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Dynamic Entanglement Routing Based on Stream Processing for Quantum Networks

IEEE/ACM Transactions on Networking 2024年

作者： Yang, Lan Zhao, Yangming Xu, Hongli Huang, Liusheng Qiao, Chunming State Key Laboratory for Novel Software Technology Nanjing University Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou215163 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China University at Buffalo The State University of New York BuffaloNY14260 United States

Quantum Networks (QNs) typically leverage teleportation to send quantum bits (called qubits) to their destinations. To teleport a data qubit from Alice to Bob, one Entanglement Connection (EC) between Alice and Bob needs to be established. Accordingly, we have to concurrently establish as many requested ECs as possible in order to maximize the network throughput. Conventional methods either assumed a known traffic matrix and calculated the Entanglement Paths (EPs) for all requests in one batch or maximized the number of ECs established between all Source-Destination (SD) pairs without considering the amount of data qubits to be teleported. These methods are not scalable in large scale QNs since it is time consuming to calculate the EPs for a batch of requests. In addition, there may be only very few data qubits to be teleported between some SD pairs. Accordingly, the latter method may establish many useless ECs. To address these issues, we propose a Dynamic Entanglement Routing () scheme which determines the EPs based on stream processing. By introducing a method to derive an appropriate purification scheme along each EP, we further extend to Dynamic Entanglement Routing with Purification () that provides fidelity guarantee to the established ECs. Through extensive simulations, we demonstrate that outperforms two representative heuristics by up to 52.79% and 61.27%, respectively, in terms of average request completion time and when we have to ensure the fidelity of the established ECs, this performance improvement will become 21.05% and 48.67%, respectively, if is adopted. © 1993-2012 IEEE.

关键词： Qubits

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Integrating Artificial Intelligence for Sustainable and Adaptable Architecture

Integrating Artificial Intelligence for Sustainable and Adap...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Valapula Lalitha Regidi Suneetha Basi Reddy A Nilesh R. Mate Syeeda Mujeebunnisa Department of CSE(AI&ML) Sai Spurthi Institute of Technology India Department of ECE Sanketika Vidya Parishad Engineering College Visakhapatnam India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India Centre for Distance and Online Education Bharati Vidyapeeth (Deemed to be University) Pune India School of Science and Computer Studies CMR University Bengaluru India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

In nature, flexibility is essential, and AI may enhance it in architecture. The article integrates AI to develop self-learning buildings that can adapt to future obstacles. This research aims to create structures that use sensors to gather environmental data and respond. Phases comprise this study's strategy. In Phase 1, AI in architecture is extensively researched. That study in Phase 1 guided Phase 2's building design. The parametric building design in phase 2 uses machine learning and computational design techniques. Grasshopper scripting and Rhino modelling were used to create an algorithm that would create shapes that would both biomimetic the process of coral growth and be tailored to the project's chosen location. The selection procedure for the produced experimental investigations is shown in Phase 3. To determine which type used the least amount of energy, many evaluations were conducted, including measurements of sunshine, radiation, and shadows. Phase 4 involves developing the shape to improve the building's efficiency. Phase 5 involves conducting performance assessments to demonstrate that the final form is highly adaptable and climatic or ecologically sensitive. This building's radiation exposure, according to the research, is between 200 and 300 kWh/m 2 . According to the shadow study, the building shape casts a shadow for eight hours. According to the analysis, the building's shape makes it more adaptive by reducing its energy use. An AI engine technology is used in the last stage to forecast the building's future growth. By incorporating technology into building design, we may create flexible structures that reduce building energy use and contribute to the creation of a sustainable world

关键词： Phase measurement Shape Architecture Shape measurement Buildings Energy measurement computer architecture Energy efficiency Sensors Artificial intelligence

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MLRegTest: A Benchmark for the Machine Learning of Regular Languages

arXiv

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arXiv 2023年

作者： van der Poel, Sam Lambert, Dakotah Kostyszyn, Kalina Gao, Tiantian Verma, Rahul Andersen, Derek Chau, Joanne Peterson, Emily Clair, Cody St. Fodor, Paul Shibata, Chihiro Heinz, Jeffrey School of Mathematics Georgia Institute of Technology United States Department of Computer Science Haverford College United States Department of Linguistics Institute of Advanced Computational Science Stony Brook University United States Department of Computer Science Stony Brook University United States Department of Linguistics Stony Brook University United States Department of Advanced Sciences Graduate School of Science and Engineering Hosei University Japan

