检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

57,254 篇 会议
184 册 图书

馆藏范围

57,431 篇 电子文献
7 种 纸本馆藏

日期分布

学科分类号

57,042 篇 工学
- 56,774 篇 计算机科学与技术...
- 7,556 篇 控制科学与工程
- 2,463 篇 软件工程
- 2,057 篇 电气工程
- 483 篇 信息与通信工程
- 360 篇 生物医学工程（可授...
- 67 篇 机械工程
- 57 篇 土木工程
- 23 篇 石油与天然气工程
- 18 篇 生物工程
- 15 篇 仪器科学与技术
4,454 篇 理学
- 3,698 篇 数学
- 368 篇 物理学
- 274 篇 生物学
- 53 篇 科学技术史(分学科...
- 52 篇 统计学（可授理学、...
- 22 篇 系统科学
- 18 篇 化学
2,190 篇 医学
- 1,233 篇 基础医学(可授医学...
- 1,065 篇 临床医学
- 36 篇 特种医学
2,181 篇 文学
- 2,178 篇 外国语言文学
1,355 篇 管理学
- 1,256 篇 管理科学与工程(可...
- 84 篇 图书情报与档案管...
1,231 篇 教育学
- 709 篇 教育学
- 507 篇 心理学(可授教育学...
- 15 篇 体育学
57 篇 法学
- 56 篇 社会学
36 篇 艺术学
- 36 篇 音乐与舞蹈学
35 篇 哲学
- 35 篇 哲学
13 篇 经济学
1 篇 农学
1 篇 军事学

主题

1,258 篇 artificial intel...
1,112 篇 machine learning
978 篇 deep learning
919 篇 data mining
780 篇 multi agent syst...
725 篇 semantics
527 篇 classification
510 篇 reinforcement le...
474 篇 learning systems
463 篇 clustering
439 篇 neural networks
415 篇 ontology
353 篇 natural language...
257 篇 computers
249 篇 rough sets
236 篇 fuzzy sets
230 篇 feature selectio...
219 篇 computer circuit...
213 篇 knowledge repres...
205 篇 logic programmin...

机构

191 篇 univ chinese aca...
107 篇 chinese acad sci...
65 篇 harbin inst tech...
63 篇 polish acad sci ...
62 篇 wroclaw univ sci...
59 篇 soochow univ sch...
55 篇 delft univ techn...
53 篇 carnegie mellon ...
48 篇 beijing univ tec...
44 篇 chinese acad sci...
44 篇 shanghai jiao to...
43 篇 tu wien austria
42 篇 univ sci & techn...
41 篇 czech tech univ ...
40 篇 univ ulsan sch e...
39 篇 beijing jiaotong...
38 篇 tsinghua univ de...
38 篇 north carolina s...
37 篇 nanjing univ sta...
36 篇 tsinghua univ sc...

作者

47 篇 gelbukh alexande...
42 篇 wang lei
36 篇 reis luis paulo
35 篇 pelachaud cather...
35 篇 malerba donato
34 篇 liu yang
33 篇 novais paulo
33 篇 cpalka krzysztof
32 篇 li li
30 篇 ricci alessandro
27 篇 lau nuno
26 篇 wang hui
26 篇 napoli amedeo
25 篇 boryczka urszula
25 篇 rutkowski leszek
25 篇 wang jing
25 篇 pluhacek michal
25 篇 bickmore timothy
25 篇 calvo hiram
24 篇 villasenor-pined...

语言

57,321 篇 英文
116 篇 中文
1 篇 丹麦文

检索条件"丛书名=Lecture notes in artificial intelligence,"

共 57438 条记录，以下是361-370 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Attention to Phonetics: A Visually Informed Explanation of Speech Transformers 27th

Attention to Phonetics: A Visually Informed Explanation of S...

引用

27th International Conference on Text, Speech, and Dialogue (TSD)

作者： Shams, Erfan A. Carson-Berndsen, Julie Univ Coll Dublin Sch Comp Sci ADAPT Res Ctr Dublin Ireland

ISBN: (纸本)9783031705656;9783031705663

Self-supervised learning based on the transformer architecture has improved the performance of Automatic Speech Recognition (ASR) systems hugely in recent years while the interpretability of such transformer-based models, by design, has received less attention. Considering this, we investigate post-hoc explainability methodologies to explore the types of phonetic information that are encoded within the black box of transformer-based ASR models. We propose an exploratory visual environment based on the encoded parameters in the self-attention (SA) component of the models as a first step in explaining transformer-based ASRs via interactive exploration of the SA heads. The visualisations reveal clues about the functionality of specific SA heads that in turn support the choice of a suitable domain-informed post-hoc explainability method for a deeper analysis. We apply this method to identify the impact of certain SA heads in encoding sub-phonetic information in the model embeddings and demonstrate that specialised SA heads can be potentially identified via combined visualisation and post-hoc analysis.

