检索结果-内蒙古大学图书馆

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shubham Gupta Nandini Saini Suman Kundu Chiranjoy Chattopadhyay Debasis Das Computer Science & Engineering Indian Institute of Technology Jodhpur Rajasthan India School of Computing and Data Sciences FLAME University Pune Maharashtra India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

As remote sensing capabilities continue to advance, there is a growing interest in leveraging computer vision and natural language processing for enhanced interpretation of remote sensing scenes. This paper explores the integration of textual information with images to augment traditional disaster classification methods. Our approach utilizes a predefined vision-language model to generate descriptive captions for images, fostering a more nuanced understanding of the remote sensing data. Next, we seamlessly integrate the generated textual information with image data through multimodal training, employing a multimodal deep learning method for disaster classification. The system categorizes input data into predefined disaster categories, presenting a comprehensive and accurate approach to emergency response system development. Experimental evaluations conducted on the AIDER dataset (Aerial Image database for Emergency Response applications) showcase the efficacy of our approach, demonstrating improved accuracy compare to unimodal approach and reliability in disaster classification. This research contributes to the advancement of intelligent emergency response systems by harnessing the synergy between vision and language in the context of remote sensing.

关键词： Training Accuracy Social networking (online) Image databases Disasters Emergency services Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Automatic Smart Crawling on Twitter for Weather Information in Indonesia

引用

Procedia computer science 2023年 227卷 795-804页

作者： Kartika Purwandari Reza Bayu Perdana Join W.C. Sigalingging Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta 10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate.

关键词： Social media Twitter SVM smart crawling weather

来源：评论

学校读者我要写书评

暂无评论

Distance Magic Labeling of the Halved Folded n-Cube 16th

Distance Magic Labeling of the Halved Folded n-Cube

引用

16th International Conference on Algorithmic Aspects in Information and Management, AAIM 2022

作者： Tian, Yi Kang, Na Wu, Weili Du, Ding-Zhu Gao, Suogang School of Mathematical Sciences Hebei Normal University Shijiazhuang050024 China School of Big Data Science Hebei Finance University Baoding071051 China School of Mathematics and Science Hebei GEO University Shijiazhuang050024 China Department of Computer Science University of Texas at Dallas RichardsonTX75080 United States Hebei International Joint Research Center for Mathematics and Interdisciplinary Science Shijiazhuang050024 China

ISBN: (纸本)9783031160806

Hypercube is an important structure for computer networks. The distance plays an important role in its applications. In this paper, we study a magic labeling of the halved folded n-cube which is a variation of the n-cube. This labeling is determined by the distance. Let G be a finite undirected simple connected graph with vertex set V(G), distance function ∂ and diameter d. Let D⊆ { 0, 1, ⋯, d} be a set of distances. A bijection l: V(G) → { 1, 2, ⋯, | V(G) | } is called a D-magic labeling of G whenever ∑x∈GD(v)l(x) is a constant for any vertex v∈ V(G), where GD(v) = { x∈ V(G) : ∂(x, v) ∈ D}. A { 1 } -magic labeling is also called a distance magic labeling. We show that the halved folded n-cube has a distance magic labeling (resp. a { 0, 1 } -magic labeling) if and only if n= 16 q2 (resp. n= 16 q2+ 16 q+ 6 ), where q is a positive integer. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Undirected graphs

来源：评论

学校读者我要写书评

暂无评论

Finding Top-k Important Edges on Bipartite Graphs: Ego-betweenness Centrality-based Approaches

Finding Top-k Important Edges on Bipartite Graphs: Ego-betwe...

引用

International Conference on data Engineering

作者： Tongfeng Weng Xu Zhou Yixiang Fang Kian-Lee Tan Kenli Li College of Computer Science and Electronic Engineering Hunan University China School of Data Science The Chinese University of Hong Kong Shenzhen School of Computing National University of Singapore Singapore

Bipartite graph is an important data structure that widely exists in disease prevention and control, community detection, and other real-life applications. In a bipartite graph, edges not only connect entries of different types but also are bridges of different communities in the above applications. However, research to date has not yet focused on edge importance in bipartite graphs. Inspired by this, we study a new problem of top-k edge search in bipartite graphs with the goal of finding k most important edges for a given bipartite graph; these edges are crucial bridges among communities. In particular, we introduce the measure of ego-betweenness for evaluating the importance of edges. To handle this problem effectively, a lazy bound-based algorithm is first proposed by integrating an upper bound pruning strategy. After that, to further get better efficiency, a greedy bound-based heuristic algorithm is explored on the basis of a tighter upper bound which contributes to reducing redundant computation for calculating ego-betweenness. Last but not least, two parallel techniques with different levels of granularity, called P -src and P -task, are respectively introduced to further improve the search efficiency. The experimental results on both real-world and synthetic graphs demonstrate the efficiency and scalability of the proposed algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Contrastive Graph Auto-Encoder for Graph Embedding

