2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第3页

Modeling Uncertainty and Inaccuracy on Data from Crowdsourcing Platforms: MONITOR 众包平台数据的不确定性和不准确性建模:MONITOR

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00112

Constance Thierry, Jean-Christophe Dubois, Y. Gall, Arnaud Martin

引用次数: 5

Facial Expression Recognition: Residue Learning Using SVM 面部表情识别:基于SVM的残差学习

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00246

Fangjun Wang, Liping Shen

引用次数: 0

Budget-Constrained Demand-Weighted Network Design for Resilient Infrastructure 弹性基础设施的预算约束需求加权网络设计

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00070

Amrita Gupta, B. Dilkina

{"title":"Budget-Constrained Demand-Weighted Network Design for Resilient Infrastructure","authors":"Amrita Gupta, B. Dilkina","doi":"10.1109/ICTAI.2019.00070","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00070","url":null,"abstract":"Our work is motivated by an important network design problem in climate adaptation. As floods become more frequent and severe due to climate change, it is increasingly crucial that road infrastructure be strategically upgraded to support post-disaster recovery efforts and normal functionality. We focus on the problem of allocating a fixed budget towards restoring edges to maximize the satisfied travel demand between locations in a network, which we formalize as the budget-constrained prize-collecting Steiner forest problem. We prove that the satisfiable travel demand objective exhibits restricted supermodularity over forests, and utilize this property to design an iterative algorithm based on maximizing successive modular lower bounds for the objective that finds better solutions than a baseline greedy approach. We also propose an extremely fast heuristic for maximizing modular functions subject to knapsack and graph matroid constraints that can be used as a subroutine in the iterative algorithm, or as a standalone method that matches the greedy baseline in terms of quality but is orders of magnitude faster. We evaluate the algorithms on synthetic data, and apply them to a real-world instance of retrofitting the Senegal national road network against flooding.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"2 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":"128672291","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

Regularized Non-Negative Spectral Embedding for Clustering 用于聚类的正则化非负谱嵌入

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00075

Yifei Wang, Rui Liu, Yong Chen, Hui Zhang, Zhiwen Ye

引用次数: 1

Threshold Based Optimization of Performance Metrics with Severely Imbalanced Big Security Data 基于阈值的严重不平衡大安全数据性能指标优化

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00184

Chad L. Calvert, T. Khoshgoftaar

{"title":"Threshold Based Optimization of Performance Metrics with Severely Imbalanced Big Security Data","authors":"Chad L. Calvert, T. Khoshgoftaar","doi":"10.1109/ICTAI.2019.00184","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00184","url":null,"abstract":"Proper evaluation of classifier predictive models requires the selection of appropriate metrics to gauge the effectiveness of a model's performance. The Area Under the Receiver Operating Characteristic Curve (AUC) has become the de facto standard metric for evaluating this classifier performance. However, recent studies have suggested that AUC is not necessarily the best metric for all types of datasets, especially those in which there exists a high or severe level of class imbalance. There is a need to assess which specific metrics are most beneficial to evaluate the performance of highly imbalanced big data. In this work, we evaluate the performance of eight machine learning techniques on a severely imbalanced big dataset pertaining to the cyber security domain. We analyze the behavior of six different metrics to determine which provides the best representation of a model's predictive performance. We also evaluate the impact that adjusting the classification threshold has on our metrics. Our results find that the C4.5N decision tree is the optimal learner when evaluating all presented metrics for severely imbalanced Slow HTTP DoS attack data. Based on our results, we propose that the use of AUC alone as a primary metric for evaluating highly imbalanced big data may be ineffective, and the evaluation of metrics such as F-measure and Geometric mean can offer substantial insight into the true performance of a given model.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"64 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":"127226953","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: 10

Accelerating Nash Q-Learning with Graphical Game Representation and Equilibrium Solving 用图形博弈表示和均衡求解加速纳什q -学习

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00133

Yunkai Zhuang, Xingguo Chen, Yang Gao, Yujing Hu

引用次数: 1

Robust Point Set Registration with Mixture Re-Weighting Based on Relative Geometric Structures 基于相对几何结构的混合重加权鲁棒点集配准

