Kehua Guo

DHSR: A Novel Semantic Retrieval Approach for Ubiquitous Multimedia

Semantic features are critical intelligence information for mobile ubiquitous multimedia, how to manage and retrieve the semantic information has been an important issue. In this paper, a novel semantic retrieval approach named Data Hiding based Semantic Retrieval (DHSR) for ubiquitous multimedia is proposed. This approach consists of the following features: (1) Every multimedia document has to be semantically annotated by several users before saved into multimedia database. (2) Semantic information described by object ontology will be hidden in the multimedia document data. (3) Semantic information will not be lost even if the multimedia document is copied, cut or leave the database. Our work provides a search engine with convenient user interfaces. The experimental results show that DHSR can search the multimedia documents reflecting users’ query intent more effectively compared with some traditional approaches.

A Component-Based Localization Algorithm for Sparse Sensor Networks Combining Angle and Distance Information

Location information of sensor nodes plays a critical role in many wireless sensor network (WSN) applications and protocols. Although many localization algorithms have been proposed in recent years, they usually target at dense networks and perform poorly in sparse networks. In this paper, we propose two component-based localization algorithms that can localize many more nodes in sparse networks than the state-of-the-art solution. We first develop the Basic Common nodes-based Localization Algorithm, namely BCLA, which uses both common nodes and measured distances between adjacent components to merge components. BCLA outperforms CALL, the state-of-the-art component-based localization algorithm that uses only distance measurements to merge components. In order to further improve the performance of BCLA, we further exploit the angular information among nodes to merge components, and propose the Component-based Localization with Angle and Distance information algorithm, namely CLAD. We prove the merging conditions for BCLA and CLAD, and evaluate their performance through extensive simulations. Simulations results show that, CLAD can locate more than 90 percent of nodes in a sparse network with average node degree 7.5, while CALL can locate only 78 percent of nodes in the same scenario.

A secure data collection scheme based on compressive sensing in wireless sensor networks

Abstract The compressive sensing (CS) based data collection schemes can effectively reduce the transmission cost of wireless sensor networks (WSNs) by exploring the sparsity of compressible signals. Although many recent works explained CS as a symmetric cryptosystem, CS-based data collection schemes still face security threats, due to the complex deployment environment of WSNs. In this paper, we first propose two feasible attack models for specific applications. Then, we present a se cure d ata c ollection scheme based on compressive sensing (SeDC), which enhances the data privacy by the asymmetric semi-homomorphic encryption scheme, and reduces the computation cost by sparse compressive matrix. More specifically, the asymmetric mechanism reduces the difficulty of secret key distribution and management. The homomorphic encryption allows the in-network aggregation in cipher domain, and thus enhances the security and achieves the network load balance. The sparse measurement matrix reduces both the computation cost and communication cost, which compensates the increasing cost caused by the homomorphic encryption. We also introduce a joint recovery model to improve the recovery accuracy. Experimental evaluation based on real data shows that the proposed scheme achieves a better performance compared with the most related works.

CASP: A Context-Aware Transparent Active Service Provision Architecture in a Mobile Internet Environment

Driven by the development of the mobile Internet and the emergence of heterogeneous mobile devices, context-aware applications are attracting growing interest. Based on the idea of transparent computing, the authors propose a novel context-aware service provision (CASP) architecture to transparently and actively provide suitable services to clients. In this article, they present the system model and describe a series of key technologies in CASP, including the client parameter acquisition scheme, the user behavior analysis approach, the service choice algorithm, and the transmission optimization method. Based on the established architecture, they developed software and ran a performance evaluation, proving that CASP offers a good user experience and outstanding generality.

3D image retrieval based on differential geometry and co-occurrence matrix

Abstract3D image retrieval approach is a challenging problem in the research of content-based image retrieval. In this paper, a novel retrieval approach combined differential geometry and co-occurrence matrix is presented. Firstly, Gaussian curvature and mean curvature are utilized to represent the inherent characteristic of spatial surface, and then we use co-occurrence matrix to store the shape information of 3D images. Secondly, normalization process is applied to the co-occurrence matrix and the invariants independence of the translation, scaling, and rotation transforms are proved. In comparison with the recent methods, experiments indicate a lower computation complexity and a better retrieval rate to 3D images with slight different shape characteristic.

