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International Conference on Advances in Information Technology (ICAIT)

作者： N. Duraimutharasan N.Venkatesvara Rao N. Poongavanam K.V. Kanimozhi S.P. Manikandan Faculty of Advanced Computing Sciences AMET UNIVERSITY Chennai Tamil Nadu India Department of Computer Science and Engineering J.N.N.INSTITUTE OF ENGINEERING Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India School of Engineering and Technology CMR UNIVERSITY Bengaluru Karnataka

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Cybersecurity still remains an all-time issue before the technological age as the enterprises continue to deal with the evolving nature of the threats arising every day. Traditional cybersecurity approaches are becoming deficient in the situation of constantly improving, swiftly mutating threats, which leads to the revision and refocus of the current approaches to the preventative reactivity type. For that, machine learning (ML) has come into the arena as a strong tool for improving cybersecurity capacity through the.. p roactive” recognition and the “mitigation” of unknown threats. The focus of this paper is the function of ML in cyber security. Here is considered anomaly detection, malware classification, threat intelligence analysis, and security form adversarial attacks. We conduct literature search, detail a novel research methodology embracing data acquisition, preprocessing, ML model training, and deployment. We present experiments versus conventional security approaches from different ML domains used confirming the superiority in cybersecurity of the presented models. Moreover, resistance to chicanery such as adversarial attacks and the models understandability are discussed. Next, research vector and innovation directions in the ML cybersecurity are highlighted. As a whole, this study provides the example of how ML can strengthen previous alliance strategies to cybersecurity and prevent the risks associated with interchanging cyber threats.

关键词： Deep learning Heuristic algorithms Prevention and mitigation Malware Data models Real-time systems computer crime Anomaly detection Monitoring Resilience

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Estimating Multiple Socioeconomic Attributes via Home Location-A Case Study in China

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Journal of Social computing 2021年第1期2卷 71-88页

作者： Shichang Ding Xin Gao Yufan Dong Yiwei Tong Xiaoming Fu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 276800China Department of Sociology Tsinghua UniversityBeijing 100085China Institute of Computer Science University of GottingenGottingen 37077Germany Shanghai Hejin Information Technology Company Shanghai 200100China

Inferring people’s Socioeconomic Attributes(SEAs),including income,occupation,and education level,is an important problem for both social sciences and many networked applications like targeted advertising and personalized *** works mainly focus on estimating SEAs from peoples’cyberspace behaviors and relationships,such as the content of tweets or the social networks between online *** cyberspace data,alternative data sources about users’physical behavior,like their home location,may offer new *** specifically,in this paper,we study how to predict a person’s income level,family income level,occupation type,and education level from his/her home *** a case study,we collect people’s home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in *** further enrich home location with the knowledge from real estate websites,government statistics websites,online map services,*** learn a shared representation from input features as well as attribute-specific representations for different SEAs,we propose H2SEA,a factorization machine-based multi-task learning method with attention *** experiment results show that:(1)Home location can clearly improve the estimation accuracy for all SEA prediction tasks(e.g.,80.2%improvement in terms of F1-score in estimating personal income level);(2)The proposed H2SEA model outperforms alternative models for SEA inference in terms of various evaluation metrics,such as Area Under Curve(AUC),F-measure,and specificity;(3)The performance of specific SEA prediction tasks(e.g.,personal income)can be further improved if H2SEA only focuses on cities or villages due to urban-rural gap in China;(4)Compared with online crawled housing price data,the area-level average income and Points Of Interest(POI)are more important features for SEA inferences in China.

关键词： personal income family income occupation education multi-task learning

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Details Enhancement in Unsigned Distance Field Learning for High-fidelity 3D Surface Reconstruction 39

Details Enhancement in Unsigned Distance Field Learning for ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Cheng Hou, Fei Wang, Wencheng Qin, Hong Zhang, Zhebin He, Ying State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China School of Advanced Interdisciplinary Sciences University of Chinese Academy of Sciences China Department of Computer Science Stony Brook University United States InnoPeak Technology United States College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)157735897X

While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures. Despite their flexibility, UDFs encounter significant challenges in high-fidelity 3D reconstruction, such as non-differentiability at the zero level set, difficulty in achieving the exact zero value, numerous local minima, vanishing gradients, and oscillating gradient directions near the zero level set. To address these challenges, we propose Details Enhanced UDF (DEUDF) learning that integrates normal alignment and the SIREN network for capturing fine geometric details, adaptively weighted Eikonal constraints to address vanishing gradients near the target surface, unconditioned MLP-based UDF representation to relax non-negativity constraints, and DCUDF for extracting the local minimal average distance surface. These strategies collectively stabilize the learning process from unoriented point clouds and enhance the accuracy of UDFs. Our computational results demonstrate that DEUDF outperforms existing UDF learning methods in both accuracy and the quality of reconstructed surfaces. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Surface reconstruction

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Global Reliable Diagnosis of Networks Based on Self-Comparative Diagnosis Model and G-Good-Neighbor Property

