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ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts;A Foundation for computerized ECG Image Digitization and Analysis

arXiv

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arXiv 2024年

作者： Reyna, Matthew A. Deepanshi Weigle, James Koscova, Zuzana Campbell, Kiersten Shivashankara, Kshama Kodthalu Saghafi, Soheil Nikookar, Sepideh Motie-Shirazi, Mohsen Kiarashi, Yashar Seyedi, Salman Clifford, Gari D. Sameni, Reza Department of Biomedical Informatics Emory University AtlantaGA30322 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA30332 United States

Objective: We introduce ECG-Image-Database, a large and diverse collection of electrocardiogram (ECG) images generated from ECG time-series data that exhibit real-world scanning, imaging, and physical artifacts. This dataset addresses the need for digitizing paper-based and non-digital ECGs for computerized analysis, providing a foundation for developing robust machine and deep learning models capable of ECG image digitization. Approach: We used ECG-Image-Kit, an open-source Python toolkit, to generate realistic images of 12-lead ECG printouts from raw ECG time-series data. We include images with realistic distortions, including noise, wrinkles, stains, and perspective shifts generated both digitally and physically. The toolkit was applied to 977 12-lead ECG records from the PTB-XL database and 1,000 from Emory Healthcare to create high-fidelity synthetic ECG images. These 1,977 unique images were subjected to both programmatic distortions using ECG-Image-Kit and to physical effects, such as soaking, staining, and mold growth, followed by scanning and photography under various lighting conditions to create real-world artifacts, producing a total of 35,595 images. Main results: The resulting dataset includes 35,595 software-labeled ECG images (generated from 1,977 unique records) from different sources (in Germany and the USA) with a wide range of imaging artifacts and distortions. The dataset provides ground truth time-series data alongside the corresponding images, offering a reference for training and evaluating machine and deep learning models for ECG digitization and classification. The images vary in quality, ranging from clear scans of clean papers to noisy photographs of degraded papers, enabling the development of more generalizable and robust digitization algorithms. Significance: ECG-Image-Database addresses a critical gap in cardiovascular research by supporting the development of machine learning models capable of classifying or converting ECG images int

关键词： Photography

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Improving Pairwise Rank Aggregation via Querying for Rank Difference

Improving Pairwise Rank Aggregation via Querying for Rank Di...

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Guoxi Zhang Jiyi Li Hisashi Kashima Graduate School of Informatics Kyoto University Kyoto Japan Department of Computer Science and Engineering University of Yamanashi Kofu Japan

ISBN: (纸本)9781665473316

Pairwise rank aggregation (PRA) aims at learning a ranking from pairwise comparisons between objects that specify their relative ordering. The present study proposes the use of rank difference information for PRA, which characterizes the extent winners in paired comparisons beat their opponents. While such information can be effortlessly recognized by annotators, to our knowledge, it has not been utilized for PRA before. The challenge is three-fold: how to solicit such information, how to utilize it in rank aggregation, and how to overcome the noise from heterogeneous annotators. This study proposes a new query for soliciting information about rank difference that imposes limited cognitive burden on annotators. As prior methods for PRA abounds, it is of interest to empower them with information on rank difference. To this end, this study proposes a conservative learning objective that can be combined seamlessly with many existing PRA algorithms. The third contribution is a new method for PRA called mixture of exponentials (MoE). Annotators from a heterogeneous population might have diverse views concerning rank difference. For example, an annotator might be good at recognizing rank difference only for a subset of items but not the rest. This means that information about rank difference is likely to be perturbed. Unfortunately, such an object-dependent error pattern cannot be modeled with existing approaches. MoE assumes that each annotator uses a mixture of ranking functions in generating answers, and the mixture components can capture object-related patterns in data. The present study evaluates the proposals with extensive experiments on both real and synthetic datasets. The results confirm the efficacy of the proposals and shed light on their practical usage.

关键词： Sociology Performance gain Data science Linear programming Robustness Proposals Noise measurement

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Differential Evolution Algorithm for Medical Image Registration 12th

Differential Evolution Algorithm for Medical Image Registrat...

