OpenRaiser/Intern-Atlas huggingface dataset:
CamDrop: A New Explanation of Dropout and A Guided Regularization Method for Deep Neural Networks
Authors: Hongjun Wang, Guangrun Wang, Guanbin Li, Liang Lin
Venue: CIKM
Year: 2019

To force convolutional networks to explore more discriminative evidence throughout spatial regions, this paper presents a novel CamDrop to improve the conventional dropout in two aspects. First, by considering the intensity of class activation mapping (CAM) all around, CamDrop selectively abandons some specific spatial regions in predominating visual patterns at each iteration. In many classification tasks, CamDrop demonstrates its effectiveness and achieves considerable improvements on robust predictions for adversarial examples. Second, although dropout is a widely adopted technique that has been applied to regularize large models, the improvement in performance always attributes to better preventing DNN from overfitting. Here we give a new explanation of dropout from the perspective of optimization that it makes the upper bound of the magnitude of gradients much tighter, which leads to a more stable behavior of the gradients and effectively avoids neurons falling into the saturation region of the nonlinear activation, even when using high learning rates. Extensive experiments have been performed to prove the above two strengths of CamDrop.
Video-level Multi-model Fusion for Action Recognition
Authors: Xiaomin Wang, Junsan Zhang, Leiquan Wang, Philip S. Yu, Jie Zhu, Haisheng Li
Venue: CIKM
Year: 2019

The approaches based on spatio-temporal features for video action recognition have emerged such as two-stream based methods and 3D convolution based methods. However, current methods suffer from the problems caused by partial observation, or restricted to single information modeling, and so on. Segment-level recognition results obtained from dense sampling can not represent the entire video and, therefore lead to partial observation. And a single model is hard to capture the complementary information on spacial, temporal and spatio-temporal information from video at the same time. Therefore, the challenge is to build the video-level representation and capture multiple information. In this paper, a video-level multi-model fusion action recognition method is proposed to solve these problems. Firstly, an efficient video-level 3D convolution model is proposed to get the global information in the video which assembling segment-level 3D convolution models. Secondly, a multi-model fusion architecture is proposed for video action recognition to capture multiple information. The spatial, temporal and spatio-temporal information are aggregate with SVM classifier. Experimental results show that this method achieves the state-of-the-art performance on the datasets of UCF-101(97.6%) without pre-training on Kinetics.
Knowledge Transfer based on Multiple Manifolds Assumption
Authors: Pengfei Wei, Yiping Ke
Venue: CIKM
Year: 2019

Unsupervised domain adaptation is a popular but challenging problem setting. Existing unsupervised domain adaptation methods are based on the single manifold assumption, i.e., data are sampled from a single low-dimensional manifold, and thus may not well capture the complex characteristic of the real-world data. In this paper, we propose to transfer knowledge across domains under the multiple manifolds assumption that assumes the data are sampled from multiple low-dimensional manifolds. Specifically, we develop a multiple manifolds information transfer framework (MMIT). The proposed MMIT aims to transfer the multiple manifolds information, which is represented by the data manifold neighborhood structure, with the the best adaptation capacity. To do so, we propose to couple the multiple manifolds information transfer with the domain distribution discrepancy minimization in the adaptation procedure. Experimental studies demonstrate that MMIT achieves the promising adaptation performance on various real-world adaptation tasks.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
Authors: Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Weinan Zhang, Yanmin Zhu, Kai Xu, Zhenhui Li
Venue: CIKM
Year: 2019

Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.
Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy
Authors: Jun Wu, Jingrui He
Venue: CIKM
Year: 2019

Networks are ubiquitous in many real-world applications due to their capability of representing the rich information in the data. One fundamental problem of network analysis is to learn a low- dimensional vector representation for nodes within the attributed networks. However, there is little work theoretically considering the information heterogeneity from the attributed networks, and most of the existing attributed network embedding techniques are able to capture at most k-th order node proximity, thus leading to the information loss of the long-range spatial dependencies between individual nodes across the entire network. To address the above problems, in this paper, we propose a novel MAnifold-RegularIzed Network Embedding (MARINE) algorithm inspired by minimizing the information discrepancy in a Reproducing Kernel Hilbert Space via Maximum Mean Discrepancy. In particular, we show that MARINE recursively aggregates the graph structure information as well as individual node attributes from the entire network, and thereby preserves the long-range spatial dependencies between nodes across the network. The experimental results on real networks demonstrate the effectiveness and efficiency of the proposed MARINE algorithm over state-of-the-art embedding methods.
Long- and Short-term Preference Learning for Next POI Recommendation
Authors: Yuxia Wu, Ke Li, Guoshuai Zhao, Xueming Qian
Venue: CIKM
Year: 2019

