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Outcome of a Magnetic Field on Heat Transfer of Carbon Nanotubes (CNTs)-Suspended Nanofluids by Shooting Type Laplace–Adomian Decomposition Method (LADM) 1

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International Conference on Sustainable Building Materials and Construction, ICSBMC 2021

作者： Vennila, B. Nithya, N. Kabilan, M. Department of Mathematics School of Basic Sciences College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9789811684968

ISBN: (纸本)9789811684951

This paper focuses on the impact of carbon nanotubes (CNTs) on nanofluid flow and heat transfer in the occurrence of a magnetic field. Various parameters’ consequences on fluid velocity and temperature have been addressed. To obtain the precise analytic solution for the velocity and temperature distribution, a shooting form Laplace–Adomian decomposition method is used. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Carbon nanotubes

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Vertex Partitions Based Approach to Solve the Graph Colouring Problem

Vertex Partitions Based Approach to Solve the Graph Colourin...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Ajay Shukla Neeraj Kumar Pandey Srabanti Maji Ashok Kumar Roosha Shamoon Computer Science & Engineering Graphic Era Hill University Dehradun India Computer Science & Engineering Graphic Era Deemed to be University Dehradun India School of Computing DIT University Dehradun India Computer Science & Engineering Uttaranchal University Dehradun India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The graph colouring problem is a NP- class problem explored in the literature due to its numerous applications in real world engineering problems. Graph colouring problem involves to assign colours in such a fashion that neighbouring nodes have distinct colours. In this paper our objective is to find the number of distinct colours that can be used to colour the vertices in the graph. During the colouring process the objective is to use the least number of colours corresponding to considered graph. In this work we devise a new colouring algorithm using greedy technique by exploring the adjacent vertices which is simple and effective and can be used to compute the graph colouring problem in more enhanced way. The algorithm starts with the calculation of links of each vertex in the graph and then unexplored highest degree vertex will be selected for computation of adjacent nodes for colouring. Finally, we examined the presented algorithm on standard DIMACS graph colouring instances to evaluate the success of the devised algorithm. The result obtained after running the proposed algorithm for several categories of graphs like myciel, mugg, mulsol, david, huck, jean in terms of number of colours is equal to the chromatic number of the graph instances.

关键词： Color Approximation algorithms Partitioning algorithms Standards

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An Automated SQL Query Grading System Using An Attention-Based Convolutional Neural Network

arXiv

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arXiv 2024年

作者： Schwartz, Donald R. Rivas, Pablo School of Computer Science and Mathematics Department of Computing Technology Marist College New York United States School of Engineering and Computer Science Department of Computer Science Baylor University TX United States

Grading SQL queries can be a time-consuming, tedious and challenging task, especially as the number of student submissions increases. Several systems have been introduced in an attempt to mitigate these challenges, but those systems have their own limitations. This paper describes our novel approach to automating the process of grading SQL queries. Unlike previous approaches, we employ a unique convolutional neural network architecture that employs a parameter-sharing approach for different machine learning tasks that enables the architecture to induce different knowledge representations of the data to increase its potential for understanding SQL statements. Copyright © 2024, The Authors. All rights reserved.

关键词： Grading

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Using Structural Similarity and Kolmogorov-Arnold Networks for Anatomical Embedding of Cortical Folding Patterns

Using Structural Similarity and Kolmogorov-Arnold Networks f...

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IEEE International Symposium on Biomedical Imaging

作者： Minheng Chen Chao Cao Tong Chen Yan Zhuang Jing Zhang Yanjun Lyu Xiaowei Yu Lu Zhang Tianming Liu Dajiang Zhu Department of Computer Science and Engineering University of Texas at Arlington USA Department of Computer Science Indiana University Indianapolis USA School of Computing University of Georgia USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The 3-hinge gyrus (3HG) is a newly defined folding pattern, which is the conjunction of gyri coming from three directions in cortical folding. Many studies demonstrated that 3HGs can be reliable nodes when constructing brain networks or connectome since they simultaneously possess commonality and individuality across different individual brains and populations. However, 3HGs are identified and validated within individual spaces, making it difficult to directly serve as the brain network nodes due to the absence of cross-subject correspondence. The 3HG correspondences represent the intrinsic regulation of brain organizational architecture, traditional image-based registration methods tend to fail because individual anatomical properties need to be fully respected. To address this challenge, we propose a novel self-supervised framework for anatomical feature embedding of the 3HGs to build the correspondences among different brains. The core component of this framework is to construct a structural similarity-enhanced multi-hop feature encoding strategy based on the recently developed Kolmogorov-Arnold network (KAN) for anatomical feature embedding. Extensive experiments suggest that our approach can effectively establish robust cross-subject correspondences when no one-to-one mapping exists. The code is available at ***/m1nhengChen/SSE-CortexEmbed.

