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arXiv 2024年

作者： Buettner, Kyle Kovashka, Adriana Intelligent Systems Program Department of Computer Science University of Pittsburgh United States

There is a scarcity of multilingual vision-language models that properly account for the perceptual differences that are reflected in image captions across languages and cultures. In this work, through a multimodal, multilingual retrieval case study, we quantify the existing lack of model flexibility. We empirically show performance gaps between training on captions that come from native German perception and captions that have been either machine-translated or human-translated from English into German. To address these gaps, we further propose and evaluate caption augmentation strategies. While we achieve mean recall improvements (+1.3), gaps still remain, indicating an open area of future work for the community. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine translation

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Improvement of Spatial Ambiguity in Multi-Channel Speech Separation Using Channel Attention

Improvement of Spatial Ambiguity in Multi-Channel Speech Sep...

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2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021

作者： Hong, Qian-Bei Wu, Chung-Hsien Nguyen, Thanh Binh Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9789881476890

Multi-channel speech separation has been successfully applied in a complex real-world environment such as the far-field condition. The common solution to deal with the far-field condition is using a multi-channel signal captured by a structured microphone array and leveraging the inner difference between channels to enhance the speech separation performance. The spatial feature has been widely used in recent speech separation research. This feature appears to be insufficient when the location information becomes ambiguous. This is known as the spatial ambiguity problem. In order to deal with the spatial ambiguity problem, this study proposes an attention mechanism for the Temporal-Spatial Neural Filter (TSNF), in which the channel attention on merged features and the feature map of 1D convolution block in the temporal convolution network is proposed. The proposed method is evaluated on the multi-channel reverberant dataset which is built based on the WSJ0-2mix dataset. The dataset is simulated in the real-environment room by using the Room Impulse Response generator. In the experimental results, the proposed methods produced the SI -SNR improvement of about 1.2dB in close speakers' case, while a small decrease of 0.1dB in other cases. © 2021 APSIPA.

关键词： Convolution

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Implementing and Improving a method for non-invasive elicitation of probabilities for bayesian networks 07

Implementing and Improving a method for non-invasive elicita...

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International Conference on computer systems and Technologies and Workshop for PhD Students in Computing, CompSysTech '07

作者： De Jongh, Martinus Druzdzel, Marek Rothkrantz, Leon Faculty of Electrical Engineering Mathematics and Computer Science Man-Machine Interaction Group Delft University of Technology Delft Netherlands Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA United States

ISBN: (纸本)9789549641509

Knowledge elicitation is difficult for expert systems that are based on probability theory. The elicitation of probabilities for a probabilistic model requires a lot of time and interaction between the knowledge engineer and the expert. Druzdzel and van der Gaag [5] have proposed a theoretical framework that would allow the use of multiple types of probabilistic information for elicitation of Bayesian networks. This framework has been used as a starting point for the implementation of a non-invasive elication method. © 2007 ACM.

关键词： Linear programming

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Large language models for diabetes training:a prospective study

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science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

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Adding Argumentation into Human Evaluation of Long Document Abstractive Summarization: A Case Study on Legal Opinions 4

Adding Argumentation into Human Evaluation of Long Document ...

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4th Workshop on Human Evaluation of NLP systems, HumEval 2024

作者： Elaraby, Mohamed Xu, Huihui Gray, Morgan Ashley, Kevin Litman, Diane Department of Computer Science School of Computing and Information United States Learning Research and Development Center United States Intelligent Systems Program School of Computing Information University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9782493814418

Human evaluation remains the gold standard for assessing abstractive summarization. However, current practices often prioritize constructing evaluation guidelines for fluency, coherence, and factual accuracy, overlooking other critical dimensions. In this paper, we investigate argument coverage in abstractive summarization by focusing on long legal opinions, where summaries must effectively encapsulate the document’s argumentative nature. We introduce a set of human-evaluation guidelines to evaluate generated summaries based on argumentative coverage. These guidelines enable us to assess three distinct summarization models, studying the influence of including argument roles in summarization. Furthermore, we utilize these evaluation scores to benchmark automatic summarization metrics against argument coverage, providing insights into the effectiveness of automated evaluation methods. © 2024 European Language Resources Association (ELRA).

关键词： Human Evaluation Legal Summarization Summarization

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Plan generation, plan management, and the design of computational agents

Plan generation, plan management, and the design of computat...

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International Conference on Multi Agent systems

作者： M.E. Pollack Department of Computer Science Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA

A significant amount of prior research effort in the field of artificial intelligence has gone into the design and analysis of planning algorithms. For the most part, the work has been guided by several strong, simplifying assumptions, most notably, that the plans will be performed in static, deterministic environments. Although these assumptions have made rigorous formal analysis possible, they make sense only for a limited number of applications, in which planning is done more or less in isolation of other reasoning tasks, and also in isolation of plan execution. Once one turns attention to agents that perform autonomously in dynamic, uncertain environments-including multi-agent environments-the assumptions made by traditional planners are violated, and it becomes necessary to rethink the traditional AI approaches to planning.

