Nowadays, the ubiquity of sharing economy and the booming of ride-sharing services prompt Mobility-on-Demand (MoD) platforms to explore and develop new business modes. Different from forcing full-time drivers to serve the dispatched orders, these modes usually aim to attract part-time drivers to share their vehicles and employ a 'driver-choose-order' pattern by displaying a sequence of orders to drivers as a candidate set. A key issue here is to determine which orders should be displayed to each driver. In this work, we propose a novel framework to tackle this issue, known as the Information Disclosure problem in MoD systems. The problem is solved in two steps combining estimation with optimization: 1) in the estimation step, we investigate the drivers' choice behavior and estimate the probability of choosing an order or ignoring the displayed candidate set. 2) in the optimization step, we transform the problem into determining the optimal edge configuration in a bipartite graph, then we develop a Minimal-Loss Edge Cutting (MLEC) algorithm to solve it. Through extensive experiments on both the simulation and the real-world data from Huolala business, the proposed method remarkably improves users experience and platform efficiency. Based on these promising results, the proposed framework has been successfully deployed in the real-world MoD system in Huolala.

Skeleton Learning (SL) is the task for learning an undirected graph from the input data that captures their dependency relations. SL plays a pivotal role in causal learning and has attracted growing attention in the research community lately. Due to the high time complexity, anytime SL has emerged which learns a skeleton incrementally and improves it overtime. In this paper, we first propose and advocate the reliability requirement for anytime SL to be practically useful. Reliability requires the intermediately learned skeleton to have precision and persistency. We also present REAL, a novel Reliable and Efficient Anytime Learning algorithm of skeleton. Specifically, we point out that the commonly existing Functional Dependency (FD) among variables could make the learned skeleton violate faithfulness assumption, thus we propose a theory to resolve such incompatibility. Based on this, REAL conducts SL on a reduced set of variables with guaranteed correctness thus drastically improves efficiency. Furthermore, it employs a novel edge-insertion and best-first strategy in anytime fashion for skeleton growing to achieve high reliability and efficiency. We prove that the skeleton learned by REAL converges to the correct skeleton under standard assumptions. Thorough experiments were conducted on both benchmark and real-world datasets demonstrate that REAL significantly outperforms the other state-of-the-art algorithms.

Spreadsheets are popular and widely used for data presentation and management, where users create tables in various structures to organize and present data. Table formatting is an important yet tedious task for better exhibiting table structures and data relationships. However, without the aid of intelligent tools, manual formatting remains a tedious and time-consuming task. In this paper, we propose CellGAN, a neural formatting model for learning and recommending formats of spreadsheet tables. Based on a novel conditional generative adversarial network (cGAN) architecture, CellGAN learns table formatting from real-world spreadsheet tables in a self-supervised fashion without requiring human labeling. In CellGAN we devise two mechanisms, row/column-wise pooling and local refinement network, to address challenges from the spreadsheet domain. We evaluate the effectiveness of CellGAN against real-world datasets using both quantitative metrics and human perception studies. The results indicate remarkable performance gains over rule-based methods, graphical models or direct application of the state-of-the-art cGANs used in visual synthesis tasks. Neural Formatting is the first step towards auto-formatting for spreadsheet tables with promising results.

Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layouts on the spreadsheet. Considering the analogy between a cell matrix as spreadsheet and a pixel matrix as image, and encouraged by the successful application of Convolutional Neural Networks (CNN) in computer vision, we have developed TableSense, a novel end-to-end framework for spreadsheet table detection. First, we devise an effective cell featurization scheme to better leverage the rich information in each cell; second, we develop an enhanced convolutional neural network model for table detection to meet the domain-specific requirement on precise table boundary detection; third, we propose an effective uncertainty metric to guide an active learning based smart sampling algorithm, which enables the efficient build-up of a training dataset with 22,176 tables on 10,220 sheets with broad coverage of diverse table structures and layouts. Our evaluation shows that TableSense is highly effective with 91.3% recall and 86.5% precision in EoB-2 metric, a significant improvement over both the current detection algorithm that are used in commodity spreadsheet tools and state-of-the-art convolutional neural networks in computer vision.

Training kernel SVM on large datasets suffers from high computational complexity and requires a large amount of memory. However, a desirable property of SVM is that its decision function is solely determined by the support vectors, a subset of training examples with non-vanishing weights. This motivates a novel efficient algorithm for training kernel SVM via support vector identification. The efficient training algorithm involves two steps. In the first step, we randomly sample the training data without replacement several times, each time a small subset of training data is sampled. Then a kernel SVM is trained on each subset, and the resulting kernel SVM models are used to identify the support vectors on the margin. In the second step, an optimization problem is solved to estimate the Lagrange multipliers corresponding to these support vectors. After obtaining the support vectors and Lagrange multipliers, we can approximate the decision function of kernel SVM. Due to the cubic complexity of standard kernel SVM training algorithm, training many kernel SVMs on small subsets of training data is much more efficient than training a single kernel SVM on the whole training data especially for large datasets. Therefore, our algorithm has better scalability than kernel SVM. Besides, training SVMs on each subset can be done independently, and hence our algorithm can be easily parallelized for further speedup. Since our algorithm only identifies the support vectors on the margin, it produces less number of support vectors as compared to that produced by standard kernel SVM. This makes our algorithm more efficient in prediction too. Experimental results show that our method outperforms state-of-the-art methods and achieves performance on par with the kernel SVM albeit with much improved efficiency.

Multiple-Instance (MI) learning is an important supervised learning technique which deals with collections of instances called bags. While existing research in MI learning mainly focused on classification, in this paper we propose a new approach for MI retrieval to enable effective similarity retrieval of bags of instances, where training data is presented in the form of similar and dissimilar bag pairs. An embedded scheme is devised as encoding each bag into a single bag feature vector by exploiting a similarity-based transformation. In this way, the original MI problem is converted into a single-instance version. Furthermore, we develop a principled approach for optimizing bag features specific to similarity retrieval through leveraging pairwise label information at the bag level. The experimental results demonstrate the effectiveness of the proposed approach in comparison with the alternatives for MI retrieval.

Feature aggregation is an important step in semantic music retrieval that accumulates features obtained from local frames to produce a global song-level representation. A good aggregation scheme should capture both feature correlations and temporal information, while existing schemes only focus on one of the two respects and lack in the other. In this paper, we present a new feature aggregation scheme to model the dependencies in both feature and temporal domains. This is achieved by augmenting local feature vectors with second-order monomials that capture the correlations between different variables and performing temporal integration over the augmented features. To cope with increased feature dimensions, we further employ an embedded technique for feature selection by training an $\ell_{2,1}$ regularized linear classifier model for all label classes. The use of $\ell_{2,1}$ regularization produces a group sparse solution for classifier weight vectors, thus automatically eliminating irrelevant feature variables with varnishing weights. Our preliminary results demonstrate the effectiveness of the proposed feature aggregation scheme over existing aggregation schemes for large-scale music retrieval and annotation.

We propose a direct approach to learning sparse kernel classifiers for multi-instance (MI) classification to improve efficiency while maintaining predictive accuracy. The proposed method builds on a convex formulation for MI classification by considering the average score of individual instances for bag-level prediction. In contrast, existing formulations used the maximum score of individual instances in each bag, which leads to nonconvex optimization problems. Based on the convex MI framework, we formulate a sparse kernel learning algorithm by imposing additional constraints on the objective function to enforce the maximum number of expansions allowed in the prediction function. The formulated sparse learning problem for the MI classification is convex with respect to the classifier weights. Therefore, we can employ an effective optimization strategy to solve the optimization problem that involves the joint learning of both the classifier and the expansion vectors. In addition, the proposed formulation can explicitly control the complexity of the prediction model while still maintaining competitive predictive performance. Experimental results on benchmark data sets demonstrate that our proposed approach is effective in building very sparse kernel classifiers while achieving comparable performance to the state-of-the-art MI classifiers.

Multiple instance learning (MIL) is a paradigm in supervised learning that deals with the classification of collections of instances called bags. Each bag contains a number of instances from which features are extracted. The complexity of MIL is largely dependent on the number of instances in the training data set. Since we are usually confronted with a large instance space even for moderately sized real-world data sets applications, it is important to design efficient instance selection techniques to speed up the training process without compromising the performance. In this paper, we address the issue of instance selection in MIL. We propose MILIS, a novel MIL algorithm based on adaptive instance selection. We do this in an alternating optimization framework by intertwining the steps of instance selection and classifier learning in an iterative manner which is guaranteed to converge. Initial instance selection is achieved by a simple yet effective kernel density estimator on the negative instances. Experimental results demonstrate the utility and efficiency of the proposed approach as compared to the state of the art.

In this paper, we develop a novel approach to object-material identification in spectral imaging by combining the use of invariant spectral absorption features and statistical machine-learning techniques. Our method hinges on the relevance of spectral absorption features for material identification and casts the problem into a pattern-recognition setting by making use of an invariant representation of the most discriminant band segments in the spectra. Thus, here, we view the identification problem as a classification task, which is effected based upon those invariant absorption segments in the spectra which are most discriminative between the materials under study. To robustly recover those bands that are most relevant to the identification process, we make use of discriminant learning. To illustrate the utility of our method for purposes of material identification, we perform experiments on both terrestrial and remotely sensed hyperspectral imaging data and compare our results to those yielded by an alternative.

Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.

In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ all audio features extracted from local windows for classification instead of just using a single song-level feature vector produced by compressing the local features. Two variants of naive Bayes classifiers are studied based on the extensions of standard nearest neighbor and support vector machine classifiers. Experimental results have demonstrated superior performance achieved by the proposed naive Bayes classifiers for both music classification and retrieval as compared to the alternative methods.

In this paper, we address the problem of combining linear support vector machines (SVMs) for classification of large-scale nonlinear datasets. The motivation is to exploit both the efficiency of linear SVMs (LSVMs) in learning and prediction and the power of nonlinear SVMs in classification. To this end, we develop a LSVM mixture model that exploits a divide-and-conquer strategy by partitioning the feature space into subregions of linearly separable datapoints and learning a LSVM for each of these regions. We do this implicitly by deriving a generative model over the joint data and label distributions. Consequently, we can impose priors on the mixing coefficients and do implicit model selection in a top-down manner during the parameter estimation process. This guarantees the sparsity of the learned model. Experimental results show that the proposed method can achieve the efficiency of LSVMs in the prediction phase while still providing a classification performance comparable to nonlinear SVMs.

Multiple-instance learning (MIL) is a new paradigm of supervised learning that deals with the classification of bags. Each bag is presented as a collection of instances from which features are extracted. In MIL, we have usually confronted with a large instance space for even moderately sized data sets since each bag may contain many instances. Hence it is important to design efficient instance pruning and selection techniques to speed up the learning process without compromising on the performance. In this paper, we address the issue of instance selection in multiple instance learning and propose the IS-MIL, an instance selection framework for MIL, to tackle large-scale MIL problems. IS-MIL is based on an alternative optimisation framework by iteratively repeating the steps of instance selection/updating and classifier learning, which is guaranteed to converge. Experimental results demonstrate the utility and efficiency of the proposed approach compared to the alternatives.

In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L1and L2regularisation techniques. An L1regularised logistic regression model is first learned to find out the sparse pattern of the features. To train the L1model efficiently, we employ a convex differentiable approximation of the L1cost function which can be solved by a quasi Newton method. We then train an L2regularised logistic regression model only on the subset of features with nonzero weights returned by the L1logistic regression. Experimental results demonstrate the utility and efficiency of the proposed approach compared to a number of alternatives.

In this paper, we propose a hierarchical approach to image segmentation based on the use of a graph regularisation algorithm. The initial segmentation map is obtained using the normalized cut segmentation algorithm. We then refine the segmentation results by iteratively propagating the class-labels from coarse-to-fine sampling levels. Image segmentation at each intermediate level is recast as a constrained graph regularisation problem that can be solved efficiently. The multi-level nature of our method achieves low computational cost and robustness to noise corruption. We provide experimental results on the Berkeley Image Database and show the efficacy of our method for segmentation of high resolution images.

In this paper, we investigate linear discriminant analysis (LDA) methods for multiclass classification problems in hyperspectral imaging. We note that LDA does not consider pairwise relations between different classes, it rather assumes equal within and between-class scatter matrices. As a result, we present a pairwise discriminant analysis algorithm for learning class categories. Our pairwise linear discriminant analysis measures the separability of two classes making use of the class centroids and variances. Our approach is based upon a novel cost function with unitary constraints based on the aggregation of pairwise costs for binary classes. We view the minimisation of this cost function as an unconstrained optimisation problem over a Grassmann manifold and solve using a projected gradient method. Our approach does not require matrix inversion operations and, therefore, does not suffer of stability problems for small training sets. We demonstrate the utility of our algorithm for purposes of learning material catergories in hyperspectral images.

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

In this paper, we aim at presenting a survey on automatic absorption recovery methods for imaging spectroscopy. We commence by viewing the algorithms in the literature from a technical perspective and presenting an overview of the derivative analysis, fingerprint, and maximum modulus wavelet transform techniques. In addition to these methods, we also present a novel absorption recovery approach based upon unimodal regression and continuum removal. With this technical review of the methods under study, we perform a complexity analysis and examine the implementation issues pertaining to each of the alternatives. We show how detected absorption bands can be used for purposes of material identification. We conclude this paper by providing a performance study and providing identification results on hyperspectral imagery. To this end, we make use of a number of distance measures to evaluate the quality of the recovered absorptions, as compared to continuum-removed spectra.

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene.

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

In this paper, we proposed a hierarchical clustering framework to classify vehicle motion trajectories in real traffic video based on their pairwise similarities. First raw trajectories are pre-processed and resampled at equal space intervals. Then spectral clustering is used to group trajectories with similar spatial patterns. Dominant paths and lanes can be distinguished as a result of two-layer hierarchical clustering. Detection of novel trajectories is also possible based on the clustering results. Experimental results demonstrate the superior performance of spectral clustering compared with conventional fuzzy K-means clustering and some results of anomaly detection are presented.

This paper presents an adaptive approach to skin detection. First, we propose a nonlinear relationship among R, G and B components and use a closed curve to identify the skin cluster region. Then, a split machine is designed that aids the extraction of the pixels with similar low-level features from images. Finally, a nonlinear skin color classifier with an adaptive threshold is developed by analyzing the properties of the extracted pixels in the HSL, YCbCr, YUV and YIQ color spaces. Experimental results show that our proposed method works very well in skin detection.