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Knowledge-Driven Drug-Use NamedEntity Recognition with Distant Supervision 18

Knowledge-Driven Drug-Use NamedEntity Recognition with Dista...

18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Bajaj, Goonmeet Kursuncu, Ugur Gaur, Manas Lokala, Usha Hyder, Ayaz Parthasarathy, Srinivasan Sheth, Amit Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Division of Environmental Health Sciences College of Public Health The Ohio State University ColumbusOH United States Ai Institute University of South Carolina ColumbiaSC United States

ISBN: (纸本)9781643682648

As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for human labor and train models incorporating domain-specific (e.g., drug use) external knowledge to recognize domain specific entities. We capture entities related the drug use and their trends in government epidemiology reports, with an improvement of 8% in F1-score. © 2022 International Medical Informatics Association (IMIA) and IOS Press.

关键词： Digital storage

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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Addressing the regulatory gap: moving towards an EU ai audit ecosystem beyond the ai Act by including civil society

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ai and Ethics 2024年 1-22页

作者： Hartmann, David de Pereira, José Renato Laranjeira Streitbörger, Chiara Berendt, Bettina Faculty of Electrical Engineering and Computer Science TU Berlin Berlin Germany Weizenbaum Institute for the Networked Society Berlin Germany LAPIN - Laboratory of Public Policy and Internet Public Policy Division Brasília Brazil Bonn Sustainable AI Lab University of Bonn Bonn Germany Kammergericht Berlin Berlin Germany Department of Computer Science KU Leuven Leuven Belgium

The European legislature has proposed the Digital Services Act (DSA) and Artificial Intelligence Act (aiA) to regulate platforms and Artificial Intelligence (ai) products. We review to what extent third-party audits are part of both laws and how is access to information on models and the data provided. By considering the value of third-party audits and third-party data access in an audit ecosystem, we identify a regulatory gap in that the aiA does not provide access to data for researchers and civil society. Our contributions to the literature include: (1) Defining an ai audit ecosystem incorporating compliance and oversight. (2) Highlighting a regulatory gap within the DSA and aiA regulatory framework, preventing the establishment of an ai audit ecosystem that has effective oversight by civil society and academia. (3) Emphasizing that third-party audits by research and civil society must be part of that ecosystem, we call for aiA amendments and delegated acts to include data and model access for certain ai products. Furthermore, we call for the DSA to provide NGOs and investigative journalists with data access to platforms by delegated acts and for adaptions and amendments of the aiA to provide third-party audits and data and model access, at least for high-risk systems. Regulations modeled after EU ai regulations should enable data access and third-party audits, fostering an ai audit ecosystem that promotes compliance and oversight mechanisms.

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Enhanced Fault and Attack Detection in IoT Systems with Naive Bayes and Long Short-Term Memory Models

Enhanced Fault and Attack Detection in IoT Systems with Naiv...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sathish Srinivasan Vidhyadar Unni Krishnan A. S Mahesh Kumar B K Madhu Harshal Shah Amol Sapatnekar Oracle Cloud Infrastructure Division Machine Learning & AI Development California USA Visa Commercial Solutions division Visa Inc Foster City California USA Department of Electronics and Communication Engineering Maharaja Institute of Technology Srirangapatna India Department of Computer Science and Engineering Vidya Vikas Institute of Engineering and Technology Alanahalli India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Technology Parul University Vadodara India Faculty of Law Symbiosis Law School Pune Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

As the number of IoT-dependent gadgets increases in the market, stringent security and fault detection solutions are required. The characteristics and variability of IoT systems may not fully capture classical detection techniques, leading to the use of more advanced machine learning-based techniques. This research employs a dual detection model consisting of Naive Bayes and Long Short-Term Memory (LSTM). The approach analyzes byte streams originating from IoT networks and device patterns, normalizes and encodes the data, and divides the classification process into two phases: screening through Naive Bayes and detailed analysis through LSTM. The proposed hybrid model achieved an overall fault detection accuracy of 97.3 % and an attack detection accuracy of 96.8 % for various IoT scenarios. The system achieved 95.2% precision and 94.8% recall, as well as a 2.1% false-positive rate. The results demonstrated that the proposed hybrid approach is 12 and 15 percent faster than the standalone implementations. In critical scenarios, particularly those where attackers employ multi-vector attacks against IoT systems and hard-to-detect faults in the communication and runtime environments, the proposed framework offers a viable solution to address these issues and challenges.

关键词： Runtime environment Accuracy Fault detection Network security Bayes methods Internet of Things Circuit faults Integrated circuit modeling Long short term memory Streams

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Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis

Quantitative Manipulation of Custom Attributes on 3D-Aware I...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hoseok Do EunKyung Yoo Taehyeong Kim Chul Lee Jin young Choi AI Lab CTO Division LG Electronics South Korea Dept. of Electrical and Computer Engineering ASRI Seoul National University South Korea Dept. of Biosystems Engineering Seoul National University South Korea

While 3D-based GAN techniques have been successfully applied to render photo-realistic 3D images with a variety of attributes while preserving view consistency, there has been little research on how to fine-control 3D images without limiting to a specific category of objects of their properties. To fill such research gap, we propose a novel image manipulation model of 3D-based GAN representations for a fine-grained control of specific custom attributes. By extending the latest 3D-based GAN models (e.g., EG3D), our user-friendly quantitative manipulation model enables a fine yet normalized control of 3D manipulation of multi-attribute quantities while achieving view consistency. We validate the effectiveness of our proposed technique both qualitatively and quantitatively through various experiments.

