable computers Side-channel attacks Lakes Phasor measurement units Time measurement Hardware

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ADBA:Approximation Decision Boundary Approach for Black-Box Adversarial Attacks 39

ADBA:Approximation Decision Boundary Approach for Black-Box ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Feiyang Zuo, Xingquan Huang, Hai Chen, Gang School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Services Ministry of Education Beijing China School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)157735897X

Many machine learning models are susceptible to adversarial attacks, with decision-based black-box attacks representing the most critical threat in real-world applications. These attacks are extremely stealthy, generating adversarial examples using hard labels obtained from the target machine learning model. This is typically realized by optimizing perturbation directions, guided by decision boundaries identified through query-intensive exact search, significantly limiting the attack success rate. This paper introduces a novel approach using the Approximation Decision Boundary (ADB) to efficiently and accurately compare perturbation directions without precisely determining decision boundaries. The effectiveness of our ADB approach (ADBA) hinges on promptly identifying suitable ADB, ensuring reliable differentiation of all perturbation directions. For this purpose, we analyze the probability distribution of decision boundaries, confirming that using the distribution’s median value as ADB can effectively distinguish different perturbation directions, giving rise to the development of the ADBA-md algorithm. ADBA-md only requires four queries on average to differentiate any pair of perturbation directions, which is highly query-efficient. Extensive experiments on six well-known image classifiers clearly demonstrate the superiority of ADBA and ADBA-md over multiple state-of-the-art black-box attacks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Normal distribution

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