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12th International Symposium on Intelligence Computation and Applications, ISICA 2021

作者： Li, Kangshun Wang, Wenxiang Wang, Hui School of Computer and Information Dongguan City University Guangdong Dongguan523808 China College of Mathematics and Informatics South China Agricultural University Guangdong Guangzhou510642 China School of Software Engineering Shenzhen Institute of Information Technology Guangdong Shenzhen518000 China

ISBN: (纸本)9789811941085

Image registration plays an important role in medical image fusion and surgical navigation. Iterative nearest point algorithm (INPA) is a high-precision image registration algorithm, but it also has the problems of huge computation cost and low efficiency. Therefore, we use the differential evolution algorithm to optimize the search process of the iterative nearest point algorithm, so that to improve its registration efficiency. Then a mutation operator selection algorithm based on fitness roughness and a slack selection strategy for differential evolution algorithm were proposed to meet the requirements of high precision and low delay of medical image registration. The registration experiments of chest CT images show that the proposed differential evolution algorithm can not only accelerate the speed of iterative nearest point algorithm, but also improve its registration accuracy. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Image registration

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Round-optimal n-Block Broadcast Schedules in Logarithmic Time

arXiv

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arXiv 2023年

作者： Träff, Jesper Larsson TU Wien Faculty of Informatics Institute of Computer Engineering Research Group Parallel Computing 191-4 Treitlstrasse 3 5th Floor Vienna1040 Austria

We give optimally fast O(log p) time (per processor) algorithms for computing round-optimal broadcast schedules for message-passing parallel computing systems. This affirmatively answers the questions posed in Träff (2022). The problem is to broadcast n indivisible blocks of data from a given root processor to all other processors in a (subgraph of a) fully connected network of p processors with fully bidirectional, one-ported communication capabilities. In this model, n − 1 + ⌈log2 p⌉ communication rounds are required. Our new algorithms compute for each processor in the network receive and send schedules each of size ⌈log2 p⌉ that determine uniquely in O(1) time for each communication round the new block that the processor will receive, and the already received block it has to send. Schedule computations are done independently per processor without communication. The broadcast communication subgraph is the same, easily computable, directed, ⌈log2 p⌉-regular circulant graph used in Träff (2022) and elsewhere. We show how the schedule computations can be done in optimal time and space of O(log p), improving significantly over previous results of O(p log2 p) and O(log3 p). The schedule computation and broadcast algorithms are simple to implement, but correctness and complexity are not obvious. All algorithms have been implemented, compared to previous algorithms, and briefly evaluated on a small 36 × 32 processor-core cluster. © 2023, CC BY-NC-ND.

关键词： Message passing

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Analyzing the behaviors of pedestrians and cyclists in interactions with autonomous systems using controlled experiments: A literature review

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Transportation Research Part F: Traffic Psychology and Behaviour 2025年 114卷 270-307页

作者： Danya Li Wencan Mao Francisco C. Pereira Yu Xiao Xiang Su Rico Krueger Department of Technology Management and Economics Technical University of Denmark (DTU) Denmark Information Systems Architecture Science Research Division National Institute of Informatics (NII) Japan Department of Information and Communications Engineering Aalto University Finland Department of Computer Science and Department of Agricultural Sciences University of Helsinki Finland

