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arXiv 2024年

作者： Schäfer-Zimmermann, Julian C. Demartsev, Vlad Averly, Baptiste Dhanjal-Adams, Kiran Duteil, Mathieu Gall, Gabriella Faiß, Marius Johnson-Ulrich, Lily Stowell, Dan Manser, Marta B. Roch, Marie A. Strandburg-Peshkin, Ariana Department for the Ecology of Animal Societies Max Planck Institute of Animal Behavior Konstanz Germany Department of Biology University of Konstanz Konstanz Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany Kalahari Research Centre Northern Cape Van Zylsrus South Africa Zukunftskolleg University of Konstanz Konstanz Germany Department of Evolutionary Biology and Environmental Studies University of Zurich Zurich Switzerland Department of Cognitive Science and Artificial Intelligence Tilburg University Tilburg Netherlands Naturalis Biodiversity Center Leiden Netherlands Interdisciplinary Center for the Evolution of Language University of Zurich Zurich Switzerland Department of Computer Science San Diego State University San DiegoCA United States

Bioacoustic research, vital for understanding animal behavior, conservation, and ecology, faces a monumental challenge: analyzing vast datasets where animal vocalizations are rare. While deep learning techniques are becoming standard, adapting them to bioacoustics remains difficult. We address this with animal2vec, an interpretable large transformer model, and a self-supervised training scheme tailored for sparse and unbalanced bioacoustic data. It learns from unlabeled audio and then refines its understanding with labeled data. Furthermore, we introduce and publicly release MeerKAT: Meerkat Kalahari Audio Transcripts, a dataset of meerkat (Suricata suricatta) vocalizations with millisecond-resolution annotations, the largest labeled dataset on non-human terrestrial mammals currently available. Our model outperforms existing methods on MeerKAT and the publicly available NIPS4Bplus birdsong dataset. Moreover, animal2vec performs well even with limited labeled data (few-shot learning). animal2vec and MeerKAT provide a new reference point for bioacoustic research, enabling scientists to analyze large amounts of data even with scarce ground truth information. © 2024, CC BY.

关键词： Biotic

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Design and implementation of a digital twin for a stone-cutting machine: a case study

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Procedia computer science 2024年 232卷 990-1000页

作者： Carlos Cremonini Joel Vasco Carlos Capela Agostinho da Silva Marcelo Gaspar School of Technology and Management Polytechnic Institute of Leiria 2411-901 Leiria Portugal Institute for Polymers and Composites (IPC) University of Minho 4800-058 Guimarães Portugal Centre for Mechanical Engineering Materials and Processes (CEMMPRE) University of Coimbra 3030-788 Coimbra Portugal CEFAGE-UE Center for Advanced Studies in Management and Economics 7000-809 Évora Portugal MARE Marine and Environmental Sciences Centre Polytechnic Institute of Leiria 2520-630 Leiria Portugal CIIC Computer Science and Communication Research Centre Polytechnic Institute of Leiria 2411-901 Leiria Portugal ADAI-LAETA - Industrial Aerodynamics Development Association Coimbra University 3030-289 Coimbra Portugal

The use of digital twins in the development and management of stone-cutting devices presents real challenges in the field of industrial stone manufacturing. Digital twins help manufacturers achieve increased precision, efficiency, and cost-effectiveness, ultimately transforming the landscape of stone manufacturing operations. Such digital twins enable real-time monitoring, predictive maintenance, and virtual simulations. This paper explores two options for connecting a physical stone-cutting machine in a company to its digital twin. The first option involves using Industrial Internet of Things (IIoT) devices and a Message Queuing Telemetry Transport (MQTT) broker to enable real-time data exchange. This approach provides agile and efficient communication between the physical machine and its digital twin. The second option employs conventional automation devices and a VPN connection to ensure data security during transmission. The two options were evaluated by considering criteria such as communication speed, security, scalability, ease of implementation, cost, complexity, and technical requirements. The results of the comparative analysis provide valuable insights for the implementation of the digital twin of the stone-cutting machine, allowing the selection of the most suitable option based on these considerations.

