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arXiv 2023年

作者： Liu, Zhenwei Zhang, Meirong Saberi, Ali Stoorvogel, Anton A. College of Information Science and Engineering Northeastern University Shenyang110819 China School of Engineering and Applied Science Gonzaga University SpokaneWA99258 United States School of Electrical Engineering and Computer Science Washington State University PullmanWA99164 United States Department of Electrical Engineering Mathematics and Computer Science University of Twente P.O. Box 217 Enschede Netherlands

This paper aims to achieve scalable exact output and regulated output synchronization for discrete-time multi-agent systems in presence of disturbances and measurement noise with known frequencies. Both homogeneous and heterogeneous multi-agent systems are considered, with parts of agents’ states accessible in the latter case. The key contribution of this paper is on the distributed protocol that only uses the information of agent models, rather than the communication network information and the agent number, so as to achieve the scalable exact synchronization under disturbances and measurement noise. The validity of the protocol is verified by numerical simulations with arbitrarily chosen number of agents. © 2023, CC BY-NC-ND.

关键词： Multi agent systems

Computing Autotopism Groups of Partial Latin Rectangles

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ACM Journal of Experimental Algorithmics 2020年第pp1–39期25卷 1-39页

作者： Stones, Rebecca J. Falcón, Raúl M. Kotlar, Daniel Marbach, Trent G. College of Computer Science Nankai University Tianjin China Department of Mathematics Beijing Jiaotong University Beijing100044 China School of Building Engineering Department of Applied Mathematics I Universidad de Sevilla Seville41012 Spain Computer Science Department TelHai College Upper Galilee1220800 Israel Department of Mathematics Ryerson University 350 Victoria St. TorontoON Canada

Computing the autotopism group of a partial Latin rectangle (PLR) can be performed in multiple ways. This study has two aims: comparing some of these methods experimentally to identify those that are competitive;and identifying design goals for developing practical software. We compare six families of algorithms (two backtracking and four graph-theoretic methods), with and without using entry invariants (EIs), in a range of settings. Two EIs are considered: frequencies of row, column, and symbol representatives;and 2 × 2 submatrices. The best approach to computing autotopism groups varies. When PLRs have many autotopisms (such as having very few entries or being a group table), the McKay, Meynert, and Myrvold (MMM) method computes generators for the autotopism group efficiently. (The MMM method is the standard way to compute autotopisms.) Otherwise, PLRs ordinarily have trivial or small autotopism groups, and the task is to verify this. The so-called PLR graph method is slightly more efficient in this setting than the MMM method (in some circumstances, around twice as fast). With an intermediate number of entries, the quick-to-compute strong EIs are effective at reducing the need for computation without introducing significant overhead. With a full or almost-full PLR, a more sophisticated EI is needed to reduce down-the-line computation. These results suggest a hybrid approach to computing autotopism groups: The software decides on suitable EIs based on the input;and the user chooses between the MMM or the PLR graph methods, depending on their dataset. This article expands the authors' previous article Computing autotopism groups of PLRs: a pilot study. © 2020 ACM.

关键词： Geometry

Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning

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arXiv 2023年

作者： De Lellis, Francesco Coraggio, Marco Russo, Giovanni Musolesi, Mirco Bernardo, Mario di The Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy The Scuola Superiore Meridionale School for Advanced Studies Naples Italy The Department of Computer and Electrical Engineering & Applied Mathematics University of Salerno DIEM Salerno Italy The Department of Computer Science University College London United Kingdom The Department of Informatics - Science and Engineering University of Bologna Bologna Italy

In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment. Motivated by this necessity, we present a set of results and a systematic reward shaping procedure that (i) ensures the optimal policy generates trajectories that align with specified control requirements and (ii) allows to assess whether any given policy satisfies them. We validate our approach through comprehensive numerical experiments conducted in two representative environments from OpenAI Gym: the Inverted Pendulum swing-up problem and the Lunar Lander. Utilizing both tabular and deep reinforcement learning methods, our experiments consistently affirm the efficacy of our proposed framework, highlighting its effectiveness in ensuring policy adherence to the prescribed control requirements. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

Significance of slip velocity and viscosity variation on squeezed film couple-stress properties between a rough plate and a cylinder

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Partial Differential Equations in applied mathematics 2025年 14卷

作者： K, Arshiya Kousar A, Salma Althobaiti, Saja Abdulrahman BN, Hanumagowda Tawade, Jagadish V Abduvalieva, Dilsora Waqas, M. Saeed, Mohammed Azeez Gupta, Manish Department of Mathematics School of Applied Science REVA University India Department of Chemistry College of Sciences & Humanities Prince Sattam Bin Abdulaziz University Saudi Arabia Department of Mathematics Vishwakarma University Pune 411048 India Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent 100070 Uzbekistan Department of Computer Science National University of Technology Islamabad 44000 Pakistan Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Environmental Engineering College of Engineering Knowlwdge University Erbil 44001 Iraq Division of Research and Development Lovely Professional University Phagwara India

