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Multimodal Event Detection on Chinese Glyphs 19th

Multimodal Event Detection on Chinese Glyphs

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Si, Qianqian Wang, Zhongqing Li, Peifeng Soochow Univ Suzhou Peoples R China

ISBN: (纸本)9789819947515;9789819947522

At present, most of the event detection researches focus on the single text modality, and multimodal event extraction is in its infancy, which mainly used the feature information of images to enhance text information. However, obtaining the corresponding high-quality images for texts still is a challenge for those plain texts. SinceChinese characters are pictographs, someradical features of event trigger words in their glyphs have certain and auxiliary effects on detecting trigger words. In this paper, we propose a multimodal event detection method based on Chinese text and the glyph of Chinese character. Specifically, we use different fonts to transform texts into text images, and then extract the features of the text images through residual network. Finally, we fuse them with the text features obtained from the dynamic multi-pooling mechanism to form the feature vectors for multimodal event detection. Experimental results on the ACE 2005 Chinese corpus show that our proposed model outperforms the best baseline by 1.9 in F1-score.

关键词： Multimodal event detection Pictograph Chinese glyph

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Technical Function Evaluation of Two Smart Wearables and Data Analysis Methods for Step Counts 17th

Technical Function Evaluation of Two Smart Wearables and Dat...

引用

17th International Conference on Augmented Cognition (AC)

作者： Boles, Katrina K. Gallimore, Malaika R. Howland, Chelsea Emezue, Chuka Reeder, Blaine Univ Missouri MU Inst Data Sci & Informat Columbia MO 65211 USA Univ Missouri Sinclair Sch Nursing Columbia MO USA Univ Iowa Coll Nursing Iowa City IA 52242 USA Rush Univ Coll Nursing Chicago IL 60612 USA

ISBN: (纸本)9783031350160;9783031350177

Smart wearable devices that capture physical activity data are increasingly used for health research and show potential for augmented cognition. These devices must be tested to understand their function before use in research and everyday life. However, there are few standards for the evaluation of step count comparisons between devices. We completed a technical function evaluation of two consumer-grade devices - Fitbit Versa 3 and generation 2 Oura Ring - against research-grade gold standard ActiGraph devices - wGT3X-BT and GT9X-Link. We compared data analysis methods to evaluate smart wearable physical activity data to inform development of standards and guidance for data analysis. Based on this effort, we suggest the use of Median Absolute Percent Difference along with Spearman's Rho as a correlation measure and Bland-Altman plots to visualize the agreement. This combination of measures provides a multi-perspective view of step counts and can assist researchers in determining limitations and best uses for smart wearable devices.

关键词： Smart watch fitness tracker digital health usability evaluation

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On the Distributional Convergence of Temporal Difference Learning

On the Distributional Convergence of Temporal Difference Lea...

引用

5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Dai, Jie Chen, Xuguang Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (纸本)9783031434204;9783031434211

Temporal Difference (TD) learning is one of the most simple but efficient algorithms for policy evaluation in reinforcement learning. Although the finite-time convergence results of the TD algorithm are abundant now, the distributional convergence has still been blank. This paper shows that TD with constant step size simulates Markov chains converging to some stationary distribution under both i.i.d. and Markov chain observation models. We prove that TD enjoys the geometric distributional convergence rate and show how the step size affects the expectation and covariance of the stationary distribution. All assumptions used in our paper are mild and common in the TD community. Our proved results indicate a tradeoff between the convergence speed and accuracy for TD. Based on our theoretical findings, we explain why the Jacobi preconditioner can accelerate the TD algorithms.

关键词： Temporal Difference Learning Markov Chain Noise Distributional Convergence

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Cluster Equality Validity Index and Efficient Clustering Optimization Strategy 1

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Huang, Zebin Yu, Ning Wu, Qingqiang Liu, KunHong Xiamen Univ Sch Informat Xiamen Peoples R China Xiamen Univ Key Lab Digital Protect & Intelligent Proc Lntang Cultural Heritage Fujian & Taiwan Minist Culture Xiamen Peoples R China Xiamen Univ Sch Film Xiamen Peoples R China

ISBN: (数字)9789819947522

ISBN: (纸本)9789819947515;9789819947522

Cluster analysis is an important research direction in data mining. The research on Cluster analysis can be divided into two parts: cluster validity index and clustering algorithm. We propose a novel external cluster validity index called CEI (Cluster Equality Index) and an efficient strategy called COS (Clustering Optimization Strategy). CEI constructs the equal confusion matrix by treating all clusters equally with greedy cluster pairing. Then it is used to match the clustering results and the ground truth labels for the clustering quality evaluation. COS is designed to judge whether a cluster should be reclustered by comparing the density of clusters' centroids with that at their edges. This optimization strategy can be combined with existing clustering algorithms to further improve the quality of their clusters. The public available artificial datasets and real-world datasets are deployed in experiments. The results show that CEI offers more accurate evaluation compared with other indexes, and COS can be further improve the quality of clustering.

