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检索条件"任意字段=14th Symposium on Large Data Analysis and Visualization"

共 540 条记录，以下是21-30 订阅

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EmoSpeech: A Corpus of Emotionally Rich and Contextually Detailed Speech Annotations 14

EmoSpeech: A Corpus of Emotionally Rich and Contextually Det...

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14th International symposium on Chinese Spoken Language Processing

作者： Bian, Weizhen Zhou, Yubo Zhang, Kaitai Gu, Xiaohan Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China

ISBN: (纸本)9798331516833;9798331516826

Advances in text-to-speech (TTS) technology have significantly improved the quality of generated speech, closely matching the timbre and intonation of the target speaker. However, due to the inherent complexity of human emotional expression, the development of TTS systems capable of controlling subtle emotional differences remains a formidable challenge. Existing emotional speech databases often suffer from overly simplistic labelling schemes that fail to capture a wide range of emotional states, thus limiting the effectiveness of emotion synthesis in TTS applications. To this end, recent efforts have focussed on building databases that use natural language annotations to describe speech emotions. However, these approaches are costly and require more emotional depth to train robust systems. In this paper, we propose a novel process aimed at building databases by systematically extracting emotion-rich speech segments and annotating them with detailed natural language descriptions through a generative model. this approach enhances the emotional granularity of the database and significantly reduces the reliance on costly manual annotations by automatically augmenting the data with high-level language models. the resulting rich database provides a scalable and economically viable solution for developing a more nuanced and dynamic basis for developing emotionally controlled TTS systems.

关键词： Targeted Speech Extraction Emotion Recognition Automatic Speech Recognition large Language Model Speech analysis

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Demo: Cohort visualization and analysis of Patients with Inflammatory Bowel Disease

Demo: Cohort Visualization and Analysis of Patients with Inf...

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14th IEEE Workshop on Visual Analytics in Healthcare (VAHC)

作者： Sessler, David Ahmad, Salmah Kohlhammer, Jorn Fraunhofer IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350330243

this demo paper introduces the final version of a cohort analysis module for the support of treating patients with inflammatory bowel disease (IBD). It is not trivial to correctly diagnose the specific IBD in patients, and wrongly treated patients have to endure the disease effects for a long time, with large costs for the individuals and the healthcare systems. the goal of this work is complementing the examination of individual patients with interactive analyses of cohorts and populations with similar disease patterns to support learning from such similarities for future treatments. We report on additional data and functionality compared to 2021 and discuss an evaluation with eight IBD experts.

关键词： Human-centered computing visualization Visual Analytics Applied computing Life and medical sciences Health care information systems

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Text2analysis: A Benchmark of Table Question Answering with Advanced data analysis and Unclear Queries 38

Text2Analysis: A Benchmark of Table Question Answering with ...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： He, Xinyi Zhou, Mengyu Xu, Xinrun Ma, Xiaojun Ding, Rui Du, Lun Gao, Yan Jia, Ran Chen, Xu Han, Shi Yuan, Zejian Zhang, Dongmei Xi An Jiao Tong Univ Xian Peoples R China Microsoft Redmond WA 98052 USA Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)1577358872

Tabular data analysis is crucial in various fields, and large language models show promise in this area. However, current research mostly focuses on rudimentary tasks like Text2SQL and TableQA, neglecting advanced analysis like forecasting and chart generation. To address this gap, we developed the Text2analysis benchmark, incorporating advanced analysis tasks that go beyond the SQL-compatible operations and require more in-depth analysis. We also develop five innovative and effective annotation methods, harnessing the capabilities of large language models to enhance data quality and quantity. Additionally, we include unclear queries that resemble real-world user questions to test how well models can understand and tackle such challenges. Finally, we collect 2249 query-result pairs with 347 tables. We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.

关键词： data handling

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Beyond Axial Lines High-Resolution Geometric analysis of London’s Urban Fabric Using LIDAR Scans 14

Beyond Axial Lines High-Resolution Geometric Analysis of Lon...

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14th International Space Syntax symposium, SSS 2024

作者： Varoudis, Tasos Hanna, Sean Space Syntax Laboratory Bartlett School of Architecture UCL United Kingdom

ISBN: (纸本)9791256690329

Space Syntax urban analyses have typically been performed on high-level approximations of the spatial network, in the form of axial lines (Penn et al. 1998), street segments (Hillier & Iida 2005), or similar, which necessarily lack some information at the smallest scales. Yet some key theoretical principles rely explicitly on locally observable features: intelligibility (Hillier et al. 1987), for example, is the correlation between long-range structure and that of the local. this paper investigates the highest resolution model of London’s geometry yet available to determine the degree to which local surface geometries correlate with long-range structural features of the urban network, particularly global measures of network centrality: integration and choice. By employing a LIDAR scan of ground and building surface geometry encompassing 2800 km^2 around London, captured as a dense cloud of points and transformed into 200-meter squares of 200 pixels, the research first queried the degree to which long-range structural features of the network are immediately evident in the local surface geometry data through clustering and unsupervised learning methods and trace its relationship to longer-scale centrality measures. We conclude that traditional measures of network centrality can be learned to be predicted from local features, suggesting an alternative to traditional syntactical intelligibility. Finally, we determine the scale at which the differentiation between foreground and background networks is most clearly discernible through local features is approximately 4 km, which coincides with that seen to best predict movement and is within the range of a typical journey length. © 14th International Space Syntax symposium, SSS 2024.

