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A polynomial time algorithm to detect PQI interval orders

International Transactions in Operational Research 2000年第6期7.0卷 609-623页

作者： The, A.N. Tsoukiàs, A. Vincke, P. LAMSADE - CNRS Université Paris Dauphine Paris 75775 France LAMSADE - CNRS Université Paris Dauphine Paris 75775 France SMG - ISRO Université Libre de Bruxelles France

Let S be a PQI preference structure on a finite set A, where the relations P, Q, I stand for ‘strict preference’, ‘weak preference’ and ‘indifference,’ respectively. Two specific preference structures: PQI semi orders and PQI interval orders, have been considered and characterised recently in such a way that is possible to associate to each element of A an interval such that P holds when one interval is completely to the right of the other, I holds when one interval is included to the other and Q holds when one interval is to the right of the other, but they do have a non empty intersection (Q medelling the hesitation). While the detection of a PQI semiorder is straightforward, the case of the PQI interval order is more difficult as the theorem of existence consists in a second-order formula. The paper presents an algorithm for detecting a PQI interval order and demonstrates that it is backtracking free. This result leads to a matrix version of the algorithm which can be proved to be polynomial. © 2000 Blackwell Publishing Ltd.

关键词： Complexity detection algorithm Interval orders PQI interval orders

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A real-time TV logo tracking method using template matching

A real-time TV logo tracking method using template matching

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Conference on Optoelectronic Imaging and Multimedia Technology II

作者： Li, Zhi Sang, Xinzhu Yan, Binbin Leng, Junmin Beijing Univ Posts & Telecommun Sate Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

ISBN: (纸本)9780819493132

A fast and accurate TV Logo detection method is presented based on real-time image filtering, noise eliminating and recognition of image features including edge and gray level information. It is important to accurately extract the optical template using the time averaging method from the sample video stream, and then different templates are used to match different logos in separated video streams with different resolution based on the topology features of logos. 12 video streams with different logos are used to verify the proposed method, and the experimental result demonstrates that the achieved accuracy can be up to 99%.

关键词： TV Logo detection algorithm real-time

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Hybrid Quantum-Classical Neural Network For Diagnosis of Autism Spectrum Disorder 5

Hybrid Quantum-Classical Neural Network For Diagnosis of Aut...

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2024 International Conference on Quantum Computing and Engineering

作者： Kim, Anthony Bergen Cty Acad Dept Phys Hackensack NJ 07601 USA

ISBN: (纸本)9798331541378

Autism Spectrum Disorder (ASD) affects almost 3% of children worldwide, and due to the complexity and heterogeneity of ASD, accurate and efficient diagnosis is difficult to achieve. Qualitative diagnosis based on observation of behavior has a major risk of misdiagnosis. Meanwhile, the most efficient quantitative algorithms in modern literature are similarly problematic, with misdiagnosis rates of up to 44%, execution times of over 7 hours, and all algorithms are purely classical. This project is an implementation of a hybrid quantum classical neural network (HQCNN) to improve upon classical neural networks by applying quantum algorithms while only using fMRI data. The proposed neural network implements a variational quantum deflation (VQD) algorithm, an extension of the variational quantum eigensolver (VQE) framework for finding the first k excited states of a Hamiltonian. This study shows that the VQD algorithm produces eigenvalues effectively equivalent to the classically computed ones, and the general algorithm has up to 70% accuracy with less than an hour of execution time, a major improvement from the 56% accuracy and over 7 hour runtimes of most classical algorithms. With the development of quantum computing technology, the VQD algorithm will continue to be optimized. Furthermore, the HQCNN is generally scalable, and only requires fMRI data for analysis. This study shows a faster and more accurate diagnosis of ASD using quantum algorithms.

关键词： neural network quantum network variational quantum eigensolver autism spectrum disorder detection algorithm IBM quantum lab

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A Linear Method for Quantification of Collagen Fiber Orientation in Cornea 41

A Linear Method for Quantification of Collagen Fiber Orienta...

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41st Annual Northeast Biomedical Engineering Conference (NEBEC)

作者： McLean, James Northeastern Univ 360 Huntington Ave Boston MA 02115 USA

A generalized, linear algorithm is used for unbiased quantification of the orientation of corneal collagen fibers. Its application to SHG microscopy for determining macro structure of the collagen extracellular matrix... 详细信息

ISBN: (纸本)9781479983605

关键词： collagen SHG microscopy detection algorithm ex-vivo biomedical optics

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Towards Developing Visual Statistical Cues for Biodiversity, Abundance, Biomass around Mariana Trench in an Embeddable Smart Module

Towards Developing Visual Statistical Cues for Biodiversity,...