Urban transportation is set to undergo a profound transformation with the advent of autonomous systems such as autonomous vehicles, automated buses, and sidewalk delivery robots. To promote safe, sustainable, and inclusive urban mobility, understanding and predicting the behaviors of pedestrians and cyclists, including their intentions, decisions, and movements, when they interact with autonomous systems becomes crucial. Gaining a thorough understanding of these complex interactions can not only improve the safety, efficiency, and acceptance of autonomous systems but also enhance the design and implementation of these technologies. Through a comprehensive review of the literature spanning the years 2014 to 2023, we identify 99 articles that empirically investigate the interactions of humans and autonomous systems. Based on our overview of progress and challenges within this field, we further identify five research gaps that future research should address to enhance human-autonomous system interactions, including: (1) scaling up experimental scenarios to multi-user and multi-modal setups to better represent real-world challenges, (2) emphasizing safety-critical scenarios that are difficult to achieve in real-world environments by applying virtual reality, (3) incorporating diverse behavioral data from the human perspective to deepen the understanding of vulnerable road user behavior and decisions, (4) embracing continuous and real-time interaction to better predict dynamic future environments, and (5) enhancing the generalization ability to ensure realism and broad applicability. This review article offers valuable insights for the growing human-autonomous system research community, specifically those interested in leveraging controlled experiments to enhance the understanding and prediction of pedestrians' and cyclists' behaviors in future urban environments.

关键词： Human-autonomous system interaction Human behavioral modeling Autonomous vehicles Virtual reality Controlled experiment

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Covert Vehicle Misguidance and Its Detection: A Hypothesis Testing Game over Continuous-Time Dynamics

arXiv

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arXiv 2024年

作者： Tanaka, Takashi Sawada, Kenji Watanabe, Yohei Iwamoto, Mitsugu School of Aeronautics and Astronautics School of Electrical and Computer Engineering Purdue University United States The Info-Powered Energy System Research Center The University of Electro-Communications Japan Department of Informatics The University of Electro-Communications Japan

We formulate a stochastic zero-sum game over continuous-time dynamics to analyze the competition between the attacker, who tries to covertly misguide the vehicle to an unsafe region, versus the detector, who tries to detect the attack signal based on the observed trajectory of the vehicle. Based on Girsanov’s theorem and the generalized Neyman-Pearson lemma, we show that a constant bias injection attack as the attacker’s strategy and a likelihood ratio test as the detector’s strategy constitute the unique saddle point of the game. We also derive the first-order and the second-order exponents of the type II error as a function of the data length. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Exploratory Study on User Experience (UX) of Public E-Procurement System in Malaysia: A Qualitative Approach

Exploratory Study on User Experience (UX) of Public E-Procur...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Emma Nuraihan Mior Ibrahim Amiruddin Kusaini Juhaida Ismail Anis Hasliza Abu Hashim Mudiana Mokhsin Misron College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Hallenweg 15 Netherlands

The public e-Procurement system in Malaysia is a crucial component of the e-Government services. Its primary function is to provide a platform for government agencies to efficiently procure goods and services from business users. The user experience (UX) of this e-Procurement system has emerged as a pivotal factor in the success of the broader e-Government digital transformation. Ensuring that the digitalization of the public e-Procurement system aligns with its objectives and delivers a top-tier Government Electronic Procurement System is imperative. This research aims to gain a comprehensive understanding of the UX issues and challenges faced by stakeholders when using the public e-Procurement system. The study employs a qualitative approach, delving into the perspectives and insights of e-Procurement system users. Data from interview sessions involving nine (9) participants have been meticulously interpreted, coded, and subjected to thematic analysis, revealing five distinct dimensions of issues and challenges: usability, performance, operational, quality, and user satisfaction. To address these identified concerns, we proposed several recommendations. Enhancements in system user interface (UI), navigational aspects, system features, performance, and operational aspects are essential to ensure sustained quality and user satisfaction within the e-Procurement system. The study also explores avenues for future work to further refine and augment these improvements.

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TokenAuditor: Detecting Manipulation Risk in Token Smart Contract by Fuzzing

TokenAuditor: Detecting Manipulation Risk in Token Smart Con...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Mingpei Cao Yueze Zhang Zhenxuan Feng Jiahao Hu Yuesheng Zhu School of Electronic and Computer Engineering Peking University Shenzhen China Department of Informatics Technical University of Munich Munich Germany

Decentralized cryptocurrencies are influential smart contract applications in the blockchain, drawing interest from industry and academia. The capacity to govern and manage token behavior provided by the token smart contract adds to thriving decentralized applications. However, token smart contracts face security challenges in technology weakness and manipulation risks. In this work, we briefly describe the manipulation risk and propose TokenAuditor, a fuzzing framework detecting those risks in token smart contracts. TokenAuditor constructs basic blocks based on the contract bytecodes and adopts the rarity selection and mutation strategy to generate test cases. The main idea is to select the test cases that have hit rare basic blocks since the fuzzing started as candidates and perform mutation operations on them. In our evaluation, TokenAudiotr discovered 664 manipulation risks of four types in 4021 real-world token contracts.