关键词： Cluster analysis Cluster Equality Index Clustering Optimization Strategy K-means Algorithm

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Hybrid Heterogeneous Graph Neural Networks for Fund Performance Prediction 1

引用

16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Hao, Siyuan Dai, Le Zhang, Le Zhang, Shengming Wang, Chao Qin, Chuan Xiong, Hui Univ Sci & Technol China Hefei Peoples R China Baidu Res Business Intelligence Lab Beijing Peoples R China Rutgers State Univ New Brunswick NJ USA Hong Kong Univ Sci & Technol Guangzhou Guangzhou HKUST Fok Ying Tung Res Inst Guangzhou Peoples R China Tsinghua Univ PBC Sch Finance Beijing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China

ISBN: (数字)9783031402869

ISBN: (纸本)9783031402852;9783031402869

Fund performance prediction aims to evaluate the performance of financial funds. It is of great interest to financial investors to achieve high returns while maintaining sustainable fund development. Existing studies primarily focus on analyzing the performance of individual fund managers using empirical analysis based on statistics, with limited attention given to leveraging social connections between managers and the latent characteristics of the fund learned from various financial market information for fund performance appraisal. In this paper, we propose to develop Hybrid Heterogeneous Graph Neural Networks for Fund Performance Prediction with Market Awareness. Specifically, we aim to build a heterogeneous fund information network to measure the social relationships between fund managers and the influence of funds on the investment relationship of stocks. Given the challenges of heterogeneous graph neural networks in dealing with relationship fusion of different types, we have designed a hybrid approach that combines heterogeneous graph embedding and tensor factorization to fuse two different sources of information over the heterogeneous fund information network. Furthermore, we incorporate a market-aware scheme, which combines static fund representation with dynamic market trends, to capture the dynamic factors of the market and enhance the accuracy of fund performance prediction. We conduct extensive experiments on real fund market datasets to confirm the effectiveness of our proposed framework, and the investment simulation shows that our approach can achieve higher returns compared to the baselines.

关键词： Fund Performance Prediction Graph Representation Learning Hybrid Heterogeneous Graph Tensor Factorization

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Estimation of Soft Body Deformation by Using Light 1

引用

24th Annual Conference on Towards Autonomous Robotic Systems (TAROS)

作者： Sirithunge, Chapa Hashem, Ryman Iida, Fumiya Univ Cambridge Dept Engn Bioinspired Robot Lab Cambridge CB2 1PZ England UCL Dept Med Phys & Biomed Engn London WC1E 6BT England

ISBN: (数字)9783031433603

ISBN: (纸本)9783031433597;9783031433603

Soft robots are becoming increasingly popular due to their ability to mimic the flexibility and adaptability of biological systems. However, they still face challenges in terms of sensing and feedback methods. Elastomers can absorb light when exposed to a light source, and the absorption intensity can be varied when elastomers are on dynamic behaviour. In this paper, we explore the potential of using different light absorption properties of elastomers as a sensing and feedback technique in soft robots. We present experimental results that show the response of two most commonly encountered deformations of soft robots: linear and spherical, towards light absorption upon deformation. This concept can be used for assisting soft robotic sensing through freely available forms of light in the surrounding. Our findings suggest that using elastomers with different light absorption properties can significantly enhance the sensing performance of soft robots and open up new avenues for research in this field.

关键词： Soft robots light sensing deformation of materials

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Unsupervised Text Style Transfer Through Differentiable Back Translation and Rewards 1

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Bandyopadhyay, Dibyanayan Ekbal, Asif Indian Inst Technol Patna Dept Comp Sci & Engn Patna India

ISBN: (数字)9783031333835

ISBN: (纸本)9783031333828;9783031333835

In this paper, we propose an end-to-end system for unsupervised text style transfer (UTST). Prior studies on UTST work on the principle of disentanglement between style and content features, which successfully accomplishes the task of generating style-transferred text. The success of a style transfer system depends on three criteria, viz. Style transfer accuracy, Content preservation of source, and Fluency of the generated text. Generated text by disentanglement-based method achieves better style transfer performance but suffers from the lack of content preservation as the previous works suggest. To develop an all-around solution to all three aspects, we use a reinforcement learning-based training objective that gives rewards to the model for generating fluent style transferred text while preserving the source content. On the modeling aspect, we develop a shared encoder and style-specific decoder architecture which uses the Transformer architecture as a backbone. This modeling choice enables us to frame a differentiable back translation objective aiding better content preservation as shown through a careful ablation study. We conclude this paper with both automatic and human evaluation, showing the superiority of our proposed method on sentiment and formality style transfer tasks. Code is available at https://***/newcodevelop/Unsupervised-TST.

