2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第6页

Distributed Support Vector Machine Based on Distributed Loss 基于分布式损失的分布式支持向量机

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00019

Yuefeng Ma, Mengwei Wang

引用次数: 0

Doing Analogical Reasoning in Dynamic Assumption-based Argumentation Frameworks 在基于动态假设的论证框架中进行类比推理

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00063

Teeradaj Racharak

引用次数: 0

MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting 多变量时间序列预测的掩码自编码器

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00150

Peiwang Tang, Xianchao Zhang

{"title":"MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting","authors":"Peiwang Tang, Xianchao Zhang","doi":"10.1109/ICTAI56018.2022.00150","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00150","url":null,"abstract":"Large-scale self-supervised pre-training Transformer architecture have significantly boosted the performance for various tasks in natural language processing (NLP) and computer vision (CV). However, there is a lack of researches on processing multivariate time-series by pre-trained Transformer, and especially, current study on masking time-series for self-supervised learning is still a gap. Different from language and image processing, the information density of time-series increases the difficulty of research. The challenge goes further with the invalidity of the previous patch embedding and mask methods. In this paper, according to the data characteristics of multivariate time-series, a patch embedding method is proposed, and we present an self-supervised pre-training approach based on Masked Autoencoders (MAE), called MTSMAE, which can improve the performance significantly over supervised learning without pre-training. Evaluating our method on several common multivariate time-series datasets from different fields and with different characteristics, experiment results demonstrate that the performance of our method is significantly better than the best method currently available.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130643165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Expand-Extract: A Parallel Corpus Mining Framework from Comparable Corpora for English-Myanmar Machine Translation 扩展-提取:一种基于可比语料库的英汉缅语机器翻译并行语料库挖掘框架

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00045

May Myo Zin, Teeradaj Racharak, Minh Le Nguyen

{"title":"Expand-Extract: A Parallel Corpus Mining Framework from Comparable Corpora for English-Myanmar Machine Translation","authors":"May Myo Zin, Teeradaj Racharak, Minh Le Nguyen","doi":"10.1109/ICTAI56018.2022.00045","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00045","url":null,"abstract":"High-quality neural machine translation (NMT) systems rely on the availability of large-scale and reliable parallel data. Since Myanmar language is a low-resource language, the parallel corpus of English-Myanmar language pair is sparse in volume. In this paper, we present a simple yet effective framework to create a parallel corpus from the available comparable corpora. Our proposed system first uses self-training and back-translation approaches together with the denoising-based automatic post-editing (DbAPE) system for augmenting synthetic datasets that are used to expand the size of existing comparable corpora. Then, LaBSE-based sentence embeddings and the proposed scoring function are applied to extract parallel sentences from the expanded comparable corpora. The extracted parallel sentences can be used to supplement parallel corpus when training the low-resource English-Myanmar NMT systems. We investigate the effectiveness of our methods by evaluating the NMT systems trained on the concatenation of parallel data created by our framework and an existing dataset. We show that the proposed framework is capable of creating a reliable parallel corpus, and that the created corpus substantially increases translation quality of MT systems trained on the existing parallel data, as measured by automatic evaluation metrics.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117191900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Event Detection with Cross-Sentence Graph Convolutional Networks 基于交叉句子图卷积网络的事件检测

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00055

Shiming He, Yu Hong, Zhongqiu Li, Jianmin Yao, Guodong Zhou

{"title":"Event Detection with Cross-Sentence Graph Convolutional Networks","authors":"Shiming He, Yu Hong, Zhongqiu Li, Jianmin Yao, Guodong Zhou","doi":"10.1109/ICTAI56018.2022.00055","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00055","url":null,"abstract":"The goal of Event Detection (ED) task is to identify the words that mark the occurrence of events in text, and classify them into a set of event types. To model informative word semantics, some researchers apply Graph Convolutional Network (GCN) to exploit the syntactic graph transformed from the dependency tree, within one sentence. We are motivated to simultaneously leverage syntactic clues and context information across sentences. To this end, we propose a novel ED model with Cross-Sentence Graph Convolutional Networks (CSGCN). The CSGCN contains two main components, including a tree extension module and the syntax-aware graph convolution. Each sentence is parsed to a dependency tree by an automatic toolkit. The first module merges entity-specific subtrees from neighbor sentences into the dependency tree of current sentence, which constructs a cross-sentence dependency tree. On this basis, we transform the tree into an undirected graph. After that, a syntax-aware attention mechanism is employed in the computation of graph convolution. This mechanism dynamically captures syntax-relevant information from neighbor nodes via the graph structure. Finally, we devise an entity aggregation module to aggregate key entity information for trigger candidates. We conduct experiments on the ACE 2005 and KBP 2017 datasets. The results show that our model achieves satisfactory and competitive performance on ACE 2005, and outperforms all State-of-The-Art models on KBP 2017.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132366522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A grouping-based AdaBoost method for factor investing 基于分组的AdaBoost要素投资方法

