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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Frederik Pahde Galip Ümit Yolcu Alexander Binder Wojciech Samek Sebastian Lapuschkin Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Germany Technische Universität Berlin Germany ICT Cluster Singapore Institute of Technology Singapore University of Oslo Norway BIFOLD – Berlin Institute for the Foundations of Learning and Data Germany

Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being applied to modern model architectures including innovative layer building blocks, which is caused by two reasons. Firstly, the high flexibility of rule-based XAI methods leads to numerous potential parameterizations. Secondly, many XAI methods break the implementation-invariance axiom because they struggle with certain model components, e.g., BatchNorm layers. The latter can be addressed with model canonization, which is the process of restructuring the model to disregard problematic components without changing the underlying function. While model canonization is straightforward for simple architectures (e.g., VGG, ResNet), it can be challenging for more complex and highly interconnected models (e.g., DenseNet). Moreover, there is only little quantifiable evidence that model canonization is beneficial for XAI. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures, including VGG, ResNet, EfficientNet, DenseNets, as well as Relation Networks. We further suggest a XAI evaluation framework with which we quantify and compare the effects of model canonization for various XAI methods in image classification tasks on the Pascal VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI. Moreover, addressing the former issue outlined above, we demonstrate how our evaluation framework can be applied to perform hyperparameter search for XAI methods to optimize the quality of explanations. Code is available on https://***/frederikpahde/xai-canonization.

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Building a data Lake for Power BI in the Cloud: A Review on Utilizing Cloud Storage Services for Large datasets

Building a Data Lake for Power BI in the Cloud: A Review on ...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Viraj Vijay Gurbade Prateek Verma Swapnil Gundewar Vijendra Singh Bramhe Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business Intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets.

关键词： Cloud computing Scalability Decision making Buildings Organizations Big data applications Security data mining Business intelligence Self-service

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Advancing Alzheimer's Nodule Detection Through a Comprehensive Multi-Scene Deep learning Framework

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： C. Premkumar R. Rajeshwari R. Remya M. S. Cholaathiraj A. Reethika S. Bharathidasan Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D institute of science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering Erode Sengunthar Engineering College Erode India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAD modules, including MRI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene Deep learning Framework (MSDLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSDLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives.

关键词： Deep learning Training Solid modeling Accuracy Sensitivity Image recognition Magnetic resonance imaging Brain modeling Classification algorithms Alzheimer's disease

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The Role of Blockchain technology in Supply chain Management for data Security

The Role of Blockchain Technology in Supply chain Management...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Dipika R. Birari Vijay U. Rathod Aarti Dandavate Nilesh Shelke Ranjit Kumar Masira M. S Kulkarni Department of Information Technology Army Institute of Technology Pune Department of Artificial Intelligent and Machine Learning G H Raisoni College of Engineering and Management Wagholi Pune Department of Computer Engineering Dhole Patil College of Engineering Pune Department of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Department of Computer Engineering Ajeenkya D Y Patil University Pune Department of Cyber Security and Data Science G H Raisoni College of Engineering and Management Wagholi Pune

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

In a traditional supply chain system, the process of producing raw materials so that a product may be delivered to a customer is a manual operation with insufficient data and transaction security. The entire process is time consuming and requires a lot of effort. The undivided process is generally ineffective and unreliable for consumers. The data security, authenticity, the inclusion of will significantly improve the time management and transaction procedures of conventional supply chain management systems. data is shielded from illegal access via the ground-breaking, decentralized technology known as blockchain. After smart contracts are put into place, the complete supply chain management (SCM) will be satisfied with the customer. When the mediator is contracted, which is possible in various ways, the plan becomes more reliable. This article places particular emphasis on lowering third party involvement in the supply chain system and enhancing data security. Conventional supply chain management techniques have several serious shortcomings. Some of them include a lack of traceability, difficulties maintaining product safety and quality, a failure to track and manage inventories in stores and warehouses, escalating supply chain costs, and so on. The reduction of third-party involvement in the supply chain system and the improvement of data security are the main goals of this article. This enhances process accessibility, effectiveness, and timeliness overall. They will feel safer throughout the payment procedure, which is the main benefit. Yet in this investigation, a smart contract and a peer-to-peer encrypted system were both used. There are a few additional features as well.

关键词： Supply chain management Costs Supply chains Smart contracts Reliability engineering Raw materials Blockchains Peer-to-peer computing Product safety

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From 5G to 6G: The Role of AI, Machine learning, and Deep learning in Wireless Systems

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Pradnyawant M. Gote Praveen Kumar Prateek Verma Prajyot Yesankar Adesh Pawar Saniya Saratkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible.