关键词： Explainable AI Automatic Speech Recognition Phonetics Transformer-based ASR

来源：评论

学校读者我要写书评

暂无评论

Assessing Lexical Psycholinguistic Properties in Mandarin Discourse Production by Patients with Aphasia 24th

Assessing Lexical Psycholinguistic Properties in Mandarin Di...

引用

24th Chinese Lexical Semantics Workshop (CLSW)

作者： Chen, Juqiang Chang, Hui Shanghai Jiao Tong Univ Sch Foreign Languages 800 Dongchuan Rd Shanghai 200240 Peoples R China

ISBN: (纸本)9789819705825;9789819705832

Due to its high ecological validity, oral discourse production has been increasingly recognized as an important source for assessing language deficits in patients with aphasia (PWA). The present study investigated lexical psycholinguistic properties, such as concreteness, imageability and familiarity, in Mandarin PWA discourse via large-scale psycholinguistic norms. We found that PWA used more concrete, imageable and familiar words than the control group and there were variations among different task types. These findings suggest that psycholinguistic lexical properties need to be incorporated into current aphasia discourse assessment frameworks in addition to lexical variability and grammatical categories.

关键词： Psycholinguistic Norms Concreteness Imageability Familiarity Aphasia Mandarin Chinese

来源：评论

学校读者我要写书评

暂无评论

The Unexpected Effects of Google Smart Compose on Open-Ended Writing Tasks 25th

The Unexpected Effects of Google Smart Compose on Open-Ended...

引用

25th International Conference on artificial intelligence in Education (AIED)

作者： Cummings, Robert E. Thai Le Shrestha, Sijan Smith, Carrie Veronica Univ Mississippi University MS 38677 USA

ISBN: (纸本)9783031643019;9783031643026

In this work we seek to understand the effects of Google Smart Compose's auto-complete suggestions on writing products and processes in open-ended writing tasks. We recruited 119 human subjects to a carefully designed user study to write timed responses to a common prompt via word processors while recording interactions with Google Smart Compose behind the scene. Our experiment demonstrates the impacts of text suggestions on writing length, structure, cohesion, and complexity, utilizing Coh-metrix values. We also introduce new metrics for measuring and understanding detailed human subject interactions with text suggestions. We find that writers utilizing Google Smart Compose write the same amount of text as those without Smart Compose, and that the structure of written work produced with Google Smart Compose enabled is not significantly distinguishable from writing produced with Google Smart Compose disabled. In addition, there was no strong evidence that writing process was significantly impacted by Google Smart Compose. Finally, this work identifies factors that can be used in future studies measuring language model interactions with human writers.

关键词： Auto Completion AI-Assisted Writing Google Smart Compose Predictive Text Suggestions

来源：评论

学校读者我要写书评

暂无评论

Active Sensing for Epidemic State Estimation Using ABM-Guided Machine Learning 24th

Active Sensing for Epidemic State Estimation Using ABM-Guide...

引用

24th International Workshop on Multi-Agent-Based Simulation (MABS)

作者： Saliba, Sami Dadgostari, Faraz Hoops, Stefan Mortveit, Henning S. Swarup, Samarth Univ Virginia Charlottesville VA 22904 USA Montana State Univ Bozeman MT USA

ISBN: (纸本)9783031610332;9783031610349

During an epidemic, it can be difficult to get an estimate of the actual number of people infected at any given time. This is due to multiple reasons, including some cases being asymptomatic and sick people not seeking healthcare for mild symptoms, among others. Large scale random sampling of the population for testing can be expensive, especially in the early stages of an epidemic, when tests are scarce. Here we show how an adaptive prevalence testing method can be developed to obtain a good estimate of the disease burden by learning to intelligently allocate a small number of tests for random testing of the population. Our approach uses a combination of an agent-based simulation and deep learning in an active sensing paradigm. We show that it is possible to get a good state estimate with relatively minimal prevalence testing, and that the trained system adapts quickly and performs well even if the disease parameters change.

关键词： Computational Epidemiology Neural Networks Agent-based Modeling and Simulation Active Sensing

来源：评论

学校读者我要写书评

暂无评论

Teaching Chinese Pattern Extraction and Its Knowledge Base Construction for Specific Domain Texts 24th

Teaching Chinese Pattern Extraction and Its Knowledge Base C...

