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arXiv 2023年

作者： Cobb, Adam D. Roy, Anirban Elenius, Daniel Heim, F. Michael Swenson, Brian Whittington, Sydney Walker, James D. Bapty, Theodore Hite, Joseph Ramani, Karthik McComb, Christopher Jha, Susmit Computer Science Laboratory SRI International United States Mechanical Engineering Division Southwest Research Institute United States Intelligent Systems Division Southwest Research Institute United States Institute of Software Integrated Systems Vanderbilt University United States School of Mechanical Engineering Purdue University United States Department of Mechanical Engineering Carnegie Mellon University United States

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details;a STandard for the Exchange of Product (STEP) model data;a 3D CAD design using a stereolithography (STL) file format;a 3D point cloud for the shape of the design;and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446, baseline models and code at https://***/SRI-CSL/AircraftVerse, and the dataset description at https://***/. © 2023, CC BY-SA.

关键词： computer aided design

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Report-Guided Cross-Modal Representation Learning for Predicting EGFR Mutations by Whole Slide Image

Report-Guided Cross-Modal Representation Learning for Predic...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qi Qiao Jun Shi Zhiguo Jiang Wei Wang Haibo Wu Yushan Zheng School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China Image Processing Center School of Astronautics Beihang University Beijing China Department of Pathology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China Hefei China School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Traditional PCR/NGS-based multigene panel testing is time-consuming and costly. Predicting EGFR mutations directly from H&E stained whole slide images (WSIs) can alleviate these limitations. Furthermore, histopathological reports contain valuable textual information that correlates with tissue areas in WSIs. However, recent research mainly analyses EGFR mutation status only from a single modality, ignoring rich information contained in reports. In this paper, we propose a report-guided cross-modal representation learning method for predicting EGFR mutations by WSIs. Specifically, we reconstruct report-level embeddings through exploring intrinsic relationships between diagnostic words in histopathological reports and tissue areas in WSIs. Finally, reconstructed histopathological report embedding and aggregated WSI embedding are fused for final prediction. More importantly, molecular testing report is also introduced as prior supervision information at the training stage to guarantee semantic consistency of fused feature and molecular report embedding. We evaluate our method on the TCGA-EGFR public benchmark dataset and an in-house clinical dataset (USTC-EGFR). Experimental results demonstrate that our method outperforms existing approaches in EGFR mutation prediction, highlighting the benefits of cross-modal learning in enhancing feature representational ability. The code is available at https://***/HFUT-miaLab/RCRL.

关键词： Representation learning Training Codes Supervised learning Semantics Medical treatment Benchmark testing Bioinformatics Image reconstruction Biomedical imaging

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Federated Learning for Recognizing Private Handwritten Numbers

Federated Learning for Recognizing Private Handwritten Numbe...

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2021 IEEE International Conference on Data science and computer Application, ICDSCA 2021

作者： Wang, Di Jiang, Yuchen Zhao, Yibo Chen, Panyang Li, Ang Pan, Kun Institution of Information Engineering Chinese Academy of Sciences State Key Laboratory of Information Security Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Computer Science and Technology College of Computer Science Chongqing University Chongqing China Zibo Shiyan High School International Division Zibo Shandong China Computer Science Department of Computer Science and Software Engineering Auburn University Auburn United States Shanghai Shangde Experimental School Shanghai China School of Computing University of Leeds Leeds United Kingdom

ISBN: (纸本)9781665440530

Federated learning (FL) is a new technology in the field of machine learning, which trains an algorithm across multiple decentralized edge devices or servers holding data samples, without exchanging their data samples. Horizontal Federated Learning (HFL) is a FL method focusing on using the same feature space across all users, which is suitable for situations where users' data feature dimensions overlap more. Because FL has the advantage of privacy protection in the field of manufacturing, we propose an HFL-based deep learning method named FedCNN, which realizes the digits handwritten recognition without violating the privacy or data sharing. Three different methods (local model, centralized model, and HFL-based FedCNN) are used on a public data set (MINIST) to compare the pros and cons of the solutions. The experimental results demonstrate that FedCNN scheme can simultaneously achieve practical privacy preservation and good model generalization performance with Homomorphic Encryption. © 2021 IEEE.

