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quantum speedup and limitations on matroid property problems

Frontiers of computer science 2024年第4期18卷 189-196页

作者： Xiaowei HUANG Jingquan LUO Lvzhou LI Institute of Quantum Computing and Computer Theory School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China

Matroid theory has been developed to be a mature branch of mathematics and has extensive applications in combinatorial optimization,algorithm design and so *** the other hand,quantum computing has attracted much attention and has been shown to surpass classical computing on solving some computational ***,crossover studies of the two fields seem to be missing in the *** paper initiates the study of quantum algorithms for matroid property *** is shown that quadratic quantum speedup is possible for the calculation problem of finding the girth or the number of circuits(bases,flats,hyperplanes)of a matroid,and for the decision problem of deciding whether a matroid is uniform or Eulerian,by giving a uniform lower boundΩ■on the query complexity of all these *** the other hand,for the uniform matroid decision problem,an asymptotically optimal quantum algorithm is proposed which achieves the lower bound,and for the girth problem,an almost optimal quantum algorithm is given with query complexityO■.In addition,for the paving matroid decision problem,a lower boundΩ■on the query complexity is obtained,and an O■ quantum algorithm is presented.

关键词： quantum computing matroid quantum algorithm quantum query complexity

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A Novel WTS-EDC Network for Multi-Source ECG Signal Classification

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Journal of The Institution of Engineers (India): Series B 2024年 1-10页

作者： Rana, Ankur Kumar, Vivek Gupta, Anish Department of Computer Science and Engineering Quantum University Uttarakhand Roorkee247667 India

Electrocardiography (ECG) is a commonly used diagnostic tool in the clinical setting for detecting cardiovascular diseases (CVDs). However, its manual interpretation can be time-consuming and prone to human error. With the recent advancements in deep learning techniques, there has been an increasing interest in using more robust and generalized deep learning algorithms for automated analysis and interpretation of ECG signals. This article provides a novel model, namely Wavelet Time Scattering-Ensembled Dilated Convolution Network (WTS-EDCN), for ECG signal classification. The classifier is an ensemble of dilated convolutional networks, with each network utilizing wavelet time scattering features extracted from multi-source (MS) ECG signal data. The WTS features efficiently constitute the time–frequency characterization of an ECG signal. WTS features are extracted from different ECG signals containing normal and abnormal rhythms. These features are exploited with a deep neural network (DNN) composed of an ensemble of convolutional nets (CNN) with varied dilation rates. Each varied dilation-rated CNN captures the time–frequency information at different scales, and the ensemble ensures the combined use of the information from individual CNNs. The WTS-EDCN model is experimented on MS ECG signals, which have been diagnosed as having either a normal sinus rhythm, Arrhythmia, or congestive heart failure. The model is successful in classifying the diagnosis with high accuracy. These WTS features have proved significant for multi-source ECG signal classification. The EDC classifier reflects a robust and well-generalized classifier since it constitutes an ensembled approach that is a proven strategy for creating better generalized classification models, especially with multi-source datasets. © The Institution of Engineers (India) 2024.

关键词： Electrocardiography

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Flu-Net: two-stream deep heterogeneous network to detect flu like symptoms from videos using grey wolf optimization algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7733-7745页

作者： Gupta, Himanshu Imran, Javed Sharma, Chandani Department of Computer Science and Engineering Quantum University Roorkee India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

The outbreak of COVID-19 (also known as Coronavirus) has put the entire world at risk. The disease first appears in Wuhan, China, and later spread to other countries, taking a form of a pandemic. In this paper, we try to build an artificial intelligence (AI) powered framework called Flu-Net to identify flu-like symptoms (which is also an important symptom of Covid-19) in people, and limit the spread of infection. Our approach is based on the application of human action recognition in surveillance systems, where videos captured by closed-circuit television (CCTV) cameras are processed through state-of-the-art deep learning techniques to recognize different activities like coughing, sneezing, etc. The proposed framework has three major steps. First, to suppress irrelevant background details in an input video, a frame difference operation is performed to extract foreground motion information. Second, a two-stream heterogeneous network based on 2D and 3D Convolutional Neural Networks (ConvNets) is trained using the RGB frame differences. And third, the features extracted from both the streams are combined using Grey Wolf Optimization (GWO) based feature selection technique. The experiments conducted on BII Sneeze-Cough (BIISC) video dataset show that our framework can 70% accuracy, outperforming the baseline results by more than 8%. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Projection-algebras and quantum Logic

