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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Saad, Serat Giri, Anish Ganiyu, Mubarak Nizovsky, David Owens, David Lalrindiki, Mandy Vanderbilt University Dept. of Physics and Astronomy NashvilleTN United States Vanderbilt University Dept. of Computer Science NashvilleTN United States The Wond'ry Vanderbilt University NashvilleTN United States Vanderbilt University Dept. of Engineering NashvilleTN United States

ISBN: (纸本)9798350343236

The Wondry Quantum Studio (TWQS) at Vanderbilt University has been a pioneering force in the arena of quantum education and learning. Our concerted efforts have provided a fresh perspective on quantum education. Strategically collaborating with industry leaders, such as IBM Quantum, Zapata Computing, and D-Wave, TWQS has been able to amalgamate and optimize a variety of pedagogical techniques into an impactful and highly effective curriculum. This synergy not only imparts practical knowledge but also provides learners with crucial exposure to real-world quantum opportunities, thereby nurturing a competent quantum workforce. In this poster, we exhibit our noteworthy initiatives and the resulting achievements, all of which have significantly contributed to the nurturing of a robust quantum ecosystem. The highlights include our unique pedagogical approach, successful collaborations that yielded unique learning outcomes, and direct exposure of students to varied quantum opportunities. Furthermore, we underscore the importance of our core values, primarily inclusivity and collaboration. Lastly, we share the key insights and experiences gathered, shedding light on the quantum leap that has been achieved in terms of quantum education and the path that lies ahead for TWQS. © 2023 IEEE.

关键词： Studios

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Online Hierarchical Conformance Refinement Planning for Autonomous Robots 28

Online Hierarchical Conformance Refinement Planning for Auto...

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28th International Conference on Automation and Computing, ICAC 2023

作者： Kamperis, Oliver Michael Wang, Yongjing Castellani, Marco University of Birmingham Dept. of Computer Science BirminghamB15 2TT United Kingdom University of Birmingham Dept. of Mechanical Engineering BirminghamB15 2TT United Kingdom

ISBN: (纸本)9798350335859

Planning is a fundamental reasoning ability required by any general-purpose autonomous robot that will be deployed into real-world domains. This paper presents our ongoing work towards a novel domain-independent planning paradigm, called Hierarchical Conformance Refinement (HCR), capable of rapid online partial-planning (that is planning partly interleaved with execution), which aims to maximize productivity and minimize down-times of robots. HCR planning achieves this by employing two core concepts involved in human decision making;abstract reasoning and problem decomposition. Our approach combines the expressive power of Answer Set Programming (ASP) with the speed of hierarchical refinement planning. Together, these allow large and complex problems with long plan lengths, to be broken down and solved as many smaller and more manageable partial-problems, in a general way. This exponentially reduces execution latency and total planning times, for the trade-off of small losses in plan quality. Simulated experiments ran on an extension of the blocks world domain show that HCR planning reduced overall planning time by between 43.8 to 97.6 %, and execution latency time by 87.1 to 98.7%, over classical ASP based planning, but could increase plans lengths by 0.0 to 9.0% over the minimum possible length, from our easiest to hardest problems tested. © 2023 IEEE.

关键词： Decision making

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Optimizing Healthcare(Nephrology) Automation to Enhance Patient Care and Operational Efficiency

Optimizing Healthcare(Nephrology) Automation to Enhance Pati...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Latha L Amaresh M Ashwath A Krithinka M Lokesh P Sai Yeswant S Dept. of Information Technology Kumaraguru College of Technology Coimbatore TamilNadu Dept. of Computer Science and Engineering Kumaraguru College of Technology Coimbatore TamilNadu Dept. of Information Science and Engineering Kumaraguru College of Technology Coimbatore TamilNadu

