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Enhancing Industrial Productivity through AI-Driven Systematic Literature Reviews 19

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Enhancing Industrial Productivity through AI-Driven Systemat...

19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Coelho, Jaqueline Gutierri Bispo, Guilherme Dantas Vergara, Guilherme Fay Saiki, Gabriela Mayumi Serrano, André Luiz Marques Weigang, Li Neumann, Clovis Martins, Patricia Helena de Oliveira, Welber Santos Albarello, Angela Brigida Casonatto, Ricardo Accorsi Missel, Patrícia de Medeiros Junior, Roberto de Oliveira Gomes, Jefferson Rosano-Peña, Carlos da Costa, Caroline Cabral F. Latitude Decision Making Technologies Lab University of Brasilia Campus Darcy Ribeiro Brasilia Brazil Projectum Research Group for Math. Methodologies Applied to Management University of Brasilia Brasilia Brazil SENAI National Service of Industrial Learning Federal District Brazil Department of Production Engineering Faculty of Technology University of Brasília Federal District Brazil Department of Computer Science University of Brasilia Federal District Brazil Department of Economics University of Brasília Federal District Brazil UNITEC Innovation and Technology Unit National Service of Industrial Learning Federal District Brazil Department of Electrical University of Brasilia Federal District Brazil Department of Administration University of Brasilia Federal District Brazil

ISBN: (纸本)9789897586729

The advent of Artificial Intelligence (AI) has opened up new possibilities for improving productivity in various industry sectors. In this paper, we propose a novel framework aimed at optimizing systematic literature reviews (SLRs) for industrial productivity. By combining traditional keyword selection methods with AI-driven classification techniques, we streamline the review process, making it more efficient. Leveraging advanced natural language processing (NLP) approaches, we identify six key sectors for optimization, thereby reducing workload in less relevant areas and enhancing the efficiency of SLRs. This approach helps conserve valuable time and resources in scientific research. Additionally, we implemented four machine learning models for category classification, achieving an impressive accuracy rate of over 75%. The results of our analyses demonstrate a promising pathway for future automation and refinements to boost productivity in the industry. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Natural language processing systems

Fairness and Bias in Robot Learning

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arXiv 2022年

作者： Londoño, Laura Hurtado, Juana Valeria Hertz, Nora Kellmeyer, Philipp Voeneky, Silja Valada, Abhinav Department of Computer Science University of Freiburg Germany Dep. International Law and Ethics of Law University of Freiburg Germany School of Business Informatics and Math. University of Mannheim Germany Department for Neurosurgery University Medical Center Freiburg Germany

Machine learning has significantly enhanced the abilities of robots, enabling them to perform a wide range of tasks in human environments and adapt to our uncertain real world. Recent works in various machine learning domains have highlighted the importance of accounting for fairness to ensure that these algorithms do not reproduce human biases and consequently lead to discriminatory outcomes. With robot learning systems increasingly performing more and more tasks in our everyday lives, it is crucial to understand the influence of such biases to prevent unintended behavior toward certain groups of people. In this work, we present the first survey on fairness in robot learning from an interdisciplinary perspective spanning technical, ethical, and legal challenges. We propose a taxonomy for sources of bias and the resulting types of discrimination due to them. Using examples from different robot learning domains, we examine scenarios of unfair outcomes and strategies to mitigate them. We present early advances in the field by covering different fairness definitions, ethical and legal considerations, and methods for fair robot learning. With this work, we aim to pave the road for groundbreaking developments in fair robot learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Intelligent robots

Reduced basis method with parameterized boundary conditions for room acoustics 51

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Reduced basis method with parameterized boundary conditions ...

