Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data. However, they still suffer from several obstacles: First, most of existing hashing methods are trained in a batch mode, yielding inefficiency for training streaming data. Second, the computational cost and the memory consumption increase extraordinarily in the big data setting, which perplexes the training procedure. Third, the lack of labeled data hinders the improvement of the model performance. To address these difficulties, we utilize online sketching hashing (OSH) and present a FasteR Online Sketching Hashing (FROSH) algorithm to sketch the data in a more compact form via an independent transformation. We provide theoretical justification to guarantee that our proposed FROSH consumes less time and achieves a comparable sketching precision under the same memory cost of OSH. We also extend FROSH to its distributed implementation, namely DFROSH, to further reduce the training time cost of FROSH while deriving the theoretical bound of the sketching precision. Finally, we conduct extensive experiments on both synthetic and real datasets to demonstrate the attractive merits of FROSH and DFROSH.

The proliferation of telco networks and mobile terminals brings the accumulation of tremendous amounts of measure report(MR) data at a rapid pace. The MR data is generated by mobile objects while connecting to data services and is stored in backend data centers. To geo-tag or localize such MR data is believed to have a profound effect on the analytics and optimizations of telco and traffic networks. However, MR records are of noisy and partial observations regarding to mobile objects' geo-locations and hence pose challenges to accurate telco data localization. There have been quite a few attempts. Single-point localization methods map a MR record to a location, but come out with limited accuracies due to the ignorance of spatiotemporal coherence of successive MR records. Recent efforts on sequential localization techniques alleviate this by mapping a sequence of MR records to a trajectory. However, existing solutions are often with assumptions on specific models, e.g., mobility and signal strength distributions, or priori knowledge on topology space, e.g., road networks, limiting the deployment in practice. To this end, we propose a data-driven framework to tackle the challenges in sequential telco localization. We solely use raw MR records and a public third-party GPS dataset for the learning of the correlations between mobile objects' locations and MR records, requiring no model assumptions and priori knowledge. To handle the data-intensive workloads during the learning process, we use materialized views for efficient online localization and light-weighted indexing techniques for periodical parameters tuning, in order to improve the efficiency and scalability. Results on real data show that our solution achieves 58.8 percent improvement in median localization errors compared with state-of-art sequential localization techniques that require hypothesis models and priori knowledge, making our solution superior in terms of effectiveness, efficiency, and employability.

The widespread deployment of deep models necessitates the assessment of model vulnerability in practice, especially for safety- and security-sensitive domains such as autonomous driving and medical diagnosis. Transfer-based attacks against image classifiers thus elicit mounting interest, where attackers are required to craft adversarial images based on local proxy models without the feedback information from remote target ones. However, under such a challenging but practical setup, the synthesized adversarial samples often achieve limited success due to overfitting to the local model employed. In this work, we propose a novel mechanism to alleviate the overfitting issue. It computes model attention over extracted features to regularize the search of adversarial examples, which prioritizes the corruption of critical features that are likely to be adopted by diverse architectures. Consequently, it can promote the transferability of resultant adversarial instances. Extensive experiments on ImageNet classifiers confirm the effectiveness of our strategy and its superiority to state-of-the-art benchmarks in both white-box and black-box settings.

With the growing prevalence of convolutional neural networks (CNNs), there is an urgent demand to explain their behaviors. Global explanations contribute to understanding model predictions on a whole category of samples, and thus have attracted increasing interest recently. However, existing methods overwhelmingly conduct separate input attribution or rely on local approximations of models, making them fail to offer faithful global explanations of CNNs. To overcome such drawbacks, we propose a novel two-stage framework, Attacking for Interpretability (AfI), which explains model decisions in terms of the importance of user-defined concepts. AfI first conducts a feature occlusion analysis, which resembles a process of attacking models to derive the category-wide importance of different features. We then map the feature importance to concept importance through ad-hoc semantic tasks. Experimental results confirm the effectiveness of AfI and its superiority in providing more accurate estimations of concept importance than existing proposals.

Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications. In this paper, we propose a data-aware weighted sampling-based covariance matrix estimator, namely DACE, which can provide an unbiased covariance matrix estimation and attain more accurate estimation under the same compression ratio. Moreover, we extend our proposed DACE to tackle multiclass classification problems with theoretical justification and conduct extensive experiments on both synthetic and real-world data sets to demonstrate the superior performance of our DACE.

Wasserstein Generative Adversarial Nets~(GANs) are newly proposed GAN algorithms and widely used in computer vision, web mining, information retrieval, etc. However, the existing algorithms with approximated Wasserstein loss converge slowly due to heavy computation cost and usually generate unstable results as well. In this paper, we solve the computation cost problem by speeding up the Wasserstein GANs from a well-designed communication efficient parallel architecture. Specifically, we develop a new problem formulation targeting the accurate evaluation of Wasserstein distance and propose an easily parallel optimization algorithm to train the Wasserstein GANs. Compared to traditional parallel architecture, our proposed framework is designed explicitly for the skew parameter updates between the generator network and discriminator network. Rigorous experiments reveal that our proposed framework achieves a significant improvement regarding convergence speed with comparable stability on generating images, compared to the state-of-the-art of Wasserstein GANs algorithms.

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN.

Check-in prediction is an important task for location-based systems, which maps a noisy estimate of a user's current location to a semantically meaningful point-of-interest (POI), such as a restaurant or store. In this paper, we leverage the personalized preference and sequential check-in pattern to improve the traditional methods that base on the geographical and temporal contexts. In our approach, we propose a Gaussian mixture model and a histogram distribution estimation model to learn the contextual features from relevant spatial and temporal information, respectively. Furthermore, we employ user and POI embeddings to model the personalized preference and leverage a stacked Long-Short Term Memory (LSTM) model to learn the sequential check-in pattern. Combining the contextual features and the personalized sequential patterns together, we propose a wide and deep neural network for the check-in prediction task. Experimental evaluations on two real-life datasets demonstrate that our proposed method outperforms state-of-the-art models.

