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Shared control of robot-robot collaborative lifting with agent postural and force ergonomic optimization

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arXiv 2021年

作者： rapetti, Lorenzo Tirupachuri, Yeshasvi ranavolo, Alberto Kawakami, Tomohiro Yoshiike, Takahide Pucci, daniele Dynamic Interaction Control at Istituto Italiano di Tecnologia Center for Robotics Technologies Genova Italy Machine Learning and Optimisation University of Manchester Manchester United Kingdom Department of Occupational and Environmental Medicine Epidemiology and Hygiene INAIL Roma Italy Honda R&D Co. Ltd. Saitama Japan

Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple agents acting on a shared environment. In this paper we present a control framework for robot-robot collaborative lifting. The proposed shared controller takes into account the joint action of both the robots thanks to a centralized controller that communicates with them, and solves the whole-system optimization. Efficient collaboration is ensured by taking into account the ergonomic requirements of the robots through the optimization of posture and contact forces. The framework is validated in an experimental scenario with two iCub humanoid robots performing different payload lifting sequences. Copyright © 2021, The Authors. All rights reserved.

关键词： Ergonomics

BErTgrid: Contextualized embedding for 2d document representation and understanding

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arXiv 2019年

作者： denk, Timo I. reisswig, Christian SAP SE Machine Learning R&D Berlin Germany

For understanding generic documents, information like font sizes, column layout, and generally the positioning of words may carry semantic information that is crucial for solving a downstream document intelligence task. Our novel BErTgrid, which is based on Chargrid by Katti et al. (2018), represents a document as a grid of contextualized word piece embedding vectors, thereby making its spatial structure and semantics accessible to the processing neural network. The contextualized embedding vectors are retrieved from a BErT language model. We use BErTgrid in combination with a fully convolutional network on a semantic instance segmentation task for extracting fields from invoices. We demonstrate its performance on tabulated line item and document header field extraction. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

A framework for developing a knowledge management platform

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arXiv 2024年

作者： Mendoza, Marie Lisandra Zepeda Agarwal, Sonali Blackshaw, James A. Bol, Vanesa Fazzi, Audrey Fiorini, Filippo Foreman, Amy Louise George, Nancy Johnson, Brett r. Martin, Brian McComb, dave Mutasa-Gottgens, Euphemia Parkinson, Helen romacker, Martin russell, rolf Ségard, Valérien Tan, Shawn Zheng Kai Teh, Wei Kheng Winstanley, F.P. Wong, Benedict Smith, Adrian M. Machine Intelligence AI & Digital Research Novo Nordisk Research Centre Oxford United Kingdom GSK Global Capability Center Bagmane Capital Mahadevapura Karnataka Bengaluru India EMBL-EBI Wellcome Genome Campus Hinxton United Kingdom GSK Vaccines R&D Rue de l'Institut 89 Rixensart Belgium GSK Via Fiorentina Siena53100 Italy Global Genes 28 Argonaut Suite 150 Aliso ViejoCA United States AbbVie 1 N Waukegan Road AP4/B North ChicagoIL United States Semantic Arts 123 N College AvenueSuite 218 Fort CollinsCO United States F.Hoffmann-La Roche Grenzacherstrasse 124 BaselCH-4070 Switzerland TXI 10 S Riverside Plaza Suite 875 ChicagoIL United States Data Representation Novo Nordisk A/S Måløv Denmark Semantic Arts Kilcreggan United Kingdom Unilever R&D Colworth Science Park Sharnbrook Bedford United Kingdom

Knowledge management (KM) involves collecting, organizing, storing, and disseminating information to improve decision-making, innovation, and performance. Implementing KM at scale has become essential for organizations to effectively leverage vast accessible data. This paper is a compilation of concepts that emerged from KM workshops hosted by EMBL-EBI, attended by SMEs and industry. We provide guidance on envisioning, executing, evaluating, and evolving knowledge management platforms. We emphasize essential considerations such as setting knowledge domain boundaries and measuring success, as well as the importance of making knowledge accessible for downstream applications and non-computational users and highlights necessary personal and organizational skills for success. We stress the importance of collaboration and the need for convergence on shared principles and commitment to provide or seek resources to advance KM. The community is invited to join the journey of KM and contribute to the advancement of the field by applying and improving on the guidelines described. © 2024, CC BY.

