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检索条件"机构=School of Computer Scinece and Engineering"

共 23 条记录，以下是1-10 订阅

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Combining Band-Limited OTSDF Filter and Directional Representation for Palmprint Recognition 16th

Combining Band-Limited OTSDF Filter and Directional Represen...

引用

16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Hou, Chaoxiang Jia, Wei School of Computer Scinece and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9783031202322

Correlation filter is one of important and powerful tools in pattern recognition. Recently, the method of band-limited phase-only correlation (BLPOC) has been successfully used for biometrics. Motivated by the method of BLPOC, we propose the band-limited optimal tradeoff synthetic discriminant function (BLOTSDF) filter in this paper. Compared with BLPOC and original OTSDF filter, BLOTSDF filter has faster matching speed and can achieve better recognition performance. We then propose a method combining BLOTSDF filter and directional representation (DR) for palmprint recognition. Since DR is insensitive to illumination changes and contains rich information and the BLOTSDF filter has good recognition ability, the proposed method can achieve promising recognition performance, which is comparable with that of other state-of-the-art methods. Particularly, the matching speed of the proposed method is about 20 times faster than that of several classical directional coding algorithms, which is very suitable for industrial applications. The experiments results obtained on two palmprint databases validate the effectiveness of the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biometrics

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Benchmarking Ontology-Based Query Rewriting Systems 26

Benchmarking Ontology-Based Query Rewriting Systems

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26th AAAI Conference on Artificial Intelligence, AAAI 2012

作者： Imprialou, Martha Stoilos, Giorgos Grau, Bernardo Cuenca Department of Computer Scinece University of Oxford Oxford United Kingdom School of Electrical and Computer Engineering National Technical University of Athens Greece

Query rewriting is a prominent reasoning technique in ontology-based data access applications. A wide variety of query rewriting algorithms have been proposed in recent years and implemented in highly optimised reasoning systems. Query rewriting systems are complex software programs;even if based on provably correct algorithms, sophisticated optimisations make the systems more complex and errors become more likely to happen. In this paper, we present an algorithm that, given an ontology as input, synthetically generates "relevant" test queries. Intuitively, each of these queries can be used to verify whether the system correctly performs a certain set of "inferences", each of which can be traced back to axioms in the input ontology. Furthermore, we present techniques that allow us to determine whether a system is unsound and/or incomplete for a given test query and ontology. Our evaluation shows that most publicly available query rewriting systems are unsound and/or incomplete, even on commonly used benchmark ontologies;more importantly, our techniques revealed the precise causes of their correctness issues and the systems were then corrected based on our feedback. Finally, since our evaluation is based on a larger set of test queries than existing benchmarks, which are based on hand-crafted queries, it also provides a better understanding of the scalability behaviour of each system. Copyright © 2012, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ontology

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Benchmarking ontology-based query rewriting systems

Benchmarking ontology-based query rewriting systems

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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： Imprialou, Martha Stoilos, Giorgos Cuenca Grau, Bernardo Department of Computer Scinece University of Oxford Oxford United Kingdom School of Electrical and Computer Engineering National Technical University of Athens Greece

ISBN: (纸本)9781577355687

关键词： Ontology

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Research on trustworthy Software Testing Techniques Based on Large Models 10

Research on trustworthy Software Testing Techniques Based on...

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Han, Qiang Shi, Zijie Zhao, Zhenyu North Minzu University School of Computer Scinece and Engineering Yinchuan750021 China Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China

ISBN: (纸本)9798350362930

This paper focuses on trustworthy software testing techniques enabled by large language models. We first give an overview of large language models and their applications in software engineering. Then we analyze the challenges of applying these models to software testing, which include overfitting, bias and trust issues. We evaluate LMTester on both synthetic and real-world programs compared with baseline testing tools. The results demonstrate its advantages in improving test coverage, detecting bugs, and assuring trustworthiness. We also discuss the limitations of current techniques and point out several future directions. Our work is expected to facilitate trustworthy applications of large language models in software testing and quality assurance. © 2024 IEEE.

关键词： Software testing

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Study on Mask R-CNN with Data Augmentation for Retail Product Detection

Study on Mask R-CNN with Data Augmentation for Retail Produc...

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Kuo, Matthew Chan, Hung-Tse Hsia, Chih-Hsien Taipei American School Taipei Taiwan National Ilan University Dept. of Computer Scinece and Information Engineering Yilan Taiwan

ISBN: (纸本)9781665419512

In deep learning (DL), object detection is a computer vision technology that can be applied in many fields, including face detection and tracking the soccer ball during a match. Previously, when dealing with detecting objects from small retail stores, most studies used Faster R-CNN with ResNet101 as its backbone. However, this method sometimes incorrectly and ineffectively identifies overlapped objects or objects with parts of their edges cut off from the image. This paper use Mask R-CNN with data augmentation (DA) to perform the object detection in retail product recognition. We seek to minimalize the error caused by overlapped objects or adjacent objects having similar colors. In this experiment result, the proposed method on Snacks dataset can achieve mAP of 98.92% with a superior recognition rate in the recognition task compared to the state-of-the-art methods. © 2021 IEEE.