关键词： Digital Twin IIot Security and Real-Time Data Exchange

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Integrating Artificial Intelligence and Machine Learning for Competitive Advantage in Developing Business Ecosystems

Integrating Artificial Intelligence and Machine Learning for...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Srinivas D C. Vijai Krishan Bansal Shaunak Pal Sumeet Gupta Punamkumar Hinge School of Business SR University Warangal Telangana Department of Commerce and Business Administration Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Lovely Professional University School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Professor and Cluster Head –Global Economics and Finance Cluster School of Business University of Petroleum and Energy Studies Management Indus Business School Pune Maharashtra

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The previous few years have witnessed significant shifts in people's aspirations and lives as a result of technology improvements. When discussing the advancements in technology over the past twenty years, we must first discuss the pre-Internet era before moving on to the Internet *** that, systems that are service-oriented replaced those of the Internet age. After then, we entered the era of social applications and left behind service-oriented systems. The social application era was eventually superseded by the age of artificial intelligence and the Internet of *** theory, subsequent development, and integration of intelligent behavior akin to that of humans in machines is known as artificial intelligence, or AI. A branch of AI called machine learning has important uses in many different fields of *** recent years, there has been a substantial advancement in the business model framework for artificial intelligence and machine learning. Nonetheless, there is one area that needs more investigation and understanding even though it is crucial for academics, policymakers, and aspiring managers to know: how companies are evolving their AI business model frameworks to achieve ongoing value *** and experience on AI business model frameworks have consequences for machine learning (ML) in this investigation. In order to create and adapt their company model and maintain value generation, executives can use this tool to reproduce and categorize significant difficulties. We want to investigate the theoretical underpinnings of the Business Model Framework in this study, which uses Consumer Business Analytics to transform the business market ecology.

关键词： Adaptation models Analytical models Quantum computing Biological system modeling Machine learning Transforms Companies

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Recording provenance of workflow runs with RO-Crate

arXiv

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arXiv 2023年

作者： Leo, Simone Crusoe, Michael R. Rodríguez-Navas, Laura Sirvent, Raül Kanitz, Alexander De Geest, Paul Wittner, Rudolf Pireddu, Luca Garijo, Daniel Fernández, José M. Colonnelli, Iacopo Gallo, Matej Ohta, Tazro Suetake, Hirotaka Capella-Gutierrez, Salvador de Wit, Renske Kinoshita, Bruno P. Soiland-Reyes, Stian CA Pula Italy Vrije Universiteit Amsterdam Amsterdam Netherlands DTL Projects Netherlands Forschungszentrum Jülich Germany Barcelona Supercomputing Center Barcelona Spain Biozentrum University of Basel Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland VIB Data Core Gent Belgium Faculty of Informatics Masaryk University Brno Czech Republic Institute of Computer Science Masaryk University Brno Czech Republic BBMRI-ERIC Graz Austria Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Computer Science Department Università degli Studi di Torino Torino Italy Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Shizuoka Japan Institute for Advanced Academic Research Chiba University Chiba Japan Sator Incorporated Tokyo Japan Department of Computer Science The University of Manchester Manchester United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands

Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and *** to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain. © 2023, CC BY.