SSRN

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SSRN 2024年

作者： Wang, Mujiangshan Xu, Shuhao Jiang, Jincheng Xiang, Dong Hsieh, Sun-Yuan Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Software Tsinghua University Beijing100084 China Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Guangdong Academy of Agricultural Sciences China

In the research field of system-level diagnosis, people used to focus on studying different diagnosabilities with restrictions. By studying this, reliability measured by these diagnosabilities of various of networks is obtained. However, for the decentralized parallel computing systems, there are always upper bonds for diagnosabilities, which means there exists an undiagnosable subnetwork cut off by a faulty vertex cut if the subnetwork is large enough. In this paper, we set up a 2-stage plan for the global reliable diagnosis of networks combining Self-Comparative diagnosis (SC) model and the g-good-neighbor property, which means every vertex in the networks is diagnosed with high accuracy. Six algorithms are proposed for the realization of the plan and diagnosis model for g≥2. In the end, we code a simulator for the plan and test how well it works and the codes are provided via Github link for readers' check. © 2024, The Authors. All rights reserved.

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A Dual-mode Local Search Algorithm for Solving the Minimum Dominating Set Problem

arXiv

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arXiv 2023年

作者： Zhu, Enqiang Zhang, Yu Wang, Shengzhi Strash, Darren Liu, Chanjuan Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Department of Computer Science Hamilton College ClintonNY13323 United States School of Computer Science and Technology Dalian University of Technology Dalian116024 China

Given a graph, the minimum dominating set (MinDS) problem is to identify a smallest set D of vertices such that every vertex not in D is adjacent to at least one vertex in D. The MinDS problem is a classic NP-hard problem and has been extensively studied because of its many disparate applications in network analysis. To solve this problem efficiently, many heuristic approaches have been proposed to obtain a good solution within an acceptable time limit. However, existing MinDS heuristic algorithms are always limited by various tie-breaking cases when selecting vertices, which slows down the effectiveness of the algorithms. In this paper, we design an efficient local search algorithm for the MinDS problem, named DmDS—a dual-mode local search framework that probabilistically chooses between two distinct vertex-swapping schemes. We further address limitations of other algorithms by introducing vertex selection criterion based on the frequency of vertices added to solutions to address tie-breaking cases, and a new strategy to improve the quality of the initial solution via a greedy-based strategy integrated with perturbation. We evaluate DmDS against the state-of-the-art algorithms on seven datasets, consisting of 346 instances (or families) with up to tens of millions of vertices. Experimental results show that DmDS obtains the best performance in accuracy for almost all instances and finds much better solutions than state-of-the-art MinDS algorithms on a broad range of large real-world graphs. Copyright © 2023, The Authors. All rights reserved.

关键词： Heuristic algorithms

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A Hybrid Approach Integrating Deep Fuzzy Dual Support Vector Regression with Evolutionary Computation and Ensemble Learning

A Hybrid Approach Integrating Deep Fuzzy Dual Support Vector...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Jagendra Singh Neeraj Kamal Upreti Sonali Gaur A. K. Shrivastav Mohit Tiwari Computer Science Engineering Alliance School of Advanced Computing Alliance University Banglore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science CHRIST (Deemed to be University) Ghaziabad India IILM Institute for Higher Education New Delhi India Department of Mathematics & Humanities MMEC Maharishi Markandashwar (Deemed University) Ambala India Dept. of Computer Science & Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

This research endeavors to revolutionize the precision of photovoltaic (PV) power generation predictions through the introduction of a novel Hybrid Ensembled Model. Motivated by the imperative to enhance the reliability of renewable energy technologies, the study amalgamates the strengths of fuzzy logic, support vector regression, ensemble learning, and evolutionary computation. The model's design addresses the inherent challenges of PV power generation prediction, notably uncertainties and non-linear dynamics. The investigation unfolds in distinct phases, beginning with a meticulous exploration of the significance of precise PV power generation prediction. Acknowledging the limitations of existing methodologies, the research establishes a robust foundation for the development of the Hybrid Ensembled Model. Methodologically, the incorporation of Double-Input-Fuzzy-Modules (DIFM), Extreme Learning Machine (ELM), and evolutionary computation ensures a comprehensive and adaptive framework. This research propels the discourse on renewable energy prediction models, presenting a Hybrid Ensembled Model as a pioneering solution for precise and reliable PV power generation forecasts. The implications extend beyond academia, envisioning a future where sustainable energy systems are underpinned by accurate predictions, fostering optimal resource utilization and contributing to a resilient and sustainable energy landscape.

关键词： Fuzzy logic Renewable energy sources Adaptation models Computational modeling Electricity Evolutionary computation Predictive models Hybrid power systems Reliability Ensemble learning

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Stitch-avoiding Global Routing for Multiple E-Beam Lithography

Stitch-avoiding Global Routing for Multiple E-Beam Lithograp...

