2021 IEEE International Conference on Data Mining (ICDM)最新文献

{"title":"Nonlinear Causal Structure Learning for Mixed Data","authors":"Wenjuan Wei, Lu Feng","doi":"10.1109/ICDM51629.2021.00082","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00082","url":null,"abstract":"Causal discovery from observational data is a fundamental problem. A large number of algorithms have been proposed over the years for that purpose, but they usually handle the data of a single type, either continuous or discrete variables only. Recently, a few causal structure discovery algorithms have been developed for mixed data types, and received many applications. In this paper, we propose a structural equation model for mixed data types, which allows the causal mechanisms to be nonlinear and can consequently model many read-world situations. We prove that the causal structure is identifiable from the data distribution generated by the model under certain conditions. Moreover, we propose a maximum likelihood estimator and develop an efficient order search algorithm benefiting from a novel method of order space cutting, which can handle several hundred variables. We adopt automatic relevance determination kernel-based variable selection after order learning to recover the causal structure. Experiments on synthetic datasets demonstrate the accuracy and scalability of our approach. Especially, we apply our method to publicly available causal-effect pairs and show its superiority in the causal direction identification of mixed causal pairs. In addition, we show that our method can sensibly recover causal relationships on a publicly available real dataset and a private real-world dataset.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114222890","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}

{"title":"Joint Scence Network and Attention-Guided for Image Captioning","authors":"Dongming Zhou, Jing Yang, Canlong Zhang, Yanping Tang","doi":"10.1109/ICDM51629.2021.00201","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00201","url":null,"abstract":"Image captioning is an interesting and challenging task. The previously established image captioning approach is based mainly on the encoder-decoder architecture, but it suffers from problems such as inaccurate captioning information, and the generated captioning sentences are not sufficiently rich. This paper proposes a novel image captioning model that is based on a self-attention network and a scene graph relationship network. First, an improved self-attention network is added to the extraction of visual features to evaluate the effectiveness of image global information for image generation. Then, we design a visual intensity parameter to coordinate the strategies of visual features and language model for word generation. Finally, a graph convolutional network is designed to extract the relationships from the scene information to render the generated caption more exciting and to increase the accuracy of the fine-grained captioning. We demonstrated the satisfactory performance of the model on the MS-COCO and Flickr 30K datasets. The experimental results demonstrate that the proposed model realizes state-of-the-art performance.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"312 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121173679","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}

{"title":"Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions","authors":"Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra, L. Vig, Gautam M. Shroff","doi":"10.1109/ICDM51629.2021.00026","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00026","url":null,"abstract":"We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, or activity recognition tasks over different individuals from multiple wearable sensors. We focus on two under-explored practical challenges arising in such settings: (i) Each task may have a different subset of sensors, i.e., providing different partial observations of the underlying ‘system’. This restriction can be due to different manufacturers in the former case, and people wearing more or less measurement devices in the latter (ii) We are not allowed to store or re-access data from a task once it has been observed at the task level. This may be due to privacy considerations in the case of people, or legal restrictions placed by machine owners. Nevertheless, we would like to (a) improve performance on subsequent tasks using experience from completed tasks as well as (b) continue to perform better on past tasks, e.g., update the model and improve predictions on even the first machine after learning from subsequently observed ones. We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks. In this work, we address this shortcoming of existing methods. To this end, we learn task-specific generative models and classifiers, and use these to augment data for target tasks. Since the input dimensions across tasks vary, we propose a novel conditioning module based on graph neural networks to aid a standard recurrent neural network. We evaluate the efficacy of the proposed approach on three publicly available datasets corresponding to two activity recognition tasks (classification) and one prognostics task (regression). We demonstrate that it is possible to significantly enhance the performance on future and previous tasks while learning continuously from VID tasks without storing data.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121208224","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}

{"title":"DCF: An Efficient and Robust Density-Based Clustering Method","authors":"Joshua Tobin, Mimi Zhang","doi":"10.1109/ICDM51629.2021.00074","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00074","url":null,"abstract":"Density-based clustering methods have been shown to achieve promising results in modern data mining applications. A recent approach, Density Peaks Clustering (DPC), detects modes as points with high density and large distance to points of higher density, and hence often fails to detect low-density clusters in the data. Furthermore, DPC has quadratic complexity. We here develop a new clustering algorithm, aiming at improving the applicability and efficiency of the peak-finding technique. The improvements are threefold: (1) the new algorithm is applicable to large datasets; (2) the algorithm is capable of detecting clusters of varying density; (3) the algorithm is competent at deciding the correct number of clusters, even when the number of clusters is very high. The clustering performance of the algorithm is greatly enhanced by directing the peak-finding technique to discover modal sets, rather than point modes. We present a theoretical analysis of our approach and experimental results to verify that our algorithm works well in practice. We demonstrate a potential application of our work for unsupervised face recognition.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"682 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122702710","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}

