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34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024

作者： Dantsker, Or D. Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

This paper presents two hands-on, project-based courses on unmanned aerial systems recently offered by the Intelligent systems engineering program at Indiana University. In Fall 2023, ENGR-E399/599 Autonomous Sports was redeveloped to focus on integrated vehicle design and development of lighter-than-air vehicles in a capstone-like, project-based course format. And in Spring 2024, ENGR-E399/599 Unmanned Aerial systems was developed to introduce students to UAS in a lecture-lab-project format where students learned about underlying aircraft concepts, developed a micro-controller-based autopilot, ultimately integrated it into a small fixed-wing unmanned aircraft that was test flown. These courses, geared towards advanced undergraduate and graduate students, allow them to apply their prerequisite knowledge to these hardware-lab heavy courses, enabling experiential learning, i.e. "learn-by-doing". © 2024, International Council of the Aeronautical Sciences. All rights reserved.

关键词： Students

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Evaluation Methods and Measures for Causal Learning Algorithms

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第6期3卷 924-943页

作者： Cheng, Lu Guo, Ruocheng Moraffah, Raha Sheth, Paras Candan, K. Selcuk Liu, Huan Arizona State University School Of Computing Informatics And Decision Systems Engineering TempeAZ85287 United States City University Of Hong Kong School Of Data Science Hong Kong

The convenient access to copious multifaceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-based learning, i.e., causal machine learning (causal learning). Recent years have, therefore, witnessed great effort in developing causal learning algorithms aiming to help artificial intelligence (AI) achieve human-level intelligence. Due to the lack of ground-truth data, one of the biggest challenges in current causal learning research is algorithm evaluations. This largely impedes the cross-pollination of AI and causal inference and hinders the two fields to benefit from the advances of the other. To bridge from conventional causal inference (i.e., based on statistical methods) to causal learning with Big Data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning. We focus on the two fundamental causal inference tasks and causality-aware machine learning tasks. Limitations of current evaluation procedures are also discussed. We, then, examine popular causal inference tools/packages and conclude with primary challenges and opportunities for benchmarking causal learning algorithms in the era of Big Data. The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. In doing so, we hope to broaden the discussions and facilitate collaboration to advance the innovation and application of causal learning. © 2020 IEEE.

关键词： Maximum likelihood estimation

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Efficient 3D Hilbert Curve Encoding and Decoding Algorithms

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Chinese Journal of Electronics 2022年第2期31卷 277-284页

作者： JIA Lianyin LIANG Binbin LI Mengjuan LIU Yong CHEN Yinong DING Jiaman Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Library Yunnan Normal University School of Information Science and Engineering Guangxi University for Nationalities School of Computing Informatics and Decision Systems Arizona State University

Hilbert curve describes a one-to-one mapping between multidimensional space and 1 D *** traditional 3D Hilbert encoding and decoding algorithms work on order-wise manner and are not aware of the difference between different input data and spend equivalent computing costs on them, thus resulting in a low efficiency. To solve this problem, in this paper we design efficient 3D state views for fast encoding and decoding. Based on the state views designed, a new encoding algorithm(JFK-3HE) and a new decoding algorithm(JFK-3HD) are proposed. JFK-3HE and JFK-3HD can avoid executing iteratively encoding or decoding each order by skipping the first 0 s in input data, thus decreasing the complexity and improving the efficiency. Experimental results show that JFK-3HE and JFK-3HD outperform the state-of-the-arts algorithms for both uniform and skew-distributed data.

关键词： order-wise manner multidimensional space encoding state-of-the-arts algorithms encoding algorithm parallel algorithms JFK-3HE statistical distributions decoding solid modelling design efficient 3D JFK-3HD traditional 3D Hilbert encoding equivalent computing costs different input data decoding algorithm Hilbert curve state views iterative methods

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Generation of Routes Using Genetic Algorithms to Optimize the Domestic Solid Waste Collection 8th

Generation of Routes Using Genetic Algorithms to Optimize th...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Vega-Huerta, Hugo Marcos, Jordy Mayhuay Acosta, Marx Campos Monrroy, Xavier Villanueva Maquen-Niño, Gisella Luisa Elena Puelles-Bulnes, María Gil-Calvo, Rubén School of Systems Engineering Universidad Nacional Mayor de San Marcos Lima Peru School of Computing and Informatics Engineering Universidad Nacional Pedro Ruiz Gallo Lambayeque Peru

ISBN: (纸本)9789819738588

The district of San Juan de Lurigancho (SJL) in Lima—Peru, is one of the districts crowed in the capital and has one of the highest amounts solid domestic wastes. To fix this problem, a model was developed to map the distance and calculate the best route to collect the domes-tic solid waste in the whole San Carlos neighborhood in SJL district. Our model was developed using genetic algorithms in JavaScript with Node JS to calculate the shortest path. For the data visualization was used the React JS for the frontend and Google Maps API for the display of an interactive map. The results obtained allowed were able to find an optimal route that joins two opposite points within the map of the San Carlos neighborhood, which is made up of 5 nodes and measures 514 m away. The model developed could generate populations of 10 to 100 individuals with the map nodes through selection, crossing and mutation processes, obtaining an optimal route that facilitates the domestic waste collection processes with the shortest distance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collection Genetic algorithm Minimum route Pathfinding Solid waste

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Evaluating Non-Expert Stakeholder Interaction with Artificial Intelligence on Energy Urban Domain Using VIRTSI: The Case of ChatGPT 15

Evaluating Non-Expert Stakeholder Interaction with Artificia...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Tsihrintzis, George A. Sarmas, Elissaios Marinakis, Vangelis Panagoulias, Dimitrios Tsihrintzi, Evangelia-Aikaterini Virvou, Maria University of Piraeus Department of Informatics Piraeus Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunicatios Athens Greece