Aspect-level sentiment classification is a fine-grained sentiment analysis task, which aims to predict the sentiment of a text in different aspects. One key point of this task is to allocate the appropriate sentiment words for the given aspect.Recent work exploits attention neural networks to allocate sentiment words and achieves the state-of-the-art performance. However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect. To solve this problem, we propose a HiErarchical ATtention (HEAT) network for aspect-level sentiment classification. The HEAT network contains a hierarchical attention module, consisting of aspect attention and sentiment attention. The aspect attention extracts the aspect-related information to guide the sentiment attention to better allocate aspect-specific sentiment words of the text. Moreover, the HEAT network supports to extract the aspect terms together with aspect-level sentiment classification by introducing the Bernoulli attention mechanism. To verify the proposed method, we conduct experiments on restaurant and laptop review data sets from SemEval at both the sentence level and the review level. The experimental results show that our model better allocates appropriate sentiment expressions for a given aspect benefiting from the guidance of aspect terms. Moreover, our method achieves better performance on aspect-level sentiment classification than state-of-the-art models.

In smart cities, green cellular networks play a crucial role to support wireless access for numerous devices anywhere and anytime with efficiency and sustainability. Because base stations (BSes) consume more than 70% of overall cellular network infrastructure energy, saving the power consumption of BSes is the key task to build a green cellular network. Except for low power design of the BS hardware and software, the traffic-driven BS sleeping operation is an economical way to improve existing cellular networks, which can reduce the BS power consumption at low traffic load. However, prior BS sleeping strategies establish on the static temporal characteristics of traffic load, which ignore the fact that network traffic is influenced by many factors such as time, human mobility, holiday, weather, etc. Hence, prior traffic estimation is coarse, and the BS sleeping strategies cannot apply to the changing network traffic. In this paper, we exploit a machine learning method to estimate the BS traffic and propose a BS sleeping strategy based on predicted traffic for power saving in the cellular network. We analyze network traffic in multi-views: temporal influence, spatial influence, and event influence. Then, we propose a multi-view ensemble learning model to predict network traffic load, which learns the traffic in multi-views and combine the results with ensemble. Furthermore, we formulate a BS sleeping strategy based on the predicted traffic load. Finally, we evaluate our traffic prediction algorithm on real cellular network data. The evaluation shows that our traffic prediction algorithm improves about 40% than state-of-the-art machine learning methods. Also, we evaluate the proposed BS sleeping strategy, which yields about 10% more energy savings and less device damage than the competitors in the simulated environment.

Urbanization's rapid progress has modernized a large number of human beings' lives. This urbanization progress is accompanied by the increase of a variety of shops (e.g., restaurants and fitness centers) to meet the increasing citizens, which means business opportunities for the investors. Nevertheless, it is difficult for the investors to catch such opportunities because opening what kind of business at which place is not easy to decide. In this paper, we take this challenge and define the business opportunity mining problem, which recommends new business categories at a partitioned business district. Specifically, we exploit the data from location-based social networks (LBSNs) to mine the business opportunities, guiding the business owners to open new commercial shops in certain categories at a particular area. First, we define the properties of a business district and propose a greedy algorithm to partition a city into different districts. Next, we propose an embedding model to learn latent representations of categories, which captures the functional correlations among business categories. Furthermore, we propose a ranking model based on the pairwise loss to recommend categories for a specific district. Finally, we conduct experiments on Yelp data, and experimental results show that our proposed method outperforms the baseline methods and resolves the problem well.

Successive point-of-interest (POI) recommendation in location-based social networks (LBSNs) becomes a significant task since it helps users to navigate a number of candidate POIs and provides the best POI recommendations based on users’ most recent check-in knowledge. However, all existing methods for successive POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences, but ignore an important fact that successive POI recommendation is a time-subtle recommendation task. In fact, even with the same previous check-in information, users would prefer different successive POIs at different time. To capture the impact of time on successive POI recommendation, in this paper, we propose a spatial-temporal latent ranking (STELLAR) method to explicitly model the interactions among user, POI, and time. In particular, the proposed STELLAR model is built upon a ranking-based pairwise tensor factorization framework with a fine-grained modeling of user-POI, POI-time, and POI-POI interactions for successive POI recommendation. Moreover, we propose a new interval-aware weight utility function to differentiate successive check-ins’ correlations, which breaks the time interval constraint in prior work. Evaluations on two real-world datasets demonstrate that the STELLAR model outperforms state-of-the-art successive POI recommendation model about 20% in Precision@5 and Recall@5.

Web service recommendation has recently drawn much attention with the growing amount of Web services. Previous work usually exploits the collaborative filtering techniques for Web service recommendation, but suffers from the data sparsity problem that leads to inaccurate results. Our analysis on a real-world Quality of Service (QoS) dataset shows that there is a hidden correlation among users and services. We define such hidden correlation with an asymmetric matrix (namely asymmetric correlation), in which each entry presents the hidden correlation between a user pair or between a service pair. The goal of this work is to employ such asymmetric correlation among users and services to alleviate the data sparsity problem and further enhance the prediction accuracy in service recommendation. Specifically, we propose an asymmetric correlation regularized matrix factorization (MF) framework, in which asymmetric correlation and asymmetric correlation propagation have been naturally integrated. Finally, experimental results on a well-known real-world QoS dataset validate that the use of asymmetric correlation among users and services is effective in improving prediction accuracy for Web service recommendation.