2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第4页

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2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ictai.2019.00002

引用次数: 0

Information-Theoretic Ensemble Learning for DDoS Detection with Adaptive Boosting 基于信息论集成学习的自适应增强DDoS检测

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00140

M. Bhuyan, M. Ma, Y. Kadobayashi, E. Elmroth

引用次数: 0

SARC: Split-and-Recombine Networks for Knowledge-Based Recommendation 基于知识的推荐的拆分和重组网络

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00096

Weifeng Zhang, Yi Cao, Congfu Xu

{"title":"SARC: Split-and-Recombine Networks for Knowledge-Based Recommendation","authors":"Weifeng Zhang, Yi Cao, Congfu Xu","doi":"10.1109/ICTAI.2019.00096","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00096","url":null,"abstract":"Utilizing knowledge graphs (KGs) to improve the performance of recommender systems has attracted increasing attention recently. Existing path-based methods rely heavily on manually designed meta-paths, while embedding-based methods focus on incorporating the knowledge graph embeddings (KGE) into recommender systems, but rarely model user-entity interactions, which can be used to enhance the performance of recommendation. To overcome the shortcomings of previous works, we propose SARC, an embedding-based model that utilizes a novel Split-And-ReCombine strategy for knowledge-based recommendation. Firstly, SARC splits the user-item-entity interactions into three 2-way interactions, i.e., the user-item, user-entity and item-entity interactions. Each of the 2-way interactions can be cast as a graph, and we use Graph Neural Networks (GNN) and KGE to model them. Secondly, SARC recombines the representation of users and items learned from the first step to generates recommendation. In order to distinguish the informative part and meaningless part of the representations, we utilize a gated fusion mechanism. The advantage of our SARC model is that through splitting, we can easily handle and make full use of the 2-way interactions, especially the user-entity interactions, and through recombining, we can extract the most useful information for recommendation. Extensive experiments on three real-world datasets demonstrate that SARC outperforms several state-of-the-art baselines.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125529539","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: 1

Feature-Selected and -Preserved Sampling for High-Dimensional Stream Data Summary 高维流数据汇总的特征选择和保留采样

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00198

Ling Lin, Qian Yu, Wen Ji, Yang Gao

{"title":"Feature-Selected and -Preserved Sampling for High-Dimensional Stream Data Summary","authors":"Ling Lin, Qian Yu, Wen Ji, Yang Gao","doi":"10.1109/ICTAI.2019.00198","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00198","url":null,"abstract":"Along with the prosperity of the Mobile Internet, a large amount of stream data has emerged. Stream data cannot be completely stored in memory because of its massive volume and continuous arrival. Moreover, it should be accessed only once and handled in time due to the high cost of multiple accesses. Therefore, the intrinsic nature of stream data calls facilitates the development of a summary in the main memory to enable fast incremental learning and to allow working in limited time and memory. Sampling techniques are one of the commonly used methods for constructing data stream summaries. Given that the traditional random sampling algorithm deviates from the real data distribution and does not consider the true distribution of the stream data attributes, we propose a novel sampling algorithm based on feature-selected and -preserved algorithm. We first use matrix approximation to select important features in stream data. Then, the feature-preserved sampling algorithm is used to generate high-quality representative samples over a sliding window. The sampling quality of our algorithm could guarantee a high degree of consistency between the distribution of attribute values in the population (the entire data) and that in the sample. Experiments on real datasets show that the proposed algorithm can select a representative sample with high efficiency.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126830373","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: 1

Swarm Filter - A Simple Deep Learning Component Inspired by Swarm Concept Swarm Filter -一个受Swarm概念启发的简单深度学习组件

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00221

Nguyen Ha Thanh, Le-Minh Nguyen

引用次数: 2

Multi-graph Convolution Network with Jump Connection for Event Detection 具有跳跃连接的多图卷积网络用于事件检测

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00108

Xiangbin Meng, Pengfei Wang, Haoran Yan, Liutong Xu, Jiafeng Guo, Yixing Fan

{"title":"Multi-graph Convolution Network with Jump Connection for Event Detection","authors":"Xiangbin Meng, Pengfei Wang, Haoran Yan, Liutong Xu, Jiafeng Guo, Yixing Fan","doi":"10.1109/ICTAI.2019.00108","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00108","url":null,"abstract":"Event detection is an important information extraction task in nature language processing. Recently, the method based on syntactic information and graph convolution network has been wildly used in event detection task and achieved good performance. For event detection, graph convolution network (GCN) based on dependency arcs can capture the sentence syntactic representations and the syntactic information, which is from candidate triggers to arguments. However, existing methods based on GCN with dependency arcs suffer from imbalance and redundant information in graph. To capture important and refined information in graph, we propose Multi-graph Convolution Network with Jump Connection (MGJ-ED). The multi-graph convolution network module adds a core subgraph splitted from dependency graph which selects important one-hop neighbors' syntactic information in breadth via GCN. Also the jump connection architecture aggregate GCN layers' representation with different attention score, which learns the importance of neighbors' syntactic information of different hops away in depth. The experimental results on the widely used ACE 2005 dataset shows the superiority of the other state-of-the-art methods.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123219932","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