{"title":"Exploring Reflective Limitation of Behavior Cloning in Autonomous Vehicles","authors":"Mohammad Nazeri, M. Bohlouli","doi":"10.1109/ICDM51629.2021.00153","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00153","url":null,"abstract":"To become a standard part of our daily lives, autonomous vehicles must ensure human safety. This safety comes from knowing what will happen in the future. The most common approach in state-of-the-art methods for sensorimotor driving is behavior cloning. These models struggle to anticipate what will happen in the near future to better plan their actions. Humans do so by first observing what objects are present in the environment, and by studying their type and history, they can predict how they may evolve in the near future. Based on this observation, we first demonstrate the limitation of behavior cloning in making safe and reliable decisions. Then, we propose a hierarchical approach to teach an agent how to make safer decisions based on the plausible future. The key idea is instead of hand-picking future features we integrate a high-dimensional prediction module such as predicting future RGB/semantically segmented frames into our model to allow the model to learn the required features by itself. In the end, we demonstrate qualitatively and quantitatively that this approach yields safer decisions by the agent.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122841335","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}

{"title":"AdaBoosting Clusters on Graph Neural Networks","authors":"Li Zheng, Jun Gao, Zhao Li, Ji Zhang","doi":"10.1109/ICDM51629.2021.00199","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00199","url":null,"abstract":"Graph Neural Networks (GNNs), combining node features and structure information flexibly, have been widely studied and applied in many fields. The growth of graph size and rich features generates a considerable demand for achieving scalability while maintaining good classification performance in the research of GNNs. Graph partition technique, as used in a recent work ClusterGCN, which divides the graph into several sub-graphs, has become an important strategy to achieve the scalability, but the loss of information still affects the results. In this paper, AdClusterGCN is proposed to establish the interaction between graph partition and node classification, in which they can promote each other, and the effectiveness and efficiency of the model can be ensured at the same time. AdClusterGCN combines GNN models trained on a sequence of graph partitions to capture different features, where the current partition is affected using adjusted node/edge weights computed from the results of GNN models on previous partitions. The PageRank and resampling techniques are adopted to keep sufficient attention on important nodes in different models. We implement our method with TensorFlow and experimental studies show that AdClusterGCN achieves state-of-the-art performance on several public benchmarks.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132767774","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}

{"title":"Disentangled Deep Multivariate Hawkes Process for Learning Event Sequences","authors":"Xixun Lin, Jiangxia Cao, Peng Zhang, Chuan Zhou, Zhao Li, Jia Wu, Bin Wang","doi":"10.1109/ICDM51629.2021.00047","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00047","url":null,"abstract":"Multivariate Hawkes processes (MHPs) are classic methods to learn temporal patterns in event sequences of different entities. Traditional MHPs with explicit parametric intensity functions are friendly to model interpretability. However, recent Deep MHPs which employ various variants of recurrent neural networks are hardly to understand, albeit more expressive towards event sequences. The lack of model interpretability of Deep MHPs leads to a limited comprehension of complicated dynamics between events. To this end, we present a new Disentangled Deep Multivariate Hawkes Process $(mathrm{D}^{2}$ MHP) to enhance model expressiveness and meanwhile maintain model interpretability. $mathrm{D}^{2}$ MHP achieves state disentanglement by disentangling the latent representation of an event sequence into static and dynamic latent variables, and matches these latent variables to interpretable factors in the intensity function. Moreover, considering that an entity typically has multiple identities, $mathrm{D}^{2}$ MHP further splits these latent variables into factorized representations, each of which is associated with a corresponding identity. Experiments on real-world datasets show that $mathrm{D}^{2}$ MHP yields significant and consistent improvements over state-of-the-art baselines. We also demonstrate model interpretability via the detailed analysis.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134561225","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}