关键词： Unsupervised Text Style Transfer Back Translation Reinforcement Learning

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Integrating Ontological Knowledge with Probability Data to Aid Diagnosis in Radiology 21st

Integrating Ontological Knowledge with Probability Data to A...

引用

21st International Conference on artificial intelligence in Medicine (AIME)

作者： Kahn, Charles E., Jr. Univ Penn Dept Radiol Philadelphia PA 19104 USA Univ Penn Inst Biomed Informat Philadelphia PA 19104 USA

ISBN: (纸本)9783031343438;9783031343445

Radiological diagnosis requires integration of imaging observations with patient factors, such as age, sex, and medical history. Imaging manifestations of a disease can be highly variable;conversely, one imaging finding may suggest several possible causes. To account for the inherent uncertainty of radiological diagnosis, this report explores the integration of probability data with an ontology of radiological diagnosis. The Radiology Gamuts Ontology (RGO) incorporates 16,839 entities that define diseases, interventions, and imaging observations of relevance to diagnostic radiology. RGO's 55,564 causal ("may cause") relationships link disorders and their potential imaging manifestations. From a cohort of 1.7 million radiology reports on more than 1.3 million patients, the frequency of individual RGO entities and of their pairwise co-occurrence was identified. These data allow estimation of conditional probabilities of pairs of entities. A user interface enables one to traverse the ontology's network of causal relations with associated conditional-probability data. The system generates Bayesian network models that integrate an entity's age and sex distribution with its causally related conditions.

关键词： Ontology Knowledge Representation Probability Radiological Diagnosis Data Mining

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Continually Learning Out-of-Distribution Spatiotemporal Data for Robust Energy Forecasting

Continually Learning Out-of-Distribution Spatiotemporal Data...

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Prabowo, Arian Chen, Kaixuan Xue, Hao Sethuvenkatraman, Subbu Salim, Flora D. UNSW Sydney NSW Australia CSIRO Newcastle NSW Australia

ISBN: (纸本)9783031434297;9783031434303

Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to adjust energy use to improve energy efficiency and reduce costs. This importance is magnified during anomalous periods, such as the COVID-19 pandemic, which have disrupted occupancy patterns and made accurate forecasting more challenging. Forecasting energy usage during anomalous periods is difficult due to changes in occupancy patterns and energy usage behavior. One of the primary reasons for this is the shift in distribution of occupancy patterns, with many people working or learning from home. This has created a need for new forecasting methods that can adapt to changing occupancy patterns. Online learning has emerged as a promising solution to this challenge, as it enables building managers to adapt to changes in occupancy patterns and adjust energy usage accordingly. With online learning, models can be updated incrementally with each new data point, allowing them to learn and adapt in real-time. Continual learning methods offer a powerful solution to address the challenge of catastrophic forgetting in online learning, allowing energy forecasting models to retain valuable insights while accommodating new data and improving generalization in out-of-distribution scenarios. Another solution is to use human mobility data as a proxy for occupancy, leveraging the prevalence of mobile devices to track movement patterns and infer occupancy levels. Human mobility data can be useful in this context as it provides a way to monitor occupancy patterns without relying on traditional sensors or manual data collection methods. We have conducted extensive experiments using data from four buildings to test the efficacy of these approaches. However, deploying these methods in the real world presents several challenges.

关键词： Forecasting Energy Continual Learning COVID

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ONA for Autonomous ROS-Based Robots 15th

ONA for Autonomous ROS-Based Robots

引用

15th International Conference on artificial General intelligence (AGI)

作者： Hammer, Patrick Isaev, Peter Lofthouse, Tony Johansson, Robert Stockholm Univ Dept Psychol Stockholm Sweden Temple Univ Dept Comp Sci Philadelphia PA 19122 USA

ISBN: (纸本)9783031199066;9783031199073;9783031199066

In this paper we will show that OpenNARS for Applications (ONA) can be used for enhanced autonomous robots operating in the real world. It is done by utilizing state-of-the-art object detection along with Simultaneous Localization and Mapping techniques while inheriting the strengths of means-end reasoning and adding robust learning at runtime into the picture. This is showcased in an experiment where an ONA-controlled mobile robot with manipulator arm is learning about its environment to collect bottles to be returned to a human operator.

关键词： Reasoning under uncertainty Non-axiomatic reasoning system Procedure learning Means-end reasoning Autonomous robots

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