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Quantum Remote State Preparation Based on Quantum Network Coding

computers, Materials & Continua 2022年第10期73卷 119-132页

作者： Zhen-Zhen Li Zi-Chen Li Yi-Ru Sun Haseeb Ahmad Gang Xu Xiu-Bo Chen School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Information Security Center State key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

As an innovative theory and technology,quantum network coding has become the research hotspot in quantum network *** this paper,a quantum remote state preparation scheme based on quantum network coding is *** with the general quantum remote state preparation schemes,our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network,by designing the appropriate encoding and decoding steps for quantum network *** is worth mentioning,from the network model,this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly ***,it can be treated as an efficient quantum network preparation scheme due to the characteristics of network coding,and it also makes the proposed scheme more applicable to the large-scale quantum *** addition,the fact of an arbitrary unknown quantum state remotely prepared means that the senders do not need to know the desired quantum ***,the security of the proposed scheme is ***,this scheme can always achieve the success probability of 1 and 1-max flow of value ***,the communication efficiency of the proposed scheme is ***,the proposed scheme turns out to be practicable,secure and efficient,which helps to effectively enrich the theory of quantum remote state preparation.

关键词： Quantum remote state preparation quantum network coding quantum k-pair network

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Modeling and Analyzing of Breast Tumor Deterioration Process with Petri Nets and Logistic Regression

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Complex system Modeling and Simulation 2022年第3期2卷 264-272页

作者： Xuyue Wang Wangyang Yu Zeyuan Ding Xiaojun Zhai Sangeet Saha Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourismand School of Computer ScienceShaanxi Normal UniversityXi’an 710100China School of Computer Science Shaanxi Normal UniversityXi’an 710100China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease *** the occurrence of the disease is caused by many factors,and the pathogenesis process is also *** is necessary to use interpretable and visual modeling methods to characterize this complex *** learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic ***,in most cases,it is *** formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent,and can well visualize the analysis *** Petri Nets(CPN)is a powerful formal *** paper presents a modeling approach with CPN and machine learning in breast cancer,which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of *** evaluating the performance of several common machine learning algorithms,we finally choose the logistic regression algorithm to analyze the data,and integrate the obtained prediction model into the CPN *** method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.

关键词： coloured Petri nets visual modeling machine learning breast cancer

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OCDB: REVISITING CAUSAL DISCOVERY WITH A COMPREHENSIVE BENCHMARK AND EVALUATION FRAMEWORK

arXiv

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arXiv 2024年

作者： Zhou, Wei Huang, Hong Zhang, Guowen Shi, Ruize Yin, Kehan Lin, Yuanyuan Liu, Bang Huazhong University of Science and Technology China DIRO Université de Montréal & Mila Canada CIFAR AI Chair Canada The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Large language models (LLMs) have excelled in various natural language processing tasks, but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes fields. Causal discovery offers a promising approach to improve transparency and reliability. However, current evaluations are often one-sided and lack assessments focused on interpretability performance. Additionally, these evaluations rely on synthetic data and lack comprehensive assessments of real-world datasets. These lead to promising methods potentially being overlooked. To address these issues, we propose a flexible evaluation framework with metrics for evaluating differences in causal structures and causal effects, which are crucial attributes that help improve the interpretability of LLMs. We introduce the Open Causal Discovery Benchmark (OCDB), based on real data, to promote fair comparisons and drive optimization of algorithms. Additionally, our new metrics account for undirected edges, enabling fair comparisons between Directed Acyclic Graphs (DAGs) and Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show significant shortcomings in existing algorithms' generalization capabilities on real data, highlighting the potential for performance improvement and the importance of our framework in advancing causal discovery techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Directed graphs

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SQLStateGuard: Statement-Level SQL Injection Defense Based on Learning-Driven Middleware 15

SQLStateGuard: Statement-Level SQL Injection Defense Based o...

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15th Annual ACM Symposium on Cloud computing, SoCC 2024

作者： Liu, Xin Huang, Yuanyuan Wang, Tianyi Li, Song Niu, Weina Shen, Jun Zhou, Qingguo Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Gansu Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Computing and Information Technology University of Wollongong WollongongNSW Australia Kansai University Osaka Suita Japan

ISBN: (纸本)9798400712869

SQL injection is a significant and persistent threat to web services. Most existing protections against SQL injections rely on traffic-level anomaly detection, which often results in high false-positive rates and can be easily bypassed by attackers. This paper introduces SQLStateGuard, the world's first middleware-driven statement-level SQL injection defense approach, to address these issues. The SQLStateGuard uses a custom SQL middleware based on the idea of Runtime Application Self-Protection to capture raw SQL statements. These statements are then analyzed by SQLSG-Net, a database-oriented detection network based on gated linear units. If SQLSG-Net detects malicious SQL statements, the SQL middleware will block them. Experiments show that the detection accuracy of SQLStateGuard exceeds 99%, outperforming existing approaches, and it can identify the type of a specific SQL injection. Additionally, SQLStateGuard has no fingerprint and does not respond to SQL syntax errors, making it more challenging for attackers to gather information. This paper also presents a novel dataset generation process for SQLStateGuard and shares two statement-level SQL injection datasets with the research community, including over 145,000 malicious SQL statements categorized by the type of SQL injection. © 2024 ACM.

