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6th IEEE International Conference on Trust, Privacy and Security in Intelligent systems, and Applications, TPS-ISA 2024

作者： Chandra, Nitish K. Kaur, Eneet Seshadreesan, Kaushik P. University of Pittsburgh School of Computing & Information Department of Informatics & Networked Systems PittsburghPA15260 United States Cisco Quantum Lab Los AngelesCA90404 United States

ISBN: (纸本)9798350386745

Realizing distributed architectures for quantum computing is crucial to scaling up computational power. A key component of such architectures is a scheduler that coordinates operations over a short-range quantum network required to enable the necessary non-local entangling gates between quantum processing units (QPUs). It is desirable to determine schedules of minimum make span, which in the case of networks with constrained resources hinges on their efficient usage. Here we compare and contrast two approaches to solving the make span minimization problem, an approach based on the resource constrained project scheduling (RCPSP) framework, and another based on a greedy heuristic algorithm. The workflow considered is as follows. Firstly, the computational circuit is partitioned and assigned to different QPUs such that the number of nonlocal entangling gates acting across partitions is minimized while the qubit load is nearly uniform on the individual QPUs, which can be accomplished using, e.g., the METIS solver. Secondly, the nonlocal entangling gate requirements with respect to the partitions are identified, and mapped to network operation sequences that deliver the necessary entanglement between the QPUs. Finally, the network operations are scheduled such that the make span is minimized. As illustrative examples, we analyze the implementation of a small instance of the Quantum Fourier Transform algorithm over instances of a simple hub and spoke (star) network architecture comprised of a quantum switch as the hub and QPUs as spokes, each with a finite qubit resource budget. In one instance, our results show the RCPSP approach outperforming the greedy heuristic. In another instance, we find the two performing equally well. Our results thus illustrate the effectiveness of the RCPSP framework, while also underlining the relevance and usefulness of greedy heuristics. © 2024 IEEE.

关键词： Budget control

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Sequential Patch Analysis Framework for Lung Disease Classification 11th

Sequential Patch Analysis Framework for Lung Disease Classi...

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11th International Conference on Future Data and Security Engineering, FDSE 2024

作者： Le, Kim-Ngoc T. Le, Duc-Tai Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of College of Computing and Informatics Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819604364

Deep neural network (DNN) models have shown promise in lung disease classification using spectrogram transformations of respiratory sounds. However, these models typically process entire images, which can be computationally expensive, and they require large datasets for effective training. With the largest available respiratory dataset, ICBHI, containing only 920 recordings, training a robust DNN becomes challenging. To address this issue, this paper introduces a simplified DNN framework that incorporates novel techniques such as patch splitting and transfer learning. Specifically, the proposed framework splits the spectrogram image into a sequence of fixed-size patches, effectively reducing computational demands. These patches are processed with the position embeddings by DNN encoders to capture acoustic characteristics, leveraging a pre-trained ImageNet model to make efficient use of the limited dataset. Evaluation experiments on the ICBHI dataset show that the proposed method improves the state-of-the-art 8-class classification accuracy by 1.91%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category) 1

Model of Visualization and Analytics for Open Data (Case: El...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Wibowo, Ripto Mukti Fakieh, Bahjat Ramzan, Muhammad Sher Alzahrani, Ahmed Saeed Siddiqui, Muazzam Alzahrani, Bander King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia Indiana University Luddy School of Informatics Computing and Engineering Bloomington United States

ISBN: (纸本)9781665472753

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

关键词： Open Data

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SQL versus NoSQL databases to assess their appropriateness for big data application

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Recent Advances in Computer Science and Communications 2021年第4期14卷 1098-1108页

作者： Kausar, Mohammad A. Nasar, Mohammad Department of Information Systems University of Nizwa Nizwa Oman Department of Computing & Informatics Mazoon College Muscat Oman

Background: Nowadays, the digital world is rising rapidly and becoming very difficult in nature’s quantity, diversity, and speed. Recently, there have been two major changes in data management, which are NoSQL databases and Big Data Analytics. While evolving with the diverse reasons, their independent growths balance each other and their convergence would greatly benefit organization to make decisions on-time with the amount of multifaceted data sets that might be semi structured, structured, and unstructured. Though several software solutions have come out to support Big Data analytics on the one hand, on the other hand, there have been several packages of NoSQL database available in the market. Aim and Methods: The main goal of this article is to give comprehension of their perspective and a complete study to associate the future of the emerging several important NoSQL data models. Results: Evaluating NoSQL databases for Big Data analytics with traditional SQL performance shows that NoSQL database is a superior alternative for industry condition need high-performance analytics, adaptability, simplicity, and distributed large data scalability. Conclusion: In this article we conclude with the adoption of NoSQL in various markets. © 2021 Bentham Science Publishers.