关键词： Knowledge management

Ethical and legal challenges with IoT in home digital twins

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MethodsX 2025年 14卷

作者： d. dhinakaran S. Edwin raja A. ramathilagam G. Vennila A. Alagulakshmi Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of Artificial Intelligence and Machine Learning School of Computing Mohan Babu University Tirupati India Department of Information Technology Ramco Institute of Technology Rajapalayam India

Home digital Twins represent a transformative application of IoT in the home environment, turning conventional living spaces into intelligent ecosystems. This paper explores the ethical and legal challenges associated with these technologies, focusing on critical issues such as privacy, data security, and accountability. The study integrates real-world case studies of privacy controversies and cybersecurity breaches to illustrate potential vulnerabilities in IoT-enabled systems. Furthermore, it examines the complexities of regulatory compliance, including cross-border data flows and liability concerns in the event of system failures, with a focus on frameworks such as GdPr, CCPA, and India’s digital Personal data Protection Bill. The methodology includes an in-depth analysis of existing legal frameworks, industry best practices, and technical mitigation strategies to propose actionable guidelines for addressing these challenges. Key findings emphasize the necessity of robust legal frameworks, user-centered design principles, and transparent data practices to foster trust and security in IoT systems. By advocating for a balance between technological innovation and ethical accountability, this paper highlights opportunities for sustainable and responsible IoT development that upholds user rights and societal values.

关键词： Internet of Things Home digital twins Privacy data security Trust Cybersecurity Ethico-Legal IoT Compliance Framework (ELICF)

deep learning-based Synthetic High-resolution In-depth Imaging Using an Attachable dual-element Endoscopic Ultrasound Probe

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arXiv 2023年

作者： Lew, Hah Min Kim, Jae Seong Lee, Moon Hwan Park, Jaegeun Youn, Sangyeon Kim, Hee Man Kim, Jihun Hwang, Jae Youn Department of Electrical Engineering and Computer Science DGIST Daegu42988 Korea Republic of Deep Learning Research Team KLleon R&D Center Seoul04637 Korea Republic of Department of Information and Communication Engineering DGIST Daegu42988 Korea Republic of Software Team ALPINION MEDICAL SYSTEMS Anyang-si14117 Korea Republic of Department of Health Promotion Center Yonsei University College of Medicine Gangnam Severance Hospital Seoul06273 Korea Republic of Department of Electrical Engineering and Computer Science The Interdisciplinary Studies of Artificial Intelligence DGIST Daegu42988 Korea Republic of

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. recently, super-resolution (Sr) ultrasound imaging studies, including the Sr task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures but rather they were conventional Sr techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues. Copyright © 2023, The Authors. All rights reserved.

关键词： Phantoms

Plant diseases detection and Soil Quality Identification Techniques for Agriculture Professionals

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Plant Diseases Detection and Soil Quality Identification Tec...

Advanced Computing Technologies (ICoACT), International Conference on

作者： M. A. Y. Peer Mohamed Appa V. Ganesh Karthikeyan Tasneem K. H. Khan r. Vaishnavi Karthika d. Vijaya Kumar S. P. ramesh r. Saravanakumar Department of Artificial Intelligence and Machine Learning School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India School of Computing SASTRA Deemed to be University Thirumalaisamudram Thanjavur Tamil Nadu India Department of Chemistry Anjuman College of Engineering & Technology Sadar Nagpur India Department of Computer Science and Engineering VSB College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Human civilization requires food production. Plant leaves and harvesting illnesses affected scientists' soil quality assessments. Plagues slow species growth. The right disease classification may reduce plant damage. Many diseases are hard to classify. Farmers misinterpret soil and plant leaf diseases. Hand-made soil quality and pathogen estimates are difficult. Without soil quality monitoring and plant disease classification, agricultural goods might be severely destroyed. Image processing may boost agricultural yield. Several methods utilize images of damaged leaves or crops to diagnose plant diseases and soil quality. research has improved sickness categorization and diagnosis in several ways. recent statements, actions, and debates need more. Global farming uses technology. CNN and ANN are artificial neural networks. deep or traditional machine learning (ML) relies on the issue, data, and computing power. When processing and data resources are available, deep learning (dL), which uses convolutional neural networks (CNNs) and artificial neural networks (ANNs), is used for several challenging visual identification and classification tasks. It excels on its own datasets but struggles on others. This study's data, assessment criteria, and conclusions will help plant leaf and crop detection and soil quality measuring researchers.

