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10th international conference on Learning Representations, ICLR 2022

作者： Schimel, Marine Kao, Ta-Chu Jensen, Kristopher T. Hennequin, Guillaume Department of Engineering University of Cambridge Cambridge United Kingdom Gatsby Computational Neuroscience Unit University College London London United Kingdom

Understanding how neural dynamics give rise to behaviour is one of the most fundamental questions in systems neuroscience. To achieve this, a common approach is to record neural populations in behaving animals, and model these data as emanating from a latent dynamical system whose state trajectories can then be related back to behavioural observations via some form of decoding. As recordings are typically performed in localized circuits that form only a part of the wider implicated network, it is important to simultaneously learn the local dynamics and infer any unobserved external input that might drive them. Here, we introduce iLQR-VAE, a control-based approach to variational inference in nonlinear dynamical systems, capable of learning both latent dynamics, initial conditions, and ongoing external inputs. As in recent deep learning approaches, our method is based on an input-driven sequential variational autoencoder (VAE). The main novelty lies in the use of the powerful iterative linear quadratic regulator algorithm (iLQR) in the recognition model. Optimization of the standard evidence lower-bound requires differentiating through iLQR solutions, which is made possible by recent advances in differentiable control. Importantly, the recognition model is naturally tied to the generative model, greatly reducing the number of free parameters and ensuring high-quality inference throughout the course of learning. Moreover, iLQR can be used to perform inference flexibly on heterogeneous trials of varying lengths. This allows for instance to evaluate the model on a single long trial after training on smaller chunks. We demonstrate the effectiveness of iLQR-VAE on a range of synthetic systems, with autonomous as well as input-driven dynamics. We further apply it to neural and behavioural recordings in non-human primates performing two different reaching tasks, and show that iLQR-VAE yields high-quality kinematic reconstructions from the neural data. © 2022 ICLR 2022 - 10th

关键词： Dynamics

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A Fuzzy Logic Approach for Self-managing Energy Efficiency in IoT Nodes 4th

A Fuzzy Logic Approach for Self-managing Energy Efficiency i...

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4th IFIP international Cross-Domain conference on Internet of Things (IoT) (IFIPIoT)

作者： Melo, Victoria Funchal, Gustavo Queiroz, Jonas Leitao, Paulo Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9783030964665;9783030964658

The collection and analysis of data assume a crucial importance in the digital transformation era. Internet of Things (IoT) technologies allow to gather data from heterogeneous sources and make them available for data-driven systems aiming, e.g., monitoring, diagnosis, prediction and optimization. Several applications require that these IoT nodes be located remotely without connection to the electrical grid and being powered by batteries or renewable sources, thus requiring a more efficient management of the energy consumption in their operation. This paper aims to study and develop intelligent IoT nodes that embed Artificial Intelligence techniques to optimize their operation in terms of energy consumption when operating in constrained environments and powered by energy harvesting systems. For this purpose, a Fuzzy Logic system is proposed to determine the optimal operation strategy, considering the node's current resource demands, the current battery condition and the power charge expectation. The proposed approach was implemented in IoT nodes measuring environmental parameters and placed in a university campus with Wi-Fi coverage. The achieved results show the advantage of adjusting the operation mode taking into consideration the battery level and the weather forecasts to increase the energy efficiency without compromising the IoT nodes' functionalities and QoS.

关键词： Internet of Things Energy efficiency Fuzzy logic

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Scalable Finite-Element Viscoelastic Crustal Deformation Analysis Accelerated with data-driven Method 13

Scalable Finite-Element Viscoelastic Crustal Deformation Ana...