关键词： Data Analytics

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Public Interest Research on Digital Learning Content Produced by Artificial Intelligence 7

Public Interest Research on Digital Learning Content Produce...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Kurniawan, Yohannes Jordan Ongko, Jastin Gabriel Julianto Anwar, Norizan School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Information Science College of Computing Informatics and Media Universiti Teknologi Mara Shah Alam Malaysia

ISBN: (纸本)9798350307504

The development of Artificial Intelligence (AI) has been extensively applied in education. Especially in Indonesia, there is an urgency to apply AI in education as it improves the quality of education due to poor management of learning systems, human resource deficits, and perverse incentive structures. A poor learning environment can lead to students lacking knowledge and resources. Besides that, the widespread adoption of AI in various fields is inevitable, so students must prepare for future challenges by starting to adopt AI themselves. This research aims to study the use of AI to create digital learning content that supports knowledge, tools, and learning empowerment. The study used a qualitative approach to analyze video learning created using AI tools and collected respondents' assessments through Google Forms. As a result, AI-generated video has a positive effect on student learning, increasing their understanding and providing them with reliable information. Furthermore, this research found that students are excited about using AI in creating digital learning content, as they believe it can help them learn and improve their learning methods. Although digital learning content is not as interactive and engaging as expected, users still believe that with the help of AI, they may improve and create a better learning environment. © 2023 IEEE.

关键词： Learning systems

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Understanding the Student Experience Better: Analyzing Remote Learning Tools Through Engagement, Participation, and Liveness Concepts 17

Understanding the Student Experience Better: Analyzing Remot...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Gomes, Genildo Silva, Paulo Magalhães, Sâmila Galvão, Leandro Lauschner, Tanara Steinmacher, Igor Conte, Tayana Gadelha, Bruno Institute of Computing Federal University of Amazonas AM Manaus Brazil Department of Design and Graphic Expression Federal University of Amazonas AM Manaus Brazil School of Informatics Computing and Cyber Systems Nothern Arizona University FlagstaffAZ United States

ISBN: (纸本)9789897587467

Technologies have changed how students engage and participate during remote classes, whether by encouraging interaction via chat on platforms such as Google Meet or by using voting features such as Poll. Our research aims to better understand the role of these tools in the educational context and how their interactive features influence student experience through the lens of engagement, participation, and liveness. In this sense, we address the following question: How can educational technologies be classified by the Engagement, Participation, and Liveness lens? To explore the relationship between the lens and the interactive features of Mentimeter, Google Meet, and Google Presentations, we conducted a study with three independent evaluations, measuring agreement using Cohen's Kappa coefficient. Our results suggest that these interactive features can be used as a lens to understand different types of experiences and how they can be applied to promote various dynamics, such as competitive and collaborative activities. Our results expand the professor's perspective on the student experience, providing ways for professors to understand the emotional and student behavioral reactions from using interactive tools in remote learning environments. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecurity-Based Approach

Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecur...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Phan, Quoc Bao Nguyen, Linh Nguyen, Tuy Tan Nguyen, Dinh C. Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350324136

This paper presents a novel two-stage approach to enhance the quality and privacy of X-ray medical images. The first stage leverages generative adversarial networks (GANs) for effective denoising, eliminating noise and artifacts from X-ray images while improving the visibility of critical anatomical structures. Subsequently, number-Theoretic transform (NTT) polynomial multiplication is integrated with Kyber to accelerate the encryption and decryption of the denoised X-ray images. This encryption safeguards the privacy of sensitive patient data and provides resilience against potential quantum computing attacks, ensuring long-Term data security. Implementing Kyber-based encryption and decryption on a graphics processing unit (GPU) architecture significantly reduces latency, enabling real-Time and secure access to critical healthcare information. © 2024 IEEE.