关键词： Representation learning Codes Adaptive systems Spread spectrum communication Encoding Regulation Reliability Image reconstruction Biomedical imaging

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Advancing general robotic manipulation with multimodal foundation models: An embodied AI paradigm

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science China(Technological sciences) 2025年第5期68卷 290-292页

作者： Shifeng HUANG He WANG Xing ZHOU Wenkai CHEN Haibin YANG Jianwei ZHANG Department of Mechanical and Aerospace Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Foshan Institute of Intelligent Equipment Technology Foshan 528234China Foshan Huashu Robot Co. Ltd.Foshan 528234China Center on Frontiers of Computing Studies(CFCS) School of Computer SciencePeking UniversityBeijing 100871China Beijing Academy of Artificial Intelligence Beijing 100084China Department of Informatics Universität HamburgHamburg 20146Germany

Can current robotic technologies truly replicate the full scope and intricacies of human labour?In practice,the adoption of robots remains limited,especially in open,unstructured environments commonly encountered in everyday scenarios such as services,healthcare,agriculture,construction,and numerous other *** the perspective of general robotic manipulation,the challenges arise from three factors.(1)High operational barriers:human operators are obliged to master specialized robotic programming languages and gain a deep understanding of the tasks at *** tasks need to be broken down into action-level robotic programs,which results in high labour costs.(2)Limited autonomous task execution:robots lack the capability to independently plan and execute actions required to achieve the target *** limitation renders them unsuitable for deployment in open,unstructured environments that demand sophisticated interaction and seamless collaboration with humans.

关键词： multimodal foundation models autonomous task execution robotic manipulationthe general robotic manipulation robotic programming language embodied AI operational barriers robotic technologies

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Air-Ground Integrated Low-Energy Federated Learning for Secure 6G Communications

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ZTE Communications 2022年第4期20卷 32-40页

作者： WANG Pengfei SONG Wei SUN Geng WEI Zongzheng ZHANG Qiang School of Computer Science and Technology Dalian University of TechnologyDalian 116024China School of Computer Science and Technology Jilin UniversityChangchun 130015China Key Laboratory of Symbolic Computing and Knowledge Engineering Jilin UniversityChangchun 130015China

Federated learning(FL)is a distributed machine learning approach that could provide secure 6G communications to preserve user *** 6G communications,unmanned aerial vehicles(UAVs)are widely used as FL parameter servers to collect and broadcast related parameters due to the advantages of easy deployment and high ***,the challenge of limited energy restricts the populariza⁃tion of UAV-enabled FL *** airground integrated low-energy federated learning framework is proposed,which minimizes the overall energy consumption of application communication while maintaining the quality of the FL ***,a hierarchical FL framework is proposed,where base stations(BSs)aggregate model parameters updated from their surrounding users separately and send the aggregated model parameters to the server,thereby reducing the energy consumption of *** addition,we optimize the deploy⁃ment of UAVs through a deep Q-network approach to minimize their energy consumption for transmission as well as movement,thus improv⁃ing the energy efficiency of the airground integrated *** evaluation results show that our proposed method can reduce the system en⁃ergy consumption while maintaining the accuracy of the FL model.

关键词： federated learning 6G communications privacy preserving secure communication

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Mitigating Multilingual Hallucination in Large Vision-Language Models

arXiv

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arXiv 2024年

作者： Qu, Xiaoye Song, Mingyang Wei, Wei Dong, Jianfeng Cheng, Yu The School of Computer Science & Technology Huazhong University of Science and Technology China The School of Computing Science and Technology Fudan University China The School of Computing Science and Technology Zhejiang Gongshang University China The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

While Large Vision-Language Models (LVLMs) have exhibited remarkable capabilities across a wide range of tasks, they suffer from hallucination problems, where models generate plausible yet incorrect answers given the input image-query pair. This hallucination phenomenon is even more severe when querying the image in non-English languages, while existing methods for mitigating hallucinations in LVLMs only consider the English scenarios. In this paper, we make the first attempt to mitigate this important multilingual hallucination in LVLMs. With thorough experiment analysis, we found that multilingual hallucination in LVLMs is a systemic problem that could arise from deficiencies in multilingual capabilities or inadequate multimodal abilities. To this end, we propose a two-stage Multilingual Hallucination Removal (MHR) framework for LVLMs, aiming to improve resistance to hallucination for both high-resource and low-resource languages. Specifically, in the first stage, considering that most non-English languages can not follow instructions well and output non-sense answers given the input image, we boost multilingual instruction following ability with a multilingual supervised fine-tuning. The second phase is aimed at enhancing the LVLM’s ability to diminish multilingual hallucinations. Instead of relying on the intricate manual annotations of multilingual resources, we fully leverage the inherent capabilities of the LVLM and propose a novel cross-lingual alignment method, which generates multiple responses for each image-query input and then identifies the hallucination-aware pairs for each language. These data pairs are finally used for direct preference optimization to prompt the LVLMs to favor non-hallucinating responses. Experimental results show that our MHR achieves a substantial reduction in hallucination generation for LVLMs. Notably, on our extended multilingual POPE benchmark, our framework delivers an average increase of 19.0% in accuracy across 13 differen