Synthetic datasets constructed from formal languages allow fine-grained examination of the learning and generalization capabilities of machine learning systems for sequence classification. This article presents a new benchmark for machine learning systems on sequence classification called MLRegTest, which contains training, development, and test sets from 1,800 regular languages. Different kinds of formal languages represent different kinds of long-distance dependencies, and correctly identifying long-distance dependencies in sequences is a known challenge for ML systems to generalize successfully. MLRegTest organizes its languages according to their logical complexity (monadic second order, first order, propositional, or restricted propositional) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, and therefore to understand the capacities of different ML systems to learn such long-distance dependencies. Finally, the performance of different neural networks (simple RNN, LSTM, GRU, transformer) on MLRegTest is examined. The main conclusion is that performance depends significantly on the kind of test set, the class of language, and the neural network architecture. © 2023, CC BY.

关键词： Formal languages

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Improving Prediction Accuracy Using Machine Learning Classification Techniques for Alzheimer’s Disease in Healthcare Services 1

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1st International Virtual Conference on Industry 4.0, IVCI4.0 2020

作者： Shakkeera, L. Sowmiya, K. School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India Department of Information Technology School of Computer Information and Mathematical Sciences B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9789811612442

ISBN: (纸本)9789811612435

The populace is quickly maturing, and the people who suffer from dementia is to spread around 35 million by 2030, to 65 million today. Alzheimer’s disease (AD) is where dementia begins. Person with AD will have memory impairment and finally the people lose his/her ability to carry out the day to day works. The existing model used CNN for classifying Alzheimer patient. Major drawback of CNN is overfitting and it is expensive because it has to take a large dataset for training. To alleviate this, we developed a model for accurate prediction of AD. The proposed system implemented with ROI-based feature extraction, NCA feature selection and tenfold satisfied cross-validation techniques to predict the AD. The advantage of this system is better accuracy which is obtained using SVM classifier. In present days, no treatment can anticipate and fix the Alzheimer’s disease in early times. Therefore, this has become the research direction of our proposed work. The accuracy, sensitivity, specificity of the proposed model ranges from 98.15%, 98%, 97%, respectively where the existing model value ranges from 92.50%, 93%, 85%, respectively. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Inflammation Segmentation Module using Convolution Neural Network for Alzheimer's Disease Detection using Magnetic Resonance Imaging Inputs

Inflammation Segmentation Module using Convolution Neural Ne...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： Sahana Shetty Ramesh S R Nithya P. Thangaraj Antony Vijay J Sangamithrai K Department of CSE-AIML and Cyber Security School of CSE FET - Jain Bangalore India Department of Computing Technologies College of Engineering & Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of CSE Bannari Amman Institute of Technology Erode Tamilnadu India Department of CSE Builders Engineering College Tirupur Dt Tamilnadu India Department of CSE Karpagam College of Engineering Coimbatore Tamilnadu India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Magnetic Resonance Imaging (MRI) inputs have been most commonly used in diagnosing Alzheimer's Disease (AD) in recent years. The patterns of brain functions are deliberately observed in the MRI inputs for efficient AD detection. This article introduces an Inflammation Segmentation Module (ISM) using a convolution neural network (CNN) to augment better AD detection. This segmentation module identifies the textural differences in inflammation regions through training input cross-correlation. The cross-correlation process is reliable in identifying similar and differential textural features to improve the detection accuracy. The CNN is trained using an external dataset and correlated outputs for high precision. This proposed module is reliable in detecting inflammation textures with high precision and less analysis time.