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model users' general taste and recent sequential behavior. Moreover, the context information such as the category and check-in time is also important to capture user preference. To this end, we propose a long- and short-term preference learning model (LSPL) considering the sequential and context information. In long-term module, we learn the contextual features of POIs and leverage attention mechanism to capture users' preference. In the short-term module, we utilize LSTM to learn the sequential behavior of users. Specifically, to better learn the different influence of location and category of POIs, we train two LSTM models for location-based sequence and category-based sequence, respectively. Then we combine the long and short-term results to recommend next POI for users. At last, we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.
NICE: Neural In-Hospital Cost Estimation from Medical Records
Authors: Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
Venue: CIKM
Year: 2019

Estimating in-hospital costs from medical records is an important task with many applications such as accountable care. Existing methods for this task usually rely on manual feature engineering which needs massive domain knowledge, and do not exploit the textual information in medical records, e.g., diagnosis and operation texts. In this paper, we propose a neural in-hospital cost estimation (NICE) approach to estimate the in-hospital costs of patients from their admission records. Our approach can exploit the heterogeneous information in records, such as patient features, diagnosis/operation texts, and the diagnosis/operation IDs, via a multi-view learning framework. In addition, since different words, diagnoses and operations have different importance for cost estimation, we propose a hierarchical attention network to select important words, diagnoses and operations for learning informative record representations. Extensive experiments on a real-world medical dataset validate the effectiveness of our approach.
Sentiment Lexicon Enhanced Neural Sentiment Classification
Authors: Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang, Xing Xie
Venue: CIKM
Year: 2019

Sentiment classification is an important task in the sentiment analysis field. Many deep learning based sentiment classification methods have been proposed in recent years. However, these methods usually rely on massive labeled texts to train sentiment classifiers, which are expensive and time-consuming to annotate. Luckily, many high-quality sentiment lexicons have been constructed and can cover a large number of sentiment words. Since sentiment words are the basic units to convey sentiments in texts, these sentiment lexicons have the potential to improve the performance of neural sentiment classification. In this paper, we propose two approaches to exploit sentiment lexicons to enhance neural sentiment classification. In our first approach we use sentiment lexicons to learn sentiment-aware attentions. We propose a word sentiment classification task to classify the sentiments of words in a sentence based on their hidden representations in the attention network of neural sentiment classification models. We jointly train this task with neural sentiment classifier to facilitate the attention network to recognize and highlight sentiment-bearing words. In our second approach we use sentiment lexicons to learn sentiment-aware word embeddings. We design an auxiliary task to classify the sentiments of words in sentiment lexicons based on their word embeddings, and jointly train this task with neural sentiment classifier to encode sentiment information in sentiment lexicons to word embeddings. Extensive experiments on three benchmark datasets validate the effectiveness of our approach.
Neural Gender Prediction in Microblogging with Emotion-aware User Representation
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Junxin Liu, Yongfeng Huang, Xing Xie
Venue: CIKM
Year: 2019

Demographics of social media users such as gender are very important for personalized online services. However, the gender information of many users is usually not available. Luckily, the messages posted by social media users can provide rich clues for inferring their genders, since male and female users usually have differences in their message content. In addition, users with different genders often have different patterns in expressing emotions. In this paper, we propose a neural approach for gender prediction in social media based on both content and emotion of messages posted by users. The core of our approach is an emotion-aware hierarchical user representation model. Our model first learns message representations from words using message encoder and then learns user representations from messages using user encoder with hierarchical attention networks selecting important words and messages to learn informative user representations. In addition, we propose two methods to incorporate emotion information in messages into user representation learning. The first one is to incorporate emotion-aware message representations generated by a pre-trained emotion classifier into message representations. The second one is to train emotion-aware message encoders via jointly training our model with an auxiliary emotion classification task. Extensive experiments on two real-world datasets validate the effectiveness of our approach.
Collaborative Analysis for Computational Risk in Urban Water Supply Systems
Authors: Di Wu, Hao Wang, Razak Seidu
Venue: CIKM
Year: 2019

