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Puncturable ciphertext-policy attribute-based encryption scheme for efficient and flexible user revocation

Science China(Information Sciences) 2023年第7期66卷 130-146页

作者： Dilxat GHOPUR Jianfeng MA Xindi MA Yinbin MIAO Jialu HAO Tao JIANG School of Cyber Engineering Xidian University Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Xi'an Satellite Control Center

With the rapid deployment of storage services, secure and efficient user authorization and revocation data shared through the cloud have become a grand challenge hindering cloud data *** previous direct and indirect user revocation schemes implemented large-scale user revocation, they faced heavy communication and computational costs. To address these challenges, this study presents a new encryption scheme that combines ciphertext-policy attribute-based encryption(CP-ABE) with puncturable encryption to achieve efficient and flexible user revocation. We design a proxy server to reduce the computational overhead in the decryption phase. Because the puncture process is performed on a semi-honest cloud, we use the digital signature method to verify the correctness of its operation. Furthermore, we prove the security of our scheme under the chosen-plaintext attack(CPA), and compare it with other schemes to highlight its advantages. Numerical analysis and experimental simulation results reveal that our scheme is more suitable than other schemes for use in a cloud environment for user revocation.

关键词： ciphertext-policy attribute-based encryption puncturable encryption user revocation outsource decryption digital signature

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Based on the Conformer End-to-End Chinese Speech Recognition Method 4

Based on the Conformer End-to-End Chinese Speech Recognition...

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4th International Conference on Electronic Information engineering and Computer Science, EIECS 2024

作者： Feng, Haiwen Shi, Yunxiao School of Software Engineering Shenyang University of Technology Shenyang Key Laboratory of Intelligent Manufacturing of High-end Industrial Equipment Liaoning Shenyang China School of Software Engineering Shenyang University of Technology ShenganpLiaoning China

ISBN: (纸本)9798331531409

Aiming at the problem that the acoustic input network of Conformer encoder is insufficient for Fbank speech feature extraction, an end-to-end speech recognition modeling method of DM-Conformer is proposed. Firstly, the Dilated convolution is utilized in the downsampling module to increase the receptive field, which captures more speech feature information without adding extra model parameters, while ensuring that the height and width of the original input feature map remain unchanged. Then, the Mish activation function is introduced to alleviate the problem of slow convergence of the ReLU activation function due to its negative value. Finally, experiments are carried out on the public dataset AISHELL-1, and the speech recognition model with the improved downsampling module proposed in this paper reduces the word error rate by 5.6% compared with the Conformer baseline model on the test set, and the model finally achieves a word error rate of 6.15%. © 2024 IEEE.

关键词： Speech enhancement

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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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Computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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Dynamic VNF Deployment and Resource Allocation in Mobile Edge Computing 22

Dynamic VNF Deployment and Resource Allocation in Mobile Edg...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhai, Xiaolong He, Zhenli Xiao, Yuanfei Wu, Junjie Yu, Xuejie Yunnan University Yunnan Key Laboratory of Software Engineering Kunming China Yunnan University School of Software Kunming650504 China

ISBN: (纸本)9798331509712

The explosive growth of terminal devices at the network edge, coupled with advancements in communication technology, poses significant challenges to traditional cloud computing models. Despite Mobile Edge Computing (MEC) mitigating some issues by enabling real-time data processing closer to the source, it faces challenges with linear growth in computational resources insufficient to meet the exponential growth in service demand. Existing research utilizing software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Service Function Chain (SFC) technologies has made progress but still faces critical issues, such as the inability to respond in real-time to dynamic demands and inefficiencies in resource management strategies. This paper addresses these challenges by optimizing Virtual Network Function (VNF) deployment strategies in MEC environments. We propose PPO-ERA, a novel algorithm leveraging deep reinforcement learning and the Karush-Kuhn-Tucker (KKT) method. This approach provides real-time, adaptive, and dynamic deployment policies for VNFs, significantly improving both the average response delay of tasks and resource utilization. key contributions include rigorous SFC-based application modeling, dynamic VNF deployment algorithms, elastic resource allocation, uniform state representation, and extensive performance validation. These advancements enhance the adaptability, efficiency, and performance of VNF deployment strategies, addressing critical challenges in dynamic MEC environments. © 2024 IEEE.

