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High Order Semi-implicit Multistep Methods for Time-Dependent Partial Differential Equations

Communications on Applied mathematics and Computation 2021年第4期3卷 701-718页

作者： Giacomo Albi Lorenzo Pareschi Computer Science Department University of VeronaVerona 37134Italy Mathematics and Computer Science Department University of FerraraFerrara 44121Italy

We consider the construction of semi-implicit linear multistep methods that can be applied to time-dependent PDEs where the separation of scales in additive form,typically used in implicit-explicit(IMEX)methods,is not *** shown in Boscarino et al.(***.68:975-1001,2016)for Runge-Kutta methods,these semi-implicit techniques give a great flexibility,and allow,in many cases,the construction of simple linearly implicit schemes with no need of iterative *** this work,we develop a general setting for the construction of high order semi-implicit linear multistep methods and analyze their stability properties for a prototype lineal'advection-diffusion equation and in the setting of strong stability preserving(SSP)*** findings are demonstrated on several examples,including nonlinear reaction-diffusion and convection-diffusion problems.

关键词： Semi-implicit methods Implicit-explicit methods Multistep methods Strong stability preserving High order accuracy

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Investigating Vulnerabilities of Information Solicitation Process in RPL-Based IoT Networks 11th

Investigating Vulnerabilities of Information Solicitation P...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Sahay, Rashmi Sushant, Cherukuri Gaurav Department of Computer Science and Engineering Faculty of Science and Technology IcfaiTech The ICFAI Foundation For Higher Education Hyderabad India Department of Data Science and Artificial Intelligence Faculty of Science and Technology IcfaiTech The ICFAI Foundation For Higher Education Hyderabad India

ISBN: (纸本)9789819967018

The Routing Over Low power and Lossy network (ROLL) working group of IETF proposed the IPv6 Routing Protocol over Low Power and Lossy network (RPL) to fulfill multiple traffic requirements of the Internet of Things (IoT) environments. RPL structures ubiquitous IoT networks in the form of Directed Acyclic Graphs (DAGs) that comprises basic sensor nodes (motes), motes with routing capability, and one or more root (sink) nodes. RPL ensures optimal topological organization of IoT networks by its rank and version number properties. However, vulnerabilities in the RPL routing process pose threats to several protocol violations in the IoT environment. In RPL, information solicitation is the process by which sensor nodes seek network configuration information required to join an IoT network. The paper investigates the vulnerabilities associated with the information solicitation process in RPL-based IoT networks that leads to flooding of control messages. Consequently, there is energy and queue buffer depletion of sensor nodes in IoT networks. On the one hand, queue buffer depletion may lead to the dropping of packets by sensor nodes. On the other hand, energy depletion of sensor nodes may altogether make them non-functioning. Therefore, IoT network administrators must pay attention to the device’s power consumption. Recent research focuses on energy depletion due flooding of control packets. Nevertheless, flooding also affects other significant parameters like, the network convergence time, beacon interval and packet arrival rate. In this work, we perform an in-depth evaluation of the performance of RPL-based IoT networks under the threat of vulnerabilities associated with information solicitation process. The results of our analysis will assist in devising mitigation mechanisms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

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Small Shadow Partitions

arXiv

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arXiv 2024年

作者： Kopparty, Swastik Sha, Harry Department of Mathematics Department of Computer Science University of Toronto Canada Department of Computer Science University of Toronto Canada

We study the problem of partitioning the unit cube [0, 1]n into c parts so that each d-dimensional axis-parallel projection has small volume. This natural combinatorial/geometric question was first studied by Kopparty and Nagargoje [KN23] as a reformulation of the problem of determining the achievable parameters for seedless multimergers - which extract randomness from "d-where"' random sources (generalizing somewhere random sources). This question is closely related to influences of variables and is about a partition analogue of Shearer's lemma. Our main result answers a question of [KN23]: for d = n−1, we show that for c even as large as 2o(n), it is possible to partition [0, 1]n into c parts so that every n−1-dimensional axis-parallel projection has volume at most (1/c)(1+o(1)). Previously, this was shown by [KN23] for c up to O(√n). The construction of our partition is related to influences of functions, and we present a clean geometric/combinatorial conjecture about this partitioning problem that would imply the KKL theorem on influences of Boolean functions. © 2024, CC BY-NC-SA.

关键词： Boolean functions

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SConE: Simplified Cone Embeddings with Symbolic Operators for Complex Logical Queries 61

SConE: Simplified Cone Embeddings with Symbolic Operators fo...