关键词： Data privacy

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StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy

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computers in Biology and Medicine 2025年 189卷 109958-109958页

作者： Raihan, Fazla Rabby Abdulrazak, Lway Faisal Rahman, Md. Ashikur Ali, Md Mamun Ibrahim, Sobhy M. Ahmed, Kawsar Bui, Francis M. Mahmud, Imran Department of Software Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Division of Biomedical Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer therapy, being able to bind and penetrate tumor cells selectively while ignoring adjacent healthy tissues. Therefore, the computational models to predict THPs became popular very rapidly, since laboratory methods are slow and resourceful. Herein, we are proposing StackTHP, a newly developed stacking-ensemble model aimed at further improving THP prediction accuracy. StackTHP implements multiple feature extraction methods, including amino acid composition (AAC), and pseudo amino acid composition (PAAC) together with classical machine learning classifiers like Extra Trees, Random Forest, and AdaBoost, while the logistic regression-based meta-classifier is used for the stacking framework. StackTHP outperformed all other models, producing an accuracy of 91.92 %, Matthew's correlation coefficient (MCC) of 0.8415, AUC of 0.977 on benchmark datasets, indicates that it is better than approaches attempted earlier and provides a robust solution for proceeding towards the discovery and development of peptide-based cancer therapies. Future research will focus on the application of StackTHP over more diverse sets of data along with some hybrid methods to enhance the prediction capability. The dataset and the code are available at the following link: https://***/Ashikur562/StackTHP. © 2025 The Authors

关键词： Prediction models

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An Explainable AutoML-Driven Meta-Learning Scheme for Intrusion Prevention in Zero-Touch Networks Within Carbon Intelligent IIoT

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IEEE Internet of Things Journal 2025年

作者： Islam, Anik Karimipour, Hadis Gadekallu, Thippa Reddy University of Calgary Schulich School of Engineering Deptartment of Electrical and Software Engneering CalgaryT2N1N4 Canada Zhejiang A and F University The College of Mathematics and Computer Science Hangzhou311300 China Lovely Professional University Division of Research and Development Phagwara India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India

Carbon Intelligent Industrial Internet of Things (IIoT) systems are critical for achieving sustainable industrial automation but face challenges such as scalability, operational complexity, and security vulnerabilities. Zero-Touch Networks (ZTN), with their autonomous management capabilities, offer solutions to operational challenges but remain vulnerable to sophisticated cyber intrusions due to their high level of autonomy and interconnectedness. While Artificial Intelligence (AI), especially Deep Learning (DL), shows potential in intrusion detection, current approaches often encounter obstacles such as insufficient datasets, challenges in automated data preprocessing, and a lack of transparency. This paper introduces an AutoML-enabled Meta Learning-based Intrusion Prevention Scheme designed specifically for ZTN within Carbon Intelligent IIoT. The proposed framework integrates AutoML and meta-learning to streamline data preprocessing and improve model adaptability in dynamic and evolving threat environments. To ensure transparency, an Integrated Gradient-based Explainable AI (XAI) mechanism is employed, offering insights into the impact of individual features on model predictions, thereby addressing concerns related to trust and accountability in industrial applications. Experimental evaluations demonstrate the framework’s effectiveness in enhancing intrusion prevention, bolstering security, and improving transparency for ZTN in carbon intelligent IIoT, providing a comprehensive solution to prevailing challenges. © 2014 IEEE.