SSRN

引用

SSRN 2023年

作者： Zu, Shuaishuai Li, Li Shen, Jun Tang, Weitao School of Computer and Information Science Southwest University China School of Computing and Information Technology University of Wollongong Australia Department of Data Science & Artificial Intelligence Monash University Australia

Graph embedding aims to embed the information of graph data into low-dimensional representation space. Prior methods generally suffer from an imbalance of preserving structural information and node features due to their pre-defined inductive biases, leading to unsatisfactory generalization performance. In order to preserve the maximal information, graph contrastive learning (GCL) has become a prominent technique for learning discriminative embeddings. However, in contrast with graph-level embeddings, existing GCL methods generally learn less discriminative node embeddings in a self-supervised way. In this paper, we ascribe above problem to two challenges: 1) graph data augmentations, which are designed for generating contrastive representations, hurt the original semantic information for nodes. 2) the nodes within the same cluster are selected as negative samples. To alleviate these challenges, we propose Contrastive Graph Auto-Encoder (CGAE) and Contrastive Variational Graph Auto-Encoder (CVGAE). Specifically, we first propose two distribution-dependent regularizations to guide the paralleled encoders to generate contrastive representations following similar distribution, followed by theoretical derivations to verify the equivalence of the above regularizations. Then, we utilize truncated triplet loss, which only selects top-k nodes as negative samples, to avoid over-separate nodes affiliated to the same cluster. Furthermore, we give theoretical analysis of the effectiveness of our models. Experiments on several real-world datasets show that our models advanced performance over all baselines in link prediction, node clustering, and graph visualization tasks. © 2023, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Towards Harmless Rawlsian Fairness Regardless of Demographic Prior

arXiv

引用

arXiv 2024年

作者： Wang, Xuanqian Li, Jing Tsang, Ivor W. Ong, Yew-Soon School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair. © 2024, CC BY.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Vibration analysis of elastically supported sandwich beam with damaged core and functionally graded face sheets

引用

Steel and Composite Structures 2025年第1期54卷 71-83页

作者： Wang, Lulu Mohsin, Hasan F. Bains, Pardeep Singh Sharma, Rohit Bohlooli, Nasrin College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad10011 Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf54001 Iraq University Karnataka Bengaluru560069 India Department of Mechanical Engineering Vivekananda Global University Rajasthan Jaipur303012 India School of Engineering and Technology Shobhit University Uttar Pradesh Gangoh247341 India Department of Mechanical Engineering Arka Jain University Jharkhand Jamshedpur831001 India Nabi Data Science & Computational Intelligence Research Co. Tehran Iran

This paper presents the influence of carbon nanotubes (CNTs) waviness, aspect ratio and damaged core on the vibrational behavior of functionally graded nanocomposite sandwich beams resting on two-parameter elastic foundations. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness of upper and bottom layers of the sandwich beam and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes’ effective aspect ratio as the filler content increases. A damage model is introduced to provide an analytical description of an irreversible rheological process that causes the decay of the mechanical properties, in terms of engineering constants. An isotropic damage is considered for the core of the sandwich beams. The equations of motion are derived based on Timoshenko beam theory and employing Hamilton’s principle. The problem is modeled using a semi-analytical approach composed of generalized differential quadrature method (GDQM) and series solution adopted to solve the equations of motion. Detailed parametric studies are carried out to investigate the mechanical behavior of these multi-layered structures depending on the damage features, through-the-thickness distribution and boundary conditions. Also, the effects of carbon nanotubes (CNTs) waviness, CNT aspect ratio, Winkler foundation modulus, shear elastic foundation modulus and geometrical conditions on the vibrational behavior of the sandwich structure are studied. © 2025 Techno-Press, Ltd.

关键词： Aspect ratio

来源：评论

学校读者我要写书评

暂无评论

GIDCL: A Graph-Enhanced Interpretable data Cleaning Framework with Large Language Models

引用

Proceedings of the ACM on Management of data 2024年第6期2卷 1-29页

作者： Mengyi Yan Yaoshu Wang Yue Wang Xiaoye Miao Jianxin Li School of Computer Science and Engineering Beihang University Beijing CN Shenzhen Institute of Computing Sciences Shenzhen University Shenzhen Guangdong CN Shenzhen Institute of Computing Sciences Shenzhen Guangdong CN Center for Data Science Zhejiang University Hangzhou Zhejiang CN