关键词： Workflow management

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Physics-driven discovery and bandgap engineering of hybrid perovskites

arXiv

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arXiv 2023年

作者： Sanchez, Sheryl L. Foadian, Elham Ziatdinov, Maxim Yang, Jonghee Kalinin, Sergei V. Liu, Yongtao Ahmadi, Mahshid Institute for Advanced Materials and Manufacturing Department of Materials Science and Engineering The University of Tennessee Knoxville KnoxvilleTN37996 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Computer Science and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

The unique aspect of the hybrid perovskites is their tunability, allowing to engineer the bandgap via substitution. From application viewpoint, this allows creation of the tandem cells between perovskites and silicon, or two or more perovskites, with associated increase of efficiency beyond single-junction Schokley-Queisser limit. However, the concentration dependence of optical bandgap in the hybrid perovskite solid solutions can be non-linear and even non-monotonic, as determined by the band alignments between endmembers, presence of the defect states and Urbach tails, and phase separation. Exploring new compositions brings forth the joint problem of the discovery of the composition with the desired band gap, and establishing the physical model of the band gap concentration dependence. Here we report the development of the experimental workflow based on structured Gaussian Process (sGP) models and custom sGP (c-sGP) that allow the joint discovery of the experimental behavior and the underpinning physical model. This approach is verified with simulated data sets with known ground truth, and was found to accelerate the discovery of experimental behavior and the underlying physical model. The d/c-sGP approach utilizes a few calculated thin film bandgap data points to guide targeted explorations, minimizing the number of thin film preparations. Through iterative exploration, we demonstrate that the c-sGP algorithm that combined 5 bandgap models converges rapidly, revealing a relationship in the bandgap diagram of MA1-xGAxPb(I1-xBrx)3. This approach offers a promising method for efficiently understanding the physical model of band gap concentration dependence in the binary systems, this method can also be extended to ternary or higher dimensional systems. © 2023, CC BY.

关键词： Perovskite

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An Investigation Using Structural Equation Modelling on Customer Adoption of Internet of Things Assistance

An Investigation Using Structural Equation Modelling on Cust...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Aasim Mir M. Sankar Kafila Kafila Shaik Vaseem Akram D Praveena Devi Shivangi Barthwal Department of Management Studies Baba Ghulam Shah Badshah University Rajouri Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Business SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University Koneru Lakshmaiah Education Foundation Guntur AP India Department of ECE Graphic Era Deemed to be University Dehradun Uttarakhand India

The development of Internet of Things-based technologies in India includes home automation, fitness trackers devices, and smart buildings. The Internet of Things has had very little uptake thus far. Scholars and professionals alike agree that a country's economic and social growth may benefit greatly from the introduction of IoT. Several theories have been applied to study the diffusion of IoT, including the technology admission model, the theory of reasoned action, and the theory of patterned behaviors. This research fills a significant knowledge gap by utilizing a structured equation model strategy to evaluate and contrast three competing models in the context of internet of things in India. This research, when compared to the existing literature, opens the door to exploration of a wide variety of internet of things application domains, such as healthcare, senior assistance, intelligent buildings, intelligent supply chains, etc.

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Learning Robust Medical Image Segmentation from Multi-source Annotations

arXiv

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arXiv 2023年

作者： Wang, Yifeng Luo, Luyang Wu, Mingxiang Wang, Qiong Chen, Hao The Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen People’s Hospital Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image segmentation. Learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of annotations and the quality of images. In this paper, we propose an Uncertainty-guided Multi-source Annotation Network (UMA-Net), which guides the training process by uncertainty estimation at both the pixel and the image levels. First, we developed the annotation uncertainty estimation module (AUEM) to learn the pixel-wise uncertainty of each annotation, which then guided the network to learn from reliable pixels by weighted segmentation loss. Second, a quality assessment module (QAM) was proposed to assess the image-level quality of the input samples based on the former assessed annotation uncertainties. Importantly, we introduced an auxiliary predictor to learn from the low-quality samples instead of discarding them, which ensured the preservation of their representation knowledge in the backbone without directly accumulating errors within the primary predictor. Extensive experiments demonstrated the effectiveness and feasibility of our proposed UMA-Net on various datasets, including 2D chest X-ray segmentation, fundus image segmentation, and 3D breast DCE-MRI segmentation. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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Tradeoff relations for two-valued qubit measurements

