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Assessment of musical representations using a music information retrieval technique

IEEE Potentials 2021年第6期40卷 11-17页

作者： Hernandez-Lopez, Valentina Duque-Mendez, Nestor Dario Orozco-Alzate, Mauricio Management of Information Systems Universidad Nacional de Colombia-Sede Manizales Manizales Caldas Colombia Department of Informatics and Computing Universidad Nacional de Colombia-Sede Manizales Manizales Caldas Colombia

While learning to play a musical instrument, a student does not always have the support of a specialized teacher to guide and assess his or her daily practice. The lack of guidance and untimely feedback can be demotivating for successful study. Lerch et al. (2019), in a recent study, suggest that performance assessment is an important aspect in music pedagogy: 'Students rely on regular feedback from teachers to learn and improve skills.' © 1988-2012 IEEE.

关键词： Students

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Operational Level System: Transaction Processing System and Decision Making 1

Operational Level System: Transaction Processing System and ...

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1st International Conference on Computer Science and Emerging Technologies, CSET 2023

作者： Wahsheh, Firas Rashed Marie, Ziyad Farea, Mazen Mohammed Al-Haithami, Wafa Abdulkarem Mohamad, Fatma Susilawati Bisht, Neeraj Ajloun National University Faculty of Business Department of Management Information Systems Ajloun Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Public Administration Department Jericho10 Palestine Al-Madinah International University Faculty of Finance & Administrative Sciences Malaysia Mediu Faculty of Finance and Administrative Sciences Malaysia UniSZA Faculty of Informatics and Computing Malaysia Birla Institute of Applied Sciences Bhimtal India

ISBN: (纸本)9798350341737

This research paper investigates the critical role of Operational Level systems (OLS) in facilitating transaction processing and supporting decision-making within modern organizations. In today's fast-paced and data-driven business environment, OLS plays a pivotal role in capturing, processing, and disseminating real-time data to aid in operational efficiency and effective decision-making. This study aims to explore the functionalities and interconnections of Transaction Processing systems (TPS) as integral components of OLS, and their contribution to informed decision-making at the operational level. To achieve this, an extensive literature review is conducted to comprehend the theoretical underpinnings and practical implications of OLS and TPS. Additionally, empirical data are collected through surveys, interviews, and case studies from various industries to gain insights into the real-world implementation and benefits of OLS and its impact on decision-making processes. The findings reveal that OLS, with its robust TPS, provides organizations with streamlined data processing capabilities, enabling seamless handling of day-to-day transactions, such as sales, inventory management, and financial transactions. Moreover, it facilitates the efficient storage and retrieval of vast amounts of transactional data, laying the foundation for data-driven decision-making. Furthermore, the research highlights the significance of the integration between TPS and decision-making systems, emphasizing the pivotal role of OLS in supporting operational managers in making well-informed decisions. The ability to access real-time transactional data empowers decision-makers to identify trends, patterns, and potential bottlenecks, leading to timely adjustments and optimized performance. This research sheds light on the vital role of Operational Level systems, particularly Transaction Processing systems, in today's business landscape. By efficiently processing transactions and providing time

关键词： Decision making

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ROS Integration and Evaluation for Robot Navigation in a Narrow-Path Indoor Environment

ROS Integration and Evaluation for Robot Navigation in a Nar...

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IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Lilian Wei Fang Ung Richard T.K. Wong Kok Seng Eu Muhammed Basheer Jasser Bayan Issa Department of Computing and Information Systems Sunway University Sunway City Selangor Malaysia Taylor’s University Selangor Malaysia Faculty of Informatics Engineering University of Aleppo Syria

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

This project explores the implementation of ROS for robot navigation in an indoor environment with narrow pathways typically found in a grocery shop or library. The study discusses the design of the integration of different components including hardware such as sensors and actuators, and algorithms in ROS. The system uses LiDAR to first map the real-world environment to determine the static components of the environment. The purpose of the project is to determine the optimal system integration to achieve obstacle detection and avoidance specifically on dynamic obstacles in environments with narrow pathways. The results show $100 \%$ collision-free navigation either through avoiding obstacles or re-routing to an obstacle-free course.

