Video stabilization is an essential component of visual quality enhancement. Early methods rely on feature tracking to recover either 2D or 3D frame motion, which suffer from the robustness of local feature extraction and tracking in shaky videos. Recently, learning-based methods seek to find frame transformations with high-level information via deep neural networks to overcome the robustness issue of feature tracking. Nevertheless, to our best knowledge, no learning-based methods leverage 3D cues for the transformation inference yet; hence they would lead to artifacts on complex scene-depth scenarios. In this paper, we propose Deep3D Stabilizer, a novel 3D depth-based learning method for video stabilization. We take advantage of the recent self-supervised framework on jointly learning depth and camera ego-motion estimation on raw videos. Our approach requires no data for pre-training but stabilizes the input video via 3D reconstruction directly. The rectification stage incorporates the 3D scene depth and camera motion to smooth the camera trajectory and synthesize the stabilized video. Unlike most one-size-fits-all learning-based methods, our smoothing algorithm allows users to manipulate the stability of a video efficiently. Experimental results on challenging benchmarks show that the proposed solution consistently outperforms the state-of-the-art methods on almost all motion categories.

In mobile communication services, users can communicate with each other over different telecommunication carriers. For telecom operators, how to acquire and retain users is a significant and practical task. Note that telecom operators only have their own customer profiles. For the users from other telecom operators, their information is sparse. Thus, given a set of communication logs, the main theme of our work is to identify the potential users who will possibly join the target services in the near future. Since only a limited amount of information is available, one challenging issue is how to extract features from the communication logs. In this article, we propose a Communication-Based Feature Generation (CBFG) framework that extracts features and builds models to infer the potential users. Explicitly, we construct a heterogeneous information network from the communication logs of users. Then, we extract the explicit features, which refer to those calling features of users, from the potential users’ interaction behaviors in the heterogeneous information network. Moreover, from the calling behaviors of users, one could extract the possible community structures of users. Based on the community structures, we further extract the implicit features of users. In light of both explicit and implicit features, we propose an information-gain-based method to select the effective features. According to the features selected, we utilize three popular classifiers (i.e., AdaBoost, Random Forest, and SVM) to build models to target the potential users. In addition, we have designed a sampling approach to extract training data for classifiers. To evaluate our methods, we have conducted experiments on a real dataset. The results of our experiments show that the features extracted by our proposed method can be effective for targeting the potential users.

In mobile social networks, users can communicate with each other over different telecom operators. Thus, for telecom operators, how to attract new customers is a significant issue. The work of churn prediction is to determine whether a customer would leave soon. Differing from churn prediction, our work is to find those users who are likely to join target services from the competitors in the near future, where these users are called potential users. To infer potential users, we propose a framework including feature extraction, feature selection, and classifier learning to solve the problem. First, we construct a heterogeneous information network from the call detail records of users. Then, we extract the explicit features from potential users' interaction behavior in the heterogeneous information network. Moreover, because users are influenced by their community, we extract community-based implicit features of potential users. After feature extraction, we explore the Information Gain to select the effective features. We use the effective explicit and implicit features to learn potential user classifiers, and use the classifiers to determine the potential users. Finally, we conduct experiments on real datasets. The results of our experiments show that the features extracted by our proposed method can improve the accuracy of inferring potential users.

The advances in location-acquisition and smart phone technologies have led to a myriad of location-based social media. Therefore, analyzing the increasing amount of spatio-temporal data emerges as an important topic. Most studies on geographic data mining focus on exploring static mobility patterns. As the amount of incoming data streams increases, revealing the temporal aspect of user mobility patterns is worth investigating. This paper targets on mining user mobility patterns over time (referred to as mobility evolution) from streams of check-in records. Intuitively, at each time slot, a mobility pattern indicates spatial regions where users stay. Therefore, given a set of time slots, mobility evolution refers a sequence of spatial regions at each time slot. Note that nearby time slots may have similar spatial region distribution. Thus, given check-in datasets, we use the idea of data compression to obtain a sequence of representative segments, where each representative segment captures spatial region distribution at the corresponding time interval. To measure the quality of a segmentation result, we propose a representation cost function based on the Minimum Description Length (MDL) principle. In addition, because deriving the sequence of segments incurs expensive computational cost, we propose a family of greedy algorithms for segmentation to serve diverse requirements: efficient compression, informative compression, and cost-effective compression. Besides, to handle the massive amount of incoming check-in data, we also propose an incremental compression approach to incrementally update the mobility evolution. We conduct experiments on Foursquare datasets to demonstrate both the effectiveness and efficiency of our proposed algorithms.