关键词： Training Image segmentation Accuracy Convolution Magnetic resonance imaging Feature extraction Reliability

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Optimizing Sewage Systems for Real-Time Flow Analysis and Predictive Maintenance with IoT and LSTM

Optimizing Sewage Systems for Real-Time Flow Analysis and Pr...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Kannan Shanmugam N. Kasthuri T. J. Nagalakshmi N. Mohankumar M Suresh Kumar M. Rajmohan Department of Gaming Technology School of Computing Science and Engineering VIT Bhopal University Bhopal Madhapradesh India Department of Mathematics Vinayaka Mission’s Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation (DU) Salem Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

This research introduces a new real-time method for analyzing sewage flow utilizing the Internet of Things (IoT). This method relies on long-short-term memory (LSTM) networks. In urban areas, sewage systems are an integral part of the infrastructure, and keeping them in good working order is crucial to reducing pollution and protecting residents’ health. System breakdowns and expensive repairs are common results of the reactive and inefficient maintenance practices commonly used in the past. In this research, it presents preventative methods that make use of IoT sensors to gather real-time data from wastewater treatment facilities. The information is then analyzed using LSTM networks to foresee problems like backups and overflows. Municipalities and utility companies may save money and lessen their environmental impact by implementing preventative maintenance plans that address potential issues in advance. It examines the practical implications of applying IoT and LSTM-based predictive maintenance to sewage systems and gives experimental findings confirming the technique’s usefulness in properly predicting flow irregularities. This study is crucial to ensuring the long-term viability and effective operation of urban sewage systems.

关键词： Temperature measurement Pollution Water quality Real-time systems Maintenance Sensors Internet of Things Public healthcare Long short term memory Predictive maintenance

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Quantum computing Society of the Philippines: An NGO Perspective on Promoting Quantum Information, science, and technology in the Philippines

Quantum Computing Society of the Philippines: An NGO Perspec...

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Quantum computing and engineering (QCE), IEEE International Conference on

作者： Dylan Josh Domingo Lopez Elmer Peramo Jabez Mendoza Ayson Angela Nicole S. Masongsong Lance Dominic C. Raquel Bobby O. Corpus Department of Computer Technology De La Salle University Manila Philippines Department of Science and Technology Advanced Science and Technology Institute Quezon City Philippines Electronics Engineering Department Don Honorio Ventura State University Pampanga Philippines STEM Department De La Salle University Integrated School Manila Philippines Department of Physics University of San Carlos Cebu City Philippines Quantum Computing Society of the Philippines Manila Philippines

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

The development of Quantum Information science and technology (QIST) as an emerging technology among developed and developing countries has caught the attention of governments, industries, and educational institutions. However, not all countries have equal starting points in promoting QIST amongst different sectors. Several players in various countries promote QIST as a discipline and education. Although significant developments in quantum technology are focused in Western countries, great innovation can also be seen in Eastern countries, specifically Asia. The Philippines is one of several developing countries in Asia that is in the process of adopting QIST in multiple sectors. In this paper, the Quantum computing Society of the Philippines (QCSP) is introduced as one of the arms of the country for promoting QIST to the public. Further in this paper, the initial impact of QIST promotion on various communities across the Philippines through multiple activities and educational efforts is explained. Several critical success stories for different audiences were described, wherein initial acceptance of QIST was evident through hackathons and lecture series. Events held show improvement in interest and proficiency in the foundations of QIST.

关键词： Industries Technological innovation Quantum computing Program processors Asia Government Education Developing countries Quantum information science Hackathon

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A Hybrid Solution for Secure Privacy-Preserving Cloud Storage & Information Retrieval

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Human-centric computing and Information sciences 2023年 13卷

作者： Kumar, Ankit Aljrees, Turki Hsieh, Sun-Yuan Singh, Kamred Udham Singh, Teekam Raja, Linesh Samriya, Jitendra Kumar Mundotiya, Rajesh Kumar Department of Computer Engineering & Applications GLA University UP Mathura India College of Computer Science and Engineering University of Hafr Al-Batin Eastern Province Saudi Arabia Department of Computer Science and Information Engineering Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models and International Center for the Scientific Development of Shrimp Aquaculture National Cheng Kung University Tainan Taiwan School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Manipal University Jaipur Jaipur India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India School of Computer Science University of Petroleum and Energy Studies Dehradun India