关键词： Middleware

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Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys

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science China(Technological sciences) 2023年第6期66卷 1815-1830页

作者： LIU Yue DING Lin YANG ZhengWei GE XianYuan LIU DaHui LIU Wei YU Tao AVDEEV Maxim SHI SiQi School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel&Shanghai Key Laboratory of Advanced Ferrometallurgy&School of Materials Science and Engineering Shanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Institute of Functional Materials Central Iron&Steel Research InstituteBeijing 100081China Australian Nuclear Science and Technology Organisation Locked Bag 2001Kirrawee DC NSW 2232Australia School of Chemistry The University of SydneySydney 2006Australia Zhejiang Laboratory Hangzhou 311100China

Despite the huge accumulation of scientific literature,it is inefficient and laborious to manually search it for useful information to investigate structure-activity ***,we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal ***,the credibility of abstracts is quantified in terms of source timeliness,publication authority and author’s academic ***,eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts,achieving an accuracy of 85.10%.Thirdly,by formulating 12 naming rules for the alloy brands derived from the recognized entities,we extract the target entities and refine them as domain knowledge through the credibility *** this,we also map out the academic cooperative“Author-Literature-Institute”network,characterize the generations of Nickel-based single crystal superalloys,as well as obtain the fractions of the most important chemical elements in *** extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys.

关键词： Nickel-based single crystal superalloys text mining named entity recognition credibility analysis domain knowledge

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CMD: A Cache-Assisted GPU Memory Deduplication Architecture

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年

作者： Zhao, Wei Feng, Dan Tong, Wei Wei, Xueliang Wu, Bing Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology China

Massive off-chip accesses in GPUs are the main performance bottleneck. We find that many writes are duplicate, and the duplication can be inter-dup and intra-dup. While inter-dup means different memory blocks are identical, and intra-dup means all the 4B elements in a line are the same. In this work, we propose a cache-assisted GPU memory deduplication architecture named CMD to reduce the off-chip accesses via utilizing the data duplication in GPU applications. CMD includes three key design contributions which aim to reduce the three kinds of accesses: (1) A novel GPU memory deduplication architecture that removes the intra-dup and inter-dup lines. We design several techniques to manage duplicate blocks, reducing massive off-chip writes. (2) We propose a cache-assisted read scheme to reduce the reads to duplicate data. When an L2 cache miss wants to read the duplicate block, if the reference block has been fetched to L2 and it is clean, we can copy it to the L2 missed block without accessing off-chip DRAM. As for the reads to intra-dup data, CMD uses the on-chip metadata cache to get the data. (3) When a cache line is evicted, the clean sectors in the line are invalidated while the dirty sectors are written back. However, most read-only victims are re-referenced from DRAM more than twice. Therefore, we add a full-associate FIFO to accommodate the read-only (it is also clean) victims to reduce the re-reference counts. Experiments show that CMD can decrease the off-chip accesses by 31.01%, reduce the energy by 32.78% and improve performance by 42.53%. Besides, CMD can improve the performance of memory-intensive workloads by 57.56%. © 2025 IEEE.

关键词： Cache memory

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Smart Captures- An Advanced Attendance system using Facial Recognition 1

Smart Captures- An Advanced Attendance System using Facial R...

引用

1st International Conference on Cognitive computing and Engineering Education, ICCCEE 2023

作者： Gunjan, Reena Mital, Anannya Jain, Muskan Pangaonkar, Satyajit Shete, Virendra Rokade, Prakash MIT School of Computing MIT Art Design and Technology University Department of Computer Science Engineering Pune India School of Computer Science and Engineering Avantika University Madhya Pradesh Ujjain India MIT School of Engineering and Science MIT Art Design and Technology University Department of Electronics and Communication Engineering Pune India Cloud and Infrastructure Services Infosys Ltd Maharashtra Pune India

