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Kirchhoff-Law Johnson Noise Meets Web 3.0: A Statistical Physical Method of Random Key Generation for Decentralized Identity Protocols

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arXiv 2023年

作者： Chamon, Christiana Mohanasundar, Kamalesh Flanery, Sarah A. Quek, Francis K. Texas A&M University Department of Computer Science and Engineering College StationTX77843 United States Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States Texas A&M University Department of Teaching Learning and Culture College StationTX77843 United States

This paper presents a statistical physical generation of random keys for a decentralized identity ecosystem that uses Web 3.0 protocols. Web 3.0 is driven by secure keys, typically represented in hexadecimal, that are pseudo-randomly generated by an initialization vector and complex computational algorithms. We demonstrate that the statistical physical Kirchhoff-law-Johnson-noise (KLJN) scheme eliminates the additional computational power by naturally generating truly random binary keys to drive the creation of decentralized identifiers (DIDs) that are appended to an Ethereum blockchain. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet protocols

An Adaptable SVM Model for Abnormalities Detection in Chest X-ray Reports

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An Adaptable SVM Model for Abnormalities Detection in Chest ...

Information Technology for Education and Development (ITED)

作者： Abimbola Rhoda Iyanda Omolara Aminat Ogungbe Adeniyi Sunday Aderibigbe Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife Nigeria Department of Radiology Obafemi Awolowo University Teaching Hospital Complex Ile-Ife Nigeria

In Nigeria, prose format is used to present and perform analysis on chest x-ray reports and this often results in delayed response from the clinicians. Therefore, with a view to developing a system for analyzing chest x-ray reports for diagnosing cardiomegaly, linear support vector machine algorithm was utilized to formulate an adaptable model with a train-test split of 70:30 for six hundred and fifty (650) de-identified patients' information. Attributes relevant to cardiomegaly from the collected dataset were extracted using Term frequency/inverse document frequency technique. This work provides an adequate requirement for diagnosis design with accuracy of 93.69%. Its implementation in software application has the potential to reduce delay in attending to patients and can also help the clinicians focus on the findings from chest x-ray reports.

关键词： Analytical models Adaptation models Computational modeling Support vector machine classification X-rays Software X-ray imaging

The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns

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arXiv 2024年

作者： Lorello, Luca Salvatore Lippi, Marco Melacci, Stefano Department of Computer Science University of Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia via Amendola 2 Reggio Emilia42122 Italy Department of Information Engineering and Mathematics University of Siena via Roma 56 Siena53100 Italy

Artificial intelligence is continuously seeking novel challenges and benchmarks to effectively measure performance and to advance the state-of-the-art. In this paper we introduce KANDY, a benchmarking framework that can be used to generate a variety of learning and reasoning tasks inspired by Kandinsky patterns. By creating curricula of binary classification tasks with increasing complexity and with sparse supervisions, KANDY can be used to implement benchmarks for continual and semi-supervised learning, with a specific focus on symbol compositionality. Classification rules are also provided in the ground truth to enable analysis of interpretable solutions. Together with the benchmark generation pipeline, we release two curricula, an easier and a harder one, that we propose as new challenges for the research community. With a thorough experimental evaluation, we show how both state-of-the-art neural models and purely symbolic approaches struggle with solving most of the tasks, thus calling for the application of advanced neuro-symbolic methods trained over time. © 2024, CC BY-NC-SA.

关键词： Curricula

Left inferior parietal lobe and auditory cortex jointly contribute to sound knowledge retrieval

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Brain Stimulation 2025年第4期18卷 1037-1047页

作者： Kuhnke, Philipp Numssen, Ole Voeller, Johannah Cheung, Vincent K.M. Weise, Konstantin Kiefer, Markus Hartwigsen, Gesa Wilhelm Wundt Institute for Psychology Leipzig University Germany Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) Leipzig Germany Methods and Development Group Brain Networks MPI CBS Leipzig Germany TMS Group Department of Neuroscience and Biomedical Engineering Aalto University Finland TMS Lab Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Boston United States Department of Experimental Psychology University of Granada Spain Mind Brain and Behavior Research Center (CIMCYC) University of Granada Spain Sony Computer Science Laboratories Tokyo Japan Leipzig University of Applied Sciences HTWK Leipzig Germany Department of Psychiatry Ulm University Germany

