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arXiv 2023年

作者： Yigit, Yagmur Bal, Bahadir Karameseoglu, Aytac Duong, Trung Q. Canberk, Berk The Department of Computer Engineering Faculty of Computer and Informatics Istanbul Technical University Turkey The Microsoft Denmark The BTS Group Istanbul Turkey School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom The School of Engineering and Built Environment Edinburgh Napier University United Kingdom The Department of Artificial Intelligence and Data Engineering Istanbul Technical University Turkey

Existing distributed denial of service attack (DDoS) solutions cannot handle highly aggregated data rates;thus, they are unsuitable for Internet service provider (ISP) core networks. This paper proposes a digital twin-enabled intelligent DDoS detection mechanism using an online learning method for autonomous systems. Our contributions are three-fold: we first design a DDoS detection architecture based on the digital twin for ISP core networks. We implemented a Yet Another Next Generation (YANG) model and an automated feature selection (AutoFS) module to handle core network data. We used an online learning approach to update the model instantly and efficiently, improve the learning model quickly, and ensure accurate predictions. Finally, we reveal that our proposed solution successfully detects DDoS attacks and updates the feature selection method and learning model with a true classification rate of ninety-seven percent. Our proposed solution can estimate the attack within approximately fifteen minutes after the DDoS attack starts. © 2023, CC BY.

关键词： Denial-of-service attack

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Digital Imaging Analyzing and Reproducing Color, Texture, and Material Appearance

Digital Imaging Analyzing and Reproducing Color, Texture, an...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sujin Jose Arul Sajja Suneel Manish Gupta K. Varada Rajkumar Monica Garg Ali Abduhussien Hameed Department of Automobile Engineering Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Digital photography has made great strides in showing colour and texture along with specific material details. Standard approaches usually do not attain realistic representations. The Multifaceted Realism Synthesis presented in this document resolves these problems and outshines classic strategies in accuracy and realism. The MRS framework incorporates three core algorithms: With this approach we can obtain more consistent and accurate colour reproduction using the Colour Harmonisation Algorithm. The Physically Informed Material Rendering and Typical Texture Synthesis (ATS) generate more natural textures that adapt to local details rather than using median filtering or morphological methods. The MRS approach adjusts materials appearance texture and colour to deliver superior realism adaptability and precision beating current methods. This new approach can change digital photography across different areas such as entertainment and healthcare. The technique of MRS has made a substantial improvement in the quest for hyper-real visuals in digital photography.

关键词： Measurement Visualization Accuracy Image color analysis MIMICs Transforms Medical services Rendering (computer graphics) Digital photography Standards

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The computational complexity of angry birds 29

The computational complexity of angry birds

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Stephenson, Matthew Renz, Jochen Ge, Xiaoyu Department of Data Science and Knowledge Engineering Maastricht University Netherlands Research School of Computer Science Australian National University Canberra Australia

ISBN: (纸本)9780999241165

In this paper we present several proofs for the computational complexity of the physics-based video game Angry Birds. We are able to demonstrate that solving levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-hard, depending on the maximum number of birds available and whether the game engine is deterministic or stochastic. We believe that this is the first time that a single-player video game has been proven EXPTIME-hard. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Birds

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HUSS: A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

HUSS: A Heuristic Method for Understanding the Semantic Stru...

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IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S. Sheng Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science Texas Tech University Lubbock TX USA

Spreadsheets contain a lot of valuable data and have many practical applications. The key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets, e.g., identifying cell function types and discovering relationships between cell pairs. Most existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells. A few studies do, but they ignore the layout structure information of spreadsheets, which affects the performance of cell function classification and the discovery of different relationship types of cell pairs. In this paper, we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets (HUSS). Specifically, based on the existing cell function classification model [11], we propose five types of heuristic rules to extract four different types of cell pairs, based on the cell style and spatial location information. Our experimental results on two real-world datasets demonstrate that the proposed method HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Analytical models Heuristic algorithms Semantics Layout Feature extraction Classification algorithms data mining

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An Efficient Approach to Analyse Sentiment Using Deep Learning

An Efficient Approach to Analyse Sentiment Using Deep Learni...

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作者： Dawn, Indrajit Ganguly, Gopi Bondhu Chakraborty, Koyel Supreme Knowledge Foundation Group of Institutions Department of Computer Science and Engineering West Bengal Mankundu Hooghly India