This study examines the impact of surface roughness and viscosity variation on the couple stress squeeze film characteristics between a cylinder and a rough plate with slip velocity. Two distinct one-dimensional roughness patterns—longitudinal and transverse—are considered. Using Christensen's theory, the stochastic modified Reynolds equation is derived for Stokes couple stress fluid, incorporating viscosity variation with pressure. The standard perturbation technique is applied to solve the average Reynolds equation, yielding closed-form expressions for the mean fluid film pressure, load-carrying capacity, and squeeze film time. Various parameters are varied, and the results are discussed through graphical representations in 2D and 3D. This study highlights the importance of viscosity variation, couple stresses, and surface roughness in optimizing squeeze film performance. While increased viscosity and couple stresses enhance load-bearing capacity and film time, slip velocity detracts from these properties. Additionally, surface roughness has a significant impact, with transverse roughness improving, and longitudinal roughness reducing, the squeeze film characteristics. Applications of this study include improving the design and performance of bearing systems, lubrication in mechanical seals, and hydraulic systems where surface roughness and viscosity variation play a significant role in operational efficiency. © 2025

关键词： Couple stress fluid Rough surface Slip velocity Squeeze-film Viscosity variation

Narrative Analysis of True Crime Podcasts With Knowledge Graph-Augmented Large Language Models

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arXiv 2024年

作者： Leng, Xinyi Liang, Jason Mauro, Jack Wang, Xu Chapman, James Bertozzi, Andrea L. Lin, Junyuan Chen, Bohan Ye, Chenchen Daniel, Temple Brantingham, P. Jeffrey Carleton College Department of Mathematics and Statistics United States Claremont McKenna College Department of Mathematical Sciences United States University of California Los Angeles Department of Mathematics United States Loyola Marymount University Department of Mathematics Statistics and Data Science United States California Institute of Technology Computing + Mathematical Sciences Division of Engineering and Applied Science United States University of California Los Angeles Department of Computer Science United States University of California Los Angeles Department of Anthropology United States

Narrative data spans all disciplines and provides a coherent model of the world to the reader or viewer. Recent advancement in machine learning and Large Language Models (LLMs) have enable great strides in analyzing natural language. However, Large language models (LLMs) still struggle with complex narrative arcs as well as narratives containing conflicting information. Recent work indicates LLMs augmented with external knowledge bases can improve the accuracy and interpretability of the resulting models. In this work, we analyze the effectiveness of applying knowledge graphs (KGs) in understanding true-crime podcast data from both classical Natural Language Processing (NLP) and LLM approaches. We directly compare KG-augmented LLMs (KGLLMs) with classical methods for KG construction, topic modeling, and sentiment analysis. Additionally, the KGLLM allows us to query the knowledge base in natural language and test its ability to factually answer questions. We examine the robustness of the model to adversarial prompting in order to test the model's ability to deal with conflicting information. Finally, we apply classical methods to understand more subtle aspects of the text such as the use of hearsay and sentiment in narrative construction and propose future directions. Our results indicate that KGLLMs outperform LLMs on a variety of metrics, are more robust to adversarial prompts, and are more capable of summarizing the text into topics. © 2024, CC BY.

关键词： Knowledge graph

Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer

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International Journal of Thermofluids 2025年 27卷

作者： Al-Betar, Mohammed Azmi Muskens, Otto L. Hammoodi, Karrar A. Abd Elaziz, Mohamed Shambour, Qusai Yousef Fraihat, Salam Elsheikh, Ammar H. College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom College of Engineering University of Al Maarif Al Anbar31001 Iraq Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Software Engineering Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Production Engineering and Mechanical Design Faculty of Engineering Tanta University Tanta31527 Egypt Faculty of Engineering Pharos University in Alexandria Egypt

Yield augmentation of solar distiller (SD) using low-cost modifications with minimum environmental impacts is highly desirable. In this study, we developed an eco-friendly steam generation device made of carbonized Ficus sycomorus wood to boost the yield of the conventional single-basin solar distiller. Moreover, a hybrid model composed of a long short-term memory (LSTM) network and a Starling murmuration optimizer (SMO) was developed to predict the yield of the established SD. To verify the accuracy of the model, three other models were employed and the distillate yield was predicted. The predicted data of all models was evaluated using different error measures. These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). Real field data were employed during the training and testing of all models. The thermal performance of the distiller with the steam generation device was compared with that of a traditional distiller based on exergy output, exergy efficiency, and energy efficiency. The distillate yield, exergy efficiency, and energy efficiency of the modified SD were enhanced by 34 %, 49 %, and 40 %, respectively, compared with that of the traditional distiller. Moreover, the total amount of produced drinkable water using the modified SD could reach a high value of 6.1 L/day with a low cost of 0.014 $/L. © 2025

关键词： Exergy

Fake News Detection Regarding COVID-19 Tweets Using Machine Learning Approaches

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Fake News Detection Regarding COVID-19 Tweets Using Machine ...