关键词： Industries Smart contracts Software quality Fuzzing Decentralized applications Software reliability Cryptocurrency

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A Probabilistic Public-Key Encryption with Ensuring Data Integrity in Cloud Computing

A Probabilistic Public-Key Encryption with Ensuring Data Int...

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International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Mostefa Kara Konstantinos Karampidis Giorgos Papadourakis Abdelkader Laouid Muath AlShaikh LIAP Laboratory University of El Oued El Oued El Oued Algeria Department of Electrical and Computer Engineering Hellenic Mediterranean University Greece Computer Science Department College of Computing and Informatics Saudi Electronic University Riyadh KSA

Researchers are working hard to face various challenges in the fast-moving field of network security and data exchange. When it comes to encryption techniques, efforts vary to address multiple issues such as computational speed, data size requirements, application scope, etc. that are driven by the purposes of collecting and sharing sensitive data that must be stored and processed securely. This paper proposes an efficient asymmetric cryptosystem that bases on the multiplication of plaintext by a variable public key. The origin of this key is constant, but for each encryption, a random number must be added to it. Therefore, a new public key each time is generated; in other words, a simulation of a one-time pad system. For each message, the sender generates a new secret twin of the public key; The receiver can decrypt all messages encrypted by different twins with a single private key. The proposed probabilistic scheme is linear coding and hence is lightweight, easy to execute, and practical in many areas. We consider the cloud to be an untrusted part that tries to disclose data when decoded by systems, the proposed method provides the additive homomorphic property and ensures data integrity when a homomorphic operation is performed by the cloud; thus, we realize verifiable partial homomorphic encryption.

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TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction

arXiv

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arXiv 2024年

作者： Gheisi, Leila Chu, Chee-Hung Henry Gottumukkala, Raju Luo, Yan Zhu, Xingquan Wang, Mengyu Shi, Min School of Computing and Informatics University of Louisiana LafayetteLA United States Informatics Research Institute University of Louisiana LafayetteLA United States College of Engineering and Computer Science Florida Atlantic University FL United States Harvard Ophthalmology AI Lab Harvard Medical School BostonMA United States

The use of artificial intelligence (AI) in automated disease classification significantly reduces healthcare costs and improves the accessibility of services. However, this transformation has given rise to concerns about the fairness of AI, which disproportionately affects certain groups, particularly patients from underprivileged populations. Recently, a number of methods and large-scale datasets have been proposed to address group performance disparities. Although these methods have shown effectiveness in disease classification tasks, they may fall short in ensuring fair prediction of disease progression, mainly because of limited longitudinal data with diverse demographics available for training a robust and equitable prediction model. In this paper, we introduce TransFair to enhance demographic fairness in progression prediction for ocular diseases. TransFair aims to transfer a fairness-enhanced disease classification model to the task of progression prediction with fairness preserved. Specifically, we train a fair EfficientNet, termed FairEN, equipped with a fairness-aware attention mechanism using extensive data for ocular disease classification. Subsequently, this fair classification model is adapted to a fair progression prediction model through knowledge distillation, which aims to minimize the latent feature distances between the classification and progression prediction models. We evaluate FairEN and TransFair for fairness-enhanced ocular disease classification and progression prediction using both two-dimensional (2D) and 3D retinal images. Extensive experiments and comparisons with models with and without considering fairness learning show that TransFair effectively enhances demographic equity in predicting ocular disease progression. © 2024, CC BY-NC-ND.

关键词： Prediction models

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