关键词： YOLO Machine learning algorithms Three-dimensional displays Navigation Robot vision systems System integration Indoor environment Sensors Collision avoidance Tuning

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3D Printing in Fiber-Device Technology

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Advanced Fiber Materials 2021年第2期3卷 1-17页

作者： Louis van der Elst Camila Faccini de Lima Meve Gokce Kurtoglu Veda Narayana Koraganji Mengxin Zheng Alexander Gumennik Department of Intelligent Systems Engineering Luddy School of InformaticsComputing and EngineeringIndiana University Bloomington700 North Woodlawn AvenueBloomingtonIN 47408USA Fibers and Additive Manufacturing Enabled Systems Laboratory 2425 North Milo B Sampson LaneBloomingtonIN 47408USA

Recent advances in additive manufacturing enable redesigning material morphology on nano-,micro-,and meso-scale,for achieving an enhanced functionality on the *** non-planar and flexible electronic circuits,through biomechanically realistic surgical models,to shoe soles individualized for the user comfort,multiple scientific and technological areas undergo material-property redesign and enhancement enabled by 3D ***-device technology is currently entering such a *** this paper,we review the recent advances in adopting 3D printing for direct digital manufacturing of fiber preforms with complex cross-sectional architectures designed for the desired thermally drawn fiber-device ***,taking a recursive manufacturing approach,such fibers can serve as a raw material for 3D printing,resulting in macroscopic objects with enhanced functionalities,from optoelectronic to bio-functional,imparted by the fiber-devices properties.

关键词： Recursive manufacturing 3D printing Fibers Optical fibers Pervasive sensing

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Causal Deep Operator Networks for Data-Driven Modeling of Dynamical systems

Causal Deep Operator Networks for Data-Driven Modeling of Dy...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Nghiem, Truong X. Nguyen, Thang Nguyen, Binh T. Nguyen, Linh School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ United States Texas A&m University-Corpus Christi Department of Engineering Corpus ChristiTX United States Institute of Innovation Science and Sustainability Federation University Australia ChurchillVIC Australia

ISBN: (纸本)9798350337020

The deep operator network (DeepONet) architecture is a promising approach for learning functional operators, that can represent dynamical systems described by ordinary or partial differential equations. However, it has two major limitations, namely its failures to account for initial conditions and to guarantee the temporal causality - a fundamental property of dynamical systems. This paper proposes a novel causal deep operator network (Causal-DeepONet) architecture for incorporating both the initial condition and the temporal causality into data-driven learning of dynamical systems, overcoming the limitations of the original DeepONet approach. This is achieved by adding an independent root network for the initial condition and independent branch networks conditioned, or switched on/off, by time-shifted step functions or sigmoid functions for expressing the temporal causality. The proposed architecture was evaluated and compared with two baseline deep neural network methods and the original DeepONet method on learning the thermal dynamics of a room in a building using real data. It was shown to not only achieve the best overall prediction accuracy but also enhance substantially the accuracy consistency in multistep predictions, which is crucial for predictive control. © 2023 IEEE.

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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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IEEE International Conference on Consumer Electronics (ICCE)

作者： Quoc Bao Phan Linh Nguyen Tuy Tan Nguyen Dinh C. Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Department of Electrical and Computer Engineering The University of Alabama in Huntsville Huntsville AL USA

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.

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High-Precision Navigation on Analytical Trajectories Using Strapdown Inertial-Satellite Navigation System

High-Precision Navigation on Analytical Trajectories Using S...