关键词： Object detection

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Finger-Vein Recognition Using a NASNet with a Cutout

Finger-Vein Recognition Using a NASNet with a Cutout

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Wang, I Sheng Chan, Hung-Tse Hsia, Chih-Hsien Taipei Fuhsing Private School Taipei Taiwan National Ilan University Dept. of Computer Scinece and Information Engineering Yilan Taiwan

ISBN: (纸本)9781665419512

Traditional information security systems use passwords or identification cards that might be deciphered or stolen. Many methods have been developed to improve the security of personal information, such as the finger-vein recognition to replace traditional recognition. This study proposes a cutout for data augmentation (DA) and a neural architecture search network (NASNet). Experiments show that the proposed method is 98.89% accurate for the FV-USM public dataset. © 2021 IEEE.

关键词： Network architecture

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Sampling Strategies for Real-time Action Recognition

Sampling Strategies for Real-time Action Recognition

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IEEE Conference on computer Vision and Pattern Recognition

作者： Feng Shi Emil Petriu Robert Laganiere School of Electrical Engineering and Computer Scinece University of Ottawa

ISBN: (纸本)9781467364102

Local spatio-temporal features and bag-of-features representations have become popular for action recognition. A recent trend is to use dense sampling for better performance. While many methods claimed to use dense feature sets, most of them are just denser than approaches based on sparse interest point detectors. In this paper, we explore sampling with high density on action recognition. We also investigate the impact of random sampling over dense grid for computational efficiency. We present a real-time action recognition system which integrates fast random sampling method with local spatio-temporal features extracted from a Local Part Model. A new method based on histogram intersection kernel is proposed to combine multiple channels of different descriptors. Our technique shows high accuracy on the simple KTH dataset, and achieves state-of-the-art on two very challenging real-world datasets, namely, 93% on KTH, 83.3% on UCF50 and 47.6% on HMDB51.

关键词： Dense Random sampling MULTICHANNEL Recognition (Psychology) Computational efficiency interest point detector High density Dataset Action

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LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Social Media Comments 25

LSTM-ANN Based Price Hike Sentiment Analysis from Bangla Soc...

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25th International Conference on computer and Information Technology, ICCIT 2022

作者： Chakraborty, Sovon Uddin Talukdar, Muhammad Borahn Yaseen Morshed Adib, Muhammed Mitra, Sowmen Rabiul Alam, Md. Golam Brac University School of Data and Sciences Department of Computer Scinece and Engineering Dhaka1212 Bangladesh Department of Computer Science and Engineering Dhaka1209 Bangladesh Hebei University of Technology Department of Artificial Intelligence Beijing065001 China

ISBN: (纸本)9798350346022

Price hike has always been a substantial concern for people all over the world. The crisis gets more conspicuous, and people find themselves more confounded when even the bare minimum of expenses still exceeds the amount they can get to earn. This tension tends to invite chaos in society as the number of people affected increases. Bangladesh is currently undergoing a formidable wave of price hikes. People have been expressing mixed reactions on social media regarding this issue. Hence, understanding the overall public sentiment can be crucial for policymaking and preventing chaos in society. This study utilizes social media comments for analyzing underlying sentiments. Data were collected from the Facebook pages of some popular Bangladeshi media for this purpose, and thereby a specialized dataset was constructed. The dataset contains 2000 public comments annotated with three polarity values- positive, negative, and neutral. A hybrid LSTM-ANN deep architecture has been exploited in this research. The model outperforms other state-of- the-art models in terms of less trainable parameters along with an F1-score of 88.47%. © 2022 IEEE.

关键词： Sentiment analysis

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Localizing XML documents through XSLT

Localizing XML documents through XSLT

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21st IASTED International Multi-Conference on Applied Informatics

作者： Yu, Yijun Lu, Jianguo Xue, Jinghao Zhang, Yi Sun, Weiwei Electrical Engineering Department Gent University Belgium School of Computer Scinece University of Windsor Canada Muller Institute for Biomechanics University of Bern Switzerland Information Technology Department Gent University Belgium Computer Science Department Fudan University China

ISBN: (纸本)0889863415

Existing efforts on XML internationalization and localization have been focusing on the contents of XML documents instead of on the meta presentations such as tags and attributes. Although XML standard allows the use of Unicode throughout a document including the meta level presentations, most tags and attributes of XML documents are still defined in English, which makes it difficult for native people to use. This paper presents a pure XSLT stylesheet to completely translate and localize XML documents into different natural languages. Furthermore, we also describe how this technique can be applied to translation problems in programming (e.g. C and Java) or documentation (e.g. LaTeX) languages when the program or the document can be converted to and from XML documents.

关键词： XML

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A novel interesting recommendation system based on tags mining

A novel interesting recommendation system based on tags mini...

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2012 International Conference on Information Management, Innovation Management and Industrial engineering, ICIII 2012

作者： Zhao, Xiaogang Ma, Zhiqiang Ding, Ling Department of Software Engineering International School of Software Wuhan University Wuhan China Department of Electronics Information and Engineering Henan Vocational College of Agriculture Zhengzhou China Department of Computer Scinece and Technology Xianning Colleage Xianning China

ISBN: (纸本)9781467319324

With the rapid growth of social network on Internet, many people can make friends through smartphone easily. But when young guys use smart phone to browse web pages which has huge content to display, they can hardly choose what they really like unless spending much time for limited input capability. This paper designs an interesting recommendation system to recommend something or someone to the people who login to our website. With Recommendation system people can easily found something they wanted from large data with little input. This character is very important to smartphone whose input capability is poor. Recommendation algorithm in the paper is based on the tags mining through Baidu encyclopedia database and Sina microblogging. When two people have interesting with high similar degree, we recommend one to the other. Experiment shows the recommendation algorithm in the paper has good precision. ©2012 IEEE.

关键词： Recommender systems

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