关键词： Network function virtualization

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Dual-band dynamic reconfigurable intelligent surface for signal enhancement in millimeter wave

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Science China(Technological Sciences) 2024年第4期67卷 1133-1138页

作者： YAO HuiMing DU XinRun WANG HengBin GAO Song PANG YaChen ZHENG HaiRong GONG HuiWen LI Yi XU JianChun ZHANG JianHua BI Ke State Key Laboratory of Information Photonics and Optical Communications School of ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

Millimeter wave with large bandwidth,high transmission rate,and low delay is considered a reliable alternative to cope with the spectrum ***,the fast attenuation and narrow beam characteristics make it difficult to achieve long-distance or wide-range ***,a 1-bit dual-band reflective reconfigurable intelligent surface(RIS)for signal enhancement in millimeter wave with 16×16 elements is designed,fabricated,and *** from most existent RIS,dynamic programming is realized at two separate frequency bands by integrating the PIN diodes and field-programmable gate array(FPGA).Particularly,the beam deflection,dual-beam,and multi-beam are created based on the coding theory and convolution operation,proving the effectiveness of wavefront ***,the far-field patterns and signal power with different coding sequences are measured and *** is indicated that the received signal power is 6–7 dB stronger than that without coding,which shows good agreement with the desired *** proposed reconfigurable metasurface exhibits great potential in beam forming,making it a promising candidate for progressive wireless communication applications.

关键词： dual-band millimeter wave reconfigurable intelligent surface signal enhancement

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ALSTNet:Autoencoder fused long-and short-term time-series network for the prediction of tunnel structure

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Deep Underground Science and engineering 2025年第1期4卷 72-82页

作者： Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen State Key Laboratory of Software Development Environment Beihang UniversityBeijingChina State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanChina

It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex *** that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring ***,the proposed model was formalized on multiple time series of strain monitoring ***,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction *** the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of *** a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.

关键词： autoencoder deep learning structural health monitoring time-series prediction

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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Science China(Information Sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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Multi-Relational Graph Representation Learning for Financial Statement Fraud Detection

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Big Data Mining and Analytics 2024年第3期7卷 920-941页

作者： Chenxu Wang Mengqin Wang Xiaoguang Wang Luyue Zhang Yi Long School of Software Engineering and also with MoE Key Lab of Intelligent Networks and Network SecurityXi’an Jiaotong UniversityXi’an 710049China School of Software Engineering Xi’an Jiaotong UniversityXi’an 710049China Shenzhen Finance Institute The Chinese University of Hong KongShenzhen(CUHK-Shenzhen)Shenzhen 518026China

Financial statement fraud refers to malicious manipulations of financial data in listed companies'annual *** machine learning approaches focus on individual companies,overlooking the interactive relationships among companies that are crucial for identifying fraud ***,fraud detection is a typical imbalanced binary classification task with normal samples outnumbering fraud *** this paper,we propose a multi-relational graph convolutional network,named FraudGCN,for detecting financial statement fraud.A multi-relational graph is constructed to integrate industrial,supply chain,and accounting-sharing relationships,effectively encapsulating the multidimensional and complex interactions among *** then develop a multi-relational graph convolutional network to aggregate information within each relationship and employ an attention mechanism to fuse information across multiple *** attention mechanism enables the model to distinguish the importance of different relationships,thereby aggregating more useful information from key *** alleviate the class imbalance problem,we present a diffusion-based under-sampling strategy that strategically selects key nodes globally for model *** also employ focal loss to assign greater weights to harder-to-classify minority *** build a real-world dataset from the annual financial statement of listed companies in *** experimental results show that FraudGCN achieves an improvement of 3.15%in Macro-recall,3.36%in Macro-F1,and 3.86%in GMean compared to the second-best *** dataset and codes are publicly available at:https://***/XNetLab/MRG-for-Finance.

关键词： financial statement fraud class imbalance Graph Neural Networks(GNN) multi-relational graphs

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Multi-Class Task Offloading Optimization in Mobile Edge Computing 22

Multi-Class Task Offloading Optimization in Mobile Edge Comp...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Ma, Songkang He, Zhenli Feng, Libo Zhai, Xiaolong Tong, Yiyan Yunnan University Yunnan Key Laboratory of Software Engineering Kunming China Yunnan University School of Software Kunming650504 China

ISBN: (纸本)9798331509712

The proliferation of mobile devices and the increasing demand for portable services have led to a surge in computationally intensive and time-sensitive applications, necessitating efficient computation offloading in Mobile Edge Computing. Existing research often overlooks the distinct response time requirements of different applications and their mutual interference during queuing. This paper addresses these gaps by optimizing offloading strategies with multiple response time constraints, focusing on the queuing impact of various tasks. We model edge servers as M/M/1 queuing systems and develop a set of mathematical models. Using algorithms based on Karush-Kuhn-Tucker conditions, we derive optimal offloading strategies to minimize system power consumption. Our approach significantly reduces system power consumption in practical applications, enhancing resource utilization for service providers. Our work introduces a comprehensive and realistic model of task offloading, considering multiple task types, their distinct average response time requirements, and their mutual interference during queuing. This advancement ensures efficient and effective offloading strategies, addressing the complexity of multiple tasks and varying response time constraints. © 2024 IEEE.

关键词： Computation offloading

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