Journal of Applied Logics

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Journal of Applied Logics 2024年第4期11卷 463-498页

作者： Lehmann, Daniel Selim and Rachel Benin School of Computer Science and Engineering and Quantum Information Science Center Hebrew University Jerusalem91904 Israel

P-algebras are a non-commutative, non-associative generalization of Boolean algebras that are for quantum logic what Boolean algebras are for classical logic. P-algebras have type ⟨X, 0,′, ·⟩ where 0 is a constant,′ is unary and · is binary. Elements of X are called features. A partial order is defined on the set X of features by x ≤ y iff x · y = x. Features commute, i.e., x · y = y · x iff x · y ≤ x. Features x and y are said to be orthogonal iff x · y = 0 and orthogonality is a symmetric relation. The operation + is defined as the dual of · and it is commutative on orthogonal features. The closed subspaces of a separable Hilbert space form a P-algebra under orthogonal complementation and projection of a subspace onto another one. P-algebras are complemented orthomodular posets but they are not lattices. Existence of least upper bounds for ascending sequences is equivalent to the existence of least upper bounds for countable sets of pairwise orthogonal elements. Atomic algebras are defined and their main properties are studied. The logic of P-algebras is then completely characterized. The language contains a unary connective corresponding to the operation′ and a binary connective corresponding to the operation "·". It is a substructural logic of sequents where the Exchange rule is extremely limited. It is proved to be sound and complete for P-algebras. © 2024, College Publications. All rights reserved.

关键词： Hilbert spaces

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Classifying the state of knowledge-based question answering: patterns, progress, and prospects

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International Journal of computers and Applications 2025年第1期47卷 93-105页

作者： Sharma, Sumit Jain, Sarika Tiwari, Mukesh Kumar lal, Sohan Department of Computer Science and Engineering Quantum University Roorkee Uttarakhand Roorkee India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generation of QASs focuses on the types of inquiries that users typically enquire, the features of the data sources that are used, and the formats in which the right responses are produced. Many QASs have been developed since the 1960s when research on the subject first started. There are several cutting-edge frameworks for answering questions that can be used to categorize the QA system according to a number of criteria. The necessity for an extensive survey on QASs comes naturally in order to determine the future focus of research in this field. In this study, QASs are analyzed by the number of criteria used to categorize question-answering (QA) and provide the dimension using a variety of standards to cover all the QAS. We describe the current state of research in each QAS area and make recommendations for its future direction. Based on the dimension criteria outlined in the research methodology, research studies are chosen for additional review and comparison. Constructed some criteria to classify the QASs, we provide the classification and go over the benefits and drawbacks of each class's QAS, as well as the problems with the corresponding study. Results are detailed summarized and compared. As a result of this study, a general architecture of frameworks for QA over knowledge base (KB) is drawn in addition to the comparative analysis of systems, serving as a reference for academics working on related topics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Question answering

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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Egocentric Vision Action Recognition: Performance Analysis on the Coer-Egovision Dataset

Egocentric Vision Action Recognition: Performance Analysis o...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Gupta, Himanshu Kumar, Vivek Quantum University Department of Computer Science & Engineering Roorkee India

