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IEEE International Conference on Systems, Man and Cybernetics

作者： Qiliang Gu Shuo Zhao Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Shandong Engineering Research Center of Big Data Applied Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

To address the challenges associated with insufficiently extracting and utilizing features at different levels, overlooking the connection between label meanings and text, and facing problems of over-compression or information loss when extracting global information using recurrent neural networks in the field of multi-label text categorization, this paper introduces an innovative model known as MFFLEN (MultiFeature Fusion and Label Embedding Neural Network). First, a back-translated enhanced label set is constructed by back-translated splicing enhancement of the original label set. This set, together with the text, is then input into the embedding layer, which consists of the pre-trained model of bert-baseChinese, thus establishing the initial connection between the text and the labels within the same vector space. Then, to comprehensively extract multi-level semantic features, the model uses a convolutional layer to extract local features and an embedding layer to extract sentence-level features. A bidirectional attention embedded GRU (BAE-GRU) layer is used to extract hybrid finegrained features, which are then fed into the attention layer to further extract hybrid labeled features based on labeling information. Finally, these three different types of features are fused and multi-label text classification results are obtained using a classifier. The experiments proved that the MFFLEN model achieved 73.82% and 88.44% macro-F1 and 88.00% and 88.86% micro-F1 on the two datasets CAIL 2018 Small and CAIL 2018 Split, respectively, which is better than other baseline models.

关键词： Measurement Recurrent neural networks Splicing Text categorization Semantics Multi label classification Logic gates Feature extraction Vectors Data mining

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Cross-modal retrieval based on Attention Embedded Variational Auto-Encoder

Cross-modal retrieval based on Attention Embedded Variationa...

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International Joint Conference on Neural networks (IJCNN)

作者： Kaili Zhou Yiyun Xing Yushui Geng Jing Zhao Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This study proposes a cross-modal retrieval technique, which employs an Attention Embedded Variational AutoEncoder (AE-VAE) to tackle the problem of reduced retrieval accuracy resulting from data noise and data absence in cross-modal retrieval tasks. First, the variational auto-encoder(VAE) is used as an infrastructure to transform modal data into latent variables by encoding and decoding different modal features, thus preserving the semantic structure of the raw modal data and suppressing noise and missing information in the data. Secondly, to better capture the important semantic features of image and text modalities, an improved multi-attention module is introduced in the encoder. In addition, a cross-modal alignment module is designed to further align the latent variables of different modalities in the latent space to solve the confusion problem and improve the accuracy and robustness of cross-modal retrieval. Finally, experiments are conducted on four public datasets, CUB, SUN, AwA1 and AwA2, and the experimental results show that our proposed model is effective in improving the accuracy and robustness of retrieval.

关键词： Accuracy Uncertainty Noise Semantics Transforms Probabilistic logic Robustness

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Comments on "VERSA: Verifiable Secure Aggregation for Cross-Device Federated Learning"

TechRxiv

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TechRxiv 2022年

作者： Luo, Fucai Wang, Haiyan Yan, Xingfu The Department of New Networks Peng Cheng Laboratory Shenzhen China The Guangdong Provincial Key School of Computer Science and Engineering South China University of Technology Guangzhou China

Federated learning (FL) allows a large number of users to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggregation in each training iteration, without sending their raw training data. The main security issues of FL, that is, the privacy of the gradient vector and the correctness verification of the aggregated gradient, are gaining increasing attention from industry and academia. To protect the privacy of the gradient, a secure aggregation was proposed;to verify the correctness of the aggregated gradient, a verifiable secure aggregation that requires the server to provide a verifiable aggregated gradient was proposed. In 2021, Hahn et al. proposed VERSA, a verifiable secure aggregation (DOI: 10.1109/TDSC.2021.3126323). However, in this paper, we will point out a flaw in VERSA, which indicates that VERSA does not work. To address the flaw, we present several approaches with different advantages and disadvantages. We hope that by identifying the flaw, similar errors can be avoided in future designs for verifiable secure aggregation. © 2022-12-09, CC BY.

关键词： Machine learning

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A Novel Wideband Bandpass Filter by ISGW 13

A Novel Wideband Bandpass Filter by ISGW

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Sa, Yuchao Shen, Dongya Chen, Jianpei Zhang, Xiupu Yunnan University Yunnan Provincial Engineering Laboratory Of Cloud Wireless Access Heterogeneous Networks Kunming650500 China Concordia University IPhotonics Labs Department Of Electrical And Computer Engineering MontrealQCH3G1M8 Canada

ISBN: (纸本)9781665434379

A novel ISGW wideband bandpass filter was proposed for 24.7 36.2 GHz frequency band signal transmission. The filter was implemented by using five coupled stepped-impedance resonators. In this design, the first three resonant frequencies of the SIR are appropriately adjusted and placed in the middle of the structure of the broadband bandpass filter. Then, two SIRs are added to the input and output ports on both sides to generate resonance out of the band, so as to improve the ability of its out-of-band suppression. The five SIRs are coupled together with parallel coupling lines to form a broadband width. By optimizing the filter, good broadband performance is achieved theoretically. The return loss is greater than 12.5 dB and the group delay change is less than 0.45 ns in the whole wide passband. © 2021 IEEE.