Recently a huge rise regarding the amount of data in the form of texts is observed and it is equally important to understand the emotion behind these texts. The sentiment analysis of a conversation is very important to judge the mind setup of a person in this era. Sentiment analysis is a technique to explain a view regarding a discussion about a topic either related to verbal or drafted form. It is a very difficult task to analyse the sentiment of a conversation. Deep Learning is the latest process to help analyse near-perfect sentiments. It feeds a lot of data to the algorithm and in the process adjusts itself to constantly progress in the prediction process. In this paper, an attempt has been made to judge the sentiment of a conversation using deep learning techniques. The aim of this work is to establish a model for selecting sentiments from sentences. After the "sentiment" word has been identified, we plan to devise a method that will select a few prior words so that the aspect can also be determined directly from the sentence itself. © 2020, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization

arXiv

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arXiv 2023年

作者： Li, Xiao Zhu, Zhihui Li, Qiuwei Liu, Kai School of Data Science The Chinese University of Hong Kong Shenzhen China China The Department of Electrical and Computer Engineering The University of Denver United States The Decision Intelligence Lab Damo Academy Alibaba Group US United States Computer Science Division Clemson University United States

The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter admits the splitting property that allows state-of-the-art alternating-type algorithms. To overcome this issue, we first split the decision variable and transform the symmetric NMF to a penalized nonsymmetric one, paving the way for designing efficient alternating-type algorithms. We then show that solving the penalized nonsymmetric reformulation returns a solution to the original symmetric NMF. Moreover, we design a family of alternating-type algorithms and show that they all admit strong convergence guarantee: the generated sequence of iterates is convergent and converges at least sublinearly to a critical point of the original symmetric NMF. Finally, we conduct experiments on both synthetic data and real image clustering to support our theoretical results and demonstrate the performance of the alternating-type algorithms. © 2023, CC BY.

关键词： Matrix algebra

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Correction to: Multi-agent-based approach for generation expansion planning in isolated micro-grid with renewable energy sources and battery storage

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The Journal of Supercomputing 2022年第1期79卷 1156-1157页

作者： Paliwal, Priyanka Webber, Julian L. Mehbodniya, Abolfazl Haq, Mohd Anul Kumar, Anil Chaurasiya, Prem Kumar Department of Electrical Engineering MANIT Bhopal India Department of Electronics and Communication Engineering Kuwait College of Science and Technology (KCST) Doha Area Kuwait Department of Computer Science College of Computer and Information Sciences Majmaah University AlMajmaah Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India Department of Mechanical Engineering Bansal Institute of Science and Technology Bhopal India

In this article the affiliation details for Julian L. Webber were incorrectly assigned.

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On Aggregation in Ensembles of Multilabel Classifiers 23rd

On Aggregation in Ensembles of Multilabel Classifiers

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23rd International Conference on Discovery science, DS 2020

作者： Nguyen, Vu-Linh Hüllermeier, Eyke Rapp, Michael Loza Mencía, Eneldo Fürnkranz, Johannes Heinz Nixdorf Institute and Department of Computer Science Paderborn University Paderborn Germany Knowledge Engineering Group TU Darmstadt Darmstadt Germany Computational Data Analytics Group JKU Linz Linz Austria

ISBN: (纸本)9783030615260

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases

arXiv

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arXiv 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Mei, Fang Kang, Jiawen Du, Hongyang Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn36830 United States

As a form of artificial intelligence (AI) technology based on interactive learning, deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. However, DRL faces certain limitations, including low sample efficiency and poor generalization. Therefore, we present how to leverage generative AI (GAI) to address these issues above and enhance the performance of DRL algorithms in this paper. We first introduce several classic GAI and DRL algorithms and demonstrate the applications of GAI-enhanced DRL algorithms. Then, we discuss how to use GAI to improve DRL algorithms from the data and policy perspectives. Subsequently, we introduce a framework that demonstrates an actual and novel integration of GAI with DRL, i.e., GAI-enhanced DRL. Additionally, we provide a case study of the framework on UAV-assisted integrated near-field/far-field communication to validate the performance of the proposed framework. Moreover, we present several future directions. Finally, the related code is available at: https://***/GAI-enhanced-DRL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Intelligent Gates Exploring the Massiveness of Random Access Points in NGMA

Intelligent Gates Exploring the Massiveness of Random Access...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sunil Prashanth Kumar P. Aswini H Pal Thethi K Smitha R. Akhilesh Reddy Ali Albawi Department of Mechanical Engineeering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

With the rapid progress of Next-Generation Mobile Architecture come several obstacles in improving user experience and network performance. Active methods frequently face issues in allocating network resources correctly. Consequently this can lead to mismanagement or poor resource utilization affecting how users experience the network. As access requirements increase with the growth of architecture complexity protecting personal data is becoming more challenging. To tackle these issues the Dynamic Access Point Management (DAPM) framework shows great potential as it delivers a creative strategy for resource allocation and network optimization. This model modifies standard resource management practices to become sharper and more adaptable. Thorough examination is needed to confirm DAPM’s superiority over standard methods regarding the resolution of these problems comprehensively. This research will examine the gains in network efficiency and security outcomes from the sophisticated models of DAPM. The detailed methodology seeks to construct a network framework that is both dependable and secure and will boost NGMA’s operational effectiveness and durability.

关键词： data privacy Adaptation models Aerodynamics Dynamic scheduling Throughput User experience Stability analysis Security Resource management Standards

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