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arXiv 2024年

作者： Zhang, Xiaoge Wang, Tao Yan, Chao Najdawi, Fedaa Zhou, Kai Ma, Yuan Cheung, Yiu-Ming Malin, Bradley A. Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States Department of Pathology Microbiology and Immunology Vanderbilt University Medical Center NashvilleTN United States Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong Department of Mechanical Engineering Research Institute for Intelligent Wearable Systems The Hong Kong Polytechnic University Kowloon Hong Kong Department of Computer Science Hong Kong Baptist University Kowloon Tong Kowloon Hong Kong Department of Computer Science Vanderbilt University NashvilleTN United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States

Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs and 2) an ambiguity-guided elimination of tiles to filter out highly ambiguous regions, addressing data trustworthiness, as well as 3) conformal prediction to ensure controlled error rates. We systematically evaluated the framework across multiple large-scale cancer datasets, leveraging both task-specific and foundation models, illustrate that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness. These findings highlight TRUECAM as a versatile wrapper framework for digital pathology AI models with diverse architectural designs, promoting their responsible and effective applications in real-world settings. © 2024, CC BY.

关键词： Lung cancer

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Question answering system for factoid questions about COVID-19 with natural language processing approach

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AIP Conference Proceedings 2024年第1期3026卷

作者： Indriati Indriati Randy Cahya Wihandika Putra Pandu Adikara Barlian Henryranu Prasetio Lailil Muflikhah Informatics Engineering Department Faculty of Computer Science Brawijaya University Veteran No.8 Malang-Indonesia 65145

In March 2020, Indonesia faced fast-spreading COVID-19 Disease. In this regard, self-isolation serves as a solution for patients with mild symptoms due to the government’s limited isolation facilities. Self-isolation is different from COVID-19 treatment in the hospital, where patients are fully treated by health professionals. Information clarity is pivotal amid the pandemics due to a vast number of misleading information. Addressing this issue, it is important to keep self-isolation patients receiving adequate and accurate information about COVID-19. Question Answering System (QAS) with Natural Language Processing emerges as one of the solutions to provide information for users. Named Entity Recognition is one of the methods to search for the answer for factoid questions, allowing users to obtain accurate answers. A number of question types were set as keywords with 50% to 72% accuracy.

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models*

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Konstantina-Helen Tsarapatsani Antonis Sakellarios Vasilis D Tsakanikas Henrik Rudolf Hans J. Trampisch Vasileios C. Pezoulas George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina GR 45110 Ioannina Greece University Medical Center Rostock Institute for Biostatistics and Informatics in Medicine and Ageing Research Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis’s results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided.

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QoE-Aware Edge-Assisted Machine Learning-Based Fall Detection and Prediction with FBGs

QoE-Aware Edge-Assisted Machine Learning-Based Fall Detectio...

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IEEE International Conference on Communications (ICC)

作者： Matilde Rocha Hao Ran Chi Nélia Alberto Paulo André Paulo Antunes Ayman Radwan M. Fátima Domingues Instituto de Telecomunicações and University of Aveiro Aveiro Portugal Department of Electrical and Computer Engineering Instituto de Telecomunicações Instituto Superior Técnico University of Lisbon Lisbon Portugal I3N Department of Physics University of Aveiro Aveiro Portugal Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Department of Biomedical Engineering Khalifa University Abu Dhabi United Arab Emirates

Considering that fall accidents are one of the leading causes of non-natural death of elders, it is crucial to design and to implement home’ fall detection systems. Current home monitoring systems are targeting this challenge, pursuing non-invasive, low latency, and simplified fall detection algorithms. Therefore, in this paper, edge-enabled non-wearable and non-invasive fall detection system is proposed. Concretely, outperforming the conventional invasive/privacy-sensitive fall detection technologies, the proposed system comprises four photonic-based accelerometers solely relying on the fiber Bragg grating (FBG) technology, which monitor the vibrations induced by the body impact in the platform by the Bragg wavelength shifts. A newly-developed support vector machine-based multi-class fall detection algorithm is proposed, based on the data collected by the accelerometers. Moreover, feasibility analysis of the proposed fall detection algorithm also reveals the possibility of fall prediction, given the slipping as the pre-falling phenomenon. Experimental results showcase that the proposed fall detection algorithm achieves overall accuracy up to 96.5%, with average processing time achieved as 21.3 ms, indicating the sufficiency to provide high quality of experience (QoE) fall detection services. Besides, fall prediction based on the pre-falling case study of slipping is discussed, revealing that fall can be predicted~197.5 ms beforehand, which is sufficient for further fall prevention (e.g., airbag).

