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DCAS-BMT: Dynamic Construction and Adjustment of Skewed Bonsai Merkle Tree for Performance Enhancement in Secure Non-Volatile Memory

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IEEE Transactions on Computers 2025年

作者： Zhang, Yu Chen, Renhai Yan, Hangyu Wu, Hongyue Feng, Zhiyong Tianjin University College of Intelligence and Computing Tianjin300350 China Nanjing University State Key Lab. for Novel Software Technology Nanjing210023 China Yunnan University of Finance and Economics Yunnan Key Lab. of Service Computing Kunming650221 China

Traditional DRAM-based memory solutions face challenges, including high energy consumption and limited scalab.lity. Non-Volatile Memory (NVM) offers low energy consumption and high scalab.lity. However, security challenges, particularly data remanence vulnerabilities, persist. Prevalent methods such as the Bonsai Merkle Tree (BMT) are employed to ensure data security. However, the consistency requirements for integrity tree updates have led to performance issues. It is observed that compared to a secure NVM system without persistent secure metadata, the average overhead for updating and persisting the BMT root with persistent secure metadata is as high as 2.48 times. Therefore, this paper aims to mitigate these inefficiencies by leveraging the principle of memory access locality. We propose the Dynamic Construction and Adjustment of Skewed Bonsai Merkle Tree (DCAS-BMT). The DCAS-BMT is dynamically built and continuously adjusted at runtime according to access weights, ensuring frequently accessed memory blocks reside on shorter paths to the root node. This reduces the verification steps for frequently accessed memory blocks, thereby lowering the overall cost of memory authentication and updates. Experimental results using the USIMM memory simulator demonstrate that compared to the widely used BMT approach, the DCAS-BMT scheme shows a performance improvement of 34.1%. © 1968-2012 IEEE.

关键词： Scalab.lity

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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

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IEEE Transactions on Fuzzy Systems 2025年

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the lab.r-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlab.led data in complex and large-scale scenes is still challenging, such as only 20 points lab.led in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlab.led data learning through reliable long-range supervision propagation from lab.led superpoints to unlab.led superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlab.led superpoints to clusters centered on lab.led superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% lab.led points. Our code is availab.e at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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newBalance: Efficient Data Space Management and Algorithmic Optimization for Large-Scale Storage Systems

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Chinese Journal of Electronics 2017年第3期26卷 493-501页

作者： XU Guangping LIN Sheng SHI Kai ZHANG Hua Tianjin Key Laboratory of Intelligence Computing and New Software Technology Computer and Communication Engineering School Tianjin University of Technology

Fragmentation usually occurs when data space of original storage nodes has to be reallocated to new added storage nodes during the scale-out evolution of the large-scale storage *** greatly influences its performance and becomes a challenge to manage the whole *** present an efficient space management framework,called new Balance,to reduce fragmentation with the minimum data movement while keeping the storage system load *** space management framework has two phases including the collection phase and the allocation *** the collection phase,we propose a novel algorithm,called the greedy bi-direction collector,which collects enough space for the new storage *** the allocation phase,we formally represent it as a variant of the bin packing problem and then utilize some bin packing heuristics including the first fitting and the best fitting to allocate collected intervals to new added storage *** experimental results show that the amount of intervals can be reduced by 20%;5%and our algorithmic optimization improves the data lookup performance by at least 10%and the scale-out performance by 2X;X.

关键词： Large-scale storage systems Space management Algorithmic optimization Performance evaluation

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Time-Ordered Collab.rative Filtering for news Recommendation

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China Communications 2015年第12期12卷 53-62页

作者： XIAO Yingyuan AI Pengqiang Ching-Hsien Hsu WANG Hongya JIAO Xu Tianjin University of Technology Tianjin Key Lab of Intelligence Computing and Novel Software Technology Chung Hua University Donghua University

Faced with hundreds of thousands of news articles in the news websites,it is difficult for users to find the news articles they are interested ***,various news recommender systems were *** the news recommendation,these news articles read by a user is typically in the form of a time ***,traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing ***,the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will *** solve this problem,this paper proposes a time-ordered collab.rative filtering recommendation algorithm(TOCF),which takes the time sequence characteristic of user behaviors into ***,a new method to compute the similarity among different users,named time-dependent similarity,is *** demonstrate the efficiency of our solution,extensive experiments are conducted along with detailed performance analysis.