data quality is critical across many applications. The utility of data is undermined by various errors, making rigorous data cleaning a necessity. Traditional data cleaning systems depend heavily on predefined rules and constraints, which necessitate significant domain knowledge and manual effort. Moreover, while configuration-free approaches and deep learning methods have been explored, they struggle with complex error patterns, lacking interpretability, requiring extensive feature engineering or labeled data. This paper introduces GIDCL (Graph-enhanced Interpretable data Cleaning with Large language models), a pioneering framework that harnesses the capabilities of Large Language Models (LLMs) alongside Graph Neural Network (GNN) to address the challenges of traditional and machine learning-based data cleaning methods. By converting relational tables into graph structures, GIDCL utilizes GNN to effectively capture and leverage structural correlations among data, enhancing the model's ability to understand and rectify complex dependencies and errors. The framework's creator-critic workflow innovatively employs LLMs to automatically generate interpretable data cleaning rules and tailor feature engineering with minimal labeled data. This process includes the iterative refinement of error detection and correction models through few-shot learning, significantly reducing the need for extensive manual configuration. GIDCL not only improves the precision and efficiency of data cleaning but also enhances its interpretability, making it accessible and practical for non-expert users. Our extensive experiments demonstrate that GIDCL significantly outperforms existing methods, improving F1-scores by 10% on average while requiring only 20 labeled tuples.

关键词： data quality graph neural network interpretable large language models

来源：评论

学校读者我要写书评

暂无评论

Reliable Placement of Erasure-coded data in Distributed Cloud data Centers under Disaster Risks: A Multi-Objective Optimization Approach 23

Reliable Placement of Erasure-coded Data in Distributed Clou...

引用

Proceedings of the 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence

作者： Shuxian Wu Junqi Chen Yong Wang School of Computer Science and Information Security Guilin University of Electronic Technology China School of Computer Science and Information Security Guilin University of Electronic Technology China and Guangxi Educational Big Data and Cyberspace Collaborative Innovation Center Guilin University of Electronic Technology China

ISBN: (纸本)9798400716485

Geographically distributed cloud data centers are increasingly favored by internet companies and governments, attributed to their superior storage and computational capabilities. However, while data redundancy techniques, especially erasure coding, play a foundational role in ensuring data reliability, the profound threat of natural disasters challenges the resilience of such centers. This paper investigates the placement of erasure-coded data considering disaster risks to enhance the reliability of cloud storage systems in distributed *** introduce a disaster-aware approach for erasure-coded data placement, aiming to avoid areas susceptible to natural disasters, thus reducing potential damages. Central to this approach is a multi-objective optimization model specifically tailored for the nuances of erasure-coded data. It counters three main threats: the direct damage from disasters compromising data reliability; the potential decline in overall reliability due to uneven disaster exposures across a data set; and data unavailability from communication disruptions between erasure-coded blocks. To obtain an optimized solution, we employ the NSGA-II algorithm. The experimental results demonstrate that, under the circumstances of natural disasters, our method ensures greater reliability and availability of erasure-coded data compared to the random placement approach that disregards disaster risks and another method we introduced for comparison, which only optimizes for a single objective while considering disaster *** our best knowledge, this study is the first exploration in ensuring the reliable placement of erasure-coded data within distributed cloud data centers under disaster risk.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A MCDM-based framework for selection of photovoltaic cell technology using novel information measure under Pythagorean fuzzy environment

引用

International Journal of Information Technology (Singapore) 2023年第8期15卷 4233-4242页

作者： Suman Sonia Jasrotia, Rohit Singh, Sunil Pratap Department of Mathematics School of Basic and Applied Sciences Maharaja Agrasen University Himachal Pradesh Baddi India Yogananda School of AI Computers and Data Sciences Shoolini University Himachal Pradesh Solan India School of Physics and Materials Science Shoolini University Himachal Pradesh Solan India Bharati Vidyapeeth’s Institute of Computer Applications and Management New Delhi India

With the drastic reduction in natural resource reserves, renewable energy alternatives have emerged as a clean source of energy. Photovoltaic technology (PV) is the rapidly emerging renewable energy technology because it has the capacity to directly convert the sunlight into electricity, and it paves the way for a low-carbon world. In general, the selection of a suitable PV technology, out of five accessible PV technologies, is considered as a complex multiple-criteria decision making (MCDM) problem, which encompasses the appropriate evaluation of technologies on various criteria, and decision maker’s (DMs) judgements. In current study, firstly, we integrate the Pythagorean Fuzzy (PF) information measure with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to a modified novel MCDM-based approach. Then, using a case study of PV technology selection, the feasibility, efficacy, and practicability of the proposed approach are evaluated through information measure under Pythagorean Fuzzy Set (PFS). Furthermore, a comparative study has been provided to demonstrate the novelty and originality of our proposed approach. The developed MCDM-based approach gives sustainable choices to both technology selection as well as government policy supporters for the creation of low carbon society. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Information measure Low carbon society creation Multiple-criteria decision making Photovoltaic technology selection Pythagorean fuzzy set

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：