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2022 International Russian Automation Conference, RusAutoCon 2022

作者： Sokolov, Sergey Sukhanov, Andrey Polyakova, Marianna Informatics and Computers Moscow Technical University of Communications and Informatics Moscow Russia Jsc NIIAS Rostov Branch Innovation Intelligent Technologies for Digital Station Department Rostov-on-Don Russia Rostov State Transport University Computing Equipment and Automated Control Systems Rostov-on-Don Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

Analytical models of spatial trajectories of mobile objects during synthesis of algorithmic support in integrated inertial-satellite navigation systems is considered. In the paper, spatial analytical model of the trajectory means the trajectory on the field of the Earth, for which an analytical relationship between the spatial coordinates (latitude and longitude) of the object moving along this trajectory exists. In this work, the trajectory of the object in the area of the Earth with a constant azimuthal angle (so-called loxodromic model) is considered. The use of this model is possible for a very wide class of objects moving in pre-known spatial trajectories, which can be approximated with high accuracy in a set of loxodromic trajectories. One of the advantages of such representation is invariance to the nature of the object's movement and type of the physical model. Presented models have important predominant features, which are the reduction in the dimension of the evaluated vector and the fundamental problem solution in a posterior assessment of the object parameters by autonomous measurers during the disappearance of satellite messages. The illustrative example is presented for an integrated inertial navigation satellite system. © 2022 IEEE.

关键词： Trajectories

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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Computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Framework for Quality Assurance and Quality Control in DL systems

Framework for Quality Assurance and Quality Control in DL Sy...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Domingos F. Oliveira Miguel A. Brito Department of Informatics and Computing University Mandume Ya Ndemufayo Lubango Angola Department of Information Systems Centro Algoritmi University of Minho Guimarães Portugal

The use of deep learning systems represents a significant challenge for the industry, as the quality assurance and control of these systems still need to be resolved, for the simple fact that these systems suffer from defects and vulnerabilities, which can lead to severe tragedies, mainly when applied to safety-critical applications in the real world, as they present a programming paradigm that differs in the representation of decision logic, as well as their development practices are unclear and centre on the data-driven paradigm, so how to ensure and control the quality of this system is unclear. This article discusses the development of a framework that will help specialists implement quality assurance and quality control in developing these systems. The framework combines two widely used tools, namely PDCA, applied to the CRISP-DM set of activities to enable quality assurance and control in developing DL systems, specifically in DS. In order to be implemented in the industry, it needs to be evaluated and validated. To this end, case studies and expert evaluation will be used to assess and validate the framework, as this stage will be addressed in future work.

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Respiratory Anomaly and Disease Detection using Multi-Level Temporal Convolutional Networks

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Le, Kim-Ngoc T. Byun, Gyurin Raza, Syed M. Le, Duc-Tai Choo, Hyunseung The Department of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of The James Watt School of Engineering University of Glasgow Glasgow United Kingdom The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of The Department of AI Systems Engineering Department of Electrical and Computer Engineering College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

An automated analysis of respiratory sounds using Deep Learning (DL) plays a pivotal role in the early detection of lung diseases. However, current DL methods often examine the spatial and temporal characteristics of respiratory sounds in isolation, which inherently limit their potential. This study proposes a novel DL framework that captures spatial features through convolution operations and exploits the spatiotemporal correlations of these features using temporal convolution networks. The proposed framework incorporates MultiLevel Temporal Convolutional Networks (ML-TCN) to considerably enhance the model accuracy in detecting anomaly breathing cycles and respiratory recordings from lung sound audio. Moreover, a transfer learning technique is also employed to extract semantic features efficiently from limited and imbalanced data in this domain. Thorough experiments on the well-known ICBHI 2017 challenge dataset show that the proposed framework outperforms state-of-the-art methods in both binary and multi-class classification tasks for respiratory anomaly and disease detection. In particular, improvements of up to 2.29% and 2.27% in terms of the Score metric, average sensitivity and specificity, are demonstrated in binary and multi-class anomaly breathing cycle detection tasks, respectively. In respiratory recording classification tasks, the classification accuracy is improved by 2.69% for healthy-unhealthy binary classification and 1.47% for healthy, chronic, and non-chronic diagnosis These results highlight the marked advantage of the ML-TCN over existing techniques, showcasing its potential to drive future innovations in respiratory healthcare technology. © 2025 IEEE.

关键词： Lung cancer

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