Background: Conceptual knowledge is central to human cognition. Neuroimaging studies suggest that conceptual processing relies on the joint contribution of modality-specific perceptual-motor and multimodal brain regions. In particular, the multimodal left inferior parietal lobe (IPL) couples with auditory association cortex during sound knowledge retrieval and with somatomotor cortex during action feature retrieval. However, it remains unknown whether the interaction between modality-specific and multimodal cortices is causally relevant for conceptually-guided behavior. methods: To tackle this issue, we applied offline transcranial magnetic stimulation (TMS) over modality-specific cortex (somatomotor, auditory, or sham), before 24 healthy participants received online TMS over multimodal cortex (IPL, or sham) during action and sound knowledge tasks on written words. For the first time in a TMS study on higher cognition, TMS targeting and dosing were optimized using individual a priori computational electric field simulations. Results: We found that combined stimulation of auditory association cortex and multimodal IPL selectively impaired sound judgments, but not action judgments, on low sound–low action words (BF₁₀ = 5.316). Unifocal TMS over auditory cortex or IPL alone did not disrupt behavior, suggesting adaptive compensatory plasticity. Conclusions: These results indicate that the joint contribution of multimodal IPL and auditory cortex is causally relevant for sound knowledge retrieval: The functional relevance of left IPL depends on the integrity of the auditory cortex, and vice versa, as single perturbation of either region did not disrupt performance. Our findings support “hybrid theories” of conceptual processing, which propose that task-dependent functional interactions between modality-specific and multimodal regions are crucial. © 2025 The Authors

关键词： Compensation Computational modeling Concepts Plasticity Semantics TMS

Convolutional Motif Kernel Networks

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arXiv 2021年

作者： Ditz, Jonas C. Reuter, Bernhard Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tübingen Tübingen Germany

Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the knowledge advancement in research fields by obscuring the decision process and preventing scientist to fully conceptualize predicted outcomes. Furthermore, domain experts like healthcare providers need explainable predictions to assess whether a predicted outcome can be trusted in high stakes scenarios and to help them integrating a model into their own routine. Therefore, interpretable models play a crucial role for the incorporation of machine learning into high stakes scenarios like healthcare. In this paper we introduce Convolutional Motif Kernel Networks, a neural network architecture that involves learning a feature representation within a subspace of the reproducing kernel Hilbert space of the position-aware motif kernel function. The resulting model enables to directly interpret and evaluate prediction outcomes by providing a biologically and medically meaningful explanation without the need for additional post-hoc analysis. We show that our model is able to robustly learn on small datasets and reaches state-of-the-art performance on relevant healthcare prediction tasks. Our proposed method can be utilized on DNA and protein sequences. Furthermore, we show that the proposed method learns biologically meaningful concepts directly from data using an end-to-end learning scheme. © 2021, CC BY.

关键词： Forecasting

Leveraging Artificial Intelligence in Online Educational Platforms to Enhance Continuous Professional Development in the teaching Profession

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Leveraging Artificial Intelligence in Online Educational Pla...

International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： Khujakulov Sherzod Amankulovich Mannabov Javlon Tokhirovich Kadirov Tulkin Bakhridinovich Taylakov Umid Kuvandikovich Christo Ananth T. Ananth Kumar Senior Teacher of the the Samarkand Regional National Center for Training Teachers in New Methods Teacher of the Samarkand Regional National Center for Training Teachers in New Methods (PhD) Samarkand Agroinnovations and Research University Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Uzbekistan&Adjunct Faculty Samarkand State University Chennai India Computer Science and Engineering IFET College of Engineering Tamilnadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

To keep up with the dynamic nature of the modern classroom, teachers must have access to online learning platforms for professional development purposes. This investigation looks at how these platforms can be used to improve teachers' skills, make information sharing easier, and open doors to CPD opportunities. This study examines the relationship between online resources and methods to demonstrate how digital tools have an impact on teacher training and professional development by leveraging AI. It also draws attention to the fact that these platforms are helping with information-methodological support for teachers as well as the ongoing reforms in our nation. Incorporating empirical observations and case studies, the analysis demonstrates how effective online platforms are in fostering a collaborative and innovative environment for professional learning. Digital transformation has the potential to radically alter how teachers obtain professional development, and this study emphasizes that fact. This will enable teachers to effectively address the demands of modern classrooms.

关键词： Training Digital transformation Information sharing Focusing Collaboration Artificial intelligence

Collaboration and orchestration in a virtual teaching organization of environmental education 23

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Collaboration and orchestration in a virtual teaching organi...