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Findings of the Association for Computational Linguistics, ACL 2023

作者： Nguyen, Chau Duc Minh French, Tim Liu, Wei Stewart, Michael ARC Centre for Transforming Maintenance through Data Science Department of Computer Science and Software Engineering School of Physics Mathematics and Computing The University of Western Australia

ISBN: (纸本)9781959429623

Geometric representation of query embeddings (using points, particles, rectangles and cones) can effectively achieve the task of answering complex logical queries expressed in first-order logic (FOL) form over knowledge graphs, allowing intuitive encodings. However, current geometric-based methods depend on the neural approach to model FOL operators (conjunction, disjunction and negation), which are not easily explainable with considerable computation cost. We overcome this challenge by introducing a symbolic modeling approach for the FOL operators, emphasizing the direct calculation of the intersection between geometric shapes, particularly sector-cones in the embedding space, to model the conjunction operator. This approach reduces the computation cost as a non-neural approach is involved in the core logic operators. Moreover, we propose to accelerate the learning in the relation projection operator using the neural approach to emphasize the essential role of this operator in all query structures. Although empirical evidence for explainability is challenging, our approach demonstrates a significant improvement in answering complex logical queries (both non-negative and negative FOL forms) over previous geometric-based models. © 2023 Association for Computational Linguistics.

关键词： computer circuits

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Which Sentence Representation is More Informative: An Analysis on Text Classification 7

Which Sentence Representation is More Informative: An Analys...

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7th International Conference on Dependency Linguistics (Depling, GURT/SyntaxFest 2023), Depling 2023

作者： Bölücü, Necva Can, Burcu Computer Engineering Adana Alparslan Turkes Science and Technology University Adana Turkey Department of Computing Science and Mathematics University of Stirling Stirling United Kingdom

ISBN: (纸本)9781959429326

Text classification is a popular and well-studied problem in Natural Language Processing. Most previous work on text classification has focused on deep neural networks such as LSTMs and CNNs. However, text classification studies using syntactic and semantic information are very limited in the literature. In this study, we propose a model using Graph Attention Network (GAT) that incorporates semantic and syntactic information as input for the text classification task. The semantic representations of UCCA and AMR are used as semantic information and the dependency tree is used as syntactic information. Extensive experimental results and in-depth analysis show that UCCA-GAT model, which is a semantic-aware model outperforms the AMR-GAT and DEP-GAT, which are semantic and syntax-aware models respectively. We also provide a comprehensive analysis of the proposed model to understand the limitations of the representations for the problem. © 2023 Association for Computational Linguistics.

关键词： Semantics

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Faster Algorithms for Computing the Hairpin Completion Distance and Minimum Ancestor 34

Faster Algorithms for Computing the Hairpin Completion Dista...

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34th Annual Symposium on Combinatorial Pattern Matching, CPM 2023

作者： Boneh, Itai Fried, Dvir Miclăuş, Adrian Popa, Alexandru Reichman University Herzliya Israel Bar-Ilan University Ramat-Gan Israel Faculty of Mathematics and Computer Science University of Bucharest Romania

ISBN: (纸本)9783959772761

Hairpin completion is an operation on formal languages that has been inspired by hairpin formation in DNA biochemistry and has many applications especially in DNA computing. Consider s to be a string over the alphabet {A, C, G, T} such that a prefix/suffix of it matches the reversed complement of a substring of s. Then, in a hairpin completion operation the reversed complement of this prefix/suffix is added to the start/end of s forming a new string. In this paper we study two problems related to the hairpin completion. The first problem asks the minimum number of hairpin operations necessary to transform one string into another, number that is called the hairpin completion distance. For this problem we show an algorithm of running time O(n2), where n is the maximum length of the two strings. Our algorithm improves on the algorithm of Manea (TCS 2010), that has running time O(n2 log n). In the minimum distance common hairpin completion ancestor problem we want to find, for two input strings x and y, a string w that minimizes the sum of the hairpin completion distances to x and y. Similarly, we present an algorithm with running time O(n2) that improves by a O(log n) factor the algorithm of Manea (TCS 2010). © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Dynamic programming

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Cardiovascular Health Risk Prediction Using Bayesian Predictive Analytics Approach 5th

Cardiovascular Health Risk Prediction Using Bayesian Predict...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Shantanu, Kumar Singhal, Priya Mishra, Sushruta Sharma, Ruchi Obaid, Ahmed J. Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Department of ECE Bharati Vidyapeeth’s College of Engineering Delhi India Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq

ISBN: (纸本)9789819763177

A correct CVD diagnosis and outcome, on the other hand, result in expedited patient care and highly accurate treatment, as well as good results. Medicine has turned to machine learning since it is capable of discerning intricate patterns from vast data set insights. This study seeks to reduce the risk of miscategorizing CVD events and subsequently decrease CVD death rates by using machine learning methods. As a result, a more sophisticated approach outlined: RF, Decision Tree, MP, and XGB as the best current computer program in the proposed system. To refine the sorting capability of the model, a revolutionary method produce, including a K-modes grouping and a Huang starting point technique to increase sorting the stuff more effectively. Using an actual data from Kaggle dataset which had 4240 examples, our models were trained and tested. After rigorous evaluation, the models demonstrated noteworthy accuracy rates: Decision Tree achieved 72.81%, XGBoost exhibited 83.33%, Random Forest attained 83.61%, Naive Bayes achieved 81.01%, and Multilayer Perceptron outperformed others with an accuracy of 77.32%. Moreover, effectiveness of the model was calculated by using the Area Under the Curve (AUC), which resulted in outstanding predictive capabilities. Specifically, XGBoost and Random Forest both achieved an AUC of 0.58, with support vector machine (SVM) model outperforming all, boasting an accuracy level of 83.33%, an AUC score of 0.58, highlighting their robustness in predicting CVD occurrences. An observation from this study tells the superiority of the brain-like network model (MPM), particularly at the time matched with cross-checking. It resulted in achieving an exceptional success rate of 87.28%. The research developed not only focuses on the area of predicting heart diseases but also lays emphasis on the potential of machine learning methods to transform medical diagnostics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Patient treatment

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Investigation the influence related to parameters configuration of Generative Adversarial Networks in face image generation

Investigation the influence related to parameters configurat...