关键词： Artificial intelligence Microwave integrated circuits computer science intelligent systems Computational and artificial intelligence Algorithm design and analysis Environmental management Technology management Delay Monitoring

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An interactive tool for natural language processing on clinical text

arXiv

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arXiv 2017年

作者： Trivedi, Gaurav Pham, Phuong Chapman, Wendy Hwa, Rebecca Wiebe, Janyce Hochheiser, Harry Intelligent Systems Program University of Pittsburgh Dept. of Computer Science University of Pittsburgh Dept. of Biomed. Informatics University of Utah Dept. of Computer Science and Intelligent Systems Program University of Pittsburgh

Natural Language Processing (NLP) systems often make use of machine learning techniques that are unfamiliar to endusers who are interested in analyzing clinical records. Although NLP has been widely used in extracting information from clinical text, current systems generally do not support model revision based on feedback from domain experts. We present a prototype tool that allows end users to visualize and review the outputs of an NLP system that extracts binary variables from clinical text. Our tool combines multiple visualizations to help the users understand these results and make any necessary corrections, thus forming a feedback loop and helping improve the accuracy of the NLP models. We have tested our prototype in a formative think-aloud user study with clinicians and researchers involved in colonoscopy research. Results from semi-structured interviews and a System Usability Scale (SUS) analysis show that the users are able to quickly start refining NLP models, despite having very little or no experience with machine learning. Observations from these sessions suggest revisions to the interface to better support review workflow and interpretation of results. Copyright © 2017, The Authors. All rights reserved.

关键词： Visualization

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Causal Discovery Using A Bayesian Local Causal Discovery Algorithm

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11th World Congress on Medical Informatics, MEDINFO 2004

作者： Mani, Subramani Cooper, Gregory F. Center for Biomedical Informatics The Intelligent Systems Program University of Pittsburgh PA15213 United States Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53201 United States

ISBN: (纸本)1586034448

This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework BLCD initially uses heuristic greedy search to derive the Markov Blanket (MB) of a node that serves as the "locality "for the identification of pair-wise causal relationships. BLCD takes as input a dataset and outputs potential causes of the form variable X causally influences variable Y. Identification of the causal factors of diseases and outcomes, can help formulate better management, prevention and control strategies for the improvement of health care. In this study we focused on investigating factors that may contribute causally to infant mortality in the United States. We used the U.S. Linked Birth/Infant Death dataset for 1991 with more than four million records and about 200 variables for each record. Our sample consisted of 41,155 records randomly selected from the whole dataset. Each record had maternal, paternal and child factors and the outcome at the end of the first year-whether the infant survived or not. Using the infant birth and death dataset as input, BLCD output six purported causal relationships. Three out of the six relationships seem plausible. Even though we have not yet discovered a clinically novel causal link, we plan to look for novel causal pathways using the full sample. © 2004 IMIA. All rights reserved.

关键词： Bayesian networks

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Using LLMs to Discover Legal Factors 37

Using LLMs to Discover Legal Factors

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37th Annual Conference on Legal Knowledge and Information systems, JURIX 2024

作者： Gray, Morgan Savelka, Jaromir Oliver, Wesley Ashley, Kevin Intelligent Systems Program University of Pittsburgh United States School of Law University of Pittsburgh United States School of Computer Science Carnegie Mellon University United States School of Law Duquesne University United States

ISBN: (纸本)9781643685625

Factors are a foundational component of legal analysis and computational models of legal reasoning. These factor-based representations enable lawyers, judges, and AI and Law researchers to reason about legal cases. In this paper, we introduce a methodology that leverages large language models (LLMs) to discover lists of factors that effectively represent a legal domain. Our method takes as input raw court opinions and produces a set of factors and associated definitions. We demonstrate that a semi-automated approach, incorporating minimal human involvement, produces factor representations that can predict case outcomes with moderate success, if not yet as well as expert-defined factors can. © 2024 The Authors.

关键词： Case based reasoning

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Toward Automatically Identifying Legally Relevant Factors 35

Toward Automatically Identifying Legally Relevant Factors

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35th International Conference on Legal Knowledge and Information systems, JURIX 2022

ISBN: (纸本)9781643683645

In making legal decisions, courts apply relevant law to facts. While the law typically changes slowly over time, facts vary from case to case. Nevertheless, underlying patterns of fact may emerge. This research focuses on underlying fact patterns commonly present in cases where motorists are stopped for a traffic violation and subsequently detained while a police officer conducts a canine sniff of the vehicle for drugs. We present a set of underlying patterns of fact, that is, factors of suspicion, that police and courts apply in determining reasonable suspicion. We demonstrate how these fact patterns can be identified and annotated in legal cases and how these annotations can be employed to fine-tune a transformer model to identify the factors in previously unseen legal opinions. © 2022 The authors and IOS Press.

关键词： Law enforcement

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