How to improve the security and stability of wireless communication systems has become a critical issue. In this paper, physical layer security is introduced to overcome security challenges. The considered communication system is equipped with full-duplex (FD) users in contrast to conventional frameworks where half-duplex (HD) users are at hand. Under these assumptions, we propose a Q-learning based user selection algorithm to model the interaction between a source and multiple users. We also investigate the effect of self-interference and channel interference on physical layer security. The numerical results verify the superiority of the proposed algorithm and in certain conditions, demonstrate substantial performance gain over the conventional approaches.

With the explosive growth of web videos on the Internet, it becomes challenging to efficiently browse hundreds or even thousands of videos. When searching an event query, users are often bewildered by the vast quantity of web videos returned by search engines. Exploring such results will be time consuming and it will also degrade user experience. In this paper, we present an approach for event driven web video summarization by tag localization and key-shot mining. We first localize the tags that are associated with each video into its shots. Then, we estimate the relevance of the shots with respect to the event query by matching the shot-level tags with the query. After that, we identify a set of key-shots from the shots that have high relevance scores by exploring the repeated occurrence characteristic of key sub-events. Following the scheme in [6] and [22], we provide two types of summaries, i.e., threaded video skimming and visual-textual storyboard. Experiments are conducted on a corpus that contains 60 queries and more than 10 000 web videos. The evaluation demonstrates the effectiveness of the proposed approach.

Product annotation in videos is of great importance for video browsing, search, and advertisement. However, most of the existing automatic video annotation research focuses on the annotation of high-level concepts, such as events, scenes, and object categories. This article presents a novel solution to the annotation of specific products in videos by mining information from the Web. It collects a set of high-quality training data for each product by simultaneously leveraging Amazon and Google image search engine. A visual signature for each product is then built based on the bag-of-visual-words representation of the training images. A correlative sparsification approach is employed to remove noisy bins in the visual signatures. These signatures are used to annotate video frames. We conduct experiments on more than 1,000 videos and the results demonstrate the feasibility and effectiveness of our approach.

Social video sharing websites allow users to annotate videos with descriptive keywords called tags, which greatly facilitate video search and browsing. However, many tags only describe part of the video content, without any temporal indication on when the tag actually appears. Currently, there is very little research on automatically assigning tags to shot-level segments of a video. In this paper, we leverage user's tags as a source to analyze the content within the video and develop a novel system named ShotTagger to assign tags at the shot level. There are two steps to accomplish the location of tags at shot level. The first is to estimate the distribution of tags within the video, which is based on a multiple instance learning framework. The second is to perform the semantic correlation of a tag with other tags in a video in an optimization framework and impose the temporal smoothness across adjacent video shots to refine the tagging results at shot level. We present different applications to demonstrate the usefulness of the tag location scheme in searching, and browsing of videos. A series of experiments conducted on a set of Youtube videos has demonstrated the feasibility and effectiveness of our approach.

Video recommendation is an important approach for helping people to access interesting videos. In this paper, we propose a scheme to integrate rich information for video recommendation. We regard video recommendation as a ranking problem and generate multiple ranking lists by exploring different information sources. A multi-task rank aggregation approach is proposed to integrate the ranking lists for different users in a joint manner. Our scheme is flexible and can easily incorporate other methods by adding their generated ranking lists into our multi-task learning algorithm. We conduct experiments with 76 users and more than 10,000 videos. The results demonstrate the feasibility and effectiveness of our approach.

Existing community question-answering forums usually provide only textual answers. However, for many questions, pure texts cannot provide intuitive information, while image or video contents are more appropriate. In this paper, we introduce a scheme that is able to enrich text answers with image and video information. Our scheme investigates a rich set of techniques including question/answer classification, query generation, image and video search reranking, etc. Given a question and the community-contributed answer, our approach is able to determine which type of media information should be added, and then automatically collects data from Internet to enrich the textual answer. Different from some efforts that attempt to directly answer questions with image and video data, our approach is built based on the community-contributed textual answers and thus it is more feasible and able to deal with more complex questions. We have conducted empirical study on more than 3,000 QA pairs and the results demonstrate the effectiveness of our approach.

Object category recognition is a challenging task due to the low level and non-discrimination in visual representation. Most previous methods concentrate to find better high level visual features. Recently, optimally integrating various features to solve the problem attracted more interests. In this paper, we provide a novel method for object category recognition by improving the popular bag-of-words (BoW) methods from the following two aspects. First, we propose to extract a series of high level visual features which exploit both the local spatial co occurrence between low level visual words and the global spatial layout of the object parts. To obtain the global spatial features, a fast method is proposed to generate the semantic meaningful object parts by exploiting the geometric position distribution of the local salient regions. The image part patches are further quantized as semantic coherent high level visual words by using correlational spectral clustering. Based on it, simplified 2D string representation is introduced to model the global spatial patterns of the objects. Second, a multi-kernel learning framework is proposed to adaptively integrate extracted features in an optimal way. For each object class, an optimal feature weight coefficient is learned automatically and separately to combine both the low level and high level visual features by considering their contribution for the different object class. The tests on Caltech-101 and Pascal- VOC 06 dataset demonstrated that our method outperforms the baseline method BoW and state-of-the-art Multi-CM model .

This demonstration presents a system, named iComics, for automatic conversion of movie into comics. We design three components to realize the system: script-face mapping, key-scene extraction, and cartoonization. Script-face mapping utilizes face recognition and tracking techniques to accomplish the mapping between character's faces and their scripts. Key-scene extraction combines the frames derived from subshots and the extracted index frames based on subtitle to select a sequence of frames for cartoonization. Finally, the cartoonization is accomplished via four steps: panel scale, stylization, word balloon placement and comics layout.

Community-based question answering systems have become very popular for providing answers to a wide variety of "how-to" questions. However most such systems present only textual answers. In many cases, users would prefer visual answers such as videos which are more direct and intuitive.

10.1145/1459359.1459494