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A kind of new web-based method of media seamless migration for mobile service

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Journal of Information and Computational Science 2011年第10期8卷 1825-1836页

作者： Zhang, Degan Dai, Wenbo Liang, Yanpin Liu, Zhaojing Tianjin Key Lab. of Intelligent Computing and Novel software Technology Tianjin University of Technology Tianjin 300191 China Ministry of Education Tianjin 300191 China

In order to improve the mobile services under the banner of the Internet of Things, we propose and design a new kind of Web-based method of media seamless migration. The method can ensure the media seamless migration fast, smoothly and correctly. Implemented method will better solve the migration of media on the application layer of mobile service. Our tests have shown our suggested seamless migration strategies are effective. This method will serve universal Web-based E-Learning, E-office, E-business and so on. © 2011 by Binary Information Press.

关键词： Websites

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The Expansibility Research of K-means Algorithm under the GPU 7

The Expansibility Research of K-means Algorithm under the GP...

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2016 IEEE 7th International Conference on software Engineering and Service Science

作者： Sujie Zhong Sheng Lin Guangping Xu Kai Shi Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Lab.of Intelligence Computing and New Software Technology

ISBN: (纸本)9781467399050

K-Means algorithm is one of the most popular clustering analysis *** the algorithm can be easily understood and implemented,and its execution is more efficient than common clustering algorithm,it has been used *** the same time,with the increasing size of the data sets processed,CPUbasedserial K-Means implementation has been unable to meetthe people's need of data *** computing is considered well with the large data sets ***-based concurrent computation can accelerate common tasks,and especially for accelerating the compute-intensive ***(Compute Unified Device Architecture) is one of the methods that achieving the GPU-based concurrent *** the paper,the author hope to achieve a K-Means algorithm implementation can handle larger data sets via CUDA and the algorithm can be used on a common computer with NVIDIA graphics cards.

关键词： CUDA GPU k-means

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The improvement of HDFS authentication model based on token push mechanism 18

The improvement of HDFS authentication model based on token ...

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2018 International Conference on Big Data and computing, ICBDC 2018

作者： Wang, Guiyuan Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing and New Software Technology Tianjin China

ISBN: (纸本)9781450364263

In order to solve the problems of single point overload, repeated authentication, replay attack and time synchronization in the security authentication of HDFS, an improved Kerberos protocol based on token push mechanism is proposed. First of all, aiming at the problem of single point overload and repeated authentication, a three-stage access mechanism based on Agent is introduced. The KDC is responsible for the visitor's first login to HDFS for the first time. In the second phase, the visitor accesses the DataNode again through HDFS, the agent generates cross-node tokens and pushes to all involved DataNodes. In the third phase, visitor directly accesses DataNode. DataNode uses cross-node tokens to authenticate visitors. This mechanism divides users' login into three phases, which lightens the load of KDC authentication server. Secondly, reduce the number of NameNode and KDC authentication through the token push mechanism. Finally, in order to enhance the security of the authentication process and prevent replay attacks, a new parameter T-nonce is introduced in the authentication of Client, NameNode and DataNodes. This parameter is obtained by hash processing the timestamp and IP address. The timestamp and IP address are combined in the parameter generation function to ensure its real-time and uniqueness so that unauthorized users cannot implement replay attacks. This paper analyzes the security and efficiency of a single-time authentication both in the original model and the improved one, and the results show that the improved protocol can effectively improve the security and efficiency of HDFS authentication. © 2018 Association for computing Machinery.

关键词： Authentication

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TF-IDF keyword extraction method combining context and semantic classification 2020

TF-IDF keyword extraction method combining context and seman...