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作者： Yuebo He Department of Computer Science Faculty of Science Western University

Due to the excellent performance of Generative Adversarial Networks(GAN) for age regression on face images, it is particularly important to explore the effect of different parameters on model training. In this study, the origin and development of Artificial Intelligence(AI) is first discussed, from which the concept and principles of GAN are *** is followed by a brief introduction of the UTKface dataset used in this research, and the Conditional Adversarial Autoencoder(CAAE) framework based on the GAN technique. The division of labor and roles of the encoder, generator, and the two discriminators in the model are described. The various learning rates as well as batch size combinations attempted in this study are then illustrated, and the training results of the model are shown in the form of graphs and plots of the loss value function. A situation where the model stops learning is highlighted in the results, which is similar to pattern descent in GAN, and is shown to be characterized by the inability of the discriminator to successfully recognize it. Ultimately, drawing from the acquired outcomes, it can be deduced that employing a larger batch size serves to enhance the pace of model training. It is advisable to concurrently elevate the learning rate by an equivalent factor when augmenting the batch size, thereby ensuring a consistent trajectory for model convergence.

关键词： Artificial Intelligence (AI) Generative Adversarial Network (GAN) Learning Rate Batch Size

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An interpretable deep learning based approach for chronic obstructive pulmonary disease using explainable artificial intelligence

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International Journal of Information Technology (Singapore) 2024年 1-16页

作者： El-Magd, Lobna M. Abou Dahy, Ghada Farrag, Tamer Ahmed Darwish, Ashraf Hassnien, Aboul Ella Computer Science Department Misr High Institute for Commerce and Computers Mansoura Egypt Faculty of Computers and AI Cairo University Giza Egypt Department of Electrical Engineering College of Engineering Taif University Taif Saudi Arabia Faculty of Science Helwan University Helwan Egypt

Artificial intelligence has become like-humans in thinking and interpretations. But its uses are still limited and are viewed as black boxes, and this is the most important factor underlying the limited applications, especially in the field of health care. Therefore, there is a need for interpretable prediction that provide better predicts and also explain their prediction. This paper proposed an approach for identification and interpretation of chronic obstructive pulmonary disease (COPD) using exhaled breath data and providing explanations of the predictions results based on explainable artificial intelligence. This paper examined a total of 78 patients, using data collected from 8 sensors that analyzed exhaled air. Diagnostic reasons often involve the utilization of transfer learning techniques based on Deep Neural Networks (DNNs). Five pre-train CNNs were tested for recognizing COPD with global average pooling and flattening layers. For interpreting the results, the given results of the DNN are explained by a twin system that uses Case Based Reasoning. The mission of the twinning system is to convert a black box into a white box to be easier to explain. Compared to the other pre-train CNNs models, GoogleNet deep learning model produces promising results, with up to 100% test accuracy with small number of parameters. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Biosensors Case-based reasoning Chronic obstructive pulmonary Explainable artificial intelligence Transfer learning Twin system

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Resource Request Handling Mechanisms for Effective VM Placements in Cloud Environment 1

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1st International Conference on Expert Clouds and Applications, ICOECA 2023

作者： Thiruvenkadam, T. Muthusamy, A. Vijayakumar, M. Karnataka Bengaluru India Assistant Professor and Head Department of Computer Science Faculty of Science & Humanities SRM Institute of Science and Technology Tamil Nadu Tiruchirappalli India Professor School of Computer Science VET Institute of Arts and Science College Tamil Nadu Erode Tindal India

ISBN: (数字)9789819917457

ISBN: (纸本)9789819917440

In a cloud computing environment, effective resource request handling and their dynamic management can optimize the VM placement process. An efficient resource request handling can resolve the problem of increased waiting time as well as load imbalance rate during the course of running the VM resources. Load balancing techniques only helps to reschedule the VMs in the appropriate PMs based on the dynamic changes in the load. To make use of the resources in an effective way, an optimized resource scheduling and load balancing model is necessary along with the proper model to manage the resource request made by the cloud users. A rule based method utilizing the M/M/C queue model for effective management of resource request handling is presented in this work. The RB-M/M/C queue model can ensure the availability of cloud datacenter services by managing user requests. This further enriches the cloud service by increasing the physical server utilization rate and minimizing the waiting time and number of servers used to fulfill the requirements of the users. The resource request queue management algorithm is designed by utilizing some suitable modesties of queuing theory. Simulation results for performance analysis shows that the proposed model decreases the waiting time and at the same time increases the utilization rate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computation theory

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