Urban Water Supply (UWS) is one of the most critical and sensitive systems to sustain overall city operations. The European Union (EU) has strict water quality regulations that currently depend on periodic laboratory tests of selected parameters in most of the cases. The tests of some biological parameters can take up to 48 hours, which leads to the delay of risk detection and lengthens the response time for taking countermeasures in UWS. This situation increases the risk of negative impacts to the health of mass population. To address this challenge, we propose a data-driven risk analysis method which is low-cost and efficient. First, we build a framework for risk evaluation and prediction, within which a risk evaluation model is introduced considering the Quantitative Microbiological Risk Assessment (QMRA) process suggested by the World Health Organization (WHO). Second, we present a collaborative method to analyze biological risk features and similarities across different locations. Third, we propose a new risk prediction algorithm. We apply this method on the real-world data collected from 4 UWS systems in Norway. The preliminary results are depicted in risk maps and prediction accuracies are compared with different strategies. The application results show that our method is practical with good accuracy and explainability.
Multi-view Moments Embedding Network for 3D Shape Recognition
Authors: Jun Xiao, Yuanxing Zhang, Pengyu Zhao, Kecheng Xiao, Kaigui Bian, Chunli Zhang, Wei Yan
Venue: CIKM
Year: 2019

Benefited from rapid developments of deep learning, 3D shape recognition has become a remarkable subject in computer vision systems.The existing methods of multi-perspective views have shown competitive performance in 3D shape recognition.However, they have not yet fully exploited the information among all views of projection.In this paper, we propose a novel Multi-view Moments Embedding Network(MMEN) for capturing multiple moments information.MMEN obtains the similarity between different views and retains the description of the original view by generating moments matrix for representing the general features of the 3D shape.Additionally, we apply the matrix square-root layer to perform a non-linear scaling to the eigenvalues of the moment embedding matrix.We compare the performance of our proposed network with several state-of-the-art models on the ModelNet datasets, and the results of the average instance/class accuracy demonstrate the promising performance of MMEN on 3D shape recognition.
Learning Traffic Signal Control from Demonstrations
Authors: Yuanhao Xiong, Guanjie Zheng, Kai Xu, Zhenhui Li
Venue: CIKM
Year: 2019

Reinforcement learning (RL) has recently become a promising approach in various decision-making tasks. Among them, traffic signal control is the one where RL makes a great breakthrough. However, these methods always suffer from the prominent exploration problem and even fail to converge. To resolve this issue, we make an analogy between agents and humans. Agents can learn from demonstrations generated by traditional traffic signal control methods, in the similar way as people master a skill from expert knowledge. Therefore, we propose DemoLight, for the first time, to leverage demonstrations collected from classic methods to accelerate learning. Based on the state-of-the-art deep RL method Advantage Actor-Critic (A2C), training with demos are carried out for both the actor and the critic and reinforcement learning is followed for further improvement. Results under real-world datasets show that DemoLight enables a more efficient exploration and outperforms existing baselines with faster convergence and better performance.
Scalable Causal Graph Learning through a Deep Neural Network
Authors: Chenxiao Xu, Hao Huang, Shinjae Yoo
Venue: CIKM
Year: 2019

Learning the causal graph in a complex system is crucial for knowledge discovery and decision making, yet it remains a challenging problem because of the unknown nonlinear interaction among system components. Most of the existing methods either rely on predefined kernel or data distribution, or they focus simply on the causality between a single target and the remaining system. This work presents a deep neural network for scalable causal graph learning (SCGL) through low-rank approximation. The SCGL model can explore nonlinearity on both temporal and intervariable relationships without any predefined kernel or distribution assumptions. Through low-rank approximation, the noise influence is reduced, and better accuracy and high scalability are achieved. Experiments using synthetic and real-world datasets show that our SCGL algorithm outperforms existing state-of-the-art methods for causal graph learning.
Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation
Authors: Fengli Xu, Jianxun Lian, Zhenyu Han, Yong Li, Yujian Xu, Xing Xie
Venue: CIKM
Year: 2019

Recent years have witnessed a phenomenal success of agent-initiated social e-commerce models, which encourage users to become selling agents to promote items through their social connections. The complex interactions in this type of social e-commerce can be formulated as Heterogeneous Information Networks (HIN), where there are numerous types of relations between three types of nodes, i.e., users, selling agents and items. Learning high quality node embeddings is of key interest, and Graph Convolutional Networks (GCNs) have recently been established as the latest state-of-the-art methods in representation learning. However, prior GCN models have fundamental limitations in both modeling heterogeneous relations and efficiently sampling relevant receptive field from vast neighborhood. To address these problems, we propose RecoGCN, which stands for a RElation-aware CO-attentive GCN model, to effectively aggregate heterogeneous features in a HIN. It makes up current GCN's limitation in modelling heterogeneous relations with a relation-aware aggregator, and leverages the semantic-aware meta-paths to carve out concise and relevant receptive fields for each node. To effectively fuse the embeddings learned from different meta-paths, we further develop a co-attentive mechanism to dynamically assign importance weights to different meta-paths by attending the three-way interactions among users, selling agents and items. Extensive experiments on a real-world dataset demonstrate RecoGCN is able to learn meaningful node embeddings in HIN, and consistently outperforms baseline methods in recommendation tasks.
New Online Kernel Ridge Regression via Incremental Predictive Sampling
Authors: Shan Xu, Xiao Zhang, Shizhong Liao
Venue: CIKM
Year: 2019

