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1st Conference on Blockchain and Web3.0 technology Innovation and Application, BWTAC 2024

作者： Liu, Xiaolong Yuan, Bo Wu, Faguo Enterprise Technology Center Aerospace Legione Technology Co. Ltd Haidian Beijing100195 China School of Computer Science and Engineering Beihang University Haidian Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Haidian Beijing100191 China Institute of Artificial Intelligence Beihang University Haidian Beijing100191 China Beihang University Haidian Beijing100191 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Haidian Beijing100191 China

ISBN: (纸本)9789819794119

This paper proposes a hyper-converged blockchain hardware and software sys-tem based on domestic chips. By utilizing domestically firmware, the Linux operating system, X86 architecture Hygon processors, and ARM architecture Phytium processors, combined with servers, storage, network, and independently controllable blockchain technologies, the system achieves integrated hardware and software optimization. This significantly improves performance and scalability. Experimental results demonstrate that the system performs excellently in various high-security application scenarios, providing reliable solutions for government, finance, taxation, transportation, energy, and telecommunications sectors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Firmware

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LMP-GAN: Out-Of-Distribution Detection For Non-Control Data Malware Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Robust laser self-mixing displacement demodulation viacontinuous wavelet–Hilbert transform for high-precision accelerometercalibration

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Applied Optics 2025年第16期64卷 4559-4567页

作者： Zhikun Chang Song Song Dongbai Xue Chunling He Zhoumiao Shi Jing Yu Ming Kong Yuying Xie Xiao Deng Tongbao Li Xinbin Cheng Institute of Precision Optical Engineering Tongji University Shanghai 200092 China National Metrology and Testing Center for Integrated Circuit Measurement and Inspection Equipment Industry (Shanghai) Tongji University Shanghai 200092 China MOE Key Laboratory of Advanced Micro-Structured Materials Tongji University Shanghai 200092 China Shanghai Frontiers Science Center of Digital Optics Tongji University Shanghai 200092 China Shanghai Professional Technical Service Platform for Full-Spectrum and High-Performance Optical Thin Film Devices and Applications Tongji University Shanghai 200092 China School of Physics Science and Engineering Tongji University Shanghai 200092 China School of Computer Science and Technology Zhejiang University of Water Resources and Electric Power Hangzhou 310018 China Shenzhen Institute of Technology Innovation NIM Shenzhen 518107 China College of Metrology and Testing Engineering China Jiliang University Hangzhou 310018 China

The laser self-mixing grating interferometer based on Cr atomlithography gratings hasbeen applied to the primarycalibration of accelerometers due to its compact structure, low cost,high accuracy, and directon-site traceability. However, the high line density of Cr gratings(4700 l/mm) introduces dense outliers in interferometricsignals, complicating displacement demodulation via conventionalderivative-based methods and causing frequent phase jumps. To addressthis challenge, we propose a hybrid algorithm integrating thecontinuous wavelet transform and the Hilbert transform, which enablesrobust displacement demodulation under high-noise *** validation on a commercial MEMS accelerometerdemonstrated exceptional accuracy: 100 consecutive data segments weresuccessfully demodulated with a displacement fitting goodness of $R^2 = 0.9964$ ( $\sigma_{R_2} =0.0027$ ), and the derived sensitivitydeviated by only 0.1% from the nominal value. This algorithmestablishes a paradigm for the high-accuracy dynamic calibration ofinertial sensors in field applications.

关键词： Laser interferometry Lithography Phase shift Refractive index Systems design Wavelet transforms

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A geometry processor for image processing and pattern recognition

A geometry processor for image processing and pattern recogn...

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Architectures and Algorithms for Digital Image Processing III 1985

作者： Suk, Minsoo Pyo, Sam S. Department of Electrical and Computer Engineering Syracuse University SyracuseNY13244-1240 United States Center for Advanced Technology in Computer Applications and Software Engineering Syracuse University United States

A one-dimensional systolic geometry processor (SGP) which is useful in image processing and pattern recognition is described. The geometry processor can be used to enhance processing speed and throughput of the host computer by working as an attached processor. SGP algorithms for solving several primitive geometric problems such as convex hull, inclusion and intersection are shown. © 1986 SPIE. All rights reserved.