关键词： Network intrusion

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Future intelligent autonomous robots, ethical by design. Learning from autonomous cars ethics

arXiv

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arXiv 2021年

作者： Dodig-Crnknovic, Gordana Holstein, Tobias Pelliccione, Patrizio Interaction Design Unit Department of Computer Science and Engineering Chalmers | University of Technology Gothenburg Sweden Division of Computer Science and Software Engineering School of Innovation Design and Engineering Mälardalen University Västerås Sweden Software Engineering Division Department of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden Department of Information Engineering Computer Science and Mathematics University of L'Aquila L'Aquila Italy

Development of the intelligent autonomous robot technology presupposes its anticipated beneficial effect on the individuals and societies. In the case of such disruptive emergent technology, not only questions of how to build, but also why to build and with what consequences are important. Thus, questions of values, good and bad, right and wrong, must be addressed in the context of the development, implementation, testing, use and disposal of technology. The field of ethics of intelligent autonomous robotic cars is a good example of research with actionable practical value, where a variety of stakeholders, including the legal system and other societal and governmental actors, as well as companies and businesses, collaborate bringing about shared view of ethics and societal aspects of technology. It could be used as a starting platform for the approaches to intelligent autonomous robot development considering human-machine interfaces in different phases of the life cycle of technology. Drawing from our work on ethics of autonomous intelligent robocars, and the existing literature on ethics of robotics, our contribution consists of a set of values and ethical principles with identified challenges and proposed approaches for meeting them. This may help stakeholders in the field of intelligent autonomous robotics to connect ethical principles with their applications. Our recommendations of ethical requirements for autonomous cars can be used for other types of intelligent autonomous robots, with the caveat for social robots that require more research regarding interactions with the users. We emphasize that existing ethical frameworks need to be applied in a context-sensitive way, by assessments in interdisciplinary, multi-competent teams through multi-criteria analysis. Furthermore, we argue for the need of a continuous development of ethical principles, guidelines, and regulations, informed by the progress of technologies and involving relevant stakeholders. Copyright

关键词： Intelligent robots

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A Hybrid Multiple Classifier System Applied in Life Insurance Underwriting 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： He, Chun Lei Keirstead, Dave Suen, Ching Y. Advanced Analytics Canadian Division Manulife TorontoON Canada Computer Science and Software Engineering Department Concordia University MontrealQC Canada

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

In an insurance company, manual underwriting is costly, time consuming, and complex. Simulating underwriters with AI is an absolutely time saving and cost fitting solution. As a result, a Hybrid Multiple Classifier System, combining three classifiers with rejection options: XGBoost, Random Forest, and SVM, was designed and applied on production. An optimal rejection criterion on classification, so-called Linear Discriminant Analysis Measurement (LDAM), is applied the first time in industry. This system is the first AI driven underwriting system in Canadian life insurance, and it helps Manulife expand digital capabilities, reorient customer experience focus and grow its business. © 2020, Springer Nature Switzerland AG.

关键词： Decision trees

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VAIM - CFF: A variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions

arXiv

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arXiv 2024年

作者： Almaeen, Manal Alghamdi, Tareq Kriesten, Brandon Adams, Douglas Li, Yaohang Lin, Huey-Wen Liuti, Simonetta Department of Software Engineering College of Computer and Information Sciences Jouf University Sakaka Aljouf72341 Saudi Arabia Department of Computer Science Old Dominion University NorfolkVA23529 United States Department of Computer Science College of Computer Science and Information Technology Al-Baha University Al-Baha Alaqiq65779 Saudi Arabia High Energy Physics Division Argonne National Laboratory LemontIL60439 United States Department of Physics University of Virginia CharlottesvilleVA22904 United States Department of Physics Michigan State University East LansingMI48824 United States

We develop a new methodology for extracting Compton form factors (CFFs) in from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted CFFs, use of uncertainty quantification technique, and inclusion of prior physics information. We then use dimensionality reduction techniques such as principal component analysis to visualize the missing physics information tracked in the latent space of the VAIM framework. Through re-framing the extraction of CFFs as an inverse problem, we gain access to fundamental properties of the problem not comprehensible in standard fitting methodologies: exploring the limits of the information encoded in deeply virtual exclusive experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Natural computational architectures for cognitive info-communication

arXiv

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arXiv 2021年

作者： Dodig-Crnkovic, Gordana Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Division of Computer Science and Software Engineering School of Innovation Design and Engineering Mälardalen University Västerås Sweden

