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69th ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition, GT 2024

作者： Fang, Yuan Reissmann, Maximilian Pacciani, Roberto Zhao, Yaomin Ooi, Andrew S.H. Marconcini, Michele Akolekar, Harshal D. Sandberg, Richard D. Department of Mechanical Engineering University of Melbourne VIC3010 Australia Department of Industrial Engineering University of Florence Florence50139 Italy Center for Applied Physics and Technology HEDPS College of Engineering Peking University Beijing100871 China Department of Mechanical Engineering School of AI & Data Science Indian Institute of Technology Jodhpur324030 India

ISBN: (纸本)9780791888070

Previous studies have shown the potential of using a multi-objective CFD (computational fluid dynamics) - driven machine-learning approach to train both transition and turbulence models in RANS (Reynolds averaged Navier-Stokes) calculations for improved turbine flow predictions (Akolekar et al., GT2022-81091;Fang et al., GT2023-102902). However, conducting CFD-driven training incurs a high computational cost as thousands of RANS calculations are required if the starting guesses are taken from an initial population of randomly generated models. This paper, for the first time, adopts a transformer technique, belonging to the class of natural language processing models, in gene expression programming (GEP), to expedite the training process for transition and turbulence models. The efficacy of utilizing the transformer is investigated for two scenarios. In one, we introduce previously trained models to randomly generated ones in the initial population of candidate models, facilitating the generation of models with a higher likelihood of achieving lower cost function values from the outset. In the other scenario, assuming that no suitable information is available from pre-training, a dynamic approach is employed at certain training iterations, where models exhibiting significant errors are excluded and replaced by those trained on the fly by the transformer and demonstrating smaller errors. Additionally, we incorporate mathematical operators such as minimum, maximum, and exponential functions, along with a technique called a rolling window, to avoid nested functions in the trained models. This enhances the flexibility in constructing trained models while still allowing us to delve into the underlying physics and provide recommendations for developing physical models. Finally, we also introduce two additional physical features that serve as training inputs for the turbulence model that contribute to smaller errors. With these enhancements to the previous GEP framework, mo

关键词： Exponential functions

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Aspect category and sentiment polarity detection using a permutation language-based transformer model

Aspect category and sentiment polarity detection using a per...

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3rd International Conference on Machine Learning and data Engineering, ICMLDE 2024

作者： Jayaraman, Ashok Kumar Trueman, Tina Ester Ananthakrishnan, Gayathri Murugappan, Abirami Cambria, Erik Manikandan, G. Mitra, Satanik HCLTech Engineering and R&D Services Tamil Nadu Chennai India Department of Computer Science University of the People CA United States School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Science and Technology Anna University Chennai India College of Computing and Data Science Nanyang Technological University Nanyang Singapore Singapore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Management BITS Pilani India

Aspect-based sentiment analysis (ABSA) performs fine-grained analysis on text to determine a specific aspect category and a sentiment polarity. Recently, machine learning models have played a key role in ABSA tasks. In particular, transformer-based pre-trained models have achieved promising results in natural language processing tasks. Therefore, we propose a permutation based XLNet fine-tuning model for aspect category detection and sentiment polarity detection. Our model learns bidirectional contexts via positional encoding and factorization order. We evaluate the proposed permutation language model on three ABSA datasets, namely, SentiHood, SemEval 2015, and SemEval 2016. Specifically, we studied the ABSA tasks in a constrained system with a multi-class environment. Our result indicates that the proposed permutation language model achieves a better result. © 2025 The Authors. Published by Elsevier B.V.

关键词： Emotion Recognition

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Star coloring for a special class of graphs G1, d

Star coloring for a special class of graphs G1, d

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2021 International Conference on Big data Analysis and Computer science, BDACS 2021

作者： Cai, Jin Tian, ShuangLiang An, ZhuoMo School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest for Minzu University Lanzhou China

ISBN: (纸本)9781665425612

In this paper we study the star coloring for a special class of graphs whose degree is either 1 or d. They are graphs obtained by generalized corona products of path, circle, star, complete graph complete bipartite graph and complement of complete graph respectively. Vertex coloring theory of graphs is an important part of graph coloring theory. Star coloring is a special vertex coloring with limited conditions.. © 2021 IEEE.

