检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Magnetotelluric signal-noise separation method based on SVM–CEEMDWT

引用

Applied Geophysics 2019年第2期16卷 160-170,252-253页

作者： Li Jin Cai Jin Tang Jing-Tian Li Guang Zhang Xian Xu Zhi-Min Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and EngineeringHunan Normal UniversityChangsha 410081China School of Geosciences and Info-Physics Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment MonitorMinistry of EducationCentral South UniversityChangsha 410083China State Key Laboratory Breeding Base of Nuclear Resources and Environment East China University of TechnologyNanchang 330013China

To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT signal affected by strong ***,the approximate entropy,fuzzy entropy,sample entropy,and Lempel-Ziv(LZ)complexity are extracted from the magnetotelluric ***,four robust parameters are used as the inputs to the support vector machine(SVM)to train the sample library and build a model based on the different complexity of *** on this model,we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition(CEEMD)and wavelet threshold(WT)for noise *** results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise *** with the CEEMD WT,the proposed SVM-CEEMDWT method retains more low-frequency low-variability information,and the apparent resistivity curve is smoother and more ***,the results better reflect the deep electrical structure in the field.

关键词： SVM-CEEMDWT magnetotelluric signal-noise separation MT data processing

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Admission of Delay-Sensitive Tasks for Multi-Mobile Edge computing Servers

Energy-Efficient Admission of Delay-Sensitive Tasks for Mult...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jianrong Wang Yuanzhi Yue Ru Wang Mei Yu Jian Yu Hongwei Liu Xiang Ying Ruiguo Yu Tianjin International Engineering Institute Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Advanced Networking (TANK Lab) Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Foreign Language Tianjin Foreign Studies University Tianjin China

ISBN: (纸本)9781728125848

For delay-sensitive applications in mobile edge computing (MEC), task admission approach is of vital importance, and there has been a lot of researches in this field. But previous works focus on the situation with only one MEC server that is a simplification of the real world. In multi-servers situation, some devices may be within the service range of multiple MEC servers, so that they could choose which MEC server to offload. We formulate this problem to a multiple-choice integer program (MCIP) and utilize Ben's genetic algorithm to solve it. The simulation results show that our approach can significantly reduce energy consumption and every task can catch its deadline under almost all experiments.

关键词： Mobile edge computing Multiple-Choice integer program Energy-efficient algorithm Servers most economic control CCL28 gene Simulation results simplification Energy efficiency UNLOADING

来源：评论

学校读者我要写书评

暂无评论

A corpus-based study on collocation and semantic prosody in China’s english media: The case of the verbs of publicity 23rd

A corpus-based study on collocation and semantic prosody in ...

引用

23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018

作者： Huang, Qunying Xia, Lixin Xia, Yun Faculty of English Language and Culture Guangdong University of Foreign Studies Guangzhou510420 China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou510420 China Nanfang College of Sun Yat-Sen University Guangzhou510420 China

ISBN: (纸本)9783319914541

This paper explored extensively the collocability and semantic prosody of the verbs of publicity in China’s English media, and then contrasted them with those in American newspapers. The purpose of the study is to disclose the attitudinal meanings from their linguistic performance. To that end, the Corpus of China’s English Media (CCEM) was built with 29,151,028 tokens, and the newspaper branch of COCA was used as a comparable corpus. All the col-locational patterns of the verbs were extracted from the two corpora, and they were further analyzed in great detail. The major findings of the study can be summarized as below: (1) The verbs of publicity are generally used more frequently in CCEM. (2) Chinese journalists tend to use some unique collocational patterns to describe some political, cultural or social concepts that are specific to China. (3) Chinese journalists have a preference for collocational patterns with a positive or neutral semantic prosody over those with a negative semantic prosody. These findings suggest that the attitudinal meanings could be worked out from the differences in collocational behaviors and semantic prosody between the texts in CCEM and COCA. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

引用

arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

LSTM-TDNN WITH CONVOLUTIONAL FRONT-END FOR DIALECT IDENTIFICATION IN THE 2019 MULTI-GENRE BROADCAST CHALLENGE

arXiv

引用

arXiv 2019年

作者： Miao, Xiaoxiao McLoughlin, Ian School of Computing University of Kent Medway United Kingdom Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics Chinese Academy of Sciences University of Chinese Academy of Sciences National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China

This paper presents a novel Dialect Identification (DID) system developed for the Fifth Edition of the Multi-Genre Broadcast challenge, the task of Fine-grained Arabic Dialect Identification (MGB-5 ADI Challenge). The system improves upon traditional DNN x-vector performance by employing a Convolutional and Long Short Term Memory-Recurrent (CLSTM) architecture to combine the benefits of a convolutional neural network front-end for feature extraction and a back-end recurrent neural to capture longer temporal dependencies. Furthermore we investigate intensive augmentation of one low resource dialect in the highly unbalanced training set using time-scale modification (TSM). This converts an utterance to several time-stretched or time-compressed versions, subsequently used to train the CLSTM system without using any other corpus. In this paper, we also investigate speech augmentation using MUSAN and the RIR datasets to increase the quantity and diversity of the existing training data in the normal way. Results show firstly that the CLSTM architecture outperforms a traditional DNN x-vector implementation. Secondly, adopting TSM-based speed perturbation yields a small performance improvement for the unbalanced data, finally that traditional data augmentation techniques yield further benefit, in line with evidence from related speaker and language recognition tasks. Our system achieved 2nd place ranking out of 15 entries in the MGB-5 ADI challenge, presented at ASRU 2019. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Combining Convolutional Neural Networks and Word Topic Features for Chinese Short Text Sentiment Analysis

Combining Convolutional Neural Networks and Word Topic Featu...