Tradeoff relations for two-valued qubit measurements

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第40届中国控制会议

作者： Huarui Xing Shuming Cheng Lijun Liu College of Mathematics and Computer Science Shanxi Normal University The Department of Control Science and Engineering Tongji University Shanghai Institute of Intelligent Science and Technology Tongji University Institute for Advanced Study Tongji University

Quantum measurement is essentially taken to gain information encoded in quantum states, however, it also inevitably disturbs the measured state such that the postmeasurement state loses its quantum information partially or fully. Thus, it is an important issue to characterise such information gain and state disturbance associated with measurement. In this work, we study the relationships between the information gain and measurement disturbance for two-valued qubit measurements. In particular,we employ a powerful tradeoff relation between the measurement strength and reversibility to obtain, generalise, and even strengthen certain well-known tradeoff relations between information gain and measurement disturbance of qubit ***, it is pointed out that our results are useful to understand the measurement effects at the fundamental level as they are state-independent. Hence, our work is helpful for designing quantum information processing tasks aided by measurement.

关键词： Information Gain Measurement Disturbance Reversibility Tradeoff Relations

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Improving Coverage Efficiency with Autonomous and Adaptive Communications Systems

Improving Coverage Efficiency with Autonomous and Adaptive C...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rakesh Kumar Giri Vibhor Mahajan Shaweta Narula S Selvi Dharmesh Dhabliya Riyan Sisiawan Putra Department of Computer Science & Engineering College Sunrise University Alwar India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Telecommunication Nutan College of Engineering and Research Pune India Faculty of Management Studies CMS Business School Jain (deemed to be university) India Department of Information Technology Vishwakarma Institute of Technology Pune Maharashtra India Department of Management College Universitas Nahdlatul Ulama Surabaya Surabaya

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper presents a method to improve coverage efficiency in Wi-Fi communication systems by leveraging self-sustaining and adaptive communication systems. The approach seeks to exploit machine getting-to-know and related optimization techniques to enable network nodes to autonomously adjust electricity stages, pick out adjacent nodes, regulate transmission stages, and other parameters to acquire progressed insurance and higher record rates. Self-reliance getting to know an algorithm primarily based on reinforcement gaining knowledge of is used to pattern the surroundings, optimizes variables, and permit gadget adaptation even as minimizing human intervention. The paper discusses the structure and an algorithm evolved and explores the blessings of self-sufficient and adaptive communication systems over conventional non-autonomous systems.

关键词： Wireless communication Adaptive systems Electricity Insurance Modulation Dynamic scheduling Resource management Protection Wireless fidelity Optimization

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Using Deep Reinforcement Learning in Optimal Energy Management for Residential House Aggregators with Uncertain User Behaviors

Using Deep Reinforcement Learning in Optimal Energy Manageme...

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IEEE Student Conference on Electric Machines and Systems

作者： Yujun Lin Linfang Yan Hongxun Hui Yin Chen Xia Chen Jinyu Wen State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China State Grid (Suzhou) City & Energy Research Institute Suzhou China Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Electronic and Electrical Engineering University of Strathclyde Glasgow U.K.

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

In this study, the home energy management problem, which can be regarded as a high-dimensional optimization problem, for numerous residential houses, is addressed. The concept of the aggregator is utilized to reduce the state and action space and to handle the high dimensionality. A two-stage deep reinforcement learning (DRL)-based approach is proposed for the aggregators to track the schedule from a superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of an electric vehicle's charging demand is quantitatively expressed based on the driver's experience. An aggregate anxiety concept is introduced to characterize the driver's anxiety on the electric vehicle's range and uncertain events. Finally, simulations are conducted to verify the effectiveness of the proposed approach under dynamic user behaviors, and comparisons show the superiority of the proposed approach over other benchmark methods.

关键词： Schedules Uncertainty Aggregates Anxiety disorders Optimal scheduling Deep reinforcement learning Electric vehicle charging Temperature control Vehicle dynamics Resistance heating

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