Recent Advances and Innovations in engineering (ICRAIE)

作者： Muhammad Hazimmuddin Hussin Yuzi Mahmud Nurul Izzah Mohd Hanafiah Zan Azma Nasruddin Nor Hapiza Mohd Ariffin Murat Ince Fatih Ahmet Şenel School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia MIS Department Faculty of Business Sohar University Sohar Oman Department of Computer Technologies Isparta University of Applied Sciences Isparta Turkiye Department of Computer Engineering Suleyman Demiral University Isparta Turkiye

The pervasiveness of misinformation surrounding the COVID-19 pandemic has garnered heightened attention due to its implications, as a noteworthy proportion of the populace is being exposed to spurious and unsubstantiated narratives concerning the crisis. This research utilizes a dataset sourced from Codalab, comprising 8,560 tweets, with 4,480 labelled as real and 4,080 as fake. The research explores the effectiveness of different machine learning models, including logistic regression (LR), random forest (RF), and deep learning models such as Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). In addition to model comparison, experiments were conducted to analyze the impact of different data splits (70:30, 80:20, and 90:10), batch sizes (16, 32, and 64), and the number of epochs (5, 10, and 15) on model performance. The experiments provided insights into the optimal configurations for the models. The results showcase the model's capabilities, with high accuracy achieved across the different models. Specifically, logistic regression achieved an accuracy of 92%, random forest 91%, Bi-LSTM 93%, and CNN 95%. These findings highlight the potential of deep learning models, particularly CNN, in accurately detecting fake news from COVID-19-related tweets.

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Generalization of CNNs on Relational Reasoning with Bar Charts

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arXiv 2025年

作者： Cui, Zhenxing Chen, Lu Wang, Yunhai Haehn, Daniel Wang, Yong Pfister, Hanspeter School of Computer Science and Technology Shandong University China State Key Lab of CAD&CG Zhejiang University China School of Information Renmin University of China China College of Science and Mathematics University of Massachusetts Boston United States College of Computing and Data Science Nanyang Technological University Singapore School of Engineering and Applied Sciences Harvard University United States

This paper presents a systematic study of the generalization of convolutional neural networks (CNNs) and humans on relational reasoning tasks with bar charts. We first revisit previous experiments on graphical perception and update the benchmark performance of CNNs. We then test the generalization performance of CNNs on a classic relational reasoning task: estimating bar length ratios in a bar chart, by progressively perturbing the standard visualizations. We further conduct a user study to compare the performance of CNNs and humans. Our results show that CNNs outperform humans only when the training and test data have the same visual encodings. Otherwise, they may perform worse. We also find that CNNs are sensitive to perturbations in various visual encodings, regardless of their relevance to the target bars. Yet, humans are mainly influenced by bar lengths. Our study suggests that robust relational reasoning with visualizations is challenging for CNNs. Improving CNNs’ generalization performance may require training them to better recognize task-related visual properties. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

IoT Malware Detection Tool with CNN Classification for Small Devices

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IoT Malware Detection Tool with CNN Classification for Small...

International Conference on Contemporary Computing and Informatics (IC3I)

作者： Gunjan Bhatnagar Yogendra Kumar Rajoria Md. Sakeel M. Vigenesh G. Premananthan Deepika Dongre School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India Department of Mathematics School of Basic & Applied Sciences K. R. Mangalam University Gurugram India Department of Computer Applications Invertis University Bareilly India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India AI & Ds Vishwakarma Institute of Information Technology Pune India

Since the number of linked devices continues to grow quickly, the Web of Everything (IoT) is being employed change today in more industries. More issues in regards to safety, stability, and supportability are brought on by an increase in smart devices, particularly as it relates to 5G technology. We are concentrating on the safety component of IoT systems in just this article because it is a relatively new field. For varied purposes, such as cost savings or a desire to forego the use of generation system, many Internet device manufacturers need not take security into account when designing their products. The opponent may use those potentially dangerous technologies to carry out several destructive assaults. As a result, we created a device capable of recognizing malicious activity from a particular IoT networks node. They are capable to offer detection methods for IoT utilizing a primary key that may be put inside the system using a multilayer brain networks for identification and surveillance. The accomplishment dispels any misconceptions about methods for deep training while demonstrating how that systems can be simply adapted and deployed to every system.

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Norm inequalities for the generalised commutator in Banach algebras

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SUT Journal of mathematics 2021年第2期57卷 185-199页

作者： Dragomir, Silvestru Sever College of Engineering and Science Victoria University PO Box 14428 Melbourne City MC 8001 Australia DST-NRF Centre of Excellence in the Mathematical and Statistical Sciences School of Computer Science and Applied Mathematics University of the Witwatersrand Private Bag 3 Johannesburg 2050 South Africa

In this paper, by utilising the Riesz functional calculus in a Banach algebra B, we provide some norm inequalities for the generalized commutator f(y)z- zf(x) where x;y;z ϵ B and f is an analytic function for which the elements f(y) and f(x) exist. Some examples for the resolvent and exponential functions are also given. © 2021, Tokyo University of Science. All rights reserved.

关键词： Analytic functions Axponential and logarithmic function on Banach algebra Banach algebras Lipschitz type inequalities