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International Conference on VLSI Design

作者： Kritanta Saha Sudipta Paul Pritha Banerjee Susmita Sur-Kolay Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India Department of Computer Science and Engineering University of Calcutta Kolkata India

ISBN: (数字)9781665485050

ISBN: (纸本)9781665496612

In Multiple E-Beam Lithography (MEBL), a layout of an IC is partitioned into vertical stripes whose boundaries are called stitch-lines. Routing patterns in each stripe are written by different beams and/or in different writing passes. However, patterns cut by stitch-lines suffer from overlay errors resulting in severe pattern distortions. These distortions are mainly due to Via violation, Vertical Routing violation, or Short Polygon. In this paper, we have proposed a Stitch-avoiding Global Router. The proposed method selects the best possible global routing path for each net so that even a traditional detailed router can generate a Detailed Routing solution with fewer violations. Experimental results show that our proposed global router followed by a conventional detailed router generates solutions with 17.3%, 6.21% and 1.89% reduction in Via violations, Short polygon violations and routed wirelength respectively with no vertical routing violations, compared to those by traditional global routing.

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Multilevel Monte Carlo Methods for the Grad-Shafranov Free Boundary Problem

SSRN

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SSRN 2023年

作者： Elman, Howard C. Liang, Jiaxing Sánchez-Vizuet, Tonatiuh Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States Applied Mathematics & Statistics and Scientific Computing Program University of Maryland College Park United States Department of Mathematics The University of Arizona United States

The equilibrium configuration of a plasma in an axially symmetric reactor is described mathematically by a free boundary problem associated with the celebrated Grad-Shafranov equation. The presence of uncertainty in the model parameters introduces the need to quantify the variability in the predictions. This is often done by computing a large number of model solutions on a computational grid for an ensemble of parameter values and then obtaining estimates for the statistical properties of solutions. In this study, we explore the savings that can be obtained using multilevel Monte Carlo methods, which reduce costsby performing the bulk of the computations on a sequence of spatial grids that are coarser than the one that would typically be used for a simple Monte Carlo simulation. We examine this approach using both a set of uniformly refined grids and a set of adaptively refined grids guided by a discrete error estimator. Numerical experiments show that multilevel methods dramatically reduce the cost of simulation, with cost reductionstypically on the order of 60 or more and possibly as large as 200. Adaptive gridding results in more accurate computation of geometric quantities such as x-points associated with the model. © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

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A Machine Learning Approach to Predict Skin Diseases and Treatment Recommendation System

A Machine Learning Approach to Predict Skin Diseases and Tre...

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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Suganiya Murugan S.R. Srividhya S. Pradeep Kumar B. Rubini Department of Computing Technologies SRM Inst itute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Elect rical and Elect ronics Engineering Vels Inst itute of Science Technology and Advanced Studies Chennai India

In medical science, dermatology is the department dedicated to identify, diagnose, and suggest a treatment for skin disease. Diseases that occur on human skin is mostly unpredictable and tough to analyze, in recent days, the occurrence of skin diseases increased unprecedently. Skin diseases are usually caused by bacteria or other related infections. Some skin diseases like ringworm, yeast infection, allergies etc. will constantly increase and spread over the skin. This type of disease should be identified in its earlier stage to avoid spreading. The identification can be done by considering some factors like clinical parameters, which are considered for identifying the disease. The possible skin diseases are dermatitis for the age group 0-5 years, warts affect 6-11 years old children, and acne vulgaris affect 12-16 years. Dermatomyositis is a type of skin disease that affects children aged 5-15 and adults aged 40-60. Due to unawareness, patients usually ignore the early symptoms. This necessitates the need for skin disease prediction. This research work predicts the type of disease by using the clinical parameter of a patient, which is achieved by using various machine learning classification algorithms. Maximum accuracy of 98.4% is achieved by the Naive Bayes algorithm. The Graphical User Interface (GUI) was developed for easy utilization of this prediction module for both doctors and dermatologists.

关键词： Pediatrics Microorganisms Machine learning Aging Prediction algorithms Skin Naive Bayes methods

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Multilevel Monte Carlo methods for the Grad-Shafranov free boundary problem

arXiv

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arXiv 2023年

The equilibrium configuration of a plasma in an axially symmetric reactor is described mathematically by a free boundary problem associated with the celebrated Grad-Shafranov equation. The presence of uncertainty in the model parameters introduces the need to quantify the variability in the predictions. This is often done by computing a large number of model solutions on a computational grid for an ensemble of parameter values and then obtaining estimates for the statistical properties of solutions. In this study, we explore the savings that can be obtained using multilevel Monte Carlo methods, which reduce costs by performing the bulk of the computations on a sequence of spatial grids that are coarser than the one that would typically be used for a simple Monte Carlo simulation. We examine this approach using both a set of uniformly refined grids and a set of adaptively refined grids guided by a discrete error estimator. Numerical experiments show that multilevel methods dramatically reduce the cost of simulation, with cost reductions typically on the order of 60 or more and possibly as large as 200. Adaptive gridding results in more accurate computation of geometric quantities such as x-points associated with the *** Codes 65Z05, 65C05, 62P35, 35R35, 35R60 © 2023, CC BY.

关键词： Monte Carlo methods

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