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3rd International Conference on Data Science and Information technology, DSIT 2020

作者： Wang, Xinyun Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing and New Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781450376044

keyword extraction plays the same role as the cornerstone in the field of natural language processing. Text classification, information retrieval, abstract generation and text clustering are all based on keyword extraction. This article takes the research of keyword extraction model as the subject. First, it analyzes the principle and limitations of the traditional keyword extraction model TF-IDF when extracting keywords. Secondly, it focuses on the problem of ignoring context and word polysemy in the keyword extraction model. To improve, introduce the concept of context vector, construct a chain-extensible structure for polysemous words, and propose a new keyword extraction method of TF-IDF that combines context and semantic classification. The specific research contents are as follows: TF-IDF extracts keywords based on multiple texts. The target keywords are words that appear frequently in the current text, but appear in other articles that are significantly lower in frequency than the current article. This method takes into account the characteristic content of the article and makes the article distinct. However, this method ignores the influence of the context of the article and the problem of word polysemy. Obviously, the expression of the thought of the article will not only be affected by the context of the words, but also by the semantics of the words. In order to solve the above problems, this paper proposes a TF-IDF keyword extraction method that combines context and semantic classification. First, the text constructs a linked list expandable structure to solve the problem of polysemy;then, the influence of context is introduced. Before inserting a new word into the extended structure, the semantic similarity is calculated through the context vector. Finally, iterate over the entire expandable structure, sum the word frequency with semantic similarity under each semantic, and then sort the results to obtain TopN words as the text keywords of the correspond

关键词： Semantics

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keywords extraction method based on two-way feature fusion 2

Keywords extraction method based on two-way feature fusion

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2nd International Conference on Machine Learning and Intelligent Systems Engineering, MLISE 2022

作者： Li, Yongrui Ning, Hongyun Tianjin University Of Technology Tianjin Key Laboratory Of Intelligence Computing & New Software Technology Tianjin China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

In order to improve the accuracy of keyword extraction, an improved method was proposed to solve the problem of missing keywords in traditional TF-IDF keyword extraction algorithm. In this method, two routes are used to extract the features of the text, one is responsible for storing the dependency syntactic relations in the preprocessing of the text, and the other is responsible for calculating the tF-IDF value, part of speech and position of the words, and constructing the comprehensive scoring weighting formula of the candidate keywords. Finally, the feature information of these two paths is fused in the stage of combinatorial optimization and sorted to form formal extracted keywords. Experimental results show that compared with the traditional TF-IDF algorithm and Textrank algorithm, the F value of the proposed method in Sina news data set is improved by 14.4% and 16.3% respectively, which effectively verifies that the proposed method can significantly improve the keyword extraction performance. © 2022 IEEE.

关键词： Syntactics

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Text keyword Extraction Method Combined with Dependent Syntax Analysis 22

Text Keyword Extraction Method Combined with Dependent Synta...

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2nd International Conference on Control and Intelligent Robotics, ICCIR 2022

作者： Li, Yongrui Ning, Hongyun Tianjin Key Laboratory of Intelligence Computing & New Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781450397179

In order to improve the accuracy of key word extraction, an improved TF-IDF method was proposed to solve the problems that traditional TF-IDF keyword extraction algorithm could not recognize new words and polysemous words. This method first TF - IDF values, part of speech of words and position characteristics is calculated respectively, build candidate keywords weighted formula comprehensive score, then stored pretreatment before interdependence syntactic relations from the text, using the established syntactic relations to score higher candidate keywords for combinatorial optimization or reducing weight, the last order form formal extracting keywords. Experimental results show that compared with the traditional TF-IDF method, the multi-feature method based on dependency parsing improves the F value of the dataset containing new words by 10.4%, reaching 60.6%, and the F value of the dataset containing polysemous words by 17.9%, reaching 66.5%, which effectively verifies that the proposed method can improve the accuracy of keyword extraction. © 2022 ACM.

关键词： Syntactics

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