51st International Congress and Exposition on Noise Control Engineering, Internoise 2022

作者： Llopis, Hermes Sampedro Engsig-Karup, Allan P. Jeong, Cheol-Ho Pind, Finnur Hesthaven, Jan S. Rambøll Denmark Hannemanns Allé 53 Copenhagen2300 Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Matematiktorvet 303b/r108 Kongens Lyngby2800 Denmark Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Ørsteds Plads 352 Kongens Lyngby2800 Denmark Treble Technologies Bjargargata 1 Reykjavik102 Iceland Comp. Math. and Simulation Science Ecole polytechnique federale de Lausanne Rte Cantonale Lausanne1015 Switzerland

ISBN: (纸本)9781906913427

Room acoustic simulations can be performed by means of numerical methods, which typically solve the wave equation in an enclosure through discretization techniques. These methods provide high-fidelity solvers that include all the wave phenomena but are computationally costly. This paper investigates the potential of a reduced basis method for simulating room acoustics when the boundary properties are changed iteratively, e.g., for the investigation of effects of design changes in rooms where different boundary properties are simulated. Such studies increase the computational cost when using traditional high-fidelity solvers. The presented framework allows reducing the computational burden by applying reduced basis methods and solving the problem in a low-dimensional subspace where the absorption properties of complex boundary conditions are parameterized, e.g., the thickness of a porous material. The potential of the proposed framework is analyzed in terms of computational efficiency, accuracy and storage requirements. A computational speedup of two orders of magnitude is demonstated for a 2D case with an upper frequency of 2 kHz, and impressive three orders of magnitude for a 3D case with an upper frequency of 1 kHz showing a potential of having an increase in terms of speedup when increasing the size of the domain. The storage requirement for a 2D case with an upper frequency of 8 kHz is 10.8GB while for a 3D case with an upper frequency of 1 kHz is 8 GB. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

关键词： Boundary conditions

On classification of 2-dimensional evolution algebras and its applications 5

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On classification of 2-dimensional evolution algebras and it...

5th International Conference on math.matical Applications in Engineering 2019

作者： Ahmed, H. Bekbaev, U. Rakhimov, I. Department of Math. Faculty of Science UPM Selangor Malaysia Depart. of Math. Faculty of Science Taiz University Taiz Yemen Department of Mathematical and Natural Sciences TTPU Uzbekistan Department of Science in Engineering Faculty of Engineering IIUM Malaysia Department of Mathematics Faculty of Computer and Mathematical Sciences UiTM Malaysia V.I.Romanovskiy Institute of Mathematics Uzbekistan Academy of Science Uzbekistan

In the paper we give a complete classification of 2-dimensional evolution algebras over algebraically closed fields, we compare the list of representatives of the isomorphism classes with that of obtained earlier by the other authors. Also we describe their groups of automorphisms and derivation algebras. © Published under licence by IOP Publishing Ltd.

关键词： Algebra

Threshold Decision-Making Dynamics Adaptive to Physical Constraints and Changing Environment

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arXiv 2023年

作者： Amorim, Giovanna Santos, María Park, Shinkyu Franci, Alessio Leonard, Naomi Ehrich The Dept. of Mechanical and Aerospace Engineering Princeton Univ. PrincetonNJ08544 United States Computer Electrical and Math. Science and Eng. Div. Thuwal23955-6900 Saudi Arabia The Department of Electrical Engineering and Computer Science University of Liege 10 Allée de la Découverte Liège4000 Belgium The WEL Research Institute Wavre Belgium

We propose a threshold decision-making framework for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dynamics model that represents the evolution of an agent’s preference for a particular task with the physical dynamics of the agent. We prove the bifurcation that governs the behavior of the coupled dynamics. We show by means of the bifurcation behavior how the coupled dynamics are adaptive to the physical constraints of the agent. We also show how the bifurcation can be modulated to allow the agent to switch tasks based on thresholds adaptive to environmental conditions. We illustrate the benefits of the approach through a multi-robot task allocation application for trash collection. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

Kemeny consensus complexity

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arXiv 2021年

作者： Fitzsimmons, Zack Hemaspaandra, Edith Dept. of Math. and Computer Science College of the Holy Cross WorcesterMA01610 United States Department of Computer Science Rochester Institute of Technology RochesterNY14623 United States