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DISENTANGLING THE PROSODY AND SEMANTIC INFORMATION WITH PRE-TRAINED MODEL FOR IN-CONTEXT LEARNING BASED ZERO-SHOT VOICE CONVERSION

arXiv

引用

arXiv 2024年

作者： Chen, Zhengyang Wang, Shuai Zhang, Mingyang Liu, Xuechen Yamagishi, Junichi Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Chinese University of Hong Kong Shenzhen China National Institute of Informatics Tokyo Japan

Voice conversion (VC) aims to modify the speaker's timbre while retaining speech content. Previous approaches have tokenized the outputs from self-supervised into semantic tokens, facilitating disentanglement of speech content information. Recently, in-context learning (ICL) has emerged in text-to-speech (TTS) systems for effectively modeling specific characteristics such as timbre through context conditioning. This paper proposes an ICL capability enhanced VC system (ICL-VC) employing a mask and reconstruction training strategy based on flow-matching generative models. Augmented with semantic tokens, our experiments on the LibriTTS dataset demonstrate that ICL-VC improves speaker similarity. Additionally, we find that k-means is a versatile tokenization method applicable to various pre-trained models. However, the ICL-VC system faces challenges in preserving the prosody of the source speech. To mitigate this issue, we propose incorporating prosody embeddings extracted from a pre-trained emotion recognition model into our system. Integration of prosody embeddings notably enhances the system's capability to preserve source speech prosody, as validated on the Emotional Speech Database. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Quantum advantage for noisy channel discrimination

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Physical Review A 2022年第3期105卷 032401-032401页

作者： Zane M. Rossi Jeffery Yu Isaac L. Chuang Sho Sugiura Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Department of Electrical Engineering and Computer Science and Co-Design Center for Quantum Advantage Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Dr. Sunnyvale California 94085 USA

Many quantum mechanical experiments can be viewed as multiround interactive protocols between known quantum circuits and an unknown quantum process. Fully quantum “coherent” access to the unknown process is known to provide an advantage in many discrimination tasks compared to when only incoherent access is permitted, but it is unclear if this advantage persists when the process is noisy. Here, we show that a quantum advantage can be maintained when distinguishing between two noisy single-qubit rotation channels. Numerical and analytical calculations reveal a distinct transition between the performance of fully coherent and fully incoherent protocols as a function of noise strength. Moreover, the size of the region of coherent quantum advantage shrinks inverse polynomially in the number of channel uses, and in an intermediate regime an improved strategy is a hybrid of fully coherent and fully incoherent subroutines. The fully coherent protocol is based on quantum signal processing, suggesting a generalizable algorithmic framework for the study of quantum advantage in the presence of realistic noise.

关键词： Quantum algorithms Quantum channels Quantum computation Quantum information processing Quantum sensing Quantum simulation

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Morphosyntactic Probing of Multilingual BERT Models

arXiv

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arXiv 2023年

作者： Acs, Judit Hamerlik, Endre Schwartz, Roy Smith, Noah A. Kornai, Andras Budapest Hungary Budapest University of Technology and Economics Budapest Hungary Department of Applied Informatics Comenius University in Bratislava Faculty of Mathematics Physics and Informatics Bratislava Slovakia Hebrew University of Jerusalem Jerusalem Israel Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States Allen Institute for Artificial Intelligence SeattleWA United States

We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a sentence with a target word and a morphological tag as the desired label, derived from the Universal Dependencies treebanks. We find that pre-trained Transformer models (mBERT and XLM-RoBERTa) learn features that attain strong performance across these tasks. We then apply two methods to locate, for each probing task, where the disambiguating information resides in the input. The first is a new perturbation method that "masks" various parts of context;the second is the classical method of Shapley values. The most intriguing finding that emerges is a strong tendency for the preceding context to hold more information relevant to the prediction than the following context. © 2023, CC BY.

关键词： Perturbation techniques

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E-Learning: A Successful Learning Environment

E-Learning: A Successful Learning Environment

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computer Science and Emerging Technologies (CSET), International Conference on

作者： Fayiz Momani Amer Ahmad Hatamleh Wan Mohd Amir Fazamin Wan Hamzah Julaily Aida Jusoh Muhammad D. Zakaria Sunanda Das E-business & Commerce Department Faculty of Management and Financial Sciences University of Petra (UOP) Jordan Head of Departments Faculty of Business and Financial Science MIS BI and Data Analysis Irbid National University Irbid Jordan Faculty of Informatics and Computing (UniSZA) Malaysia Faculty of Informatics and Computing UniSZA Malaysia Cyber Security School of Computer Science and Engineering Jain University Bangalore India

