检索结果-内蒙古大学图书馆

CATIRI:An Efficient Method for Content-and-Text Based Image Retrieval

Journal of Computer science & Technology 2019年第2期34卷 287-304页

作者： Mengqi Zeng Bin Yao Zhi-Jie Wang Yanyan Shen Feifei Li Jianfeng Zhang Hao Lin Minyi Guo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Data and Computer Science Sun Yat-sen University Guangzhou 510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871 China School of Computing University of Utah Salt Lake City 84112 U.S.A. Alibaba Group Hangzhou 311121 China

The combination of visual and textual information in image retrieval remarkably alleviates the semantic gap of traditional image retrieval methods,and thus it has attracted much attention *** retrieval based on such a combination is usually called the content-and-text based image retrieval(CTBIR).Nevertheless,existing studies in CTBIR mainly make efforts on improving the retrieval *** the best of our knowledge,little attention has been focused on how to enhance the retrieval ***,image data is widespread and expanding rapidly in our daily ***,it is important and interesting to investigate the retrieval *** this end,this paper presents an efficient image retrieval method named CATIRI(content-and-text based image retrieval using indexing).CATIRI follows a three-phase solution framework that develops a new indexing structure called *** MHIM-tree seamlessly integrates several elements including Manhattan Hashing,Inverted index,and *** use our MHIM-tree wisely in the query,we present a set of important metrics and reveal their inherent *** on them,we develop a top-k query algorithm for *** results based on benchmark image datasets demonstrate that CATIRI outperforms the competitors by an order of magnitude.

关键词： image retrieval text-and-visual feature indexing top-k

来源：评论

学校读者我要写书评

暂无评论

Approximating Bounded Job Start Scheduling with Application in Royal Mail Deliveries Under Uncertainty 1

引用

13th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2019

作者： Bradley, Jeremy T. Letsios, Dimitrios Misener, Ruth Page, Natasha GBI/Data Science Group Royal Mail London United Kingdom Department of Computing Imperial College London London United Kingdom

ISBN: (数字)9783030364120

ISBN: (纸本)9783030364113

Motivated by mail delivery scheduling problems arising in Royal Mail, we study a generalization of the fundamental makespan scheduling problem which we call the Bounded Job Start Scheduling Problem. Given a set of jobs, each one specified by an integer processing time, that have to be executed non-preemptively by a set of m parallel identical machines, the objective is to compute a minimum makespan schedule subject to an upper bound on the number of jobs that may simultaneously begin per unit of time. We show that Longest Processing Time First (LPT) algorithm is tightly 2-approximate. After proving that the problem is strongly -hard even when, we elaborate on improving the 2-approximation ratio for this case. We distinguish the classes of long and short instances satisfying and, respectively, for each job j. We show that LPT is 5/3-approximate for the former and optimal for the latter. Then, we explore the idea of scheduling long jobs in parallel with short jobs to obtain solutions with tightly satisfied packing and bounded job start constraints. For a broad family of instances excluding degenerate instances with many very long jobs and instances with few machines, we derive a 1.985-approximation ratio. For general instances, we require machine augmentation to obtain better than 2-approximate schedules. Finally, we exploit machine augmentation and a state-of-the-art lexicographic optimization method for under uncertainty to propose a two-stage robust optimization approach for bounded job start scheduling under uncertainty attaining good trade-offs in terms of makespan and number of used machines. We substantiate this approach numerically using Royal Mail data. © 2019, Springer Nature Switzerland AG.

关键词： Scheduling

来源：评论

学校读者我要写书评

暂无评论

Self-explaining deep models with logic rule reasoning 22

Self-explaining deep models with logic rule reasoning

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Seungeon Lee Xiting Wang Sungwon Han Xiaoyuan Yi Xing Xie Meeyoung Cha KAIST School of Computing IBS Data Science Group Social Computing Group Microsoft Research Asia IBS Data Science Group KAIST School of Computing

ISBN: (纸本)9781713871088

We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision. By "human precision", we refer to the degree to which humans agree with the reasons models provide for their predictions. Human precision affects user trust and allows users to collaborate closely with the model. We demonstrate that logic rule explanations naturally satisfy human precision with the expressive power required for good predictive performance. We then illustrate how to enable a deep model to predict and explain with logic rules. Our method does not require predefined logic rule sets or human annotations and can be learned efficiently and easily with widely-used deep learning modules in a differentiable way. Extensive experiments show that our method gives explanations closer to human decision logic than other methods while maintaining the performance of deep learning models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The importance of robust and reliable energy prediction models: Next generation of smart meters 10

The importance of robust and reliable energy prediction mode...