Proceedings of the 15th International Conference on Education Technology and computers

作者： Fenghua Li Mei Wang Chunfang Zhou Maria Inmaculada Navarro Gonzalez Chong Han Yong Li PBL Teaching Innovation Research Center of Northeastern University Department of Resources and Environment School of metallurgy Northeastern University China Department of Mathematics and Computer Science University of Southern Denmark Denmark Faculty of Education National University of Distance Education Spain Department of Resources and Environment School of metallurgy Northeastern University China

ISBN: (纸本)9798400709111

In the information era, Virtual teaching Organizations (VTOs) development is an important probe of creative education. This paper explores an investigation on the inter-departmental VTO in a college-wide general elective course Environment and Sustainable Development (VTO-ESD) that involves teachers who come from six majors in four departments in a Chinese university. In order to achieve ground-breaking expectations for the teachers’ teaching and research capacity of working at the virtual community, it is critical to enhance their skills of team collaboration and communication during the development process in practice. The VTO-ESD is an example of a large curriculum teaching organization that may be organically split into divisions based on diverse factors such as environmental education research, textbooks, teaching reform, training in teaching methods, case studies, and so on. Problem-Based Cooperation has been recognized as a good way to improve trust between team members, boost organizational resilience through the synergistic impact, and accomplish sustainable development and collective progress through deliberate and in-depth communication and exchange.

关键词： Environmental education Orchestration Organization resilience Problem-Based Collaboration Virtual teaching Organization (VTO)

Understanding Fairness Surrogate Functions in Algorithmic Fairness

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arXiv 2023年

作者： Yao, Wei Zhou, Zhanke Li, Zhicong Han, Bo Liu, Yong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China TMLR Group Department of Computer Science Hong Kong Baptist University Hong Kong

It has been observed that machine learning algorithms exhibit biased predictions against certain population groups. To mitigate such bias while achieving comparable accuracy, a promising approach is to introduce surrogate functions of the concerned fairness definition and solve a constrained optimization problem. However, it is intriguing in previous work that such fairness surrogate functions may yield unfair results and high instability. In this work, in order to deeply understand them, taking a widely used fairness definition—demographic parity as an example, we show that there is a surrogate-fairness gap between the fairness definition and the fairness surrogate function. Also, the theoretical analysis and experimental results about the "gap" motivate us that the fairness and stability will be affected by the points far from the decision boundary, which is the large margin points issue investigated in this paper. To address it, we propose the general sigmoid surrogate to simultaneously reduce both the surrogate-fairness gap and the variance, and offer a rigorous fairness and stability upper bound. Interestingly, the theory also provides insights into two important issues that deal with the large margin points as well as obtaining a more balanced dataset are beneficial to fairness and stability. Furthermore, we elaborate a novel and general algorithm called Balanced Surrogate, which iteratively reduces the "gap" to mitigate unfairness. Finally, we provide empirical evidence showing that our methods consistently improve fairness and stability while maintaining accuracy comparable to the baselines in three real-world datasets. Our code is available at https://***/yw101004244/Understanding-Fairness-Surrog ate-Functions. Copyright © 2023, The Authors. All rights reserved.

关键词： Stability

Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope 15

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Intensionalizing Abstract Meaning Representations: Non-Verid...

Joint 15th Linguistic Annotation Workshop and 3rd Designing Meaning Representations Workshop, LAW-DMR 2021

作者： Williamson, Gregor Elliott, Patrick Ji, Yuxin Department of Computer Science Emory University AtlantaGA30322 United States Department of Linguistics and Philosophy Massachusetts Institute of Technology CambridgeMA02139 United States Department of Quantitative Theory and Methods Emory University AtlantaGA30322 United States

ISBN: (纸本)9781954085855

Meaning Representation (AMR) is a graphical meaning representation language designed to represent propositional information about argument structure. However, at present it is unable to satisfyingly represent non-veridical intensional contexts, often licensing inappropriate inferences. In this paper, we show how to resolve the problem of non-veridicality without appealing to layered graphs through a mapping from AMRs into Simply-Typed Lambda Calculus (STLC). At least for some cases, this requires the introduction of a new role:content which functions as an intensional operator. The translation proposed is inspired by the formal linguistics literature on the event semantics of attitude reports. Next, we address the interaction of quantifier scope and intensional operators in so-called de re/de dicto ambiguities. We adopt a scope node from the literature and provide an explicit multidimensional semantics utilizing Cooper storage which allows us to derive the de re and de dicto scope readings as well as intermediate scope readings which prove difficult for accounts without a scope node. © 2021 Association for Computational Linguistics.

关键词： Semantics