ISBN: (纸本)9798350382723

Egocentric activity recognition, also known as first-person vision, captures human actions and activities from the perspective of a wearable camera, providing a personalized and contextualized viewpoint. This unique perspective is crucial for various real-world applications, including healthcare monitoring, sports analy-sis, augmented reality, and assistive technologies. In this paper, we present a comparative analysis of various state-of-the-art algorithms for egocentric action recognition using the recently curated dataset, 'Coer-Egovision.' This dataset captures human actions and activities from the unique perspective of a wearable camera, providing a personalized and context-aware viewpoint. Such a perspective holds significant value for real-world applications, including healthcare monitoring, sports analysis, augmented reality, and assistive *** 'Coer-Egovision' dataset encompasses diverse actions, such as 'Going-Downstairs', 'Going-Upstairs', 'Texting', 'Walking' and 'Writing' allowing for a challenging benchmark to evaluate algorithms in this domain. By leveraging the wearable camera perspective, the dataset addresses the need for a more com-prehensive understanding of human interactions with the *** the evaluated algorithms, the Long-term Recurrent Convolutional Network (LRCN) emerges as the top performer, achieving an impressive accuracy of 88% in effectively modeling long-term temporal dependencies. Additionally, the Histogram of Oriented Gradients (HOG) combined with Support Vector Machine (SVM) approach demonstrates robust performance with 82% accuracy, highlighting the efficacy of feature-based methods. The study also reveals promising results for the 2-Stream Approach with 3D CNN and LSTM, achieving an accuracy of 74%. Furthermore, pretrained models like VGG16 and DenseNet201 along with LSTM exhibit competitive performances with accuracies of 80% *** results offer valuable insights into the performance of diverse algo

关键词： Support vector machines

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Unveiling the web of privacy: A comprehensive review of threats to information security in social networks 4th

Unveiling the web of privacy: A comprehensive review of thre...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Singh, Shalini Baloni, Dev Sarkar, Manash Department of Computer Science and Engineering Quantum University Roorkee India Department of Computer Science and Engineering Atria Institute of Technology Banglore India

ISBN: (纸本)9781032911571

This review paper explores emerging threats to information privacy and security within the dynamic landscape of Online Social Networks (OSNs), which serve as repositories of vast amounts of user data. The rise in social media use emphasizes how crucial it is to inform users about the dangers that come with it, like cyber-attacks and privacy violations. Even though various social media platforms have been made possible by technological advancements, consumers’ ignorance of these platforms creates serious security risks. Although academics have come up with creative security solutions for social networks, there are obstacles in the way of their actual application. It is essential to continuously assess security vulnerabilities in light of technology improvements. This study examines several dangers to online social networks and suggests countermeasures based on a variety of models, frameworks, and encryption methods. Despite advancements, privacy concerns remain unaddressed, requiring parents to actively supervise their children’s online activity. © 2025 the Author(s).

关键词： Differential privacy

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Ensemble Explainable Artificial Intelligence Model for COVID-19 Detection Using Chest X-Ray Images 1

Ensemble Explainable Artificial Intelligence Model for COVID...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Pandey, Krishna Mohan Baloni, Dev Quantum University Department of Computer Science and Engineering Uttrakhand Roorkee India

ISBN: (纸本)9798350367720

After the virus was initially detected in Wuhan, China, in December 2019, it was announced in the midst of 2020 that an outbreak of COVID-19 had occurred, marking the onset of a global pandemic. Experts have put forth the hypothesis that this particular virus has the capability to infect both the upper respiratory system, including the sinuses, nose, and throat, as well as the lower respiratory system, encompassing the trachea and lungs. The most prevalent modes of transmission are through airborne droplets and direct contact with individuals carrying the COVID-19 virus. Currently, there are countless individuals across the globe who are grappling with this illness. Despite widespread preventive measures, the number of individuals affected by this disease and the death toll associated with it continue to rise at a disconcerting pace. There is a pressing need for computerAided Diagnosis (CAD) methods that can accurately identify this disease, yet many existing models are not utilized due to their lack of explainability. In this particular investigation, we present a methodology for swiftly and precisely identifying COVID-19 in Chest X-Ray (CXR) images, as this is the most commonly used and easily accessible medical diagnostic tool. To accomplish this, we employ DenseNet 121, a Deep Learning Model, on our dataset and achieve an impressive accuracy rate of 98.88%. Furthermore, we integrate Explainable Artificial Intelligence (XAI) models, specifically GRAD CAM ++ and SHAP, as an ensemble approach to enhance the interpretability of our classification model. This integration allows for a clearer visualization of the decision-making process and supports medical professionals in understanding how the algorithm arrives at its decisions. © 2024 IEEE.

关键词： COVID-19

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(Information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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