关键词： Stars

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Hybrid Horizons: Advancing Water Potability Prediction Through Hybrid Machine Learning

Hybrid Horizons: Advancing Water Potability Prediction Throu...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Jovita Biju Chetan Badgujar Alwin Poulose School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM) Vithura Thiruvananthapuram Kerala India Biosystems Engineering and Soil Sciences The University of Tennessee Knoxville Tennessee TN United States

ISBN: (数字)9798350385298

ISBN: (纸本)9798350385304

Ensuring access to safe drinking water is a critical global concern with significant implications for public health. This paper investigates the application of the hybrid machine learning model in assessing water potability, offering a comprehensive review of current methodologies and prospects. With water quality assessment a critical component of public health management, integrating machine learning techniques shows promising avenues for improving accuracy, efficiency, and predictive capabilities. This paper synthesizes existing literature on machine learning models in water quality analysis, highlighting various approaches, such as supervised and hybrid machine learning models utilized for water potability assessment. Furthermore, it examines using diverse data sources, including the pH level of the water, water hardness, total dissolved solids in the water, Chloramines concentration, sulfate concentration, electrical conductivity, organic carbon content, Trihalomethanes concentration, and turbidity level to enhance model performance and robustness. Our experiment results on the Water Quality and Potability dataset show that the proposed hybrid machine learning model achieved 68% classification accuracy compared to traditional supervised machine learning techniques. By critically evaluating the strengths and limitations of supervised and hybrid machine learning models, our research contributes to the ongoing discourse on leveraging technology to safeguard water quality and public health, ultimately fostering sustainable water management practices.

关键词： Analytical models Adaptation models Soft sensors Machine learning Water quality Predictive models data models

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Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding 10

Security Enhancing for IRS-Aided NOMA Networks via Construct...

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10th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2024

作者： Bao, Jingying Cao, Yang Qin, Xiaoqi Xu, Lexi Zhao, Nan Niyato, Dusit School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Singapore University of Technology and Design Engineering Systems and Design Pillar Singapore487372 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Research Institute China United Network Communications Corporation Beijing100048 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798331533922

In this paper, we propose a constructive interference precoding (CIP) enabled secure intelligent reflecting surface (IRS)-non-orthogonal multiple access (NOMA) scheme, lever-aging both the inter-user interference and artificial noise to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. Based on this CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and successive interference cancellation for the near user and the IRS unit modulus constraint. To handle this non-convex problem, an alternating optimization algorithm is proposed. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA. © 2024 IEEE.

关键词： Multiple access interference

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Metric-Agnostic Continual Learning for Sustainable Group Fairness 39

Metric-Agnostic Continual Learning for Sustainable Group Fai...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lian, Heng Zhao, Chen Chen, Zhong Zhu, Xingquan Thai, My T. He, Yi School of Computing Data Sciences & Physics William & Mary Williamsburg VA United States School of Engineering and Computer Science Baylor University Waco TX United States School of Computing Southern Illinois University Carbondale IL United States College of Engineering & Computer Science Florida Atlantic University Boca Raton FL United States Department of Computer & Information Science & Engineering University of Florida Gainesville FL United States

ISBN: (纸本)157735897X

Group Fairness-aware Continual Learning (GFCL) aims to eradicate discriminatory predictions against certain demographic groups in a sequence of diverse learning tasks. This paper explores an even more challenging GFCL problem - how to sustain a fair classifier across a sequence of tasks with covariate shifts and unlabeled data. We propose the MacFRL solution, with its key idea to optimize the sequence of learning tasks. We hypothesize that high-confident learning can be enabled in the optimized task sequence, where the classifier learns from a set of prioritized tasks to glean knowledge, thereby becoming more capable to handle the tasks with substantial distribution shifts that were originally deferred. Theoretical and empirical studies substantiate that MacFRL excels among its GFCL competitors in terms of prediction accuracy and group fairness metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Adversarial machine learning

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Incidence Coloring of Product Graphs of Paths

Incidence Coloring of Product Graphs of Paths

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2021 International Conference on Big data Analysis and Computer science, BDACS 2021

作者： Peng, Lizhen Tian, Shuangliang Lu, Qiqi School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

ISBN: (纸本)9781665425612

Incidence coloring of graph refers to coloring of its all incidences in which neighborly incidences are assigned different colors. The smallest number of colors in an incidence coloring is incidence coloring number of graph. In this paper, structural coloring method and disproof method are used. we study the incidence coloring of direct products, semi-strong products and strong products of paths of different orders, and obtain the exact values of the incidence coloring numbers. The incidence coloring number of the direct product and the strong product of a path is the maximunm degree plus one. © 2021 IEEE.