关键词： Syntactics

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14th International symposium on Intelligence Computation and Applications, ISICA 2023

14th International Symposium on Intelligence Computation and...

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14th International symposium on Intelligence Computation and Applications, ISICA 2023

ISBN: (纸本)9789819743957

the proceedings contain 44 papers. the special focus in this conference is on Intelligence Computation and Applications. the topics include: Research on Smart Agriculture Big data System Based on Spark and Blockchain;a Multiparty Reversible data Hiding Scheme in Encrypted Domain Based on Hybrid Encryption;packet Performance Predictor Based on Graph Isomorphism Network for Neural Architecture Search;visualization analysis of Convolutional Neural Network Processes;multimedia Information Retrieval Method Based on Semantic Similarity;Research on Intelligent Clustering Scoring of English Text Based on XGBOOST Algorithm;multi-recipient Public-Key Authenticated Encryption with Keyword Search;machine Learning-Assisted Optimization of Direction-Finding Antenna Arrays;research on the Innovative Application of Computer Aided Design in Environmental Design;key Technology and Application Research Based on Computer Internet of things;mutate Suspicious Statements to Locate Faults;petrochemical Commodity Price Prediction Model Based on Wavelet Decomposition and Bayesian Optimization;construction and Quality Evaluation of Learning Motivation Model from the Perspective of Course Ideology and Politics;Study on TNM Classification Diagnosis of Colorectal Cancer Based on Improved Self-supervised Contrast Learning;design and Application of a Teaching Evaluation Model Based on the theory of Multiple Intelligences;edible Oil Price Forecasting: A Novel Approach with Group Temporal Convolutional Network and BetaAdaptiveAdam;artificial Intelligence in Intelligent Clothing: Design and Implementation;research on the Routing Protocol Algorithm Driven by the Dedicated Frequency Points of the Internet of things to Build a Network;research on Communication Power of Cross-Cultural Short Video Based on Qualitative Comparative analysis;research on Bayberry Traceability Platform Based on Blockchain;preface.

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From Raw data to Informed Decisions: the Development of an Online data Repository and visualization Dashboard for Transportation data 14

From Raw Data to Informed Decisions: The Development of an O...

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14th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICth 2023

作者： Tsouros, Ioannis Polydoropoulou, Amalia Tsirimpa, Athena Karakikes, Ioannis Tahmasseby, Shahram Mohammad, Anas Ahmad Nemer Alhajyaseen, Wael K.M. University of the Aegean Department of Shipping Trade and Transport Chios82132 Greece Qatar Transportation and Traffic Safety Center Department of Civil and Environmental Engineering Qatar University P.O.Box 2713 Doha Qatar Department of Civil and Environmental Engineering Qatar University P.O.Box 2713 Doha Qatar

this paper presents the design, implementation, and practical use of a specialized online data repository and an interactive visualization of a transportation dashboard. Specifically tailored to handle and interpret large-scale transportation data within the Qatari context, the combined platform serves as a comprehensive solution for managing extensive datasets, including GPS traces from taxis and e-scooters, which are examined as primary use-cases in this paper. the online data repository provides a centralized hub for efficient data storage and management of public transport including Mobility-as-a-Service (MaaS) data. Concurrently, the visualization dashboard fosters an intuitive, user-friendly interface for data exploration and analysis. through real-world applications within Qatar's transportation ecosystem, we elucidate how these innovative developments can inform data-driven policy decisions in crucial areas such as infrastructure development, resource allocation, and safety measures. Ultimately, this study underscores the pivotal role of effective data management and advanced visualization in maximizing the potential of big data, providing valuable insights for urban mobility planning and enhancing the landscape of policy-making in Qatar and worldwide. © 2024 Elsevier B.V.. All rights reserved.