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OCEANS - Marseille Conference

作者： Li, Xiaoyu Ren, Shidong Geng, Xue He, Hui Lendasse, Amaury Nian, Rui Ocean Univ China Sch Informat Sci & Engn Songling Rd 238 Qingdao 266100 Peoples R China Boston Childrens Hosp Radiol Dept Boston MA 02115 USA Harvard Med Sch Boston MA 02115 USA Arcada Univ Appl Sci Helsinki 00550 Finland Univ Houston Dept Informat & Logist Technol Houston TX USA

ISBN: (纸本)9781728114507

Most ecosystems in Mariana Trench, still remain unexplored or poorly known. Recently, advanced underwater vision systems, especially those deployed by the Remotely Operated Vehicles (ROV) and Autonomous Underwater Vehicles(AUV), provide us more possibilities to have a closer observation on the deep sea environments, communities, lifestyles, even behaviors of the inhabitants. However, online visual understanding is still a big challenge in these deepest spots of the ocean, due to the lack of prior knowledge, poor visibility, and dynamic investigation. In this paper, we are trying to make a further step to develop ecological indicators in an embeddable smart module, capturing visual statistical cues in biodiversity, abundance, biomass for those primary species observed along deep sea exploration mission, on the basis of RefineDet with our predefined scenic segmentation.

关键词： Visual Statistical Cues detection algorithm scenic segmentation Mariana Trench Biodiversity Abundance Biomass

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An Algebraic Formulation of the Division Property: Revisiting Degree Evaluations, Cube Attacks, and Key-Independent Sums 26th

An Algebraic Formulation of the Division Property: Revisitin...

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26th Annual International Conference on the Theory and Application of Cryptology and Information Security

作者： Hu, Kai Sun, Siwei Wang, Meiqin Wang, Qingju Shandong Univ Sch Cyber Sci & Technol Qingdao Shandong Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China Univ Luxembourg SnT Esch Sur Alzette Luxembourg Shandong Univ Key Lab Cryptol Technol & Informat Secur Minist Educ Qingdao Shandong Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783030648367;9783030648374

Since it was proposed in 2015 as a generalization of integral properties, the division property has evolved into a powerful tool for probing the structures of Boolean functions whose algebraic normal forms are not available. We capture the most essential elements for the detection of division properties from a pure algebraic perspective, proposing a technique named as monomial prediction, which can be employed to determine the presence or absence of a monomial in any product of the coordinate functions of a vectorial Boolean function f by counting the number of the so-called monomial trails across a sequence of simpler functions whose composition is f. Under the framework of the monomial prediction, we formally prove that most algorithms for detecting division properties in literature raise no false alarms but may miss. We also establish the equivalence between the monomial prediction and the three-subset bit-based division property without unknown subset presented at EUROCRYPT 2020, and show that these two techniques are perfectly accurate. The monomial prediction technique can be regarded as a purification of the definitions of the division properties without resorting to external multisets. This algebraic formulation gives more insights into division properties and inspires new search strategies. With the monomial prediction, we obtain the exact algebraic degrees of Trivium up to 834 rounds for the first time. In the context of cube attacks, we are able to explore a larger search space in limited time and recover the exact algebraic normal forms of complex superpolies with the help of a divide-and-conquer strategy. As a result, we identify more cubes with smaller dimensions, leading to improvements of some near-optimal attacks against 840-, 841and 842-round Trivium.

关键词： Division property Monomial prediction detection algorithm Algebraic degree Cube attack Trivium

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Innovative data treatment routines for Atomic Force Microscopy force curves

Innovative data treatment routines for Atomic Force Microsco...

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International Conference on Advances in Biomedical Engineering (ICABME)

作者： Klaimi, Rami El Kirat, Karim Univ Technol Compiegne Univ Paris 04 CNRS UMR Bioinech & Bioengn 7338 Compiegne France AZM Ctr Res Biotechnol EDST Tripoli Tripoli Lebanon

ISBN: (纸本)9781467365161

The atomic force microscopy (AFM) is an important innovation in the sample imaging fields, the signals extracted from the AFM such as the force as function of the indentation and the friction as function of the indentation can be used to extract many chemical and mechanical properties of the sample. Nanoscientists analyze these signals manually;the aim of this article is to find new algorithms to automatically analyze AFM signals.