Rain removal from a single color image is a challenging problem as no temporal information among successive images can be obtained. In this paper, we propose a single-color-image-based rain removal framework by properly formulating rain removal as an image decomposition problem based on sparse representation. In our framework, an input color image is first decomposed into a low-frequency part and a high-frequency part by using the guided image filter so that the rain streaks would be in the high-frequency part with nonrain textures/edges, and the high-frequency part is then decomposed into a rain component and a nonrain component by performing dictionary learning and sparse coding. To separate rain streaks from the high-frequency part, a hybrid feature set, including histogram of oriented gradients, depth of field, and Eigen color, is employed to further decompose the high-frequency part. With the hybrid feature set applied, most rain streaks can be removed; simultaneously nonrain component can be enhanced. To the best of our knowledge, compared with the state-of-the-art approaches, the proposed method is among the first to focus on the problem of single color image rain removal and achieves promising results with not only the rain component being removed more completely, but also the visual quality of restored images being improved.

In this study, the water quality monitoring system with wireless remote control platform was investigated. And the sensing properties of the Cl-sensing device and the pH sensor were discussed. The pH sensing membrane, ruthenium dioxide (RuO2) was deposited on the silicon substrate by radio frequency (R. F.) sputtering system. The chlorine ion selective membrane was prepared on the pH sensing membrane thin film by spin coating method. Furthermore, the response time of the Cl-sensing device and pH sensing device were less than 30 s and 10 s, respectively. Moreover, the lifetime of the Cl-sensing device was immersed in 7.3 ppm of Cl-concentration in NaClO solution at least 3 days. The lifetime of the pH sensing device was immersed in pH 7 buffer solution at least 15 days. According to the experimental results, Cl-sensing device and pH sensing device can be applied to water quality monitoring system for real-time measurement and long-term measurement.

A point of interest (POI) is a location that users can find something interesting. The POI information is typically made by the POI service provider and users can also add comments to a POI or add a new POI to enrich the POI contents. However, the information does not update frequently and some new points or time-sensitive events may not be marked in the POI service. Therefore, we construct a framework to extract the events from microblog to support an event-based POI service different from traditional POI service. With this kind of application, users can easily to get the events around them. In Web 2.0, microblog has become an important media spreading information in the web. With the help of mobile devices, microblogs can spread the information with greater spatiotemporal sensitivity. We take the spatial and temporal features of messages in microblogs to detect spatiotemporal events and use it to enrich POI service. This work aims at detecting spatiotemporal events in microblogs with an pair {location, time} from an handset device and feedbacks information to the users. To achieve this goal, we propose a framework with 4 phases: (1) profile construction; (2) feature extraction; (3) event summary detection; (4) ranking events. In this paper, we propose STF(standing for Spatio-Temporal Focus) value to evaluate the distinctiveness of a feature. Furthermore, we combine STF value with document overlap to expand an event cluster from an event seed. To extract the top-k possible event summaries, an efficient top-k soft clustering algorithm is proposed in this paper. In the experiments, we use real data set from Twitter to verify our proposed framework.

The objective of this paper is to develop a self-learning cooperative strategy for robot soccer systems. The strategy enables robots to cooperate and coordinate with each other to achieve the objectives of offense and defense. Through the mechanism of learning, the robots can learn from experiences in either successes or failures, and utilize these experiences to improve the performance gradually. The cooperative strategy is built using a hierarchical architecture. The first layer of the structure is responsible for assigning each role, that is, how many defenders and sidekicks should be played according to the positional states. The second layer is for the role assignment related to the decision from the previous layer. We develop two algorithms for assignment of the roles, the attacker, the defenders, and the sidekicks. The last layer is the behavior layer in which robots execute their behavior commands and tasks based on their roles. The attacker is responsible for chasing the ball and attacking. The sidekicks are responsible for finding good positions, and the defenders are responsible for defending competitor scoring. The robots' roles are not fixed. They can dynamically exchange their roles with each other. In the aspect of learning, we develop an adaptive Q-learning method which is modified form the traditional Q-learning. A simple ant experiment shows that Q-learning is more effective than the traditional techniques, and it is also successfully applied to the learning of the cooperative strategy.

The molecular binding problem, one of the most important problems in structure based drug design, can be formulated as a global energy optimization problem by using molecular mechanics. A novel computational algorithm is proposed to address the molecular binding problem. The algorithm is derived from genetic algorithms (GA) plus simulated annealing (SA) hybrid techniques, namely population based annealing genetic algorithms (PAG). We have applied the algorithm to find binding structures for three drug protein molecular pairs. One of the three drugs is an anti cancer drug methotrexate (MTX) and the other two are analogues of the antibacterial drug trimethoprim. Moreover, we have also studied two other well resolved ligand receptor molecular complex which are obtained from the Protein Data Bank (PDB): Thermolysin-HONH-benzylmalonyl-L-Ala-Gly-p-nitroanilide complex (5tln) and HIV-1 protease-Hydroxyethylene isostere inhibitor complex. Hydroxyethylene isostere inhibitor is one of new potential HIV-1 protease inhibitors synthesized. Through our experiments, all of the binding results not only keep the energy at low levels, but also have a promising binding geometrical structure in terms of number of hydrogen bonds formed.