引用

24th Chinese Lexical Semantics Workshop (CLSW)

作者： Wei, Zuntian Peng, Weiming Song, Jihua Wang, Shaodong Zhao, Mengyu Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Beijing Normal Univ Chinese Character Res & Applicat Lab Beijing Peoples R China Univ Penn Linguist Data Consortium Philadelphia PA 19104 USA

ISBN: (纸本)9789819705856;9789819705863

Certain specialized component collocations and high-frequency expressions, known as Sentence Pattern Forms, can be used in textbook compilation and in-person teaching. The selection of Sentence Pattern Forms and the organization of knowledge points depend on the expert's subjective opinion and lack the support of objective evidence. Manual extraction also limits the compilation's effectiveness and thoroughness. This study creates a corpus of vocational textbooks on various majors. At the same time, we develop a knowledge base of extraction principles and provide corresponding algorithms for different Sentence Pattern Forms. Candidate expressions are automatically extracted and filtered by necessary manual screening. This method is more effective and scientific than pure manual extraction since it can cover more Sentence Pattern Forms that expert opinion cannot exhaust.

关键词： Chinese teaching Sentence Pattern Forms Sentence Pattern Structure Chinese textbook corpus Automatic extraction algorithm

来源：评论

学校读者我要写书评

暂无评论

Open-Source Web Service with Morphological Dictionary-Supplemented Deep Learning for Morphosyntactic Analysis of Czech 27th

Open-Source Web Service with Morphological Dictionary-Supple...

引用

27th International Conference on Text, Speech, and Dialogue (TSD)

作者： Straka, Milan Strakova, Jana Charles Univ Prague Inst Formal & Appl Linguist Fac Math & Phys Malostranske Nam 25 Prague 11800 Czech Republic

ISBN: (纸本)9783031705625;9783031705632

We present an open-source web service for Czech morphosyntactic analysis. The system combines a deep learning model with rescoring by a high-precision morphological dictionary at inference time. We show that our hybrid method surpasses two competitive baselines: While the deep learning model ensures generalization for out-of-vocabulary words and better disambiguation, an improvement over an existing morphological analyser MorphoDiTa, at the same time, the deep learning model benefits from inference-time guidance of a manually curated morphological dictionary. We achieve 50% error reduction in lemmatization and 58% error reduction in POS tagging over MorphoDiTa, while also offering dependency parsing. The model is trained on one of the currently largest Czech morphosyntactic corpora, the PDT-C 1.0, with the trained models available at https://***/11234/1-5293. We provide the tool as a web service deployed at https://lindat. ***/services/udpipe/. The source code is available at GitHub (https://***/ufal/udpipe/tree/udpipe-2), along with a Python client for a simple use. The documentation for the models can be found at https://***/udpipe/2/models#czech pdtc1.0 model.

关键词： morphosyntactic analysis deep learning morphological dictionary POS tagging lemmatization

来源：评论

学校读者我要写书评

暂无评论

New Human-Annotated Dataset of Czech Health Records for Training Medical Concept Recognition Models 27th

New Human-Annotated Dataset of Czech Health Records for Trai...

引用

27th International Conference on Text, Speech, and Dialogue (TSD)

作者： Anetta, Kristof Horak, Ales Masaryk Univ Nat Language Proc Ctr Fac Informat Brno Czech Republic

ISBN: (纸本)9783031705625;9783031705632

Following the widespread successes of leveraging recent large language models (LLMs) in various NLP tasks, this paper focuses on medical text content understanding. Adapting a foundational LLM to the medical domain requires a special kind of datasets where core medical concepts are accurately annotated. This paper addresses the need of better medical concept recognition in free-text electronic health records in low-resourced Slavic languages and introduces CSEHR, a new human-annotated dataset of Czech oncology health records. It describes the dataset inception, management, considerations, processing, and finally presents baseline concept recognition model results. XLM-RoBERTa models trained on the dataset using 5-fold cross-validation achieved an average weighted F1 score of 0.672 in exact and 0.777 in partial medical concept recognition ranging from 0.335 to 0.857 per different concept classes. This paper then describes future plans of bootstrapping larger annotated corpora from the CSEHR dataset and of making the dataset publicly available. This endeavor is unique in the realm of Slavic languages and already at this stage it represents a major step in the field of Slavic medical concept recognition.

关键词： medical text analysis electronic health records medical concept terms medical concept dataset named entity recognition

来源：评论

学校读者我要写书评

暂无评论

Supporting Teaching-to-the-Curriculum by Linking Diagnostic Tests to Curriculum Goals: Using Textbook Content as Context for Retrieval-Augmented Generation with Large Language Models 25th

Supporting Teaching-to-the-Curriculum by Linking Diagnostic ...

