Air pollution is becoming a serious issue that threatens everyone's daily life. Accordingly how to measure the local air quality easily and quickly becomes an urgent problem. Sensor-based methods are relatively expensive, and image based air quality measurement is a promising direction as it is often less cost. Meanwhile web service of precise air quality measurement is of great importance as it allows to timely monitor the air pollution and can provide recommendations for decision makers. This paper devises an effective web service to address this challenging problem. Specifically we offer a service letting mobile device users to upload photos taken outdoor with meta information. Once the background web service computation is finished, we return the air quality level at their location to the users. In our service system, it includes three basic modules: 1) using GPS location information to get the basic air quality value from the nearest official air quality station, 2) using photo by our air quality assessment based on dictionary learning for image representation to compute air quality. In this method we add ℓ21norm to the target function to promote the nonzero coefficients of the words aggregate within similar quality level, 3) using photo uploaded by user by our CNN based photo air pollution estimation method to obtain the air quality estimation. Finally we combine the above three estimations to reach the final estimation to the end users. Empirical experiments are conducted on the real-world dataset that collaborates the efficacy of our method.

Air quality has become a major global concern for human beings involving all social stratums, for both developing and developed countries. Web service of precise and early air pollution forecasting is of great importance as it allows people to pro-actively take preventative and protective measurements. As an endeavor on the course of machine learning based air quality forecasting, this paper presents an initiative and its technological details in solving this challenging problem. Specifically, this work involves three major highlights regarding with both algorithmic innovation and deployment with its impact: 1) We propose a multi-channel ensemble learning framework, 2) We propose a new supervised feature learning and extraction method, i.e. sufficient statistics feature mapping based on Deep Boltzman Machine, which serves as a building block for our learning system, 3) We target our air pollution prediction method to the city of Beijing, China as it is at the forefront for battling against air pollution, which is embodied as a web service for prediction. Extensive experiments of real time air pollution forecasting on the real-world data demonstrates the effectiveness of the proposed method and value of the deployed web service system.

Air pollution has raised people's intensive concerns especially in developing countries such as China and India. Different from using expensive or unreliable methods like sensor-based or social network based one, photo based air pollution estimation is a promising direction, while little work has been done up to now. Focusing on this immediate problem, this paper devises an effective convolutional neural network to estimate air's quality based on photos. Our method is comprised of two ingredients: first a negative log-log ordinal classifier is devised in the last layer of the network, which can improve the ordinal discriminative ability of the model. Second, as a variant of the Rectified Linear Units (ReLU), a modified activation function is developed for photo based air pollution estimation. This function has been shown it can alleviate the vanishing gradient issue effectively. We collect a set of outdoor photos and associate the pollution levels from official agency as the ground truth. Empirical experiments are conducted on this real-world dataset which shows the capability of our method.

Sales pipeline win-propensity prediction is fundamental to effective sales management. In contrast to using subjective human rating, we propose a modern machine learning paradigm to estimate the win-propensity of sales leads over time. A profile-specific two-dimensional Hawkes processes model is developed to capture the influence from seller's activities on their leads to the win outcome, coupled with lead's personalized profiles. It is motivated by two observations: i) sellers tend to frequently focus their selling activities and efforts on a few leads during a relatively short time. This is evidenced and reflected by their concentrated interactions with the pipeline, including login, browsing and updating the sales leads which are logged by the system; ii) the pending opportunity is prone to reach its win outcome shortly after such temporally concentrated interactions. Our model is deployed and in continual use to a large, global, B2B multinational technology enterprize (Fortune 500) with a case study. Due to the generality and flexibility of the model, it also enjoys the potential applicability to other real-world problems.

This paper focuses on the problem of hyper-graph matching, by accounting for both unary and higher-order affinity terms. Our method is in line with the linear approximate framework while the problem is iteratively solved in discrete space. It is empirically found more efficient than many extant continuous methods. Moreover, it avoids unknown accuracy loss by heuristic rounding step from the continuous approaches. Under weak assumptions, we prove the iterative discrete gradient assignment in general will trap into a degenerating case - an m-circle solution path where m is the order of the problem. A tailored adaptive relaxation mechanism is devised to detect the degenerating case and makes the algorithm converge to a fixed point in discrete space. Evaluations on both synthetic and real-world data corroborate the efficiency of our method.

As an important and fundamental methodology in the fields of pattern recognition and image processing, learning middle level feature has attracted increasing interest during the recent years, where generative feature mapping has shown highly completive performance in diverse applications. In this paper, a middle level feature representation is proposed based on Deep Boltzmann Machine (DBM) and sufficient statistics (SS) feature mapping for detection. In the approach, DBM is employed to model data distribution and the hidden information inferred by DBM together with other informative variables are then exploited by SS to form the middle level features. The features, learnt from data, can be fed to standard classifiers for classification. In order to evaluate the performance of our method, we apply our feature mapping method to two challenging tasks: (1) contour detection through distinguishing border and non-border pixels, (2) sales pipeline prediction, which predicts the winning propensity of the ongoing sales opportunity in the pipeline. In comparison with other leading methods in the literature on the Berkeley Segmentation Dataset and Sales Pipeline Database (SPDB), our proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness and efficiency.

Contour detection is an important and fundamental problem in computer vision which finds numerous applications.Despite significant progress has been made in the past decades, contour detection from natural images remains a challenging task due to the difficulty of clearly distinguishing between edges of objects and surrounding backgrounds.To address this problem, we first capture multi-scale features from pixel-level to segmentlevel using local and global information.These features are mapped to a space where discriminative information is captured by computing posterior divergence of Gaussian mixture models and then used to train a random forest classifier for contour detection.We evaluate the proposed algorithm against leading methods in the literature on the Berkeley segmentation and Weizmann horse data sets.Experimental results demonstrate that the proposed contour detection algorithm performs favorably against state-of-the-art methods in terms of speed and accuracy.