A Novel Mechanical Component Retrieval Approach Based on Differential Moment

The differential geometry and moment invariants are important approaches in pattern recognition and artificial intelligence. In this paper, a novel approach for mechanical component retrieval is reported. This approach combines local (spatial curvature) signatures and global (moment) features of 3D mechanical component. Mean curvatures are integrated into the computation of 3D moment invariants, and differential moment invariants are constructed. Experiments indicate that differential moment invariants have a lower computation complexity than traditional 3D image retrieval approaches without reducing the retrieval rate.

Secure and Cost-Effective Distributed Aggregation for Mobile Sensor Networks

Secure data aggregation (SDA) schemes are widely used in distributed applications, such as mobile sensor networks, to reduce communication cost, prolong the network life cycle and provide security. However, most SDA are only suited for a single type of statistics (i.e., summation-based or comparison-based statistics) and are not applicable to obtaining multiple statistic results. Most SDA are also inefficient for dynamic networks. This paper presents multi-functional secure data aggregation (MFSDA), in which the mapping step and coding step are introduced to provide value-preserving and order-preserving and, later, to enable arbitrary statistics support in the same query. MFSDA is suited for dynamic networks because these active nodes can be counted directly from aggregation data. The proposed scheme is tolerant to many types of attacks. The network load of the proposed scheme is balanced, and no significant bottleneck exists. The MFSDA includes two versions: MFSDA-I and MFSDA-II. The first one can obtain accurate results, while the second one is a more generalized version that can significantly reduce network traffic at the expense of less accuracy loss.

Predicting the Times of Retweeting in Microblogs

Recently, microblog services accelerate the information propagation among peoples, leaving the traditional media like newspaper, TV, forum, blogs, and web portals far behind. Various messages are spread quickly and widely by retweeting in microblogs. In this paper, we take Sina microblog as an example, aiming to predict the possible number of retweets of an original tweet in one month according to the time series distribution of its top n retweets. In order to address the problem, we propose the concept of a tweet’s lifecycle, which is mainly decided by three factors, namely, the response time, the importance of content, and the interval time distribution, and then the given time series distribution curve of its top n retweets is fitted by a two-phase function, so as to predict the number of its retweets in one month. The phases in the function are divided by the lifecycle of the original tweet and different functions are used in the two phases. Experiment results show that our solution can address the problem of predicting the times of retweeting in microblogs with a satisfying precision.

Differential and Statistical Approach to Partial Model Matching

Partial model matching approaches are important to target recognition. In this paper, aiming at a 3D model, a novel solution utilizing Gaussian curvature and mean curvature to represent the inherent structure of a spatial shape is proposed. Firstly, a Point-Pair Set is constructed by means of filtrating points with a similar inherent characteristic in the partial surface. Secondly, a Triangle-Pair Set is demonstrated after locating the spatial model by asymmetry triangle skeleton. Finally, after searching similar triangles in a Point-Pair Set, optimal transformation is obtained by computing the scoring function in a Triangle-Pair Set, and optimal matching is determined. Experiments show that this algorithm is suitable for partial model matching. Encouraging matching efficiency, speed, and running time complexity to irregular models are indicated in the study.

DDA: A deep neural network-based cognitive system for IoT-aided dermatosis discrimination

Abstract The rapid development of the Internet of Things (IoT) and cognitive cyber-physical systems (CPS) has made peoples daily lives more intelligent. Additionally, emerging technologies, such as wearable devices and machine learning, have demonstrated the potential for acquiring and processing large amounts of data from the physical world. In the medical field, effectively utilizing the collected medical data and providing more intelligent systems for doctors and patients to assist in diagnoses have also become important research topics. This paper presents a deep neural network-based cognitive system named DDA (dermatosis discrimination assistant) for classifying the dermatosis images generated by confocal laser scanning microscopes. Considering the lack of labels, we increase the labeled data automatically using an incremental model based on a small amount of labeled data and propose a disease discrimination model to distinguish and diagnose the categories of the disease images. In this system, the diagnoses of seborrheic keratosis (SK) and flat wart (FW) are used as examples, and experiments are conducted using the proposed models. Experimental results show that this system performs almost as well as individual dermatologists and can identify and diagnose other common dermatoses.