关键词： 6G mobile communication Wireless communication Deep learning Tactile Internet Technological innovation Sentiment analysis 5G mobile communication Reviews Heuristic algorithms Security

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Consensus-Based Optimization with Truncated Noise

arXiv

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arXiv 2023年

作者： Fornasier, Massimo Richtárik, Peter Riedl, Konstantin Sun, Lukang Technical University of Munich School of Computation Information and Technology Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Germany King Abdullah University of Science and Technology Thuwal Saudi Arabia KAUST AI Initiative Thuwal Saudi Arabia SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Thuwal Saudi Arabia

Consensus-based optimization (CBO) is a versatile multi-particle metaheuristic optimization method suitable for performing nonconvex and nonsmooth global optimizations in high dimensions. It has proven effective in various applications while at the same time being amenable to a theoretical convergence analysis. In this paper, we explore a variant of CBO, which incorporates truncated noise in order to enhance the well-behavedness of the statistics of the law of the dynamics. By introducing this additional truncation in the noise term of the CBO dynamics, we achieve that, in contrast to the original version, higher moments of the law of the particle system can be effectively bounded. As a result, our proposed variant exhibits enhanced convergence performance, allowing in particular for wider flexibility in choosing the noise parameter of the method as we confirm experimentally. By analyzing the time-evolution of the Wasserstein-2 distance between the empirical measure of the interacting particle system and the global minimizer of the objective function, we rigorously prove convergence in expectation of the proposed CBO variant requiring only minimal assumptions on the objective function and on the initialization. Numerical evidences demonstrate the benefit of truncating the noise in CBO. © 2023, CC BY.

关键词： Global optimization

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JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj Computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus

arXiv

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arXiv 2023年

作者： Hedström, Anna Bommer, Philine Wickstrøm, Kristoffer K. Samek, Wojciech Lapuschkin, Sebastian Höhne, Marina M.-C. Department of Electrical Engineering and Computer Science Technical University of Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Germany Department of Physics and Technology UiT Arctic University of Norway Norway Department of Artificial Intelligence Fraunhofer Heinrich-Hertz-Institute Germany Department of Computer Science University of Potsdam Germany Germany

One of the unsolved challenges in the field of Explainable AI (XAI) is determining how to most reliably estimate the quality of an explanation method in the absence of ground truth explanation labels. Resolving this issue is of utmost importance as the evaluation outcomes generated by competing evaluation methods (or "quality estimators"), which aim at measuring the same property of an explanation method, frequently present conflicting rankings. Such disagreements can be challenging for practitioners to interpret, thereby complicating their ability to select the best-performing explanation method. We address this problem through a meta-evaluation of different quality estimators in XAI, which we define as "the process of evaluating the evaluation method". Our novel framework, MetaQuantus, analyses two complementary performance characteristics of a quality estimator: its resilience to noise and reactivity to randomness, thus circumventing the need for ground truth labels. We demonstrate the effectiveness of our framework through a series of experiments, targeting various open questions in XAI such as the selection and hyperparameter optimisation of quality estimators. Our work is released under an open-source license1 to serve as a development tool for XAI- and Machine learning (ML) practitioners to verify and benchmark newly constructed quality estimators in a given explainability context. With this work, we provide the community with clear and theoretically-grounded guidance for identifying reliable evaluation methods, thus facilitating reproducibility in the field. © 2023, CC BY-SA.

关键词： Benchmarking

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An iterative-based difference scheme for nonlinear fractional integro-differential equations of Volterra type

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Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Saini, Gaurav Ghosh, Bappa Chand, Sunita Mohapatra, Jugal Center for Data Science Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) India Center for Artificial Intelligence and Machine Learning Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) India Department of Mathematics Siksha ‘O’ Anusandhan (Deemed to be University) India Department of Mathematics National Institute of Technology Rourkela India

This paper presents an iterative difference scheme for solving nonlinear fractional integro-differential equations of Volterra type, which are widely used in modeling memory-dependent phenomena in various scientific and engineering applications. The scheme employs Caputo's fractional derivative to handle the non-local nature of the fractional derivative and introduces an iterative approach to enhance accuracy and stability. By discretizing the domain of interest on a uniform mesh and the present non-local differential operator using a well-established L1 technique, we derive a nonlinear difference equation that captures the essential dynamics of the underlying continuous problem. The corresponding nonlinear equation is solved by employing the Daftardar-Gejji and Jafari (DGJ) method. The convergence and stability of the proposed scheme are rigorously analyzed under sufficiently smooth initial data, and the method produces reliable numerical solutions. Numerical experiments are conducted to validate the effectiveness of the scheme, demonstrating its efficiency. © 2025 The Authors

关键词： Composite trapezoidal formula Error estimation L1 technique Nonlinear integro-differential equations

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ACTIONS ON CLASSIFIABLE C*-ALGEBRAS WITHOUT EQUIVARIANT PROPERTY (SI)

arXiv

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arXiv 2024年

作者： Gardella, Eusebio Kranz, Julian Vaccaro, Andrea Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg GothenburgSE-412 96 Sweden Data Science: Machine Learning and Data Engineering Prof. Dr. Fabian Gieseke Leonardo-Campus 3 Münster48149 Germany Mathematisches Institut Fachbereich Mathematik Informatik der Universität Münster Einsteinstrasse 62 Münster48149 Germany

We exhibit examples of actions of countable discrete groups on both simple and non-simple nuclear stably finite C*-algebras that are tracially amenable but not amenable. We furthermore obtain that, under the additional assumption of strict comparison, amenability is equivalent to tracial amenability plus the equivariant analogue of Matui-Sato's property (SI). By virtue of this equivalence, our construction yields the first known examples of actions on classifiable C*-algebras that do not have equivariant property (SI). We moreover show that such actions can be chosen to absorb the trivial action on the universal UHF algebra, thus proving that equivariant Z-stability does not in general imply equivariant property (SI). Copyright © 2024, The Authors. All rights reserved.

关键词： Algebra

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