The computational study of election problems generally focuses on questions related to the winner or set of winners of an election. But social preference functions such as Kemeny rule output a full ranking of the candidates (a consensus). We study the complexity of consensus-related questions, with a particular focus on Kemeny and its qualitative version Slater. The simplest of these questions is the problem of determining whether a ranking is a consensus, and we show that this problem is coNP-complete. We also study the natural question of the complexity of manipulative actions that have a specific consensus as a goal. Though determining whether a ranking is a Kemeny consensus is hard, the optimal action for manipulators is to simply vote their desired consensus. We provide evidence that this simplicity is caused by the combination of election system (Kemeny), manipulative action (manipulation), and manipulative goal (consensus). In the process we provide the first completeness results at the second level of the polynomial hierarchy for electoral manipulation and for optimal solution recognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Polynomials

Which Activation Function Works Best for Training Artificial Pancreas: Empirical Fact and Its Theoretical Explanation

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Which Activation Function Works Best for Training Artificial...

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Denes-Fazakas, Lehel Szilagyi, Laszlo Eigner, Gyorgy Kosheleva, Olga Ceberio, Martine Kreinovich, Vladik Doctoral School Óbuda University Physiological Controls Research Center Appl. Informatics & Appl. Math. Budapest Hungary Sapientia University Computational Intelligence Res. Group Tg. Mures Romania Physiological Controls Res. Center Óbuda University Budapest Hungary Biomatics and Applied Ai Institute Obuda University Physiological Controls Res. Center John von Neumann Faculty of Informatics Budapest Hungary University of Texas at El Paso Department of Teacher Education El PasoTX United States University of Texas at El Paso Department of Computer Science El PasoTX United States

ISBN: (纸本)9781665430654

One of the most effective ways to help patients at the dangerous levels of diabetes is an artificial pancreas, a device that constantly monitors the patient's blood sugar level and injects insulin based on this level. Patient's reaction to insulin is highly individualized, so the artificial pancreas needs to be trained on each patient. It turns out that the best training results are attained when instead of the usual ReLU neurons, we use their minor modification known as Exponential Linear Units (ELU). In this paper, we provide a theoretical explanation for the empirically observed effectiveness of ELUs. © 2023 IEEE.

关键词： artificial pancreas diabetes Exponential Linear Unit (ELU) Leaky ReLU neural network Rectified Linear Unit (ReLU)

VIETORIS THICKENINGS AND COMPLEXES HAVE ISOMORPHIC HOMOTOPY GROUPS

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arXiv 2022年

作者： Adams, Henry Frick, Florian Virk, Žiga Department of Mathematics Colorado State University Fort CollinsCO80523 United States Dept. Math. Sciences Carnegie Mellon University PittsburghPA15213 United States Inst. Math. Freie Universität Berlin Arnimallee 2 Berlin14195 Germany Faculty of Computer and Information Science Večna pot 113 LjubljanaSI-1000 Slovenia

We study the relationship between metric thickenings and simplicial complexes associated to coverings of metric spaces. Let U be a cover of a separable metric space X by open sets with a uniform diameter bound. The Vietoris complex V(U) contains all simplices with vertex set contained in some U ∈ U, and the Vietoris metric thickening Vm(U) is the space of probability measures with support in some U ∈ U, equipped with an optimal transport metric. We show that Vm(U) and V(U) have isomorphic homotopy groups in all dimensions. In particular, by choosing the cover U appropriately, we get isomorphisms between the homotopy groups of Vietoris-Rips metric thickenings and simplicial complexes πk(VRm(X;r)) ≅ πk(VR(X;r)) for all integers k ≥ 0, where both spaces are defined using the convention "diameter k(Čm(X;r)) ≅ πk(Č(X;r)) for all integers k ≥ 0, where both spaces are defined using open balls (instead of closed balls). Copyright © 2022, The Authors. All rights reserved.

关键词： Set theory

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence Preface

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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research 2021年 161卷 1-11页

作者： de Campos, Cassio Maathuis, Marloes H. Quaeghebeur, Erik Uncertainty in AI Group Dep. of Math. and Computer Science Eindhoven University of Technology Netherlands Seminar for Statistics Department of Mathematics ETH Zurich Switzerland