Cloud storage is an emerging archetype used by businesses for data storage. Clients require easy access to data in the cloud, which helps clients with limited computing power move their high-value, high-risk principle jobs to the cloud. The primary concern of this study is towards flawless verifying, checking data bundles, and determining modules to carry out the project. As discussed at several research platforms, each client is concerned about data storage and retrieval. Data security is critical in cloud computing, and thus monitoring has evolved into a vigilance that abstracts the monitoring process. Request made by clients for security planning is always computationally exclusive. As a result, each customer is concerned as to their verified condition. This paper will highlight a secure cloud communication method for client data to address the afore-mentioned issues. Synchronization creates secret hare keys, combined with a model-encrypted data packet sent to the cloud. These secure data are then sent over a public network, subject to monitoring to avert data leakage to an unauthorized party. They lack the cohesion to outsource data storage to the cloud deliberately. As an outcome of this model's distribution, the perception of authorized data owners would be changed regarding cloud storage up to 97.6%, addressing the trust issues between data owners and cloud ace affiliations © 2023, Human-centric computing and Information *** Rights Reserved.

关键词： Cryptography

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Rethinking Few-shot Class-incremental Learning: Learning from Yourself

arXiv

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arXiv 2024年

作者： Tang, Yu-Ming Peng, Yi-Xing Meng, Jingke Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University China

Few-shot class-incremental learning (FSCIL) aims to learn sequential classes with limited samples in a few-shot fashion. Inherited from the classical class-incremental learning setting, the popular benchmark of FSCIL uses averaged accuracy (aAcc) and last-task averaged accuracy (lAcc) as the evaluation metrics. However, we reveal that such evaluation metrics may not provide adequate emphasis on the novel class performance, and the continual learning ability of FSCIL methods could be ignored under this benchmark. In this work, as a complement to existing metrics, we offer a new metric called generalized average accuracy (gAcc) which is designed to provide an extra equitable evaluation by incorporating different perspectives of the performance under the guidance of a parameter α. We also present an overall metric in the form of the area under the curve (AUC) along the α. Under the guidance of gAcc, we release the potential of intermediate features of the vision transformers to boost the novel-class performance. Taking information from intermediate layers which are less class-specific and more generalizable, we manage to rectify the final features, leading to a more generalizable transformer-based FSCIL framework. Without complex network designs or cumbersome training procedures, our method outperforms existing FSCIL methods at aAcc and gAcc on three datasets. See codes at 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Complex networks

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A Novel Approach to Design Secured and Privacy Enabled Health Data Linkage System based on Cyber Security Principles

A Novel Approach to Design Secured and Privacy Enabled Healt...

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Big Data & Machine Learning (ICBDML), IEEE International Conference on

作者： Gayathri S A. Jaya Mabel Rani Anushalin P. S C.N. Ravi M. Jeyalaxmi V. Samuthira Pandi Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Biomedical Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Chennai Department of Computer Science and Engineering CMR Engineering College Hyderabad Medchal Department of Physics Prince Shri Venkateshwara Padmavathy Engineering College Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374117

Medical records from a variety of sources must be integrated into healthcare data repositories for knowledge discovery to be possible. An essential area of study is how to keep records linked when merging medical data. Researchers have proposed many solutions to this issue, all of which are relevant to industrialized nations that use patient identification numbers such as social security numbers, health insurance numbers, universal patient identifiers (UPIs), etc., to keep track of electronic health records. Because of issues with missing data, unclear patient identification, and excessive noise in patient information, these systems are not suitable for record linking of health data in underdeveloped nations. There is a serious concern that patients' personally identifiable information stored in electronic health records poses a threat to their privacy and leaves health information systems open to intrusion. We have examined the real-world challenges of healthcare data collection and integration for a national health data warehouse in this article. To accommodate the limited health data of poor nations like Bangladesh, we have developed privacy-preserving secured record linkage architecture we call Secured Health Data Handler (SHDH). For the purpose of facilitating the knowledge discovery process, our approach may anonymize identifying private data of patients while preserving record linkage in integrated health repositories. Even without a standard ID, our suggested strategy was able to connect records with sufficient accuracy for noisy data, according to the experimental results.

关键词： Couplings Data privacy Sociology Data warehouses Knowledge discovery Security Electronic medical records

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