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13th IEEE/ACM Workshop on Latest advances in Scalable Algorithms for Large-Scale Heterogeneous systems, ScalAH 2022

作者： Fujita, Kohei Murakami, Sota Ichimura, Tsuyoshi Hori, Takane Hori, Muneo Lalith, Maddegedara Ueda, Naonori The University of Tokyo Earthquake Research Institute Department of Civil Engineering Japan Center for Computational Science Riken Japan The University of Tokyo Earthq. Res. Inst. Dept. Civil Eng. Japan Center for Advanced Intelligence Project Riken Japan Japan Agency for Marine-Earth Science and Technology Research Institute for Marine Geodynamics Yokohama Japan Research Institute for Value-Added-Information Generation Japan Agency for Marine-Earth Science and Technology Yokohama Japan

ISBN: (纸本)9781665475709

Targeting viscoelastic crustal deformation analysis, we develop a scalable unstructured implicit finite-element solver accelerated by a data-driven method. Here, we combine a datadriven predictor, that uses past time step data for estimating high-accuracy initial solutions, and a multi-grid based conjugate gradient solver for efficient solving of the remaining errors. When compared to using a standard initial solution predictor on a block Jacobi-preconditioned conjugate gradient solver, a 3.19fold speedup was attained by using the data-driven predictor, and combination with a multi-grid solver attained a total speedup of 76.8-fold on Fugaku. Furthermore, as the computation of the data-driven predictor is localized and can be conducted without communication between computation nodes, the solver attained high weak scalability efficiency of 78.5% up to 73728 compute nodes of Fugaku, leading to 6.88% FP64 peak efficiency for the whole application. Such development is also expected to be useful for accelerating other PDE-based time evolution problems. © 2022 IEEE.

关键词： Finite element method

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Machine Learning driven Cardiovascular Disease Prediction Among Male Senior Adults

Machine Learning Driven Cardiovascular Disease Prediction Am...

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： Poongothai Elango Kasthuri A Mariammal G T.R. Saravanan Department of Computational Intelligence SRM Institute of Science and Technology Chennai India Department Of Computer Science Arulmigu Subramania Swamy Arts and Science College Thoothukudi India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The heart is a critical organ at the heart of the circulatory system, acting as a powerful muscle pump that keeps life going by constantly pumping blood throughout the body. Heart illnesses, often known as cardiovascular diseases (CVDs), are a broad category of ailments that affect the heart and blood arteries. The severity of these illnesses ranges from minor to life-threatening. Heart disease prediction using machine learning is essential for improving patient care, reducing healthcare costs, and addressing a major public health challenge. It leverages advanced data analytics to enable early detection, personalized treatment, and more efficient allocation of resources, ultimately leading to better health outcomes and a positive impact on public health. Heart disease prediction involves the use of machine learning and data analysis techniques to assess an individual's risk of developing heart-related conditions. This process begins with collecting a wide range of medical data from kaggle for analysis, including demographics, medical history, and clinical measurements. In this analysis, Random forest is used as a feature estimate for the study of cardiovascular disease prediction for male senior adults in the age group of 37 to 70 years since we analyzed from the existing research that it performs better for heart disease. Recursive Feature Elimination with Cross-Validation (RFECV) is used to assess feature importance. It trains the model using logistic regression and achieves an accuracy of 82.4%. Predicting cardiovascular health among senior male adults is of utmost importance to proactively manage and mitigate the risks associated with these conditions.

关键词： Heart data analysis Telemedicine Soft sensors Pumps Aging Cardiovascular diseases

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advances in Machine Learning-driven Quality Control within ERP systems: A Comprehensive Survey

Advances in Machine Learning-driven Quality Control within E...

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IEEE international conference on intelligent computing and Information systems (ICICIS)

作者： Shaymaa Ahmed Qaddoori Aldoori Mohamed Mabrouk Abeer M. Mahmoud Cairo Computer Science Department Faculty of Computer and Information Science Cairo Egypt

Enterprise Resource Planning (ERP) systems have become essential instruments in contemporary corporate operations, offering significant benefits in process optimization and efficient data management. The assurance of quality and dependability in these intricate systems is of utmost significance given that errors or inconsistencies might yield extensive ramifications. This study highlights the importance of quality control within ERP systems, emphasizing the crucial role of accuracy and integrity in facilitating efficient decision-making processes and optimizing operational efficiency. This analysis explores the increasing importance of upholding ERP quality as firms become increasingly dependent on these systems to traverse intricate business environments. In addition, this study explores the capacity of machine learning to enhance quality control procedures. Machine learning techniques, owing to their ability to examine large datasets and identify subtle patterns, provide a transformative shift away from the traditional rule-based approaches. Machine learning enhances the capacity to anticipate problems, reduce interruptions, and maintain consistent operational performance by employing real-time anomaly detection, predictive modeling, and adaptation to shifting ERP settings. This study explores the complex confluence of technology and business imperatives, highlighting the transformative impact of the collaboration between machine learning and quality control on the evolution of ERP systems.

