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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Yazhi Luo, Chuanwen Wang, Guijuan Zhang, Li Lv, Mengjie Yu, Jiguo Qilu University of Technology School of Computer Science and Technology Jinan China Beijing Forestry University School of Information Science and Technology Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Nanjing University of Posts and Telecommunications College of Computer Nanjing China Qilu University of Technology Shandong Academy of Sciences Big Data Research Institute Jinan China

ISBN: (纸本)9798350329223

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length. © 2023 IEEE.

关键词： Hypercube networks

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Quantum Secret Sharing Enhanced: Utilizing W States for Anonymous and Secure Communication

arXiv

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arXiv 2024年

作者： Li, Guo-Dong Cheng, Wen-Chuan Wang, Qing-Le Cheng, Long Mao, Ying Jia, Heng-Yue School of Control and Computer Engineering North China Electric Power University Beijing102206 China Key Lab of Information Network Security Ministry of Public Security Shanghai200031 China Department of Computer and Information Science Fordham University New York City10458 United States School of Information Central University of Finance and Economics Beijing102206 China Engineering Research Center of State Financial Security Ministry of Education Central University of Finance and Economics Beijing102206 China

Quantum secret sharing (QSS) is the result of merging the principles of quantum mechanics with secret information sharing. It enables a sender to share a secret among receivers, and the receivers can then collectively recover the secret when the need arises. To enhance the practicality of these quantum protocols, an innovative concept of quantum anonymous secret sharing (QASS) is advanced. In this paper, we propose a QASS protocol via W states, which can share secrets while ensuring recover-ability, recover-security, and recover-anonymity. We have rigorously evaluated our protocols, verifying their accuracy and fortifying their security against scenarios involving the active adversary. This includes considerations for dishonest receivers and non-receivers. Moreover, acknowledging the imperfections inherent in real-world communication channels, we have also undertaken an exhaustive analysis of our protocol’s security and effectiveness in a quantum network where some form of noise is present. Our investigations reveal that W states exhibit good performance in mitigating noise interference, making them apt for practical applications. © 2024, CC BY-NC-ND.

关键词： Quantum theory

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Multi-label Text Classification for Judicial Texts via Dual Graph and label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

引用

Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu 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 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 Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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New Cryptographic Bent Functions from Permutations and Linear Translator in Cyber security

New Cryptographic Bent Functions from Permutations and Linea...

引用

IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Qian Liu Yan Chen Zhixiong Chen Ximeng Liu Hui Lin College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Financial Information Processing Putian University Putian China Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China College of Computer and Cyber Security Fujian Normal University Fuzhou China

Cryptographic bent functions are maximally nonlinear Boolean functions with an even number of variables. They are not only closely related to some interesting combinatorial objects, but also have important applications in coding, sequence design, cryptography and cyber security. In this paper, we firstly investigate the Walsh transform of two families of functions via Maiorana-MacFarland's class. Secondly, we present new infinite families of permutations. We show that those families have nice property that one can select three elements among them which can be used to construct bent functions. Finally, by using two linear translators, we construct bent functions from the class of Maiorana-MacFarland.

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ACDNet: Attention-guided Collaborative Decision network for Effective Medication Recommendation

arXiv

引用

arXiv 2023年

作者： Mi, Jiacong Zu, Yi Wang, Zhuoyuan He, Jieyue School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Jiangsu Nanjing210018 China

Medication recommendation using Electronic Health Records (EHR) is challenging due to complex medical data. Current approaches extract longitudinal information from patient EHR to personalize recommendations. However, existing models often lack sufficient patient representation and overlook the importance of considering the similarity between a patient’s medication records and specific medicines. Therefore, an Attention-guided Collaborative Decision network (ACDNet) for medication recommendation is proposed in this paper. Specifically, ACDNet utilizes attention mechanism and Transformer to effectively capture patient health conditions and medication records by modeling their historical visits at both global and local levels. ACDNet also employs a collaborative decision framework, utilizing the similarity between medication records and medicine representation to facilitate the recommendation process. The experimental results on two extensive medical datasets, MIMIC-III and MIMIC-IV, clearly demonstrate that ACDNet outperforms state-of-the-art models in terms of Jaccard, PR-AUC, and F1 score, reaffirming its superiority. Moreover, the ablation experiments provide solid evidence of the effectiveness of each module in ACDNet, validating their contribution to the overall performance. Furthermore, a detailed case study reinforces the effectiveness of ACDNet in medication recommendation based on EHR data, showcasing its practical value in real-world healthcare scenarios. © 2023, CC BY.

关键词： Data mining

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Salient Object Detection Based on Pyramid Vision Transformer Gated network

SSRN

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SSRN 2023年

作者： Huo, Lina Zhou, Xiaoli Wang, Wei Cao, Zhiyi Key Laboratory of Network & Information Security Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang050024 China

Recently, vision transformers started to show impressive results that significantly outperform large convolution-based models. In this work, we propose a gating network based on Pyramid Vision Transformer for salient object detection. The Pyramid Vision Transformer (PVT) serves as the backbone network of this gating network, learning global and local representations with its self-attention mechanism. Multi-level gating units are used to recover more details of the saliency map by establishing cooperation among different levels of features, thus improving the discriminability of the whole network. With the help of multi-level gating units, the valuable context information from the encoder can be optimally transmitted to the decoder. The pyramid pooling module (PPM) collects high-level semantic information. Moreover, the semantic information of each level is integrated and decoded by the feature aggregation decoder (FAD). The experimental results on five challenging benchmark databases demonstrate that the proposed method achieves more favourable performance than the current state-of-the-art methods in terms of four evaluation criteria. © 2023, The Authors. All rights reserved.