关键词： visualization

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Chartthinker: A Contextual Chain-of-thought Approach to Optimized Chart Summarization 30

ChartThinker: A Contextual Chain-of-Thought Approach to Opti...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Mengsha Chen, Daoyuan Li, Yaliang Fang, Guian Shen, Ying Sun Yat-sen University China Alibaba Group China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou510006 China

ISBN: (纸本)9782493814104

data visualization serves as a critical means for presenting data and mining its valuable insights. the task of chart summarization, through natural language processing techniques, facilitates in-depth data analysis of charts. However, there still are notable deficiencies in terms of visual-language matching and reasoning ability for existing approaches. To address these limitations, this study constructs a large-scale dataset of comprehensive chart-caption pairs and fine-tuning instructions on each chart. thanks to the broad coverage of various topics and visual styles within this dataset, better matching degree can be achieved from the view of training data. Moreover, we propose an innovative chart summarization method, Chartthinker, which synthesizes deep analysis based on chains of thought and strategies of context retrieval, aiming to improve the logical coherence and accuracy of the generated summaries. Built upon the curated datasets, our trained model consistently exhibits superior performance in chart summarization tasks, surpassing 8 state-of-the-art models over 7 evaluation metrics. Our dataset and codes are publicly accessible. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： data visualization

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Outlier Ranking for large-Scale Public Health data 38

Outlier Ranking for Large-Scale Public Health Data

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Joshi, Ananya Townes, Tina Gormley, Nolan Neureiter, Luke Rosenfeld, Roni Wilder, Bryan Carnegie Mellon Univ 5000 Forbes Rd Pittsburgh PA 15213 USA

ISBN: (纸本)1577358872

Disease control experts inspect public health data streams daily for outliers worth investigating, like those corresponding to data quality issues or disease outbreaks. However, they can only examine a few of the thousands of maximally-tied outliers returned by univariate outlier detection methods applied to large-scale public health data streams. To help experts distinguish the most important outliers from these thousands of tied outliers, we propose a new task for algorithms to rank the outputs of any univariate method applied to each of many streams. Our novel algorithm for this task, which leverages hierarchical networks and extreme value analysis, performed the best across traditional outlier detection metrics in a human-expert evaluation using public health data streams. Most importantly, experts have used our open-source Python implementation since April 2023 and report identifying outliers worth investigating 9.1x faster than their prior baseline. Other organizations can readily adapt this implementation to create rankings from the outputs of their tailored univariate methods across large-scale streams.

关键词： Disease control

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Truth Forest: Toward Multi-Scale Truthfulness in large Language Models through Intervention without Tuning 38

Truth Forest: Toward Multi-Scale Truthfulness in Large Langu...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Chen, Zhongzhi Sun, Xingwu Jiao, Xianfeng Lian, Fengzong Kang, Zhanhui Wang, Di Xu, Cheng-Zhong Beihang Univ Beijing Peoples R China Tencent Inc Shenzhen Peoples R China Univ Macau Zhuhai Peoples R China

ISBN: (纸本)1577358872

Despite the great success of large language models (LLMs) in various tasks, they suffer from generating hallucinations. We introduce Truth Forest, a method that enhances truthfulness in LLMs by uncovering hidden truth representations using multi-dimensional orthogonal probes. Specifically, it creates multiple orthogonal bases for modeling truth by incorporating orthogonal constraints into the probes. Moreover, we introduce Random Peek, a systematic technique considering an extended range of positions within the sequence, reducing the gap between discerning and generating truth features in LLMs. By employing this approach, we improved the truthfulness of Llama-2-7B from 40.8% to 74.5% on TruthfulQA. Likewise, significant improvements are observed in fine-tuned models. We conducted a thorough analysis of truth features using probes. Our visualization results show that orthogonal probes capture complementary truth-related features, forming well-defined clusters that reveal the inherent structure of the dataset.

关键词： Probes

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Practical Sentiment analysis for Education: the Power of Student Crowdsourcing 38

Practical Sentiment Analysis for Education: The Power of Stu...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Kasumba, Robert Neumann, Marion Washington Univ St Louis MO 63110 USA

ISBN: (纸本)1577358872

Sentiment analysis provides a promising tool to automatically assess the emotions voiced in written student feedback such as periodically collected unit-of-study reflections. the commonly used dictionary-based approaches are limited to major languages and fail to capture contextual differences. Pre-trained large language models have been shown to be biased and online versions raise privacy concerns. Hence, we resort to traditional supervised machine learning (ML) approaches which are designed to overcome these issues by learning from domain-specific labeled data. However, these labels are hard to come by - in our case manually annotating student feedback is prone to bias and time-consuming, especially in high-enrollment courses. In this work, we investigate the use of student crowdsourced labels for supervised sentiment analysis for education. Specifically, we compare crowdsourced and student self-reported labels with human expert annotations and use them in various ML approaches to evaluate the performance on predicting emotions of written student feedback collected from large computer science classes. We find that the random forest model trained with student-crowdsourced labels tremendously improves the identification of reflections with negative sentiment. In addition to our quantitative study, we describe our crowdsourcing experiment which was intentionally designed to be an educational activity in an introduction to data science course.

关键词： Sentiment analysis

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