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Transferring knowledge distillation for multilingual social event detection

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

Some Results on Critical ( P 5 , H )-free Graphs

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Theoretical Computer Science 2025年 1051卷

作者： Wen Xia Jorik Jooken Jan Goedgebeur Shenwei Huang School of Mathematics and Statistics Ankang University Ankang 725000 China College of Computer Science Nankai University Tianjin 300071 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin 300071 China Department of Computer Science KU Leuven Campus Kulak-Kortrijk 8500 Kortrijk Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University 9000 Ghent Belgium School of Mathematical Sciences and LPMC Nankai University Tianjin 300071 China

Given two graphs H 1 and H 2 , a graph is ( H 1 , H 2 ) -free if it contains no induced subgraph isomorphic to H 1 or H 2 . A graph G is k -vertex-critical if every proper induced subgraph of G has chromatic number less than k , but G has chromatic number k . The study of k -vertex-critical graphs for specific graph classes is an important topic in algorithmic graph theory because if the number of such graphs that are in a given hereditary graph class is finite, then there exists a polynomial-time certifying algorithm to decide the k -colorability of a graph in the class. In this paper, we show that: (1) for k ≥ 1 , there are finitely many k -vertex-critical ( P 5 , K 1 , 4 + P 1 ) -free graphs; (2) for s ≥ 1 , there are finitely many 5-vertex-critical ( P 5 , K 1 , s + P 1 ) -free graphs; (3) for k ≥ 1 , there are finitely many k -vertex-critical ( P 5 , K 3 + 2 P 1 ‾ ) -free graphs. Moreover, we characterize all 5-vertex-critical ( P 5 , H ) -free graphs where H ∈ { K 1 , 3 + P 1 , K 1 , 4 + P 1 , K 3 + 2 P 1 ‾ } using an exhaustive graph generation algorithm.

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STR: Secure computation on additive shares using the share-transform-reveal strategy

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arXiv 2020年

作者： Xia, Zhihua Gu, Qi Zhou, Wenhao Xiong, Lizhi Weng, Jian Xiong, Naixue N. College of Cyber Security Jinan University Guangzhou510632 China Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing210044 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China The College of Intelligence and Computing Tianjin University Tianjin300350 China

The rapid development of cloud computing probably benefits many of us while the privacy risks brought by semi-honest cloud servers have aroused the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks to servers while ensuring the security of private data. The tasks to be outsourced are countless;however, the computations involved are similar. In this paper, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) on arbitrary n ≥ 2 servers. All protocols only require constant rounds of interactions and achieve low computation complexity. Moreover, the proposed n-party protocols ensure the security of private data even though n − 1 servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols. Copyright © 2020, The Authors. All rights reserved.

关键词： Additives

Research on System Dynamics and Agent Hybrid Modeling Method for Multi-Energy Collaborative Control under Ubiquitous Power Internet 3

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Research on System Dynamics and Agent Hybrid Modeling Method...

3rd IEEE Conference on Energy Internet and Energy System Integration, EI2 2019

作者： Zhang, Fuxing Zhang, Yingjie Zhan, Xiong Luo, Juan Miao, Honglei Gui, Yonghua Hunan University College of Computer Science and Electronic Engineering Changsha China Global Energy Interconnection Research Institute State Grid Key Laboratory of Information Network Security Beijing China HNAC Technology Co. Ltd. Hardware RD Department Changsha China

ISBN: (纸本)9781728131375

In order to promote the study of system dynamics (SD) and multi-agent theory and its wide application in the field of renewable energy, a hybrid modeling method of SD and Agent is proposed in this paper, which is suitable for the multi-energy collaborative management and control of regional cooling, heating and power under the background of the ubiquitous Internet of Things. Based on the detailed analysis of the lack of single modeling paradigm of system dynamics and the necessity and feasibility of mixed modeling of SD and Agent, the basic types of hybrid modeling of SD and Agent for regional multi-energy cooperative management and control are defined, and the application forms, simulation platforms, problems and ideas of hybrid modeling of SD and Agent in regional multi-energy cooperative management are discussed and analyzed, which can deepen the innovative application of SD and Agent hybrid modeling in multiple fields and assist relevant managers in scientific decision-making. © 2019 IEEE.