ISBN: (纸本)9798350332803

Facial Recognition is one of the most productive and important techniques in image processing. It uses Machine Learning and Artificial Intelligence techniques to identify, collect, store, and evaluate face characteristics so that they can be matched to photos of people in a database. Nowadays, many kinds of research are focusing on human detection mainly in the context of marking students' attendance. Marking attendance is a procedure of recognizing students' faces and marking their attendance for that class. The need for this application was generated because after the pandemic use of touch biometric systems was suspended in many places and the manual process of taking attendance and maintaining paper records was a very tedious job and this could be manipulated very easily by anyone. Some attendance systems with face recognition already got developed but they were very basic and only had the feature of just recognizing the face once and marking the attendance of that student. So, by bringing in some unique features, here, developed 'Smart Captures', an advanced attendance system for universities, schools, and education-based places. Smart Captures was entitled to capturing the students' movement from the classroom (in out) and marking their attendance according to the set constraints using IoT, thereafter, making it visible to the faculties on their mobile application. This provided the data analytics for regular attendance on the dashboard to the admin with the administrative power using the application. © 2023 IEEE.

关键词： Students

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FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search

arXiv

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arXiv 2024年

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd China

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services None of modern ANNS systems can address these issues simultaneously. We present FusionANNS, a high-throughput, low-latency cost-efficient, and high-accuracy ANNS system for billion scale datasets using SSDs and only one entry-level GPU The key idea of FusionANNS lies in CPU/GPU collabo rative filtering and re-ranking mechanisms, which signifi cantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We imple ment FusionANNS and compare it with the state-of-the-art SSD-based ANNS system–SPANN and GPU-accelerated in memory ANNS system–RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Artificial Impostors: An Efficient and Scalable Scheme for Location Privacy Preservation

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IEEE Transactions on services computing 2025年第3期18卷 1262-1277页

作者： Hao Tang Kunfeng Chen Zhiyang Xie Cheng Wang College of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

The progress of location-based services has led to severe concerns about location privacy leakage. However, existing methods are still incompetent for efficient and scalable location privacy preservation (LPP). They are often vulnerable to inference attacks with side information, or hard to be implemented due to the high computational complexity. In this article, we pursue the high protection quality with low computational complexity. We propose a scalable LPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them by synthesizing artificial impostors. The so-called artificial impostors refer to the synthesized traces that have similar semantic features to the actual traces, e.g., similar transition patterns, and do not contain any protected location, i.e., the exact location that needs to be protected. We devise two dedicated techniques: the station-based synthesis method and the population-level semantic model. We conduct the experiments on real datasets of two cities (Shanghai of China and Asturias of Spain) to validate the quality of privacy preservation, utility loss, and scalability of the proposed method. Based on these two datasets, the experimental results show that our method achieves the privacy preservation quality of 97.68% and 96.24%, respectively, and the time spent on building generators is only 144.96 seconds and 136.08 seconds, respectively. The experimental results also show that our method achieves a good trade-off between privacy and utility. Our study would give the research community new insights into improving the practicality of LPP paradigm via counterfeiting locations.

关键词： Privacy Semantics Feature extraction Urban areas Generators Protection Counterfeiting Computational efficiency Artificial intelligence Transforms

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Tensor and Minimum Connected Dominating Set based Confident Information Coverage Reliability Evaluation for IoT

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2024年第3期10卷 547-561页

作者： Xiao, Ziheng Zhu, Chenlu Feng, Wei Liu, Shenghao Deng, Xianjun Lu, Hongwei Yang, Laurence T. Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Company Ltd United States Network and Industrial Control Information Security Technology Department China Nuclear Power Operation Technology Company Ltd. 1011 Xiongchu Avenue Hubei Wuhan430070 China The Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of

Internet of Things (IoT) reliability evaluation contributes to the sustainable computing and enhanced stability of the network. Previous algorithms usually evaluate the reliability of IoT by enumenating the states of nodes and networks, which are difficult to handle IoT with hundreds of nodes because the computational cost. In this paper, a novel algorithm, TMCRA, is proposed to evaluate the reliability of IoT in complex network environment, which consider both coverage and connectivity. For coverage, TMCRA employs the Confident Information Coverage (CIC) model to divide the target area into independent grids and calculates the coverage rate. In terms of connectivity, TMCRA forming the Virtual Backbone Network (VBN) based on two proposed methods: TMA and MGIN, and evaluate connectivity by analyzing the VBN rather than the whole network. The TMA and MGIN are two algorithms for constructing Minimum Connected Dominant Sets (MCDS), which are suitable for different scale networks. Finally, based on the data of coverage and connectivity, TMCRA utilizes tensors for the unified modeling and representation of network structure, and calculates IoT reliability based on the tensors. Simulations are carried out for various sizes of IoT to show the advantages and effectiveness of the proposed approach in reliability evaluation. © 2016 IEEE.

关键词： Data reliability

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