Recent comprehensive overview of 40 years of research in cognitive architectures, (Kotseruba and Tsotsos 2020), evaluates modelling of the core cognitive abilities in humans, but only marginally addresses biologically plausible approaches based on natural computation. This mini review presents a set of perspectives and approaches which have shaped the development of biologically inspired computational models in the recent past that can lead to the development of biologically more realistic cognitive architectures. For describing continuum of natural cognitive architectures, from basal cellular to human-level cognition, we use evolutionary info-computational framework, where natural/physical/morphological computation leads to evolution of increasingly complex cognitive systems. Forty years ago, when the first cognitive architectures have been proposed, understanding of cognition, embodiment and evolution was different. So was the state of the art of information physics, bioinformatics, information chemistry, computational neuroscience, complexity theory, self-organization, theory of evolution, information and computation. Novel developments support a constructive interdisciplinary framework for cognitive architectures in the context of computing nature, where interactions between constituents at different levels of organization lead to complexification of agency and increased cognitive capacities. We identify several important research questions for further investigation that can increase understanding of cognition in nature and inspire new developments of cognitive technologies. Recently, basal cell cognition attracted a lot of interest for its possible applications in medicine, new computing technologies, as well as micro- and nanorobotics. Bio-cognition of cells connected into tissues/organs, and organisms with the group (social) levels of information processing provides insights into cognition mechanisms that can support the development of new AI platforms and co

关键词： Cognitive systems

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Discrimination of Quantum States Under Locality Constraints in the Many-Copy Setting

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Communications in Mathematical Physics 2023年第1期404卷 151-183页

作者： Cheng, Hao-Chung Winter, Andreas Yu, Nengkun Department of Electrical Engineering and Graduate Institute of Communication Engineering National Taiwan University Tapei 106 Taiwan Department of Mathematics National Taiwan University Tapei 106 Taiwan Center for Quantum Science and Engineering National Taiwan University Tapei 106 Taiwan Physics Division National Center for Theoretical Sciences Taipei 10617 Taiwan Hon Hai (Foxconn) Quantum Computing Center New Taipei City 236 Taiwan ICREA-Institució Catalana de Recerca i Estudis Avançats Barcelona 08010 Spain Departament de Física Física Teòrica: Informaciò i Fenòmens Quàntics Universitat Autònoma de Barcelona Barcelona 08193 Spain Computer Science Department Stony Brook University New York NY United States Centre for Quantum Software and Information and Faculty of Engineering and Information Technology University of Technology Sydney Ultimo 2007 NSW Australia

We study quantum hypothesis testing between orthogonal states under restricted local measurements in the many-copy scenario. For testing arbitrary multipartite entangled pure state against its orthogonal complement state via the local operation and classical communication (LOCC) operation, we prove that the optimal average error probability always decays exponentially in the number of copies. Second, we provide a sufficient condition for the LOCC operations to achieve the same performance as the positive-partial-transpose (PPT) operations. We further show that testing a maximally entangled state against its orthogonal complement and testing extremal Werner states both fulfill the above-mentioned condition. Hence, we determine the explicit expressions for the optimal average error probability, the optimal trade-off between the type-I and type-II errors, and the associated Chernoff, Stein, Hoeffding, and strong converse exponents. Then, we show an infinite asymptotic separation between the separable (SEP) and PPT operations by providing a pair of states constructed from an unextendible product basis (UPB). The quantum states can be distinguished perfectly by PPT operations, while the optimal error probability, with SEP operations, admits an exponential lower bound. On the technical side, we prove this result by providing a quantitative version of the well-known statement that the tensor product of UPBs is a UPB. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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