关键词： Coloring

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Real-Time Decision-Making Techniques using Artificial Intelligence and Cloud computing

Real-Time Decision-Making Techniques using Artificial Intell...

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Disruptive Technologies (ICDT), International Conference on

作者： Kuldeep Singh Kaswan Jagjit Singh Dhatterwal Naresh Kumar Shashank Awasthi Sansar Singh Chauhan School of Computing Science & Engineering Galgotias University Greater Noida UP India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India GL Bajaj Institute of Technology & Management Greater Noida India

scientific research will increasingly rely on AI and the cloud in the future; our suggested solution will allow us to use these technologies to solve a number of problems (CC). We have outlined the different issues that may be addressed via the combined efforts of cloud computing and AI and discussed how to implement such an approach. One of the most powerful exploration techniques is, for example, using cloud-based artificial intelligence algorithms to increase productivity. Drive to create apps, manufactured in the cloud, beyond the fundamental automation pro, requires the ability to predict scenarios and make continuous decisions online. In this paper, we describe a programming language for intelligent computing that will enable machines to reason and make choices for themselves, in real time.

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Sparse graph counting and Kelley–Meka bounds for binary systems

arXiv

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arXiv 2023年

作者： Filmus, Yuval Hatami, Hamed Hosseini, Kaave Kelman, Esty The Henry & Marilyn Taub Faculty of Computer Science Faculty of Mathematics Technion School of Computer Science McGill University Canada Department of Computer Science University of Rochester United States Department of Computer Science Department of Computing & Data Sciences Boston University CSAIL Massachusetts Institute of Technology United States

In a recent breakthrough, Kelley and Meka (FOCS 2023) obtained a strong upper bound on the density of sets of integers without non-trivial three-term arithmetic progressions. In this work, we extend their result, establishing similar bounds for all linear patterns defined by binary systems of linear forms, where "binary" indicates that every linear form depends on exactly two variables. Prior to our work, no strong bounds were known for such systems even in the finite field model setting. A key ingredient in our proof is a graph counting lemma. The classical graph counting lemma, developed by Thomason (Random Graphs 1985) and Chung, Graham, and Wilson (Combinatorica 1989), is a fundamental tool in combinatorics. For a fixed graph H, it states that the number of copies of H in a pseudorandom graph G is similar to the number of copies of H in a purely random graph with the same edge density as G. However, this lemma is only non-trivial when G is a dense graph. In this work, we prove a graph counting lemma that is also effective when G is sparse. Moreover, our lemma is well-suited for density increment arguments in additive number theory. As an immediate application, we obtain a strong bound for the Turán problem in abelian Cayley sum graphs: let Γ be a finite abelian group with odd order. If a Cayley sum graph on Γ does not contain any r-clique as a subgraph, it must have at most 2−Ωr(log1/16 |Γ|) · |Γ|2 edges. These results hinge on the technology developed by Kelley and Meka and the follow-up work by Kelley, Lovett, and Meka (STOC 2024). © 2023, CC BY.

关键词： Graph theory

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Machine Learning Approaches for Proactive Phishing Attack Detection 7

Machine Learning Approaches for Proactive Phishing Attack De...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Geetha, B. Malathi, P. Thirumalaikumari, T. Janakiraman, V. Basha, H. Anwer Rukmani Devi, S. Saveetha Institute of Medical and Technical Sciences Science Saveetha College of Liberal Arts and Sciences Department of Computer Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of ECE Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Phishing attacks remain a serious problem in cybersecurity, necessitating advances in detection methods. The study describes a novel method for detecting phishing attacks that use advanced machine learning (ML) techniques, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs and RNNs were chosen because of their ability to detect spatial and sequential patterns in phishing data. The system achieves improved performance by iterative model refinement and ensemble learning, beating existing rule-based systems in terms of precision (96%), recall (94%), and F1-score (95%). Furthermore, the proposed strategy has significantly lower false positive (3.8%) and false negative (5.7%) rates than existing systems, indicating greater dependability in distinguishing between legitimate and malicious emails. Cross-validation validates that the proposed system is stable and ready for real-world use. As a result, the proposed ML-driven strategy represents a substantial leap in phishing detection, providing increased accuracy (F1-score: 95%) and resilience to emerging cybersecurity threats. © 2024 IEEE.

关键词： Phishing

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