关键词： Internet of things

Deep learning techniques for DDoS attack detection: Concepts, analyses, challenges, and future directions

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Expert Systems with Applications 2025年 291卷

作者： Xingbing Fu Supeng Lou Jiaming Zheng Cheng Chi Jie Yang Dong Wang Chenming Zhu Butian Huang Xiatian Zhu School of Cyberspace Hangzhou Dianzi University Hangzhou 310018 PR China Zhejiang Electronic Information Products Inspection and Research Institute (Key Laboratory of Information Security of Zhejiang Province) Hangzhou 310007 PR China China Academy of Information and Communications Technology Beijing 100195 PR China School of Management Hangzhou Dianzi University Hangzhou 310018 PR China Hangzhou Yunphant Network Technology Co. LTD Hangzhou 311121 PR China Surrey Institute for People-Centred Artificial Intelligence CVSSP University of Surrey Guildford UK

DDoS (Distributed Denial of Service) attacks are increasingly becoming a major threat in the field of cybersecurity. They overwhelm target servers by sending large-scale requests from multiple locations, causing the servers to become unresponsive. The distributed nature of DDoS attacks also makes detection and defense even more challenging. As the damage caused by such attacks grows, the development of efficient detection and mitigation mechanisms has become an urgent priority. While traditional machine learning methods are useful, they still require manual feature extraction, which not only involves significant human intervention, but also is time-consuming. In contrast, deep learning offers an automated approach to feature extraction and can learn more abstract patterns, which leads to improved detection performance. Therefore, this paper reviews and analyzes existing deep learning methods for DDoS attack detection. We provide a comprehensive analysis of the various types of DDoS attacks and explore different deep learning models employed for attack detection. Additionally, we explore techniques such as federated learning that can be integrated with deep learning, and analyze their related literature in DDoS attack detection. Finally, we specify future research directions on DDoS attack detection using deep learning.

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Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

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arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

Android Malicious Code Detection Based on Secondary Pruning Optimization

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Android Malicious Code Detection Based on Secondary Pruning ...

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

作者： Qing Yu Kuolang Ma Zuohua Wang Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China Tianjin Times Shengshi Technology Co. Ltd. No. 216 Hongqi Road Nankai District Tianjin China

In this paper, we propose a detection algorithm for Android malicious code based on integrated multi-feature. By decompiling and processing APK files, the multi-class behavior features of Android application are extracted, and the classification is achieved through an integrated learning framework based on quadratic pruning optimization. The prototype system automatically detects the malicious code of the Android platform and analyzes the validity of the algorithm through experimental verification.

关键词： Classification algorithms Malware Feature extraction Optimization Predictive models security

Application of Mixed Reality technology in Education with the case of a Huangmei Opera Cultural Education System

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Application of Mixed Reality Technology in Education with th...

IEEE International Conference on Computer Science and Educational Informatization (CSEI)

作者： Guangjun Wang Zhichao Lu Yufei Zhang Yu Qian Haiying Zhao Deyang Liu The University Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China Institute of Network Technology Beijing University of Posts and Telecommunications Beijing China School of Computer and Information Anqing Normal University Anqing China Mobile media and cultural calculation Key Laboratory of Beijing Century college Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728170084

ISBN: (纸本)9781728170091

Mixed reality is an emerging technology that allows virtual world objects and real world objects to coexist and interact in real time. Its positioning lies between virtual reality and augmented reality, providing users with an immersive experience of combining virtual and real. In this article, we take the Huangmei Opera cultural educational system as an example, and use the current mainstream mixed reality hardware platform and its supporting content production softwares to implement interactive games and virtual museums about Huangmei Opera culture and knowledge. And to explore the application of mixed reality in education.

关键词： Virtual reality Cultural differences Solid modeling Spatial databases Education Three-dimensional displays Mathematical model

Quantum memory assisted entropic uncertainty and entanglement dynamics: Two qubits coupled with local fields and Ornstein Uhlenbeck noise

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arXiv 2021年

作者： Khan, Atta Ur Rahman Zidan, Nour Zangi, S.M. Ali, Hazrat Key Laboratory of Aerospace Information Security and Trusted Computing School of Cyber Science and Engineering Wuhan University P.O. Box 430072 Wuhan China Mathematics Department College of Science Jouf University P.O. Box 2014 Sakaka Saudi Arabia School of Physics and Astronomy Yunnan University P.O. Box 650500 Kunming China Abbottabad University of Science and technology P.O. Box 22500 Havellian KP Pakistan

Entropic uncertainty and entanglement are two distinct aspects of quantum mechanical procedures. To estimate entropic uncertainty relations, entropies are used: the greater the entropy bound, the less effective the quantum operations and entanglement are. In this regard, we analyze the entropic uncertainty, entropic uncertainty lower bound, and concurrence dynamics in two non-interacting qubits. The exposure of two qubits is studied in two different qubit-noise configurations, namely, common qubit-noise and independent qubit-noise interactions. To include the noisy effects of the local external fields, a Gaussian Ornstein Uhlenbeck process is considered. We show that the rise in entropic uncertainty gives rise to the disentanglement in the two-qubit Werner type state and both are directly proportional. Depending on the parameters adjustment and the number of environments coupled, different classical environments have varying capacities to induce entropic uncertainty and disentanglement in quantum systems. The entanglement is shown to be vulnerable to current external fields;however, by employing the ideal parameter ranges we provided, prolonged entanglement retention while preventing entropic uncertainty growth can be achieved. Besides, we have also analyzed the intrinsic behavior of the classical fields towards two-qubit entanglement without any imperfection with respect to different parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics