With the growing popularity of mobile apps, the security issues of mobile apps become critical. Similar to traditional cyber security, mobile security includes authentication, data integrity, and data privacy. Seed-based authentication is a novel approach for mobile systems. The key idea is to extract seeds from files such as images and generate random numbers for authentication. However, in seed-based authentication, the server side design is different from traditional username-password paradigm. Few work has been done to address server-side issues on seed-based authentication. In this paper, we focus on analyzing server-side design of seed-based authentication. We develop a prototype system and set up experiments to evaluate our server-side design.

Recent growth of cloud computing has driven the development of Heterogeneous Cloud Federation (HCF) that has been considered an approach of improving the diversity of cloud systems by enable cloud users to select resources provided by different service providers. Using this mechanism can divide a complicate task into a group of sub-tasks in order to increase the efficiency of application executions, which can support simultaneously executing different cloud systems. One of the resource allocation mechanisms is to schedule tasks among cloud providers. However, current challenge of implementing this mechanism is to generate an efficient task scheduling. We consider mobile cloud computing an adaptive approach for increasing the utilizations of mobile devices. Addressing this dimension, we propose our solution named Mobile Cloud-based Heterogeneous Resource Allocation Model (MC-HRAM), which is designed to minimize the execution time. The main algorithm used in the proposed model is Dynamic Heterogeneous Task Assignments Algorithm (DHTA). Our experimental evaluation has proved that our proposed schema has an advantage of saving execution time.

Data-centers play a fundamental role in cloud computing. As the market of cloud computing continues to scale, it becomes increasingly difficult to utilize resources in large data-center networks. There is a growing interest to seek for efficient data-center architectures among academia and industry. Software-defined Networking (SDN) is a promising technology to develop scalable network architectures for massive data processing. SDN decouples control planes from physical switches and centralizes network management. However, most of the previous works merging SDN with data-center networks use tree or fat-tree topologies. These topologies have fixed architectures and are difficult to make full use of the features supported by SDN. In this paper, we proposed a fabric topology to provide a flexible data-center network. With SDN controllers, our work has the potential to address critical issues such as bandwidth utilization and network capacity in datacenter networks.

Many traditional methods for shape classification involve establishing point correspondences between shapes to produce matching scores, which are in turn used as similarity measures for classification. Learning techniques have been applied only in the second stage of this process, after the matching scores have been obtained. In this paper, instead of simply taking for granted the scores obtained by matching and then learning a classifier, we learn the matching scores themselves so as to produce shape similarity scores that minimize the classification loss. The solution is based on a max-margin formulation in the structured prediction setting. Experiments in shape databases reveal that such an integrated learning algorithm substantially improves on existing methods.

Moving object classification in far-field video is a key component of smart surveillance systems. In this paper, we propose a reliable system for person-vehicle classification which works well in challenging real-word conditions, including the presence of shadows, low resolution imagery, perspective distortions, arbitrary camera viewpoints, and groups of people. Our system runsin real-time (30 Hz) on conventional machines and has low memory consumption. We achieved accurate results by relying on powerful discriminative features, including a novel measure of object deformation based on differences of histograms of oriented gradients. We also provide an interactive user interface, enabling users to specify regions of interest for each class and correct for perspective distortions by specifying different sizes indifferent positions of the camera view. Finally, we use anautomatic adaptation process to continuously update the parameters of the system so that its performance increases for a particular environment. Experimental results demonstrate the effectiveness of our system in standard dataset and a variety of video clips captured with our surveillance cameras.

Traditional stereo matching algorithms are limited in their ability to produce accurate results near depth discontinuities, due to partial occlusions and violation of smoothness constraints. In this paper, we use small baseline multi-flash illumination to produce a rich set of feature maps that enable acquisition of discontinuity preserving point correspondences. First, from a single multi-flash camera, we formulate a qualitative depth map using a gradient domain method that encodes object relative distances. Then, in a multiview setup, we exploit shadows created by light sources to compute an occlusion map. Finally, we demonstrate the usefulness of these feature maps by incorporating them into two different dense stereo correspondence algorithms, the first based on local search and the second based on belief propagation. Experimental results show that our enhanced stereo algorithms are able to extract high quality, discontinuity preserving correspondence maps from scenes that are extremely challenging for conventional stereo methods. We also demonstrate that small baseline illumination can be useful to handle specular reflections in stereo imagery. Different from most existing active illumination techniques, our method is simple, inexpensive, compact, and requires no calibration of light sources.

Currently, sharp discontinuities in depth and partial occlusions in multiview imaging systems pose serious challenges for many dense correspondence algorithms. However, it is important for 3D reconstruction methods to preserve depth edges as they correspond to important shape features like silhouettes which are critical for understanding the structure of a scene. In this paper, we show how active illumination algorithms can produce a rich set of feature maps that are useful in dense 3D reconstruction. We start by showing a method to compute a qualitative depth map from a single camera, which encodes object relative distances and can be used as a prior for stereo. In a multiview setup, we show that along with depth edges, binocular half-occluded pixels can also be explicitly and reliably labeled. To demonstrate the usefulness of these feature maps, we show how they can be used in two different algorithms for dense stereo correspondence. Our experimental results show that our enhanced stereo algorithms are able to extract high quality, discontinuity preserving correspondence maps from scenes that are extremely challenging for conventional stereo methods.

Photo album management, an application of content-based image browsing/retrieval, has attracted much attention in recent years. Identities of individuals appearing in photos are the most important aspect of photo browsing. However, face recognition generally does not work effectively in such situations due to the large variations in pose, illumination and sometimes poor quality of images. We present a system for browsing photo albums. The system automatically detects and stores information about the locations of faces in the photos. A similarity function based on face arrangement is defined. Photos are then clustered based on the similarity function using a proposed clustering algorithm. The system also represents photos of an event using a composite image. The composite image is built from representative faces and an image that represents the event. Experiments indicate that the face arrangement features are effective in representing the semantic content of the photos and are appropriate for photo albums.

Automatic annotation of photographs is one of the most desirable needs in family photograph management systems. In this paper, we present a learning framework to automate the face annotation in family photograph albums. Firstly, methodologies of content-based image retrieval and face recognition are seamlessly integrated to achieve automated annotation. Secondly, face annotation is formulated in a Bayesian framework, in which the face similarity measure is defined as maximum a posteriori (MAP) estimation. Thirdly, to deal with the missing features, marginal probability is used so that samples which have missing features are compared with those having the full feature set to ensure a non-biased decision. The experimental evaluation has been conducted within a family album of few thousands of photographs and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.