关键词： Pattern recognition

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Precipitable water vapor retrieved from FY-3G/MWRI-RM observations using machine learning models

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Advances in Space Research 2025年

作者： Yuxin Liu Xinzhi Wang Yi Zhou Atta ur Rahman School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing 210044 China Jiangsu Province Engineering Research Center of Collaborative Navigation/Positioning and Smart Applications Nanjing 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

Water vapor is an essential component of the Earth’s atmosphere, and accurate retrieval of precipitable water vapor (PWV) is critical to understanding the Earth’s climate and hydrological cycle. In this study, we use the global Fengyun-3G/Micro-Wave Radiation Imager-Rainfall Mission (FY-3G/MWRI-RM) L1 data from October 2023 to August 2024 to construct a high-precision PWV retrieval model. Three machine learning algorithms are used to construct our models:Random Forests (RF), Light Gradient Boosting Machine (Light GBM), and Gradient Boosting Regression (GBR), due to their ability to handle complex relationships between brightness temperature data and PWV retrieval with high precision. Then, the precision of retrieved PWV is evaluated using the fifth generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5) PWV and radiosonde PWV. Light GBM demonstrated the highest accuracy with root-mean-square error (RMSE) of 2.151 mm over the ocean compared to ERA5 PWV, outperforming GBR and RF. RF demonstrated the highest accuracy with RMSE of 2.323 mm over land compared to ERA5 PWV, outperforming Light GBM and GBR. In addition, this study compares the FY-3G/MWRI-RM PWV with advanced Microwave Scanning Radiometer 2 (AMSR2) TPW and AMSR2 PWL. FY-3G/MWRI-RM retrieved PWV demonstrated higher precision compared to AMSR2 water vapor products when validated against ERA5 PWV.

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Incorporating target language semantic roles into a string-to-tree translation model

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Frontiers of Information technology & Electronic engineering 2017年第10期18卷 1534-1542页

作者： Chao SU Yu-hang GUO He-yan HUANG Shu-min SHI Chong FENG School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing 100081China Beijing Advanced Innovation Center for Imaging Technology Capital Normal UniversityBeijing 100048China

The string-to-tree model is one of the most successful syntax-based statistical machine translation(SMT) models. It models the grammaticality of the output via target-side syntax. However, it does not use any semantic information and tends to produce translations containing semantic role confusions and error chunk sequences. In this paper, we propose two methods to use semantic roles to improve the performance of the string-to-tree translation model:(1) adding role labels in the syntax tree;(2) constructing a semantic role tree, and then incorporating the syntax information into it. We then perform string-to-tree machine translation using the newly generated trees. Our methods enable the system to train and choose better translation rules using semantic information. Our experiments showed significant improvements over the state-of-the-art string-to-tree translation system on both spoken and news corpora, and the two proposed methods surpass the phrase-based system on large-scale training data.

关键词： Machine translation Semantic role Syntax tree String-to-tree

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing 210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China China University of Petroleum (East China) College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Combinatorial auction distributed DNN inference dynamic scheduling edge-cloud networks resource pricing

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A hierarchical similarity based job recommendation service framework for university students

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Frontiers of computer Science 2017年第5期11卷 912-922页

作者： Rui LIU Wenge RONG Yuanxin OUYANG Zhang XIONG State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China

When people want to move to a new job, it is often difficult since there is too much job information available. To select an appropriate job and then submit a resume is tedious. It is particularly difficult for university students since they normally do not have any work experience and also are unfamiliar with the job market. To deal with the informa- tion overload for students during their transition into work, a job recommendation system can be very valuable. In this research, after fully investigating the pros and cons of current job recommendation systems for university students, we propose a student profiling based re-ranking framework. In this system, the students are recommended a list of potential jobs based on those who have graduated and obtained job offers over the past few years. Furthermore, recommended employers are also used as input for job recommendation result re-ranking. Our experimental study on real recruitment data over the past four years has shown this method＇s potential.

关键词： job recommendation students similarity time,re-ranking

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Optical neural network models applied to logic program execution

Optical neural network models applied to logic program execu...

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Neural Network Models for Optical Computing 1988

作者： Stormon, Charles D. New York State Center for Advanced Technology in Computer Applications and Software Engineering 120 Hinds Hall Syracuse University SyracuseNY13244-1190 United States

Logic programming is being used extensively by Artificial Intelligence researchers to solve problems including natural language processing and expert systems. These languages, of which Prolog is the most widely used, promise to revolutionize software engineering, but much greater performance is needed. Researchers have demonstrated the applicability of neural network models to the solution of certain NP-complete problems, but these methods are not obviously applicable to the execution of logic programs. This paper outlines the use of neural networks in four aspects of the logic program execution cycle, and discusses results of a simulation of three of these. Four neural network functional units are described, called the substitution agent, the clause filter, the structure processor, and the heuristics generator, respectively. Simulation results suggest that the system described may provide several orders of magnitude improvement in execution speed for large logic programs. However, practical implementation of the proposed architecture will require the application of optical computing techniques due to the large number of neurons required, and the need for massive, adaptive connectivity. © 1988 SPIE. All rights reserved.

关键词： Neural network models

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