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Cross-Disciplinary Remote Course for Mixed Group of Students and Professionals 23rd

Cross-Disciplinary Remote Course for Mixed Group of Students...

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23rd international conference on Interactive Collaborative Learning (ICL) / 49th IGIP international conference on Engineering Pedagogy - Educating Engineers for Future Industrial Revolutions

作者： Lahdeaho, Oskari Hilmola, Olli-Pekka LUT Univ Kouvola Unit Prikaatintie 9 Kouvola 45100 Finland Tallinn Univ Technol Estonian Maritime Acad Kopli 101 EE-11712 Tallinn Estonia

ISBN: (纸本)9783030681982;9783030681975

The vast amount of openly available information enables data-driven study of the surrounding world. The aim of this article is to present dissemination of methods to calculating exhaust gas emission and assessing the effect of weather in dispersion of said emissions that are possible to carry out with data from open data repositories. As the measures to prevent spread of COVID-19 virus has led to a temporary cease of contact learning, this course is implemented remotely online. The course offers a controlled environment to test the foreknowledge of the participants regarding environmental challenges tied in transport, and the effectiveness of the remote course in enhancing the awareness in said issues. data will be gathered on the success of the course. It is used to enable continuous improvement of the course in terms of quality of the content and successfulness of the remote setting. In addition, the gathered data is used to determine the effectiveness of remote education and the preparedness of scholars to operate under special conditions. Through collaborative effort of the course organizers and attendees, the remote implementation can be successful. Also, the mixed group of organizers, students and professionals enable problem-oriented approach to the topic that is difficult to limit into a specific discipline. The pandemic and succeeding mitigation of COVID-19 spreading showcases the importance of planning education for such special situations. While the resulting circumstances are not pleasant, they serve as a promoter for rigorous planning of educational events.

关键词： Road traffic emissions Road weather international Cross-disciplinary Education

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intelligent Online Voting System for Twenty-First Century and Smart Cities 5.0: An Empirical Approach through Blockchain with ML Techniques 1

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international conference on advances in data Science and computing Technologies, ASDC 2022

作者： Rastogi, Rohit Arora, Priyanshu Dhamija, Luv Shrivastava, Rajat ABES Engineering College Uttar Pradesh Ghaziabad India

ISBN: (数字)9789819936564

ISBN: (纸本)9789819936557

A digital voting system that allows people to vote sitting at their homes based on their face recognition. The traditional voting system does not allow people to vote while sitting in their homes. Considering the situation of COVID-19, everything is going digital. Questions on EVM from losing parties regarding some malfunctioning. Digital, secured, reliable, user-friendly, and a low-cost system. DDoS assaults, polling booth capturing, vote tampering and manipulation, malware attacks, and other security issues are all present in today’s voting systems. The voting process may be made more safe, transparent, immutable, and dependable by utilizing blockchain technology. Our current voting system is questioned by a variety of stakeholders, including political parties and ordinary citizens because votes are manipulated, citizens find voting a time-consuming task, and, most importantly, the current pandemic situation has digitized almost all areas of interest in our county. As a result, to resolve the issue produced by the physical voting system. The system we are creating is a blockchain-based decentralized app which uses machine learning for the identification of the user, it uses principles like face matching, checking confidence level, and then allows the user to vote based on the verification of her/his documents and eligibility to vote without compromising the right to vote anonymously. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Approximate Solution using Machine Learning and Evolutionary Algorithm for Large-Scale data-driven Chance Constrained Problems

Approximate Solution using Machine Learning and Evolutionary...

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international Symposium on Soft computing and intelligent systems (SCIS)

作者： Kiyoharu Tagawa Faculty of Informatics Kindai University 3-4-1 Kowakae Higashi-Osaka Japan

By using a large-scale data set, Chance-Constraint Problem (CCP) is formulated as a deterministic optimization problem called Deterministic data-driven CCP (DDCP). DDCP not only determines the feasibility of a solution by whether or not it satisfies the constraints at a specified sufficiency level, but also classifies all given data into two classes. Then an approximate solution method for DDCP is proposed by combining a Support Vector Machine (SVM) and an Evolutionary Algorithm (EA). In order to select a training set for the SVM, a stratified sampling method using principal component analysis is employed. Finally, the performance of the proposed approximate solution method is evaluated through a real-world application, namely, the base-station location problem which is formulated as DDCP.

关键词： Support vector machines Training Support vector machine classification Evolutionary computation Machine learning Sampling methods Classification algorithms

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View-Robust Neural Networks for Unseen Human Action Recognition in Videos

View-Robust Neural Networks for Unseen Human Action Recognit...