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Guest Editorial Special Section on Hybrid Human-Artificial Intelligence for Multimedia computing

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 2185-2187页

作者： Hamzaoui, Raouf Ning, Huansheng Wang, Chonggang Malekian, Reza Ding, Wei De Montfort Univ Leicester LE1 9BH Leics England Univ Sci & Technol Beijing 100083 Peoples R China InterDigital Commun Wilmington DE 19809 USA Malmo Univ S-21119 Malmo Sweden Natl Sci Fdn Div Informat & Intelligent Syst Boston MA 02125 USA Univ Massachusetts Boston MA 02125 USA

The papers in this special section focus on hybrid human-artificial intelligene (AI) for multimedia computing. Multimedia computing has experienced a tremendous growth in the last decades, with applications ranging from multimedia information retrieval and analysis to multimedia compression and communication. However, the increasing volume and complexity of multimedia data driven by the large-scale spread of various new devices and sensors is posing a serious challenge to traditional multimedia computing algorithms. Artificial intelligence (AI), in particular deep learning techniques, has improved the performance of multimedia computing algorithms for many tasks, including computer vision and natural language processing. But unlike humans, AI is poor at solving tasks across multiple domains or in dealing with an uncontrolled dynamic environment. Hybrid Human-Artificial Intelligence (HH-AI) is an emerging field that aims at combining the benefits of human intelligence, such as semantic association, inference, and generalization with the computing power of AI.

关键词： Special issues and sections Multimedia computing Artificial intelligence Rendering (computer graphics) Multimedia communication Matched filters Image retrieval Image coding

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intelligent Conversational Agents for Ambient computing 22

Intelligent Conversational Agents for Ambient Computing

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45th international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Sarikaya, Ruhi Amazon Alexa Seattle WA 98109 USA

ISBN: (纸本)9781450387323

We are in the midst of an AI revolution. Three primary disruptive changes set off this revolution: 1) increase in compute power, mobile internet, and advances in deep learning. The next decade is expected to be about the proliferation of Internet-of-Things (IoT) devices and sensors, which will generate exponentially larger amounts of data to reason over and pave the way for ambient computing. This will also give rise to new forms of interaction patterns with these systems. Users will have to interact with these systems under increasingly richer context and in real-time. Conversational AI has a critical role to play in this revolution, but only if it delivers on its promise of enabling natural, frictionless, and personalized interactions in any context the user is in, while hiding the complexity of these systems through ambient intelligence. However, current commercial conversational AI systems are trained primarily with a supervised learning paradigm, which is difficult, if not impossible, to scale by manually annotating data for increasingly complex sets of contextual conditions. Inherent ambiguity in natural language further complicates the problem. We need to devise new forms of learning paradigms and frameworks that will scale to this complexity. In this talk, we present some early steps we are taking with Alexa, Amazon's Conversational AI system, to move from supervised learning to self-learning methods, where the AI relies on customer interactions for supervision in our journey to ambient intelligence.

关键词： Any terms Ruhi Sarikaya intelligent Assistants Conversational AI Ambient computing and Intelligence

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DKIMDB: The gut microbial database of the larvae of Dendrolimus kikuchii Matsumura 4

DKIMDB: The gut microbial database of the larvae of Dendroli...