关键词： Visual languages

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Smart Health Diagnosis:Harnessing Data Mining for Predictive Healthcare

Smart Health Diagnosis:Harnessing Data Mining for Predictive...

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Advances in computing, Communication and Networking (ICAC2N), International Conference on

作者： R. Indrakumari Vishal Pandey Ashish Kumar Sinha Ankesh Kumar School of Computer Science & Engineering Galgotias University India School of Computing Application & Technology Galgotias University India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The use of Smart Health Diagnosis: Data Mining for Predictive Healthcare is to provide a user- friendly and easily accessible platform where individuals can get instant health advice through an intelligent online healthcare system. By the analysis of the reported symptoms, the system can predict potential future diseases and provide relevant recommendations to healthcare providers on courses of action. Intelligent methods accurately determine potential interferences based on patient symptoms. Medical record database was used and symptoms were assigned to underlying diseases using Naive Bayesian calculations. Fast, reliable access to health care is a cornerstone of individual and social health but many people face barriers in receiving timely care. We are trying to solve these problems by introducing Health Prediction System, a revolutionary online healthcare solution that uses data mining for quick support and customized recommendations.

关键词： Databases Smart healthcare Prediction algorithms Bayes methods Data mining Reliability Medical diagnostic imaging Faces Diseases

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Information Gerrymandering in Elections 1

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19th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2023, held in conjunction with the 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Liu, Xiaoxue Kato, Shohei Ren, Fenghui Su, Guoxin Zhang, Minjie Gu, Wen School of Computing and Information Technology University of Wollongong Wollongong Australia Department of Computer Science and Engineering Nagoya Institute of Technology Nagoya Japan Center for Innovative Distance Education and Research Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (数字)9789819978557

ISBN: (纸本)9789819978540

When making decisions, individuals often seek advice from their family, friends, and social network connections. In this way, individ-uals’ opinions are susceptible to influence from their connections’ viewpoints. For example, in elections, individuals may initially support one party, while social influence may sway their choice to another. A recent study by Stewart et al. introduced a metric, influence assortment, to quantify the phenomenon of information gerrymandering that one party can influence more voters from other parties by strategically distributing their members among the social networks. While this metric correlates strongly with voting outcomes, the finding is only suitable for two-party elections. In this paper, we define the influence assortment that incorporates the level of similarity (homophily) among neighbours in the social network and extend it to multi-party elections. We examine its ability to predict voting outcomes when all parties initially have equal votes and average degrees. Through simulations using a stochastic model of voting behaviour, we demonstrate that the correlation between the influence gap and vote difference is a strong predictor in both small and large scale-free networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Stochastic systems

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Comparative Analysis of Simulators for IoT Applications in Fog/Cloud computing

Comparative Analysis of Simulators for IoT Applications in F...

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2022 International Conference on Sustainable computing and Data Communication Systems, ICSCDS 2022

作者： Bajaj, Karan Sharma, Bhisham Singh, Raman Chitkara University School of Engineering and Technology Chitkara University Department of Computer Science Engineering Himachal Pradesh India School of Computing Engineering and Physical Sciences University of the West of Scotland Lanarkshire United Kingdom

ISBN: (纸本)9781665478847

In order to make people's life easier, a significant number of Internet of Things (IoT) applications have become a part of a global world. The internet of things is strongly intertwined to cloud computing, and sensor data handling is one of the most urgent issues with cloud computing. Sensors and other cloud-connected smart devices are frequently used to operate IoT-Cloud systems;as a result, these devices generate a large volume of data that must be preserved and processed efficiently. Simulators have the advantage of allowing complex systems to be explored without the requirement to purchase and install physical resources. The purpose of this article is to look at several cloud implementation simulators and see which ones are best for today's IoT demands. With the moving of the trend and computing needs, this paper provides a brief examination of the different most widely used simulators in the research field by categorising the simulators into three categories: cloud, edge, and fog simulators. Finally, various future research direction in accordance with the simulator's evolution is given. © 2022 IEEE.

关键词： Internet of things

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