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2022 IEEE international conference on systems, Man, and Cybernetics, SMC 2022

作者： Yu, Jiahui Ma, Tianyu Ju, Zhaojie Chen, Hang Xu, Yingke Zhejiang University Department of Biomedical Engineering Hangzhou China Binjiang Institute of Zhejiang University Innovation Center for Smart Medical Technologies & Devices Hangzhou China Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Portsmouth School of Computing Portsmouth United Kingdom

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

data-driven deep learning achieved excellent performance for human action recognition. However, unseen action recognition remains a challenge for most existing neural networks. Because the action categories, collection perspectives, and scenarios considered during data collection are limited. Compared with class-unseen action recognition, view-unseen action recognition in videos is under-explored. This paper proposes view-robust neural networks (VR-Net) to recognize unseen actions in videos. The VR-Net consists of a 3D pose estimation module, skeleton adaptive transformation neural networks, and classification modules. We first extract 3D skeleton models from the video sequence based on existing pose estimation methods. Next, we propose a skeleton representation transformation scheme and achieve it based on Convolutional Neural Networks (VR-CNN) and Graph Neural Networks (VR-GCN), resulting in the optimal skeleton representations. Futhermore, we explore an associate optimization scheme and a fused output method. We evaluate the proposed neural networks on three challenging benchmarks, i.e., NTU RGB-D dataset (NTU), Kinetics-400 dataset, and Human3.6M dataset (H3.6M). The experimental results show that view robust neural networks achieve the top performance compared to state-of-the-art RGB-based and skeleton-based works, such as 93.6% on the NTU (CV) and 94.6% on the Kinetics-400 dataset (Top-5). The proposed neural networks significantly improve the recognition performance for unseen action recognition, such as 86.8% on the H3.6M (View 2). © 2022 IEEE.

关键词： Convolutional neural networks

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Frontiers of Cognitive Informatics and intelligent computing underpinned by intelligent Mathematics (IM)

Frontiers of Cognitive Informatics and Intelligent Computing...

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Frontiers of Electronics, Information and Computation Technologies (ICFEICT), international conference on

作者： Yingxu Wang International Institute of Cognitive Informatics and Cognitive Computing (I2CICC) Stanford Univ. (2008|2016) MIT (2012) UC Berkeley (2008) Oxford Univ. (1995|2018-2023) Tsinghua Univ. (2019-2022) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary 2500 University Drive NW Calgary Alberta Canada

Latest advances in Intelligence Science (IS) [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26] underpinned by breakthroughs in intelligent Mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50] have enabled the convergence of contemporary information, computation, and electronic sciences and technologies. This trend has led to novel theories and disruptive technologies across the fields of Cognitive Informatics (CI) [6], [13] and Cognitive computing (CC) [1] as fundamental theories of intelligence science and Autonomous AI (AAI) [12].This keynote lecture presents the latest advances in electronics, information, and computing theories underpinned by cognitive informatics [5], [10], [13], cognitive computing [1], [25] and their IM foundations. It will focus on three themes: a) The fundamental mechanisms of cognitive information representations and manipulations in the brain for cognitive computing; b) The contemporary IM theories for building the theoretical framework of IS towards autonomous and real-time intelligence generation by Cognitive Computers (κC) [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50]; and c) The architecture of κC towards full AAI for unleashing machine intelligence enabled by Brain-Inspired systems (BIS) [14], [51], [73] driven by IM. Applications of KC and IM will be demonstrated by pilot projects and experiments in novel intelligent computing systems [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89].

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Keynote 1: Developing data-driven intelligent Autonomous systems: Information Fusion;Event Correlation;Decision Making and Planning

Keynote 1: Developing Data-Driven Intelligent Autonomous Sys...

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international conference on Automation and computing (ICAC)

Presents the conference keynote speech.

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498081

Presents the conference keynote speech.

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AstroFusion: A GAN-Augmented Approach for Exoplanet Detection

AstroFusion: A GAN-Augmented Approach for Exoplanet Detectio...

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Emerging Techniques in Computational Intelligence (ICETCI), international conference on

作者： Aakash Suresh Lakshmi C Gopirajan PV K Dhanasekaran School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Humanity's exploration of the cosmos spans billions of years, prompting contemplation of our position in the universe. This study advocates for proactive communication with potential intelligent species, driven by uncertainty about their existence. The focus is on exploring Generative Adversarial Networks (GANs) to enhance exoplanet detection through data augmentation. Utilizing the extensive Kepler dataset, the study evaluates the accuracy of conventional machine learning techniques: Logistic Regression, Decision Trees, and Random Forests. (RF)-trained on original data. Subsequently, it assesses the effectiveness of models trained solely on GAN-generated synthetic data and compares their performance with that of models trained on original data. Lastly, it merges original and synthetic data to create a composite dataset, evaluating the ML model's accuracy. The results indicate that models trained solely on synthetic data achieve comparable accuracy to those trained on original data. Furthermore, the combined dataset yields improved accuracy-87%, 95%, and 96% in Logistic Regression, Decision Tree, and RF, respectively-highlighting the potential of GAN-based data augmentation in enhancing exoplanet detection. This study advances space exploration by integrating technology with astronomical research, facilitating more precise and reliable exoplanet detection methods. Leveraging GANs for data augmentation promises novel insights and discoveries about the universe.