关键词： Natural frequencies

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A survey on deep learning for textual emotion analysis in social networks

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Digital Communications and networks 2022年第5期8卷 745-762页

作者： Sancheng Peng Lihong Cao Yongmei Zhou Zhouhao Ouyang Aimin Yang Xinguang Li Weijia Ji Shui Yu Laboratory of Language Engineering and Computing Guangdong University of Foreign StudiesGuangzhou510006China School of English Education Guangdong University of Foreign StudiesGuangzhou510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computing University of LeedsWood-house LaneLeedsWest YorkshireLS29JTUnited Kingdom School of Computer Science and Intelligence Education Lingnan Normal UniversityZhanjiang 524048China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University(BNU Zhuhai)Zhuhai519087China School of Computer Science University of Technology SydneySydneyNSW 2007Australia

Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in *** Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language *** trend has drawn increasing researchers away from traditional machine learning to DL for their scientific *** this paper,we provide an overview of TEA based on DL *** introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning *** then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic *** close by discussing emotion analysis challenges and future research *** hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

关键词： Text Emotion analysis Deep learning Sentiment analysis Pre-training

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Ultrahigh-coherence microcomb generation and low-noise amplification using integrated photonics

Ultrahigh-coherence microcomb generation and low-noise ampli...

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IEEE Photonics Conference (IPC)

作者： Xuguang Zhang Zixuan Zhou Yijun Guo Minxue Zhuang Warren Jin Bitao Shen Yujun Chen Jiahui Huang Weiwei Hu Haowen Shu John E. Bowers Xingjun Wang Lin Chang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

ISBN: (数字)9798350361957

ISBN: (纸本)9798350361964

We demonstrate a high-coherence parallel integrated light source utilizing a self-injection locked microcomb and injection locking amplification. Our strategy exhibits a record high on-chip gain of 60 dB, nearly 0 dB noise factor, which enable more than 30 highly coherent channels in integrated photonics whose linewidths down to 10 Hz, power exceeding 20 dBm per line and a wall plug efficiency of 19%.

关键词： Injection-locked oscillators Noise System-on-chip Gain Photonics Plugs Light sources

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Semantic Relation-Based Cross Attention Network for Image-Text Retrieval

Semantic Relation-Based Cross Attention Network for Image-Te...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image-text retrieval refers to querying the text or image of another modality given an image or text, and its key lies in the ability to accurately measure the similarity between text and image. Most of the existing retrieval methods only utilize the intramodal relations of each modality or the intermodal relations of the two modalities to perform the retrieval task. To address the above problems, we integrate the intra-modal and inter-modal relations and propose a Semantic Relation-based Cross Attention Network(SRCAN). In our proposed method, we first mine the possible intra-modal associations between regions in an image and between words in a text to obtain features with semantic relationships, and then capture the fine-grained associations between segments through the cross-attention mechanism. Finally, we balance the intra-modal and inter-modal relationships to improve the performance of the model. Our proposed method is experimentally validated on two datasets, Flickr30K and MS-COCO, and the experimental results show that our method achieves superior results.

关键词： Image segmentation Semantics Bidirectional control Feature extraction Cognition Feeds Cybernetics

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CFL: Cluster Federated Learning in Large-scale Peer-to-Peer networks

arXiv

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arXiv 2022年

作者： Chen, Qian Wang, Zilong Zhou, Yilin Chen, Jiawei Xiao, Dan Lin, Xiaodong State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xidian University Xi’an710071 China School of Computer Science University of Guelph Guelph Canada

Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' local devices. However, the parameter server setting of FL not only has high bandwidth requirements, but also poses data privacy issues and a single point of failure. In this paper, we propose an efficient and privacy-preserving protocol, dubbed CFL, which is the first fine-grained global model training for FL in large-scale peer-to-peer (P2P) networks. Unlike previous FL in P2P networks, CFL aggregates local model update parameters hierarchically, which improves the communication efficiency facing large amounts of clients. Also, the aggregation in CFL is performed in a secure manner by introducing the authenticated encryption scheme, whose key is established through a random pairwise key scheme enhanced by a proposed voting-based key revocation mechanism. Rigorous analyses show that CFL guarantees the privacy and data integrity and authenticity of local model update parameters under two widespread threat models. More importantly, the proposed key revocation mechanism can effectively resist hijack attacks, thereby ensuring the confidentiality of the communication keys. Ingenious experiments on the Trec06p and Trec07 datasets show that the global model trained by CFL has good classification accuracy, model generalization, and rapid convergence rate, and the dropout-robustness of the system is achieved. Compared to the first global model training protocol for FL in P2P networks, PPT, CFL improves communication efficiency by 43.25%. Also, CFL outperforms PPT in terms of computational efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Peer to peer networks

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The influence of proton radiation on the performance of silicon Mach-Zehnder modulator

The influence of proton radiation on the performance of sili...

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2021 Asia Communications and Photonics Conference, ACP 2021

作者： Han, Changhao Hu, Zhaoyi Tao, Yuansheng Fu, Engang Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China

The influence of proton radiation on silicon Mach-Zehnder modulator performance was studied for space environment. The modulator was sensitive to proton radiation while still demonstrated a certain working ability aft... 详细信息

ISBN: (纸本)9781957171005

关键词： Silicon

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching

FERI: Feature Enhancement and Relational Interaction for Ima...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Shuo Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.

关键词： computer vision Fuses Image matching Semantics Feature extraction Natural language processing

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