引用

IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Jianghao Lin Yeli Gu Yongmei Zhou Aimin Yang Jin Chen Xinguang Li Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9781538665664;9781538665657

The Chinese short text sentiment classification method based on word embedding and convolutional neural network has already achieved effective results. The recent classification method mainly adopts single channel CNN. In order to enhance classification performance, this study proposes a classification method based on Dual-channel CNN. The word2vec semantic feature and topic model (LDA)feature of words were used as input of Dual-channel CNN to extract sequence sentimental features which, were subsequently applied on short text sentiment classification. The experimental results show that the classification performance of Dual-channel CNN is better than those of single channel CNN and Naive Bayes on hotel review data.

关键词： Feature extraction Semantics Sentiment analysis Convolutional neural networks Classification algorithms Training Data models

来源：评论

学校读者我要写书评

暂无评论

Automatic Meta-evaluation of Low-Resource Machine Translation Evaluation Metrics

Automatic Meta-evaluation of Low-Resource Machine Translatio...

引用

International Conference on Asian language Processing (IALP)

作者： Junting Yu Wuying Liu Hongye He Lin Wang Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangdong CHINA Training Department Shaanxi provincial Party School of CPC Shaanxi CHINA Xianda College of Economics and Humanities Shanghai International Studies University Shanghai CHINA

ISBN: (数字)9781728150147

ISBN: (纸本)9781728150154

Meta-evaluation is a method to assess machine translation (MT) evaluation metrics according to certain theories and standards. This paper addresses an automatic meta-evaluation method of machine translation evaluation based on ORANGE- Limited ORANGE, which is applied in low-resource machine translation evaluation. It is adopted when the resources are limited. And take the three n-gram-based metrics - BLEUS, ROUGE-L and ROUGE-S for experiment, which is called horizontal comparison. Also, vertical comparison is used to compare the different forms of the same evaluation metric. Compared with the traditional human method, this method can evaluate metrics automatically without extra human involvement except for a set of references. It only needs the average rank of the references, and will not be influenced by the subjective factors. And it costs less and expends less time than the traditional one. It is good for the machine translation system parameter optimization and shortens the system development period. In this paper, we use this automatic meta-evaluation method to evaluate BLEUS, ROUGE-L, ROUGE-S and their different forms based on Cilin on the Russian-Chinese dataset. The result shows the same as that of the traditional human meta-evaluation. In this way, the consistency and effectiveness of Limited ORANGE are verified.

关键词： Measurement Smoothing methods Semantics Training Standards System performance

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised bootstrapping of dialogue state trackers for task oriented modelling

arXiv

引用

arXiv 2019年

作者： Tseng, Bo-Hsiang Rei, Marek Budzianowski, Pawel Turner, Richard E. Byrne, Bill Korhonen, Anna Engineering Department Cambridge University United Kingdom Computer Laboratory Cambridge University United Kingdom Language Technology Lab Cambridge University United Kingdom Department of Computing Imperial College London PolyAI Limited London United Kingdom

Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels. However, collecting these annotations is expensive and time-consuming, holding back development in the area of dialogue modelling. In this paper, we investigate semi-supervised learning methods that are able to reduce the amount of required intermediate labelling. We find that by leveraging un-annotated data instead, the amount of turn-level annotations of dialogue state can be significantly reduced when building a neural dialogue system. Our analysis on the MultiWOZ corpus, covering a range of domains and topics, finds that annotations can be reduced by up to 30% while maintaining equivalent system performance. We also describe and evaluate the first end-to-end dialogue model created for the MultiWOZ corpus. Copyright © 2019, The Authors. All rights reserved.

关键词： Speech processing

来源：评论

学校读者我要写书评

暂无评论

A Scoring System of Oral English-Chinese Translation Based on Frame Semantic Analysis

A Scoring System of Oral English-Chinese Translation Based o...

引用

International Conference on Systems and Informatics (ICSAI)

作者： XinGuang Li XiaoLan Long Yan Long ChuHua Liang Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China School of English and Education Guangdong University of Foreign Studies Guangzhou China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

In recent years, there has been an increasing interest in speech evaluation. However, most of the automatic evaluation systems focus on evaluating the quality of retelling questions and reading questions. In our best knowledge, research evaluating the quality of English-Chinese oral translation questions have not been found. This paper proposed an automatic method for scoring English-Chinese oral translation. This method leveraged three indicators, including key words, general idea of sentences and fluency, as the basis of scoring. The key words indicator was evaluated by the synonym analysis at the lexical level to evaluate while the general idea of sentences indicator was evaluated by the framework semantic analysis at the sentence level. Then, fluency indicator was scored by the speed of the speech. The experimental results demonstrated that using Mel-frequency cepstral coefficients (MFCC) and the Hidden Markov Model (HMM) algorithm to identify the key words, the system achieved average recognition rate in answering keywords of 97.3%. There is a good possibility that the proposed method may be effective for key word recognition.

关键词： Semantics Hidden Markov models Vocabulary Education Speech Standards Companies

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：