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Healthcare automation is rapidly transforming the landscape of medical care, particularly in specialized fields like nephrology, which deal with the diagnosis and treatment of kidney-related diseases. This study explores the integration of automation technologies in nephrology to enhance patient care and improve operational efficiency. Nephrology practices can leverage the advancements in AI, machine learning, robotics, and data analytics to streamline administrative workflows, optimize resource allocation, and deliver more personalized and precise care. Automated systems, such as AI-powered diagnostic tools, predictive analytics for disease progression, and automated dialysis machines, may reduce diagnostic errors, improve patient outcomes, and minimize clinician workload. This research shows how automation of routine processes, including patient scheduling, updating medical records, and treatment planning, decreases administrative burdens and allows healthcare providers to focus on critical decision-making and direct patient interaction. In addition, automation in nephrology supports the early detection and management of chronic kidney disease (CKD) by analysing large datasets to identify at-risk individuals and recommend timely interventions. Automating tasks such as laboratory data processing and dialysis monitoring will enable healthcare facilities to save a lot of money and be more operationally efficient while maintaining regulatory standards. This research study addresses the healthcare automation issues associated with data security, patient privacy, and upskilling of the workforce for operation and management of complex systems. Thus, this paper stresses the point that a multi-stakeholder approach involving providers of healthcare services, developers of technology, and policymakers is significant in achieving sustainable and effective automation strategies. This research indicates that in the field of nephrology, optimizing automation brings clinical and

关键词： Automation Nephrology Medical services Chronic kidney disease Telecommunication computing Resource management Stress Standards Robots Diseases

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Reducing Client-Side Cost in Secure Inner Product Evaluations

Reducing Client-Side Cost in Secure Inner Product Evaluation...

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International Conference on Information Networking

作者： Changhee Hahn Dongyoung Koo Junbeom Hur dept. Electrical and Information Engineering Seoul National University of Science and Technology South Korea dept. Convergence Security Hansung University South Korea dept. Computer Science and Engineering Korea University South Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

In the era of ubiquitous data collection and analysis, preserving privacy while utilizing data, such as inner product evaluations, poses a significant challenge. One such method is inner product functional encryption (IPFE), which enables the computation of inner products on encrypted vectors without exposing the vectors themselves. However, the computational intensity of IPFE decryption poses challenges, particularly for resource-limited client devices and high-dimensional vectors. To facilitate partial decryption, prior IPFE schemes require the client to derive a partial decryption key from an assigned secret key, incurring computation costs linear to the vector dimension. In this paper, we provide a framework for the challenges of IPFE, enabling efficient decryption outsourcing, maintaining data privacy, and facilitating key generation outsourcing in IPFE schemes. In the proposed framework, outsourced decryption using a partial decryption key generates a masked plaintext that only the client can unmask. Furthermore, the process of deriving the partial decryption key can be fully outsourced, thereby ensuring that the client’s computational burden remains constant.

关键词： Data privacy Costs Data collection Vectors Outsourcing Encryption Computational efficiency

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

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IEEE Region 10 International Conference TENCON

作者： Yoshihiko Tsuchiya Norisato Suga Kazunori Uruma Masaya Fujisawa Dept. of Information and Computer Technology Tokyo University of Science Tokyo Japan Dept. of Information and Communication Engineering Shibaura Institute of Technology Tokyo Japan Dept. of Computer Science Kogakuin University Tokyo Japan

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency.

关键词： Fluctuations Accuracy Surface waves Spectral efficiency Superresolution Channel estimation Line-of-sight propagation Predictive models Numerical models IEEE Regions

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum Machine Learning 3

A Comprehensive Survey on Breast Cancer Diagnostics: From Ar...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Reddy, Manideep Raya V S R S Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Patanjali CPS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum Machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.

关键词： Biopsy

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LaCK: Lung Cancer Classification and Detection using Convolutional Neural Network-based Gated Recurrent Unit Neural Network Model

LaCK: Lung Cancer Classification and Detection using Convolu...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Thara, Machakanti Navya Chatterjee, Kalyan Raju, Muntha Rout, Sandip Priyadharshini, M. Prasad, Kokkula Shiva Suraj Kumar, Samala Reddy, Mandadi Sriya Nalla Malla Reddy Engineering College Artificial Intelligence & Machine Learning Hyderabad India Nalla Malla Reddy Engineering College Computer Science & Engineering Hyderabad India Government of Odisha Mathematics Government Autonomous College Dept. of Higher Education Odisha Angul India