引用

10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2020

作者： Jurado, Sergio Nebot, Àngela Mugica, Francisco Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9789897584442

In this position paper a discussion is performed related to the importance of Energy Prediction Models (EPM) in the context of trading renewable energy in smart grids and the need to develop a new generation of Smart Meters (SMs) based on edge computing. If the electricity currently produced and consumed in the low voltage is expected to grow due to installations of PVs and the electrification of the system, we need to incentivise local energy trading and provide tools to make it possible. Currently, all energy trading mechanisms in the literature heavily rely on predictions, therefore, inaccuracy would be translated in low profitability of prosumer's investments or even worse, disappointment with the local energy markets. To guarantee robustness and reliability of predictions, we propose Flexible FIR as EPM to be used during the trading process and to integrate them in a Next Generation of SMs (NGSMs). In this document some reflections about new functionalities of NGSMs and first steps of a prototype are also addressed. © 2020 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved

关键词： Smart meters

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Fuzzy DBN-Based Broad Learning System for Forecasting ICU Admissions in Post-Transplant COVID-19 Patients 19

Semi-Supervised Fuzzy DBN-Based Broad Learning System for Fo...

引用

19th International Conference on Software Technologies, ICSOFT 2024

作者： Zhang, Xiao Nebot, Àngela Soft Computing Research Group The Intelligent Data Science and Artificial Intelligence Research Center Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9789897587061

This paper introduces a novel semi-supervised neuro-fuzzy system to predict ICU admissions among post-COVID organ transplant recipients. Addressing the challenges of small sample sizes and lacking labels in organ transplantation, our study takes on these issues by proposing a DBN-Based Dual Manifold Regularized Fuzzy Broad Learning System (D-DMR-FBLS). This system utilizes the streamlined and flat architecture of the Broad Learning System (BLS), integrating Deep Belief Networks (DBN) and Takagi-Sugeno-Kang (TSK) systems to enhance representation learning capacities during the Unsupervised Training Phase (UTP). The system combines the strong feature learning capabilities of DBN with the powerful fuzzy rule extraction capacity of the TSK system, enhancing the model’s predictive performance and generalization capability. Moreover, we propose two types of graph-based manifold regularization, sample-based and feature-based, within this novel D-DMR-FBLS framework. Our method enhances its predictive ability by exploiting both the similarity among unlabeled and labeled patient samples, as well as the correlations between features within the fuzzy feature space. Employed to predict ICU admission risks in post-transplant COVID-19 patients, the method has demonstrated superior performance over existing methods, particularly in scenarios with limited samples and labels, thereby providing more accurate decision support for medical professionals in optimizing resource allocation for transplant patients. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Evolution of the Participation of Women in University Computer science Studies in Spain and in Europe 53

Evolution of the Participation of Women in University Comput...

引用

53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Nebot, Àngela Mugica, Francisco Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politécnica de Catalunya Barcelona Spain

ISBN: (纸本)9798350336429

This paper presents a comprehensive examination of the current state of underrepresentation of women in computer science (CS) degrees in Spain. It offers an overview of the situation across autonomous communities in Spain, highlighting the evolving trends within the country, regional variations, and the distinctions between public and private universities. The research employs descriptive statistical methods to analyze data obtained from the Spanish Ministry of Universities, Eurostat, as well as recent literature reviews. Furthermore, the study contextualizes the evolution of female enrollments in Spain by comparing it with data from other European countries. The primary objective is to gain a thorough understanding of the broader Spanish landscape and suggest future research directions aimed at reducing the gender gap in CS studies. © 2023 IEEE.

关键词： computer science degree descriptive statistical methods gender imbalance

来源：评论

学校读者我要写书评

暂无评论

A NOVEL RULE-BASED OVERSAMPLING APPROACH FOR IMBALANCED data CLASSIFICATION 37

A NOVEL RULE-BASED OVERSAMPLING APPROACH FOR IMBALANCED DATA...