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4th international conference on advances in Computer Technology, Information Science and Communications, CTISC 2022

作者： Huang, Biaosheng Mao, Jiawei Xiong, Zhi Zhao, Youjie Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University School of Continuing Education Kunming China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

Gut microbes are closely related to the metabolism and growth and development of insects, which are crucial for pest control research. In this study, the pre-processed data based on the gut microbial data obtained from the previous experiments for different instars and parts of the larvae of Dendrolimus kikuchii Matsumura were stored into MySQL database. We also completed the construction and visualization of the Dendrolimus kikuchii Matsumura larvae gut microbial database (DKIMDB, http://8.136.132.83:5000/) by using Python's lightweight Flask framework, Ajax asynchronous requests and Echarts, and mapped the national diversity distribution of Dendrolimus kikuchii Matsumura larvae. DKIMDB also realizes the functions of viewing and visualizing the information of gut microbial composition of different instars of Dendrolimus kikuchii Matsumura larvae, visualizing the information of gut microbial composition of different parts of Dendrolimus kikuchii Matsumura larvae and downloading the pictures and gut microbial data of Dendrolimus kikuchii Matsumura larvae. The construction of this database provides an important data base for the study of gut microorganisms of Dendrolimus kikuchii Matsumura larvae, records the number of gut microorganisms of Dendrolimus kikuchii Matsumura larvae at different instars, reveals the dynamic changes of gut microorganisms of Dendrolimus kikuchii Matsumura larvae at different instars and in different parts of the larvae, lays the foundation for the study of the physiological functions of gut microorganisms on Dendrolimus kikuchii Matsumura larvae, has an important role for forestry pest and disease control, and finally provides new ideas for the control of Dendrolimus kikuchii Matsumura larvae. © 2022 IEEE.

关键词： database systems

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Random Forest Predictive Model for Ventilator-Associated Pneumonia Prediction with IoT data Analytics

Random Forest Predictive Model for Ventilator-Associated Pne...

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： R. Ramadevi V. J. K. Kishoresonti M. Jain Jacob V. Vaidehi N. Mohankumar M. Rajmohan Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Faculty of Management SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Vinayaka Mission's Kirupananda Variyar Arts and Science College Vinayaka Mission's Research Foundation (DU) Salem Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

To identify and forecast ventilator-associated pneumonia (VAP) early on, this study investigates the use of random forest predictive models in combination with Internet of Things (IoT) data analytics. Patients on mechanical ventilation are particularly vulnerable to VAP, a serious nosocomial infection. It uses Random Forest algorithms to examine a wide range of factors, such as vital signs, patient demographics, and ventilator settings, by using the abundance of healthcare data supplied by the IoT. The goal is to create reliable prediction models that can identify people who are at risk of getting VAP before any symptoms appear. Using data gathered from ventilator-integrated real-time monitoring devices, it creates a complete dataset. The findings reveal that random forest models are reliable and very accurate in predicting VAP. Integrating analytics from the IoT improves prediction accuracy, which in turn helps healthcare providers make better, timelier preventative decisions. Improving patient outcomes and optimizing resource allocation in critical care units may be possible as a result of this study's contributions to the expanding body of work on using sophisticated data-driven methodologies for infectious illness prediction in clinical settings.

关键词： Ventilators data analysis Pneumonia Medical services Predictive models Internet of Things Resource management

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Implementation of data Warehouse: An Improved data-driven Decision-Making Approach 2nd

Implementation of Data Warehouse: An Improved Data-Driven De...

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2nd international conference on intelligent and Cloud computing, ICICC 2021

作者： Nayak, L. S. Abinash Das, Kaberi Hota, Srilekha Sahu, Bharat Jyoti Ranjan Mishra, Deb ahuti Siksha ‘O’ Anusandhan University Orissa Bhubaneswar India Cognizant Technology Solutions Bengaluru India

ISBN: (纸本)9789811698729

IT industries or organizations are continuously growing day by day where data Warehousing Technology plays an important role. A data Warehouse has the characteristics such as subject-oriented, integrated, time variant, and non-volatile which supports the management to take necessary decisions. data warehousing can be defined as a data hub or repository, where a huge and large amount of meaningful data are stored. It gives a clear visualization for analysis of the huge data and helps to take the decisions for business growth. data warehouse stores historical information and makes it simple for business analysts to analyze data. This technology enables analysts to extract and view business data from different sources. Here a discussion has been made about the implementation of data Warehouse technology. This paper represents a framework regarding how to maintain data in a data warehouse while collecting data from different data sources and making it usable inside a data Warehouse. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data warehouses

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