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00011

Yujie Ding, Wenting Tu, Chuan Qin, Jun Chang

{"title":"A grouping-based AdaBoost method for factor investing","authors":"Yujie Ding, Wenting Tu, Chuan Qin, Jun Chang","doi":"10.1109/ICTAI56018.2022.00011","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00011","url":null,"abstract":"Constructing a quantitative factor investment strategy based on hundreds of candidate factors is a critical challenge. Existing linear models do not account for nonlinearities and variable interactions, while complex machine learning models are easily overfitting. In this paper, motivated by the portfolio sorts methods in empirical asset pricing, we propose an alternative approach called grouping-based AdaBoost by adapting the existing AdaBoost. It introduces the experience of the financial field into the algorithm design to improve the performance and generalization of machine learning-based factor investing strategies. The proposed method restricts the factor to only predict the common part of the returns of the same groups and allows the potential nonlinear relationship between a factor and the return. Moreover, to enhance the model's ability to use factors with high correlation, we extend the single-grouping AdaBoost in a multi-grouping way. Experiments on the Chinese A-share market demonstrate the effectiveness of our approach in both stock performance classification and portfolio selection and provide intuitive evidence for the generalization of the proposed method.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132791782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Path-aware Multi-hop Question Answering Over Knowledge Graph Embedding 基于知识图嵌入的路径感知多跳问答

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00074

Jingchao Wang, Weimin Li, Yixing Guo, Xiaokang Zhou

{"title":"Path-aware Multi-hop Question Answering Over Knowledge Graph Embedding","authors":"Jingchao Wang, Weimin Li, Yixing Guo, Xiaokang Zhou","doi":"10.1109/ICTAI56018.2022.00074","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00074","url":null,"abstract":"Question answering over knowledge graph (KGQA) aims at answering questions posed over the knowledge graph (KG). Multi-hop KGQA requires multi-hop reasoning on KG to achieve the correct answer. Unfortunately, KGs are usually incomplete with many missing links, which poses additional challenges to KGQA. KG embedding-based KGQA methods have recently been proposed as a way to overcome this limitation. However, existing KG embedding-based KGQA methods fail to take full advantage of semantic correlations between questions and paths. Furthermore, their inference process is not easily explainable. To address these challenges, we propose a novel path-aware multi-hop KGQA model (PA-KGQA), which can fully capture semantic correlations between the paths and the questions in a feature-interactive manner. Specifically, we introduce a case-enhanced path retriever to evaluate the importance of paths between topic entities and candidate answer entities, and then propose an interactive convolutional neural network (ICNN) to model the interactions between paths and questions for mining richer correlation features. Experiments show that PA-KGQA achieves state-of-the-art results on multiple benchmark datasets and is explainable.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128316302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Emotion Evolution Network for Emotion Recognition in Conversation 会话中情感识别的情感进化网络

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00187

Shimin Tang, Changjian Wang, Kele Xu, Zhen Huang, Minpeng Xu, Yuxing Peng

{"title":"An Emotion Evolution Network for Emotion Recognition in Conversation","authors":"Shimin Tang, Changjian Wang, Kele Xu, Zhen Huang, Minpeng Xu, Yuxing Peng","doi":"10.1109/ICTAI56018.2022.00187","DOIUrl":"https://doi.org/10.1109/ICTAI56018.2022.00187","url":null,"abstract":"Emotion recognition in conversation (ERC) aims to detect the emotion in a conversation, which has drawn increasing interests due to its widely applications. Current methodologies mainly endeavor to capture a good representation of conversation context. However, we argue that the conversation context are not always consistent with the emotion evolution. This incongruity can greatly restrict the recognition performance. To address aforementioned challenges, in this paper, we propose an emotion evolution network for emotion recognition in conversation (E2Net). Specifically, a speaker-aware modeling methodology is firstly constructed to fuse the utterance from conversations. We employ the gated recurrent unit (GRU) encodes the utterance sequentially. For encoding the interaction between speakers, a listener state is introduced to aid in analyzing conversation context. Then, a Transformer-based method is proposed to capture the emotion evolution accompanying with the emotion transformation matrix. To demonstrate the superior performance of our proposed method, extensive experiments are conducted on four REC datasets and the experimental results suggest that our method is effective and outperforms the current state-of-the-art methods on multiple datasets.","PeriodicalId":354314,"journal":{"name":"2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131902080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Methods for Animal Counting in Camera Trap Images 摄像机陷阱图像中动物计数的深度学习方法

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00143

Yizhen Wang, Yang Zhang, Yuanyao Feng, Y. Shang

引用次数: 0

Deep-Cogn: Skeleton-based Human Action Recognition for Cognitive Behavior Assessment 深度认知:基于骨骼的人类行为识别的认知行为评估

2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2022-10-01 DOI: 10.1109/ICTAI56018.2022.00107

Sayda Elmi, Morris Bell, K. Tan

引用次数: 1