引用

37th Annual European Simulation and Modelling Conference, ESM 2023

作者： Zhang, Xiao Paz, Iván Nebot, Àngela Soft Computing Research Group Intelligent Data Science and Artificial Intelligence Research Center Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9789492859280

When confronted with imbalanced datasets, traditional classifiers frequently struggle to correctly categorize samples from the minority class, adversely impacting the overall predictive performance of machine learning models. Current oversampling techniques generally focus on data interpolation through neighbor selection, often neglecting to uncover underlying data structures and relationships. This study introduces a novel application for RuLer, an algorithm originally developed for identifying sound patterns in the artistic domain of live coding. When adapted for data oversampling (as Ad-RuLer), the algorithm shows significant promise in addressing the challenges associated with imbalanced class distribution. We undertake a thorough comparative evaluation of Ad-RuLer against established oversampling algorithms such as SMOTE, ADASYN, Tomek-links, Borderline-SMOTE, and KmeansSMOTE. The evaluation employs various classifiers including logistic regression, random forest, and XGBoost, and is conducted over six real-world biomedical datasets with varying degrees of imbalance. © 2023 ESM. All Rights Reserved.

关键词： Classification data synthesis Imbalanced data Oversampling Rule-based approach

来源：评论

学校读者我要写书评

暂无评论

Two layers of annotation for representing event mentions in news stories 11

Two layers of annotation for representing event mentions in ...

引用

11th Linguistic Annotation Workshop, LAW 2017

作者： Di Buono, Maria Pia Tutek, Martin Šnajder, Jan Glavaš, Goran Bašić, Bojana Dalbelo Milić-Frayling, Nataša TakeLab Faculty of Electrical Engineering and Computing University of Zagreb Croatia Data and Web Science Group University of Mannheim Germany School of Computer Science University of Nottingham United Kingdom

ISBN: (纸本)9781945626395

In this paper, we describe our preliminary study of methods for annotating event mentions as part of our research on highprecision models for event extraction from news. We propose a two-layer annotation scheme, designed to capture the functional and the conceptual aspects of event mentions separately. We hypothesize that the precision can be improved by modeling and extracting the different aspects of news events separately, and then combining the extracted information by leveraging the complementarities of the models. We carry out a preliminary annotation using the proposed scheme and analyze the annotation quality in terms of inter-annotator agreement. © 2017 Association for Computational Linguistics.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Retrofitting Word Embeddings with the UMLS Metathesaurus for Clinical Information Extraction

Retrofitting Word Embeddings with the UMLS Metathesaurus for...

引用

2018 IEEE International Conference on Big data, Big data 2018

作者： Alawad, Mohammed Hasan, S.M. Shamimul Blair Christian, J. Tourassi, Georgia Biomedical Sciences Engineering and Computing Group Health Data Science Institute Oak Ridge National Laboratory Oak RidgeTN37831 United States

ISBN: (纸本)9781538650356

Deep learning has surged in popularity and proven to be effective for various artificial intelligence applications including information extraction from cancer pathology reports. Since word representation is a core unit that enables deep learning algorithms to understand words and be able to perform NLP, this representation must include as much information as possible to help these algorithms achieve high classification performance. Therefore, in this work in addition to the distributional information of words in large sized corpora, we use UMLS vocabulary resources to enrich the vector space representation of words with the semantic relations between words. These resources provide many terminologies pertaining to cancer. The refined word embeddings are used with a convolutional neural (CNN) model to extract four data elements from cancer pathology reports;ICD-O-3 tumor topography codes, tumor laterality, behavior, and histological grade. We observed that using UMLS vocabulary resources to enrich word embeddings of CNN models consistently outperformed CNN models without pre-training word embeddings and even with pre-trained word embeddings on a domain specific corpus across all four tasks. The results show marginal improvement on the laterality task, but a significant improvement on the other tasks, especially for the macro-f score. Specifically, the improvements are 3%, 13%, and 15% for tumor site, histological grade, and behavior tasks, respectively. This approach is encouraging to enrich word embeddings with more clinical data resources to be used for information abstraction tasks from clinical pathology reports. © 2018 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Predicting news values from headline text and emotions 2

Predicting news values from headline text and emotions

引用

EMNLP 2017 2nd Workshop on Natural Language Processing Meets Journal., NLPmJ 2017

作者： Di Buono, Maria Pia Šnajder, Jan Bašic, Bojana Dalbelo Glavaš, Goran Tutek, Martin Milic-Frayling, Natasa TakeLab Faculty of Electrical Engineering and Computing University of Zagreb Croatia Data and Web Science Group University of Mannheim Germany School of Computer Science University of Nottingham United Kingdom

ISBN: (纸本)9781945626883

We present a preliminary study on predicting news values from headline text and emotions. We perform a multivariate analysis on a dataset manually annotated with news values and emotions, discovering interesting correlations among them. We then train two competitive machine learning models - an SVM and a CNN - to predict news values from headline text and emotions as features. We find that, while both models yield a satisfactory performance, some news values are more difficult to detect than others, while some profit more from including emotion information. © EMNLP *** right reserved.

关键词： Forecasting

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：