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Maintaining Balance of Mobile Manipulators for Safe Pick-Up Tasks

Maintaining Balance of Mobile Manipulators for Safe Pick-Up ...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Francesco D'Orazio Tommaso Belvedere Spyridon G. Tarantos Giuseppe Oriolo Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy CNRS Université de Rennes Inria IRISA Rennes France Center of AI & Robotics (CAIR) and Engineering Division New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper presents a novel method to maintain the dynamic balance of a Mobile Manipulator (MM) during the pick-up of heavy objects. The approach entails the generation of a preliminary reach-to-grasp trajectory, which is subsequently refined by an Optimization-Based Controller (OBC) formulated as a Quadratic Program (QP). The trajectory is modified in a minimal fashion to ensure that the robot maintains balance during the reaching phase and remains balanced when the payload is grasped. This is accomplished by incorporating a balance constraint into the OBC that predicts the Zero Moment Point (ZMP) position of the robot at the beginning of the pick-up phase. This accounts for the gravitational and inertial effects that the object has on the robot. The method is validated through simulations conducted with the TIAGo robot in Gazebo. The results demonstrate that the proposed approach effectively prevents the robot from tipping over when the payload is considered.

关键词： Simulation Wheels Stability analysis Robustness Trajectory Safety Mobile robots Robots Manipulator dynamics Payloads

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Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication 7

Towards Cluster-Based Split Federated Learning Approach for ...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Wazzeh, Mohamad Arafeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Trois-Rivières Canada Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon New York University Division of Science Abu Dhabi United Arab Emirates Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security. © 2023 IEEE.

关键词： Internet of things

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Referenceless User Controllable Semantic Image Synthesis

Referenceless User Controllable Semantic Image Synthesis

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International Joint Conference on Neural Networks (IJCNN)

作者： Jonghyun Kim Gen Li Joongkyu Kim AI Lab CTO Division LG Electronics Seoul Republic of Korea School of Informatics University of Edinburgh Edinburgh UK Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Republic of Korea

Despite recent progress in semantic image synthesis, complete control over image style remains a challenging problem. Existing methods require reference images to feed style information into semantic layouts, which indicates that the style is constrained by the given image. In this paper, we propose a model named RUCGAN for user controllable semantic image synthesis, which utilizes a singular color to represent the style of a specific semantic region. The proposed network achieves reference-free semantic image synthesis by injecting color as user-desired styles into each semantic layout, and is able to synthesize semantic images with unusual colors. Extensive experimental results on various challenging datasets show that the proposed method outperforms existing methods, and we further provide an interactive UI to demonstrate the advantage of our approach for style controllability. The codes and UI are available at: https://***/BenjaminJonghyun/RUCGAN

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Accelerating Multi-UAV Collaborative Sensing Data Collection: A Hybrid TDMA-NOMA-Cooperative Transmission in Cell-Free MIMO Networks

arXiv

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arXiv 2024年

作者： Park, Eunhyuk Kim, Junbeom Park, Seok-Hwan Simeone, Osvaldo Shamai, Shlomo The Division of Electronic Engineering Jeonbuk National University Jeonju Korea Republic of The Department of AI Information Engineering Gyeongsang National University Jinju Korea Republic of Department of Engineering King's College London LondonWC2R 2LS United Kingdom The Department of Electrical and Computer Engineering Technion Haifa3200003 Israel

This work investigates a collaborative sensing and data collection system in which multiple unmanned aerial vehicles (UAVs) sense an area of interest and transmit images to a cloud server (CS) for processing. To accelerate the completion of sensing missions, including data transmission, the sensing task is divided into individual private sensing tasks for each UAV and a common sensing task that is executed by all UAVs to enable cooperative transmission. Unlike existing studies, we explore the use of an advanced cell-free multiple-input multiple-output (MIMO) network, which effectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Unsupervised Learning Approaches for Zooplankton Classification: Recent Trends and Advances

Unsupervised Learning Approaches for Zooplankton Classificat...

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International Symposium on Ocean Electronics (SYMPOL)

作者： Sadaf Ansari K. Y. Nisheeth Charan Reddy Dattesh V. Desai Marine Instrumentation Division (Computer Vision & AI) CSIR-National Institute of Oceanography Goa India Department of Electronics & Communication Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Biological Oceanography Division CSIR-National Institute of Oceanography Goa India

Zooplankton are key components of the aquatic food web and present a lot of taxonomic diversity. Over the years, various Machine Learning techniques have been employed for the classification of zooplankton. Supervised Learning has been widely utilised in zooplankton classification, presenting commendable performance. However, it requires a substantial amount of manually labelled images, and the volume of collected zooplankton images is extensive. Consequently, Unsupervised Learning techniques have proven exceptionally valuable and popular for clustering unlabelled zooplankton data. Our study compiles and elucidates the Unsupervised Learning methods applied to zooplankton classification, while also comparing their performance and popularity over the years, based on observations from respective experimental studies.

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