引用

25th International Conference on artificial intelligence in Education (AIED)

作者： Li, Xiu Henriksson, Aron Duneld, Martin Nouri, Jalal Wu, Yongchao Stockholm Univ NOD Huset Borgarfjordsgatan 12 S-16455 Stockholm Sweden

ISBN: (纸本)9783031643019;9783031643026

Using AI for automatically linking exercises to curriculum goals can support many educational use cases and facilitate teaching-to-the-curriculum by ensuring that exercises adequately reflect and encompass the curriculum goals, ultimately enabling curriculum-based assessment. Here, we introduce this novel task and create a manually labeled dataset where two types of diagnostic tests are linked to curriculum goals for Biology G7-9 in Sweden. We cast the problem both as an information retrieval task and a multi-class text classification task and explore unsupervised approaches to both, as labeled data for such tasks is typically scarce. For the information retrieval task, we employ state-of-the-art embedding model ADA-002 for semantic textual similarity (STS), while we prompt a large language model in the form of ChatGPT to classify diagnostic tests into curriculum goals. For both task formulations, we investigate different ways of using textbook content as a pivot to provide additional context for linking diagnostic questions to curriculum goals. We show that a combination of the two approaches in a retrieval-augmented generation model, whereby STS is used for retrieving textbook content as context to ChatGPT that then performs zero-shot classification, leads to the best classification accuracy (73.5%), outperforming both STS-based classification (67.5%) and LLM-based classification without context (71.5%). Finally, we showcase how the proposed method could be used in pedagogical practices.

关键词： Teaching-to-the-Curriculum Semantic Textual Similarity Large Language Models ChatGPT Retrieval-Augmented Generation

来源：评论

学校读者我要写书评

暂无评论

Improving the Validity of Automatically Generated Feedback via Reinforcement Learning 25th

Improving the Validity of Automatically Generated Feedback v...

引用

25th International Conference on artificial intelligence in Education (AIED)

作者： Scarlatos, Alexander Smith, Digory Woodhead, Simon Lan, Andrew Univ Massachusetts Amherst MA 01003 USA Eedi London England

ISBN: (纸本)9783031643019;9783031643026

Automatically generating feedback via large language models (LLMs) in intelligent tutoring systems and online learning platforms has the potential to improve the learning outcomes of many students. However, both feedback generation and evaluation are challenging: feedback content has to be valid especially in subjects like math, which requires models to understand the problem, the solution, and where the student's error lies. Feedback also has to be pedagogically valid to reflect effective tutoring strategies, such as explaining possible misconceptions and encouraging the student, among other desirable features. In this work, we address both problems of automatically generating and evaluating feedback while considering both correctness and alignment. First, we propose a rubric for evaluating math feedback and show that GPT-4 is able to effectively use it to annotate human-written and LLM-generated feedback. Second, we propose a framework for feedback generation that optimizes both correctness and alignment using reinforcement learning (RL). Specifically, we use GPT-4's annotations to create preferences over feedback pairs in an augmented dataset for training via direct preference optimization (DPO). We show that our methods significantly increase the correctness and alignment of generated feedback with Llama 2, an open-source LLM, qualitatively analyze our generation and evaluation systems using case studies, and outline several areas for future work. (Our code is available at https://***/umass-ml4ed/feedback-gen-dpo).

关键词： Feedback Generation Human Preference Alignment Math Education Reinforcement Learning

来源：评论

学校读者我要写书评

暂无评论

Federated Learning Analytics: Investigating the Privacy-Performance Trade-Off in Machine Learning for Educational Analytics 25th

Federated Learning Analytics: Investigating the Privacy-Perf...

引用

25th International Conference on artificial intelligence in Education (AIED)

作者： van Haastrecht, Max Brinkhuis, Matthieu Spruit, Marco Leiden Univ Leiden Inst Adv Comp Sci LIACS Leiden Netherlands Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands Leiden Univ Med Ctr LUMC Dept Publ Hlth & Primary Care PHEG Leiden Netherlands

ISBN: (纸本)9783031642982;9783031642999

Concerns surrounding privacy and data protection are a primary contributor to the hesitation of institutions to adopt new educational technologies. Addressing these concerns could open the door to accelerated impact, but current state-of-the-art approaches centred around machine learning are heavily dependent on (personal) data. Privacy-preserving machine learning, in the form of federated learning, could offer a solution. However, federated learning has not been investigated in-depth within the context of educational analytics, and it is therefore unclear what its impact on model performance is. In this paper, we compare performance across three different machine learning architectures (local learning, federated learning, and central learning) for three distinct prediction use cases (learning outcome, question correctness, and dropout). We find that federated learning consistently achieves comparable performance to central learning, but also that local learning remains competitive up to 20 local clients. We conclude by introducing FLAME, a novel metric that assists policymakers in their assessment of the privacy-performance trade-off.

关键词： privacy machine learning federated learning trust

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 33 34 35 36 37 38 39 40 41 42 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：