{"title":"STAN: Adversarial Network for Cross-domain Question Difficulty Prediction","authors":"Yeqing Huang, Wei Huang, Shiwei Tong, Zhenya Huang, Qi Liu, Enhong Chen, Jianhui Ma, Liang Wan, Shijin Wang","doi":"10.1109/ICDM51629.2021.00032","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00032","url":null,"abstract":"In intelligent education systems, question difficulty prediction (QDP) is a fundamental task of many applications, such as personalized question recommendation and test paper analysis. Previous work mainly focus on data-driven QDP methods, which are heavily relied on the large-scale labeled dataset of courses. To alleviate the labor intensity, an intuitive method is to introduce domain adaptation into QDP and consider each course as a domain. In educational psychology, there are two factors influencing difficulty common to different courses: the obstacles of comprehending the question and generating a response, namely stimulus and task difficulty. To this end, we propose a novel Stimulus and Task difficulty-based Adversarial Network (STAN) that models question difficulty from the views of stimulus and task. Then, in order to align the difficulty distribution of the source domain and the target domain, we utilize the conditional adversarial learning with readability-enhanced pseudo-labels. Meanwhile, we proposed a sampling method based on density estimation to implicit alignment. Finally, we conduct experiments on the real questions datasets to evaluate the effectiveness of our QDP model and domain adaptation method. Our method significantly improves accuracy over state-of-the-art methods on real-world question data of multiple courses.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134156906","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}

{"title":"Generating Structural Node Representations via Higher-order Features and Adversarial Learning","authors":"Wang Zhang, Yang Yu, Ting Pan, Lin Pan, Pengfei Jiao, Wenjun Wang","doi":"10.1109/ICDM51629.2021.00193","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00193","url":null,"abstract":"Role of node is defined on structural similarity or local connective pattern, describing the functions of node in the network. In real-world situation, it can denote person’s identity and status. It has been studied over the past decades, and learning role-based network representations is crucial to many downstream tasks. In this field, the important step for is extracting some measurements to evaluate structural similarity. Although some methods have been developed to capture the role features to learn the structural similarities between nodes, they all design the features of fixed types, such as global, local, and higher-order features. These features can only discover single type of roles, and simply combing them may cause damage to performance. It is very difficult to model the complex relationship between different scale features in the field of role-based network embedding. Therefore, we propose a novel adversarial framework to generate structural node representations via higher-order features and adversarial learning (SHOAL). We leverage the Auto-Encoder on higher-order features and some GNNs on its outputs to aggregate local neighbors. We believe that higher-order and local features can denote roles, and effectively integrating them will help for role discovery. So we consider the GNNs as the generator and design an adversarial game between these features, which can also improve the robustness. The experiments on real-world networks demonstrate the superiority and efficiency of our model, and the results also prove the effectiveness of integrating higher-order and local features.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132606755","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}

{"title":"Better Prevent than React: Deep Stratified Learning to Predict Hate Intensity of Twitter Reply Chains","authors":"Dhruv Sahnan, Snehil Dahiya, Vasu Goel, Anil Bandhakavi, Tanmoy Chakraborty","doi":"10.1109/ICDM51629.2021.00066","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00066","url":null,"abstract":"Given a tweet, predicting the discussions that unfold around it is convoluted, to say the least. Most if not all of the discernibly benign tweets which seem innocuous may very well attract inflammatory posts (hate speech) from people who find them non-congenial. Therefore, building upon the aforementioned task and predicting if a tweet will incite hate speech is of critical importance. To stifle the dissemination of online hate speech is the need of the hour. Thus, there have been a handful of models for the detection of hate speech. Classical models work retrospectively by leveraging a reactive strategy – detection after the postage of hate speech, i.e., a backward trace after detection. Therefore, a benign post that may act as a surrogate to invoke toxicity in the near future, may not be flagged by the existing hate speech detection models. In this paper, we address this problem through a proactive strategy initiated to avert hate crime. We propose DRAGNET, a deep stratified learning framework which predicts the intensity of hatred that a root tweet can fetch through its subsequent replies. We extend the collection of social media discourse from our earlier work [1], comprising the entire reply chains up to $sim$5k root tweets catalogued into four controversial topics Similar to [1], we notice a handful of cases where despite the root tweets being non-hateful, the succeeding replies inject an enormous amount of toxicity into the discussions. DRAGNET turns out to be highly effective, significantly outperforming six state-of-the-art baselines. It beats the best baseline with an increase of 9.4% in the Pearson correlation coefficient and a decrease of 19% in Root Mean Square Error. Further, DRAGNET’S deployment in Logically’s advanced AI platform designed to monitor real-world problematic and hateful narratives has improved the aggregated insights extracted for understanding their spread, influence and thereby offering actionable intelligence to counter them","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115149465","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}