关键词： AFM Young modulus Wavelets detection algorithm Nanobiomechanics

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An Efficient algorithm for Identifying Genomic Structural Inversion with Wide-spectrum of Length 17

An Efficient Algorithm for Identifying Genomic Structural In...

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17th IEEE International Conference on Bioinformatics and Bioengineering (BIBE)

作者： Geng, Yu Zhao, Zhongmeng Cui, Xingjian Zhang, Rong Zheng, Tian Zhang, Xuanping Wang, Jiayin Xi An Jiao Tong Univ Dept Comp Sci & Technol Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Management Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Data Sci & Informat Qual Shaanxi Engn Res Ctr Med & Hlth Big Data Xian Shaanxi Peoples R China

ISBN: (纸本)9781538613245

Genomic structural inversion is a class of structural variations, and has been widely associated to a series of complex traits and diseases. It has great significance in accurately identifying the inversions from the high-throughput sequencing data for both research and clinical practice. However, detecting inversion is a challenging computational problem. Existing approaches either limit to detect the inversions with specific length intervals or require a significant distribution of the coverage across the candidate interval. In this paper, we propose a novel detection algorithm to accurately identify the inversions with wide-spectrum of length. The proposed algorithm consists of two components: a clustering step and a segmentation and extension step. It first clusters the pair ended reads to squeeze the candidate intervals. Then, it utilizes the contig assembly strategy to reconstruct the candidate intervals. Meanwhile, a segmentation and extension strategy is implemented. For each candidate interval, a feature vector is calculated, based on the characteristic values. Finally, the algorithm combines the comparison verification results to filter out some potential false positives, and then returns the inversion breakpoints on base pair resolution. We conduct a series of simulation experiments to verify the performance of proposed algorithm and compare to two very popular approaches, DELLY and Pindel. The results demonstrate that the proposed approach provides better results on handling the inversions with wide-spectrum of length, especially when the inversions with short-to-medium length exist.

关键词： genomic structural variation genomic inversion short-to-medium length detection algorithm the next generation sequencing data

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Surface roughness detection based on image analysis

Surface roughness detection based on image analysis

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作者： Jiajun Ma Guangjie Li Qiang Zhan Dandan Wu Xiong Wei Xu Wang Qiang Zhao Jiancun Zuo Shanghai Polytechnic University Shanghai University

Roughness, as one of the evaluation indexes of processed surface quality, has a straight influence on the performance and life of products. Therefore, the roughness detection is of great significance for the control of product quality. At present, image-based roughness detection has become the mainstream technology with its advantage of high detection efficiency, large amount of information collection, and high measurement accuracy. From the perspective of image acquisition, preprocessing, and detection algorithms,this paper summarizes the existing typical methods, compares and analyzes them, and gives the challenges and future development direction of detection technology.

关键词： image analysis surface roughness image acquisition preprocessing detection algorithm

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NETWORK DATA INTRUSION detection AND DATA FEATURE EXTRACTION OF ELECTROMECHANICAL FACILITIES FROM MACHINE LEARNING

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第6期25卷 5171-5183页

作者： Xu, Ting Wang, Lijun Hu, Yanhong Tong, Xuming Hebei North Univ Zhangjiakou 075132 Hebei Peoples R China

With the rapid development of Internet technology, network security issues have become more complex and changeable. Various intrusion methods threaten the information network environment in the Electromechanical Facility (EF) system. This paper focuses on EF to study the relevant detection methods and data feature extraction in complex network intrusions. Firstly, four common machine learning algorithms are used to calculate the data set. The advantages and disadvantages of each algorithm are analyzed after tuning and comparison. Secondly, a network intrusion detection algorithm is proposed based on Recursive Feature Elimination (RFE) principal component analysis. It uses RFE to reduce the number of features and improve the elimination judgment index to align with the detection requirements of information network datasets. Finally, a fault diagnosis method is proposed based on empirical pattern decomposition and support vector machine under Renyi entropy complexity measurement. This method trains and identifies the Renyi entropy of several basic pattern components obtained by decomposing empirical patterns as feature vectors. The results show that the RFE method judged by random forest removes irrelevant features, and the evaluation index is improved to align with the network dataset's detection requirements. It reduces the data dimension, reduces the operation time, and improves the accuracy of a few attack types. The comprehensive final detection effect is better than other algorithms. Additionally, the embedded operating system construction method based on the protection mechanism realizes the separate storage of the operating system and key data. Also, it can prevent the network system from being maliciously invaded, ensuring the stability of the instrument operation under harsh working conditions.

关键词： Machine learning electromechanical facility network data intrusion data feature extraction detection algorithm

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