关键词： deep learning Plant diseases Visualization machine learning algorithms Crops Soil Feature extraction Classification algorithms Convolutional neural networks diseases

Overcoming low-utility facets for complex answer retrieval

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INFOrMATION rETrIEVAL JOUrNAL 2019年第3-4期22卷 395-418页

作者： MacAvaney, Sean Yates, Andrew Cohan, Arman Soldaini, Luca Hui, Kai Goharian, Nazli Frieder, Ophir Georgetown Univ Comp Sci Dept Informat Retrieval Lab 3700 O St NW Washington DC 20057 USA Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany Allen Inst Artificial Intelligence Seattle WA USA SAP SE Machine Learning R&D Berlin Germany

Many questions cannot be answered simply;their answers must include numerous nuanced details and context. Complex Answer retrieval (CAr) is the retrieval of answers to such questions. These questions can be constructed from a topic entity (e.g., cheese') and a facet (e.g., health effects'). While topic matching has been thoroughly explored, we observe that some facets use general language that is unlikely to appear verbatim in answers, exhibiting low utility. In this work, we present an approach to CAr that identifies and addresses low-utility facets. First, we propose two estimators of facet utility: the hierarchical structure of CAr queries, and facet frequency information from training data. Then, to improve the retrieval performance on low-utility headings, we include entity similarity scores using embeddings trained from a CAr knowledge graph, which captures the context of facets. We show that our methods are effective by applying them to two leading neural ranking techniques, and evaluating them on the TrEC CAr dataset. We find that our approach perform significantly better than the unmodified neural ranker and other leading CAr techniques, yielding state-of-the-art results. We also provide a detailed analysis of our results, verify that low-utility facets are indeed difficult to match, and that our approach improves the performance for these difficult queries.

关键词： Complex answer retrieval Knowledge graphs Neural information retrieval reranking

Cost-Effective Communication in UdN in Indoor and Outdoor Environment via machine learning

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Cost-Effective Communication in UDN in Indoor and Outdoor En...

Artificial Intelligence and Knowledge discovery in Concurrent Engineering (ICECONF), International Conference on

作者： K Nattar Karman V. Velmurugan Kommisetti Murthy raju T. Sajana V. Vijayalakshmi JoshuvaArockia dhanraj Departmeru of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala R&D Institute of science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women West Godavari Andhra Pradesh India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra dense cloud Network (UdN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra dense cloud Network (UdN). The proposed method achieves 93.28% of Cost-effective data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management.

关键词： Costs Mobility models machine learning Knowledge discovery Scheduling Energy efficiency Concurrent engineering

SCArFF: A scalable framework for streaming credit card fraud detection with spark

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INFOrMATION FUSION 2018年 41卷 182-194页

作者： Carcillo, Fabrizio dal Pozzolo, Andrea Le Borgne, Yann-Ael Caelen, Olivier Mazzer, Yannis Bontempi, Gianluca ULB Fac Sci Comp Sci Dept Machine Learning Grp Brussels Belgium Worldline R&D High Proc & Volume Team Brussels Belgium

The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor. detecting frauds in (nearly) real time setting demands the design and the implementation of scalable learning techniques able to ingest and analyse massive amounts of streaming data. recent advances in analytics and the availability of open source solutions for Big data storage and processing open new perspectives to the fraud detection field. In this paper we present a Scalable real-time Fraud Finder (SCArFF) which integrates Big data tools (Kafka, Spark and Cassandra) with a machine learning approach which deals with imbalance, nonstationarity and feedback latency. Experimental results on a massive dataset of real credit card transactions show that this framework is scalable, efficient and accurate over a big stream of transactions. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Big data Fraud detection Streaming analytics machine learning Scalable software Kafka Spark Cassandra