Online kernel ridge regression via existing sampling approaches, which aim at approximating the kernel matrix as accurately as possible, is independent of learning and has a cubic time complexity with respect to the sampling size for updating hypothesis. In this paper, we propose a new online kernel ridge regression via an incremental predictive sampling approach, which has the nearly optimal accumulated loss and performs efficiently at each round. We use the estimated ridge leverage score of the labeled matrix, which depends on the accumulated loss at each round, to construct the predictive sampling distribution, and use this sampling probability for the Nyströ m approximation. To avoid calculating the inverse of the approximated kernel matrix directly, we use the Woodbury formula to accelerate the computation and adopt the truncated incremental singular value decomposition to update the generalized inverse of the intersection matrix. Our online kernel ridge regression has a time complexity of $O(tmk+k^3 )$ for updating hypothesis at round t, where k is the truncated rank of the intersection matrix, and enjoys a regret bound of order $O(\sqrtT )$, where T is the time horizon. Experimental results show that the proposed online kernel ridge regression via the incremental predictive sampling performs more stably and efficiently than the online kernel ridge regression via existing online sampling approaches that directly approximate the kernel matrix.
A Spatio-temporal Recommender System for On-demand Cinemas
Authors: Taofeng Xue, Beihong Jin, Beibei Li, Weiqing Wang, Qi Zhang, Sihua Tian
Venue: CIKM
Year: 2019

On-demand cinemas are a new type of offline entertainment venues which have shown the rapid expansion in the recent years. Recommending movies of interest to the potential audiences in on-demand cinemas is keen but challenging because the recommendation scenario is totally different from all the existing recommendation applications including online video recommendation, offline item recommendation and group recommendation. In this paper, we propose a novel spatio-temporal approach called Pegasus. Because of the specific characteristics of on-demand cinema recommendation, Pegasus exploits the POI (Point of Interest) information around cinemas and the content descriptions of movies, apart from the historical movie consumption records of cinemas. Pegasus explores the temporal dynamics and spatial influences rooted in audience behaviors, and captures the similarities between cinemas, the changes of audience crowds, time-varying features and regional disparities of movie popularity. It offers an effective and explainable way to recommend movies to on-demand cinemas. The corresponding Pegasus system has been deployed in some pilot on-demand cinemas. Based on the real-world data from on-demand cinemas, extensive experiments as well as pilot tests are conducted. Both experimental results and post-deployment feedback show that Pegasus is effective.
Integrating Multi-Network Topology via Deep Semi-supervised Node Embedding
Authors: Hansheng Xue, Jiajie Peng, Jiying Li, Xuequn Shang
Venue: CIKM
Year: 2019

Node Embedding, which uses low-dimensional non-linear feature vectors to represent nodes in the network, has shown a great promise, not only because it is easy-to-use for downstream tasks, but also because it has achieved great success on many network analysis tasks. One of the challenges has been how to develop a node embedding method for integrating topological information from multiple networks. To address this critical problem, we propose a novel node embedding, called DeepMNE, for multi-network integration using a deep semi-supervised autoencoder. The key point of DeepMNE is that it captures complex topological structures of multiple networks and utilizes correlation among multiple networks as constraints. We evaluate DeepMNE in node classification task and link prediction task on four real-world datasets. The experimental results demonstrate that DeepMNE shows superior performance over seven state-of-the-art single-network and multi-network embedding algorithms.
Caching Scores for Faster Query Processing with Dynamic Pruning in Search Engines
Authors: Erman Yafay, Ismail Sengor Altingovde
Venue: CIKM
Year: 2019

We propose to use a score cache, which stores the score of the k-th result of a query, to accelerate top-k query processing with dynamic pruning methods (i.e., WAND and BMW). We introduce heuristics that, for a new query, generate its subsets and probe the score cache to obtain a lower-bound on its score threshold. Our experiments show up to 8.6% savings in mean processing time for the queries that are not seen before, i.e., cannot benefit from a result cache.