ISBN: (纸本)9798350361537

Cancer instills fear in people worldwide, with millions succumbing to its grasp each year. Among the various types, lung cancer stands as a prominent contributor to this alarming statistic. Therefore, this novel research Lung Cancer Classification and Detection using Convolutional Neural Network-based Gated Recurrent Unit Neural Network Model (LaCK) has focused on classifying lung cancer, notably Non-Small Cell Lung Cancer, which constitutes the majority of cases. We employed five units of gated recurrent unit neural network (GRU-NN) together to achieve this classification, utilizing logistic regression (LR) and support vector classifier (SVC) models for comparisons. Among all models, the LaCK model demonstrated remarkable accuracy, achieving approximately 98% success rate, outperforming its counterparts. Implementing this model in clinical settings enables medical professionals to establish a precise and automated approach to diagnosing lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Topic Relation Graph Construction to Identify Challenges in Introducing Programming in Indonesian K-12 Education 1

Topic Relation Graph Construction to Identify Challenges in ...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Ayub, Mewati Karnalim, Oscar Toba, Hapnes Wijanto, Maresha Caroline Dulimarta, Hans Saputra, Williyandie Kotambunan, Tabitha Emanuella Master Program of Computer Science Maranatha Christian University Bandung Indonesia Ewha Womans University Dept. of Computer Science and Engineering Seoul Korea Republic of School of Computing Grand Valley State University MI United States Informatics Engineering Maranatha Christian University Bandung Indonesia

ISBN: (纸本)9798350306156

Programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming. © 2023 IEEE.

关键词： Students

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Efficient Augmentation for Imbalanced Deep Learning 39

Efficient Augmentation for Imbalanced Deep Learning

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Dablain, Damien A. Bellinger, Colin Krawczyk, Bartosz Chawla, Nitesh V. University of Notre Dame Lucy Family Institute for Data & Society Dept. Computer Science and Engineering Notre DameIN46556 United States National Research Council of Canada OttawaK1A 0R6 Canada Virginia Commonwealth University Dept. Computer Science RichmondVA23824 United States

ISBN: (纸本)9798350322279

Deep learning models may not effectively generalize across under-represented or minority classes. We empirically study a convolutional neural network's (CNN) internal representation of imbalanced image data and measure the generalization gap between a model's feature embeddings in the training and test sets, showing that the gap is wider for minority classes. This insight enables us to design an efficient three-phase CNN training framework for imbalanced data. The framework involves training the network end-to-end on imbalanced data to learn feature embeddings, performing data augmentation in the learned embedding space to balance the training data distribution, and fine-tuning the classifier head on the embedded balanced training data. We develop Expansive Over-Sampling (EOS) as a data augmentation technique to utilize in the training framework. EOS forms synthetic training instances as convex combinations between the minority class samples and their nearest adversaries in the embedding space to reduce the generalization gap. The proposed framework improves the accuracy over leading cost-sensitive and resampling methods commonly used in imbalanced learning. Moreover, it is more computationally efficient than standard data pre-processing methods, such as SMOTE and GAN-based over-sampling, as it requires fewer parameters and less training time. The source code for the proposed framework is available at: https://***/dd1github/EOS. © 2023 IEEE.

关键词： Embeddings

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Stochastic Fundamental Diagram for Motorway Traffic Modelling

SSRN

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SSRN 2024年

作者： Erberto, Cantarella Giulio Ernesto, Cipriani Andrea, Gemma Orlando, Giannattasio Livia, Mannini Dept. of Civil Engineering University of Salerno Italy Dept. of Civil Computer Science and Aeronautical Technologies Engineering University of Roma Tre Italy

This paper presents a comprehensive methodology aimed at specifying lane specific (stable regime) speed-flow function within the framework of the Stochastic Fundamental Diagram for motorways. This methodology is consistent with the main assumptions of Demand Assignment and, additionally, it offers a process for calibrating this speed-flow function based on observed trajectories. The proposed methodology discusses lane specific speed-flow functions and speed distribution through first a complete statistical analysis, next a deterministic analysis and then a stochastic analysis, further developing the contents of a previous paper. The practical utility of the proposed methodology is demonstrated through the investigation of a real-world case, highlighting its applicability in concrete scenarios. This case study is an example of the effectiveness of the methodology in practical transport contexts. The proposed methodology is useful for the evaluation of projects, potentially considering different externalities, as a tool for the planning and programming of transport systems. With regard to motorway traffic control, S-FD can be applied to forecast the effects of speed variations (e.g. due to work zones) and speed control strategies such as variable speed limits (VSLs). © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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