关键词： GANs Images denoising Kyber number theoretic transform

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ScaDLES: Scalable Deep Learning over Streaming data at the Edge

ScaDLES: Scalable Deep Learning over Streaming data at the E...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Tyagi, Sahil Swany, Martin Computing and Engineering Indiana University Luddy School of Informatics Department of Intelligent Systems Engineering BloomingtonIN United States

ISBN: (纸本)9781665480451

Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, a s w ell a s independent and identically distributed (IID) data across all nodes. However, these assumptions don't necessarily apply on the edge, especially when training neural networks on streaming data in an online manner. computing on the edge suffers from both systems and statistical heterogeneity. systems heterogeneity is attributed to differences in compute resources and bandwidth specific to each device, while statistical heterogeneity comes from unbalanced and skewed data on the edge. Different streaming-rates among devices can be another source of heterogeneity when dealing with streaming data. If the streaming rate is lower than training batch-size, device needs to wait until enough samples have streamed in before performing a single iteration of stochastic gradient descent (SGD). Thus, low-volume streams act like stragglers slowing down devices with high-volume streams in synchronous training. On the other hand, data can accumulate quickly in the buffer if the streaming rate is too high and the devices can't train at line-rate. In this paper, we introduce ScaDLES to efficiently train on streaming data at the edge in an online fashion, while also addressing the challenges of limited bandwidth and training with non-IID data. We empirically show that ScaDLES converges up to 3.29× faster compared to conventional distributed SGD. © 2022 IEEE.

关键词： Digital storage

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A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems 13

A Modified EACOP Implementation for Real-Parameter Single Ob...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tangherloni, Andrea Coelho, Vasco Buffa, Francesca M. Cazzaniga, Paolo Bocconi Institute for Data Science and Analytics Bocconi University Department of Computing Sciences Milan Italy University of Milano-Bicocca Department of Informatics Systems and Communication Milan Italy Bocconi University Department of Computing Sciences Milan Italy Bocconi Institute for Data Science and Analytics Italy University of Bergamo Department of Human and Social Sciences Bergamo Italy

ISBN: (纸本)9798350308365

Evolutionary algorithms are effective techniques for optimizing non-linear and complex high-dimensional problems. However, most of them require a precise fine-tuning of their functioning settings to achieve satisfactory results. In this work, we propose a modified version of an evolutionary approach called the Evolutionary Algorithm for COmplex-process oPtimization (EACOP), designed to have a limited number of hyper-parameters. The base version of EACOP (bEACOP) combines different strategies, including the scatter search methodology, local searches, and a novel combination method based on path relinking to balance the exploration and exploitation phases. Our improved version (iEACOP) intensifies the exploration phase to escape from suboptimal search space areas where, on the contrary, bEACOP gets stuck. Our results show that iEACOP outperforms bEACOP on 27 out of 29 CEC 2017 test suite benchmark functions, exhibiting comparable performance against the three best algorithms of the CEC 2017 competition on single-objective bound-constrained real-parameter numerical optimization. The source code of bEACOP and iEACOP will be made publicly available on GitHub upon acceptance. © 2024 IEEE.

关键词： Optimization algorithms

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Toward a Cognitive Data Model: Exploring a Mind-Inspired Approach to Database Design

arXiv

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arXiv 2025年

作者： Pieris, Dhammika Department of Information Systems Engineering and Informatics Faculty of Computing University of Sri Jayawardenepura Sri Lanka

The Cognitive Data Model (CDM) is proposed. A novel approach to database design, inspired by the belief that the human brain operates with a logical data model independent of its anatomical structure. The study aims to identify and replicate this CDM to enhance database design, resolving limitations of the existing models in handling complex relationships, scalability, and adaptability. The methodology involves empirical observation, cognitive experiments, iterative modelling and critical thinking. Findings suggest that the information processing in the brain occurs sequentially with forming associations on atomic static data in a pairwise, time-dependent manner. This insight led to the development of a meta-framework for the brain’s information processing to design CDM based on it. The CDM offers improved data modelling and database design, efficient querying, and potential applications in AI, machine learning, and big data analytics. Future research will focus on formalizing the model, implementing it in 3D environments, and developing a query language. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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