关键词： Object detection

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StarMFND: Star Topology Multi-Domain Fake News Detection

StarMFND: Star Topology Multi-Domain Fake News Detection

引用

Data Science and information Technology (DSIT), International Conference on

作者： Pengfei Gu Yibing Chen Wanlong Bing Yahui Liu Hongliang Ma Wei Wang School of Information Science and Technology Shihezi University Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China School of Computer Science and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection.

关键词： Economics Social networking (online) Computational modeling Stars Feature extraction Data models Topology Internet Fake news information technology

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DRNet:Towards fast,accurate and practical dish recognition

引用

Science China(Technological Sciences) 2021年第12期64卷 2651-2661页

作者： CHENG SiYuan CHU BinFei ZHONG BiNeng ZHANG ZiKai LIU Xin TANG ZhenJun LI XianXian Guangxi Key Lab of Multi-Source Information Mining&Security Guangxi Normal UniversityGuilin 541004China Department of Computer Science and Technology Huaqiao UniversityXiamen 361021China Seetatech Technology Nanjing 211800China

Existing algorithms of dish recognition mainly focus on accuracy with predefined classes,thus limiting their application *** this paper,we propose a practical two-stage dish recognition framework(DRNet)that yields a tradeoff between speed and accuracy while adapting to the variation in class *** the first stage,we build an arbitrary-oriented dish detector(AODD)to localize dish position,which can effectively alleviate the impact of background noise and pose *** the second stage,we propose a dish reidentifier(DReID)to recognize the registered dishes to handle uncertain *** further improve the accuracy of DRNet,we design an attribute recognition(AR)module to predict the attributes of *** attributes are used as auxiliary information to enhance the discriminative ability of ***,pruning and quantization are processed on our model to be deployed in embedded ***,to facilitate the study of dish recognition,a well-annotated dataset is *** AODD,DReID,AR,and DRNet run at about 14,25,16,and 5 fps on the hardware RKNN 3399 pro,respectively.

关键词： neural network acceleration neural network quantization object detection reidentification dish recognition

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MMSFD: Multi-grained and Multi-modal Fusion for Short Video Fake News Detection

MMSFD: Multi-grained and Multi-modal Fusion for Short Video ...

引用

Data Science and information Technology (DSIT), International Conference on

作者： Shuai Ren Yahui Liu Yaping Zhu Wanlong Bing Hongliang Ma Wei Wang School of Information Science and Technology Shihezi University Shihezi China Key Laboratory of Computing Intelligence and Network Information Security Xinjiang Production and Construction Corps Shihezi China School of Computer Science and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Short video platforms have become an essential part of people’s daily lives, while also facing the challenge of the rapid spread of fake news. Therefore, the detection of fake news in short videos has attracted increasing attention. Existing works mostly focus on the integration of textual and visual content, but fail to fully exploit multi-modal information at both fine-grained and coarse-grained levels. Moreover, multi-modal fake news detection is vulnerable to modality ambiguity due to potential conflicts or weak correlations between different modalities. In this paper, we propose a Multi-grained and Multi-modal fusion for Short video Fake news Detection (MMSFD). We encode textual and visual content using pre-trained models at both fine-grained and coarse-grained levels and fuse them through distinct branches. To tackle the ambiguity issue, we design a multi-modal fusion branch based on similarity weighting, as well as separate single-modal branches. Additionally, this paper presents a cross-modal attention mechanism for adaptively aggregating and reweighting features. The experimental results on a real-world short video dataset demonstrate that the proposed method outperforms state-of-the-art short video fake news detection methods.

关键词： Visualization Adaptation models Attention mechanisms Quantization (signal) Computational modeling Feature extraction Rendering (computer graphics) Encoding Real-time systems Fake news

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INN-based Robust JPEG Steganography Through Cover Coefficient Selection

INN-based Robust JPEG Steganography Through Cover Coefficien...

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Fei Shang Weixiang Zhao Jingyang Wen Xiangui Kang Z. Jane Wang Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Electrical and Computer Engineering University of British Columbia Vancouver Canada

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Images transmitted through social networks generally undergo JPEG recompression, which degrades image quality and could disrupt embedded secret messages, making error-free extraction of such messages difficult. In this paper, we propose an INN-based robust JPEG steganography framework through cover coefficient selection, which selects appropriate embedding coefficients based on prior knowledge of JPEG compression and the characteristics of invertible neural networks (INNs). When subjected to JPEG recompression with quality factor (QF)=90 and QF=85 during transmission, the proposed method achieves extraction accuracy as high as 100%. Moreover, we introduce a novel learnable noise layer that incorporates rounding and truncation operations into network training, to mitigate the information loss caused by rounding and to limit pixel values of the stego image within the range of [0-255] as much as possible. When compared with existing INN-based methods, our method reduces runtime by 23% and memory usage by 82.14%.

关键词： Training Image quality Q-factor Steganography Runtime Social networking (online) Noise Neural networks Transform coding Robustness

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