Education is developing with the economy. This led to the creation of e-Iearning tools for instructors and students. In e-learning, electronic resources are used to teach. E-learning can be taught in or out of school, but computers and the Internet are essential. That is, e-learning involves delivering education to a big group of people at once or over time. So, overall, in this research is about E-learning that make a successful environment. We chose this term since it is widely used in E-learning research to improve student academic achievement. Due to the difficulties of accessing adequate study materials during the Covid-19 epidemic, distance learning has grown more popular among students. This study proposes a unique learning technique that involves choosing and organizing important learning items using a recommender system. We also studied our method's efficacy. Using a recommended system to help online learning activities was proven to be effective. Software development methodology is a collection of principles and processes used to build software. Systematic software development is the goal. The research approach utilized for this topic is Lean Development Methodology. We also examine the advantages and disadvantages of e-learning. This research also introduces various new strategies and methodologies that may be employed in the future to improve system-learning. It also presents system that we choose and why we choose it. So, it aims to eliminate waste and increase production. Following the principles can help developers avoid nonproductive chores while still producing high-quality results. The ultimate objective is to develop an efficient and error-free system. Finally, E-learning is a social revolution. It's part of a bigger rethinking of how we teach future employees and students. The eLearning platform enabled the learning data visualization dashboard beautifully. User-centered design lets the dashboard adapt to its users' demands.

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A Blockchain-Based Multiplayer Transaction for Tourism Serious Game

A Blockchain-Based Multiplayer Transaction for Tourism Serio...

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2020 International Conference on computer engineering, Network, and Intelligent Multimedia, CENIM 2020

作者： Arif, Yunifa Miftachul Pradana, Reza Putra Nurhayati, Hani Nugroho, Supeno Mardi Susiki Hariadi, Mochamad Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Universitas Islam Negeri Maulana Malik Ibrahim Department of Informatics Engineering Malang Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9781728182834

A tourism serious game requires a transaction system to handle the transaction process's visualization between players. In this paper, we propose a serious game with a blockchain-based multiplayer transaction system. To integrate the transaction system with the game engine, we use the ethereum platform. In this serious game, ethereum handles the simulation of transaction activities between several characters' choices, including tourists, ticket sellers, jeep drivers, traders, and horse rental. The experimental results show that the gas price variable affects the speed of the transaction process. Meanwhile, setting the gas limit value in this serious game affects the transaction process's success rate between players. © 2020 IEEE.

关键词： Blockchain

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Unbundling the significance of cognitive robots and drones deployed to tackle COVID-19 pandemic: A rapid review to unpack emerging opportunities to improve healthcare in sub-Saharan Africa

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Cognitive Robotics 2021年 1卷 205-213页

作者： Mbunge, Elliot Chitungo, Itai Dzinamarira, Tafadzwa Department of Computer Science Faculty of Science and Engineering University of Eswatini Kwaluseni Manzini Swaziland Department of Information Technology Faculty of Accounting and Informatics Durban University of Technology P O Box 1334 Durban4000 South Africa Department of Laboratory Diagnostic and Investigative Sciences Faculty of Medicine and Health Sciences University of Zimbabwe Harare Zimbabwe ICAP at Columbia University Kigali Rwanda School of Health Systems & Public Health University of Pretoria Pretoria0002 South Africa

The emergence of COVID-19 brought unprecedented opportunities to deploy emerging digital technologies such as robotics and drones to provide contactless services. Robots and drones transformed initial approaches to tackle COVID-19 and have proven to be effective in curbing the risk of COVID-19 in developed countries. Despite the significant impact of robots and drones in reducing the burden of frontline healthcare professionals, there is still limited literature on their utilization to fight the pandemic in sub-Saharan Africa. Therefore, this rapid review provides significant capabilities of robots and drones while introspecting at the challenges and barriers that may hinder their implementation in developing countries. The study revealed that robots and drones have been used for disinfection, delivery of medical supplies, surveillance, consultation and screening and diagnosis. The study revealed that adopting robots and drones face challenges such as infrastructural, financial, technological barriers, security and privacy issues, lack of policies and frameworks regulating the use of robots and drones in healthcare. We, therefore, propose a collaborative approach to mobilise resources and invest in infrastructure to bridge the digital divide, craft policies and frameworks for effectively integrating robots and drones in healthcare. There is a need to include robotics in the medical education and training of health workers and develop indigenous knowledge and encourage international collaboration. Partnership with civil aviation authorities to license and monitor drones to improve monitoring and security of drone activities could also be helpful. Robots and drones should guarantee superior safety features since it either directly interacts with human or works in a densely populated environment. However, future work should focus on the long term consequences of robots and drones on human behavior and interaction as well as in healthcare. © 2021

关键词： COVID-19

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