Multi-task Learning for Relation Extraction 关系抽取的多任务学习

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00210

Kai Zhou, Xiangfeng Luo, Hongya Wang, R. Xu

{"title":"Multi-task Learning for Relation Extraction","authors":"Kai Zhou, Xiangfeng Luo, Hongya Wang, R. Xu","doi":"10.1109/ICTAI.2019.00210","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00210","url":null,"abstract":"Distantly supervised relation extraction leverages knowledge bases to label training data automatically. However, distant supervision may introduce incorrect labels, which harm the performance. Many efforts have been devoted to tackling this problem, but most of them treat relation extraction as a simple classification task. As a result, they ignore useful information that comes from related tasks, i.e., dependency parsing and entity type classification. In this paper, we first propose a novel Multi-Task learning framework for Relation Extraction (MTRE). We employ dependency parsing and entity type classification as auxiliary tasks and relation extraction as the target task. We learn these tasks simultaneously from training instances to take advantage of inductive transfer between auxiliary tasks and the target task. Then we construct a hierarchical neural network, which incorporates dependency and entity representations from auxiliary tasks into a more robust relation representation against the noisy labels. The experimental results demonstrate that our model improves the predictive performance substantially over single-task learning baselines.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123081134","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: 1

Learning Effective Neural Nets for Outcome Prediction from Partially Labelled Log Data 从部分标记的日志数据中学习有效的神经网络预测结果

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00196

Francesco Folino, G. Folino, M. Guarascio, L. Pontieri

{"title":"Learning Effective Neural Nets for Outcome Prediction from Partially Labelled Log Data","authors":"Francesco Folino, G. Folino, M. Guarascio, L. Pontieri","doi":"10.1109/ICTAI.2019.00196","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00196","url":null,"abstract":"The problem of inducing a model for forecasting the outcome of an ongoing process instance from historical log traces has attracted notable attention in the field of Process Mining. Approaches based on deep neural networks have become popular in this context, as a more effective alternative to previous feature-based outcome-prediction methods. However, these approaches rely on a pure supervised learning scheme, and unfit many real-life scenarios where the outcome of (fully unfolded) training traces must be provided by experts. Indeed, since in such a scenario only a small amount of labeled traces are usually given, there is a risk that an inaccurate or overfitting model is discovered. To overcome these issues, a novel outcome-discovery approach is proposed here, which leverages a fine-tuning strategy that learns general-enough trace representations from unlabelled log traces, which are then reused (and adapted) in the discovery of the outcome predictor. Results on real-life data confirmed that our proposal makes a more effective and robust solution for label-scarcity scenarios than current outcome-prediction methods.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126254546","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: 7

Fine-Grained Image Classification Combined with Label Description 结合标签描述的细粒度图像分类

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00148

Xiruo Shi, Liutong Xu, Pengfei Wang

{"title":"Fine-Grained Image Classification Combined with Label Description","authors":"Xiruo Shi, Liutong Xu, Pengfei Wang","doi":"10.1109/ICTAI.2019.00148","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00148","url":null,"abstract":"Fine-grained image classification faces huge challenges because fine-grained images are similar overall, and the distinguishable regions are difficult to find. Generally, in this task, label descriptions contain valuable semantic information that is accurately compatible with discriminative features of images (i.e., the description of the \"Rusty Black Bird\" corresponding to the morphological characteristics of its image). Bringing these descriptions into consideration is benefit to discern these similar images. Previous works, however, usually ignore label descriptions and just mine informative features from images, thus the performance may be limited. In this paper, we try to take both label descriptions and images into consideration, and we formalize the classification task into a matching task to address this issue. Specifically, Our model is based on a combination of Convolutional Neural Networks (CNN) over images and Graph Convolutional Networks(GCN) over label descriptions. We map the resulting image representations and text representations to the same dimension for matching and achieve the purpose of classification through the matching operation. Experimental results demonstrate that our approach can achieve the best performance compared with the state-of-the-art methods on the datasets of Stanford dogs and CUB-200-2011.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130127662","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: 3

An Expansion Convolution Method Based on Local Region Parameter Sharing 基于局部区域参数共享的展开卷积方法

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00204

Qimao Yang, Jun Guo

引用次数: 1