ISBN: (纸本)9798350368833

This paper investigates the interaction of non-expert stakeholders with Artificial Intelligence (AI) in the energy urban domain, using the VIRTSI model and focusing on the capabilities of ChatGPT. VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), is a rigorous computational model for human-AI Interaction that simulates human trust states, spanning from overtrust to distrust, through user modelling and quantifies the efficiency of the interaction in VIRTSI-adapted confusion matrices. The research employed an 16-question survey, evaluating the accuracy and usefulness of ChatGPT's responses regarding energy consumption, cost-effective solutions, and renewable energy production for residential buildings in Greece. Each answer was assessed by human stakeholders who were non-expert in the energy Urban Domain (e.g house owners, building managers, etc.), who either accepted or rejected the responses based on validation processes. The analysis highlighted key aspects such as repetition, specification, and objections in the interaction with ChatGPT, offering insights into the effectiveness of AI in supporting energy-related decisions. The findings reveal that while AI can provide valuable information, user validation and expert consultation are critical for practical implementation. This highlights both the potential and limitations of integrating AI tools like ChatGPT in enhancing non-expert stakeholder engagement and decision-making in urban energy management, emphasizing the ongoing need for human expert involvement. © 2024 IEEE.

关键词： decision making

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Distributed intelligence for IoT-based smart cities: a survey

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Neural computing and Applications 2024年第27期36卷 16621-16656页

作者： Hashem, Ibrahim Abaker Siddiqa, Aisha Alaba, Fadele Ayotunde Bilal, Muhammad Alhashmi, Saadat Mehmood Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science Federal College of Education Zaria Kaduna Tudun Wada810282 Nigeria Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32610 United States Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smart city

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First-Order Stable Model Semantics with Intensional Functions

arXiv

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arXiv 2023年

作者： Bartholomew, Michael Lee, Joohyung School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

In classical logic, nonBoolean fluents, such as the location of an object, can be naturally described by functions. However, this is not the case in answer set programs, where the values of functions are pre-defined, and nonmonotonicity of the semantics is related to minimizing the extents of predicates but has nothing to do with functions. We extend the first-order stable model semantics by Ferraris, Lee, and Lifschitz to allow intensional functions - functions that are specified by a logic program just like predicates are specified. We show that many known properties of the stable model semantics are naturally extended to this formalism and compare it with other related approaches to incorporating intensional functions. Furthermore, we use this extension as a basis for defining Answer Set Programming Modulo Theories (ASPMT), analogous to the way that Satisfiability Modulo Theories (SMT) is defined, allowing for SMT-like effective first-order reasoning in the context of ASP. Using SMT solving techniques involving functions, ASPMT can be applied to domains containing real numbers and alleviates the grounding problem. We show that other approaches to integrating ASP and CSP/SMT can be related to special cases of ASPMT in which functions are limited to non-intensional ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Circumstance Evaluation Using Extreme Value Analysis on Charging Station Data: The Case of DEI Blue in Greece 14th

Circumstance Evaluation Using Extreme Value Analysis on Cha...

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14th International Conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Panagoulias, Dimitrios P. Sarmas, Elissaios Marinakis, Vangelis Tsihrintzis, George A. Department of Informatics University of Piraeus Piraeus18534 Greece Decision Support Systems Lab School of Electrical and Computer Engineering National Technical University of Athens Zografou-Athens15773 Greece

ISBN: (纸本)9783031674259

We apply Extreme Value Analysis on sets of Electric Vehicle (EV) charging data with the goal to extract indications in the data of future trends in the demand for EV charging. The datasets have been provided by DEI blue, a service designed to provide integrated e-mobility solutions to individuals, companies and public administration throughout Greece. Eventhough the data are limited as they have been collected during a period of only twelve months, our analysis still reveals trends in the data highlighting a probable seasonality. The seasonality is observed during periods of extreme heat, regional population mobility and incoming visitors. These occurrences impact technical aspects of battery efficiency, grid capacity and a shift of behavioural usage patterns. Our findings cannot be ignored by policy makers as they are expected to intensify in the years to come due to the fast-rising number of EVs in use. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electrification

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Development of Kansei-Based Visualization Pattern for E-Learning Website 4th

Development of Kansei-Based Visualization Pattern for E-Lear...

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4th International Conference on Advances in Computational Science and engineering, ICACSE 2023

作者： Aning, Alberto Mohd Naim, Nur Faraha Baharum, Aslina Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu88450 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819729760

Through their websites, a lot of businesses and people directly offer their services. The way in which these services are offered may affect users emotionally. A website that elicits pleasant emotions in its users can make them feel happy, engaged, trustworthy, or satisfied. Well-known for its ability to analyze customer emotional demands and define the connection between emotions and product design aspects, Kansei engineering (KE) is a user-centered technology. The development of e-learning software, e-learning authoring tools, learning management systems, and other related technologies is currently a major investment made by universities, training centers, and government bodies. Even though there are many advantages for educators using e-learning technology, getting students to use these services can be difficult, especially for the e-learning websites of Malaysia’s higher education institutions (HEIs). Few standards exist for user interface (UI) design that encourages emotional connection on the e-learning platforms of Malaysian HEIs. Students were disengaging from e-learning platforms in substantial numbers because of this issue. Hence, this paper aims to explore users’ needs and emotions while using an HEI website and produce a new guideline for HEIs e-learning website. Using the card sorting technique, a visualization pattern has been presented as a design guideline for an e-learning graphical user interface (GUI) website for HEIs in Malaysia. Positive outcomes have been obtained by implementing the suggested guideline on the GUI of the prototype website. The purpose of this guideline is to help HEIs as well as website developers and designers create e-learning websites that will keep students interested in e-learning over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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