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00114

Yucheng Shu, Zhenlong Liao, Dan Luo

{"title":"Robust Point Set Registration with Mixture Re-Weighting Based on Relative Geometric Structures","authors":"Yucheng Shu, Zhenlong Liao, Dan Luo","doi":"10.1109/ICTAI.2019.00114","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00114","url":null,"abstract":"Point set registration is one of the challenging tasks in computer vision. One critical step is to find the corresponding relationship between the model point set and the scene point set. Existing registration algorithms primarily utilize the information of global and local shape, yet neglect the credibility of corresponding relation, therefore they may lead to the insufficient estimation of spatial transformation. To tackle this problem, we firstly adopt a relative polar coordinate system, it performs spatial pooling operation and further divides the feature extraction region into sub-areas with different scales. Then, based on the Relative Average Distance (RAD) and the Relative Average Offset Angle (RAOA), we propose multi-granular MRGS descriptor to extract visual structures of the point set. The similarity between the model point set and scene point set is then represented by the Gaussian Mixture Model, where the weights can be dynamically adjusted during the process of registration. Finally, we apply the robust mixture re-weighting to reduce the impact of false corresponding pairs and reinforce the weight of correct matching points. Experimental results on synthetic data and medical image data not only show that our method outperform state-of-the-art methods, but also demonstrate the robustness of our method when the non-grid transformation of point sets suffers from deformations, noises and outliers.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"11 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":"127514726","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

Agnostic Local Explanation for Time Series Classification 时间序列分类的不可知论局部解释

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00067

Maël Guillemé, Véronique Masson, L. Rozé, A. Termier

{"title":"Agnostic Local Explanation for Time Series Classification","authors":"Maël Guillemé, Véronique Masson, L. Rozé, A. Termier","doi":"10.1109/ICTAI.2019.00067","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00067","url":null,"abstract":"Recent advances in Machine Learning (such as Deep Learning) have brought tremendous gains in classification accuracy. However, these approaches build complex non-linear models, making the resulting predictions difficult to interpret for humans. The field of model interpretability has therefore recently emerged, aiming to address this issue by designing methods to explain a posteriori the predictions of complex learners. Interpretability frameworks such as LIME and SHAP have been proposed for tabular, image and text data. Nowadays, with the advent of the Internet of Things and of pervasive monitoring, time-series have become ubiquitous and their classification is a crucial task in many application domains. Like in other data domains, state-of-the-art time-series classifiers rely on complex models and typically do not provide intuitive and easily interpretable outputs, yet no interpretability framework had so far been proposed for this type of data. In this paper, we propose the first agnostic Local Explainer For TIme Series classificaTion (LEFTIST). LEFTIST provides explanations for predictions made by any time series classifier. Our thorough experiments on synthetic and real-world datasets show that the explanations provided by LEFTIST are at once faithful to the classification model and understandable by human users.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"22 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":"126963699","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: 27

Towards Explainable Multi-Label Classification 迈向可解释的多标签分类

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00152

Karim Tabia

引用次数: 1

Feature Space Regularization for Person Re-identification with One Sample 单样本人再识别的特征空间正则化

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00208

TIan Xu, Jiangli Li, Hao Wu, Huafeng Yang, Xiaoming Gu, Yanqiu Chen

{"title":"Feature Space Regularization for Person Re-identification with One Sample","authors":"TIan Xu, Jiangli Li, Hao Wu, Huafeng Yang, Xiaoming Gu, Yanqiu Chen","doi":"10.1109/ICTAI.2019.00208","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00208","url":null,"abstract":"Few Shot Learning is a solution to relieve the huge annotation cost in Person Re-Identification. We concentrate on one sample setting in this work, where each identity has only one labeled sample along with many unlabeled samples. Training with one sample setting, the model is easily biased towards certain identities. Moreover, a reliable pseudo-label estimation scheme can greatly improve the final performance of the model. Targeting to solve the issues above, we propose two simple and effective solutions. (a) We design the Feature Space Regularization (FSR) Loss to adjust the distribution of samples in feature space. The FSR loss make the difference in distance of all labeled samples to unlabeled samples as small as possible. (b) We propose combining the Nearest Neighbor distance with inter-class distance to estimate pseudo-label for unlabeled data, which we called Joint-Distance. Notably, the Rank-1 accuracy of our method outperforms the state of the art method by a large margin of 12.1 points (absolute, i.e., 67.9% vs. 55.8%) on Market-1501, and 10.1 points (absolute, i.e., 58.9% vs. 48.8%) on DukeMTMC-reID, respectively. We will release all the code in https://github.com/Freedomxt/Feature_Space_Regularization_for_person_Re-Identification_with_One_Sample.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"55 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":"132008412","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: 6