关键词： Radio frequency Logistic regression Accuracy Uncertainty Computational modeling Heuristic algorithms Exoplanet data augmentation data models Synthetic data

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Application of open data and integer linear programming to real-time generation of one-stroke traveling route for collecting and distributing for food loss reduction

Application of open data and integer linear programming to r...

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international Symposium on Soft computing and intelligent systems (SCIS)

作者： Itaru Kaneko Emi Yuda Hitoshi Okada Center for Data-driven Science and Artificial Intelligence Tohoku University Sendai Japan Information and Society Research Division National Institute of Informatics Chiyoda-ku Tokyo

We believe that alleviating poverty and reducing food loss can contribute to the SDGs [1], and we have designed PoC for real time generation of one-stroke routes for real-time collection and distribution of food loss.... 详细信息

关键词： Food waste Integer programming Integer linear programming Real-time systems Automobiles Open data

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Integration of COTS Processing Architectures in Small Satellites for Onboard computing Using Fault Injection Testing Methodology

Integration of COTS Processing Architectures in Small Satell...

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19th international conference on Software Engineering and Formal Methods (SEFM)

作者： Gamazo-Real, Jose-Carlos Zamorano-Flores, Juan Rafael Sanz-Andres, Angel Univ Politecn Madrid Univ Inst Micrograv Ignacio Da Riva IDR Madrid 28031 Spain

ISBN: (纸本)9783031124297;9783031124280

Over the last decade, the interest of the space industry has increased towards smaller missions with reduced instruments. advances in miniaturization technologies for electronics have increased the development of small spacecrafts, such as nanosatellites and microsatellites, from commercial-off-the-shelf (COTS) devices of lowsize, weight, and power. However, the effects of harsh space environment conditions on COTS electronic devices limit their use in high-performance multicore and heterogenous architectures, such as onboard edge computing based on Graphics Processing Units (GPU) for Artificial Intelligence (AI) applications. This article analyses themain considerations for adopting the Fault Injection Testing (FIT) methodology for software-intensive developments based on COTS, primarily intended to test embedded faults that emulate potential faults triggering to allow verification of fault detection, isolation, reconfiguration, and recovery capabilities in fault-tolerant and safety-critical systems. Two variants of techniques can be considered in the FIT methodology, based on hardware and software, but it is the software FIT variant (SFIT) that provides an inexpensive and time-efficient framework to replicate the fault triggering effects by injecting faults into code, data, and interfaces. A proposal is presented to apply the FIT methodology in the testing of future versions of the UPMSat-2 microsatellite payload at Spanish University Institute of Microgravity "Ignacio Da Riva" (IDR/UPM). The scope of the proposed methodology aims to facilitate the future validation of satellite realtime control systems, designed with model-driven engineering processes, and the future onboard integration of high-performance COTS GPUs to process sensorfusion payload data and to implement auxiliary controllers with AI computing techniques.

关键词： Fault Injection Testing (FIT) Commercial-off-the-self (COTS) Small satellite Onboard computing Graphics Processing Unit (GPU) Fault-tolerant Safety-critical Real-time control system Sensor-fusion payload data

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7th EAI international conference on the Internet of Things as a Service, IoTaaS 2021

7th EAI International Conference on the Internet of Things a...

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7th EAI international conference on the Internet of Things as a Service, IoTaaS 2021

ISBN: (纸本)9783030959869

The proceedings contain 17 papers. The special focus in this conference is on Internet of Things as a Service. The topics include: Machine Learning and Deep Learning for Predicting Indoor and Outdoor IoT Temperature Monitoring systems;a Novel Dual Prediction Scheme for data Communication Reduction in IoT-Based Monitoring systems;ioT-Based data driven Prediction of Offshore Wind Power in a Short-Term Interval Span;Channel Estimation for Millimeter Wave MIMO System: A Sequential Analysis Approach;A Comprehensive Study on the Energy Efficiency of IoT from Four Angles: Clustering and Routing in WSNs, Smart Grid, Fog computing and MQTT & CoAP Application Protocols;reinforcement Learning Based intelligent Management of Smart Community Grids;energy Inefficacy in IoT Networks: Causes, Solutions and Enabling Techniques;preface;SLA Negotiation and Renegotiation in Cloud SLA Management: Issue and Challenges;Introducing the BrewAI AutoML Tool;review-Based Recommender System for Hedonic and Utilitarian Products in IoT Framework;An Enhancement to Channel Access Mechanism for the IEEE 802.15.3C MillimeterWave (5G) Standard to Support Stringent QoS Requirements of IoT;challenges and Issues of the Internet of Things: Factoring Elements from the Social, Political and Information systems;security Requirements in IoT Environments;IiCE: A Proposed System Based on IoTaaS to Study Administrative Efficiency in Primary Schools.

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