Zhigao Zheng

KDE based outlier detection on distributed data streams in multimedia network

Multimedia networks hold the promise of facilitating large-scale, real-time data processing in complex environments. Their foreseeable applications will help protect and monitor military, environmental, safety-critical, or domestic infrastructures and resources. Cloud infrastructures promise to provide high performance and cost effective solutions to large scale data processing problems. This paper focused on the outlier detection over distributed data stream in real time, proposed kernel density estimation (KDE) based outlier detection algorithm KDEDisStrOut in Storm, firstly formalized the problem of outlier detection using the kernel density estimation technique and update the transported data incrementally between the child node and the coordinator node which reduces the communication cost. Then the paper adopted the exponential decay policy to keep pace with the transient and evolving natures of stream data and changed the weight of different data in the sliding window adaptively made the data analysis more reasonable. Theoretical analysis and experiments on Storm with synthetic and real data show that the KDEDisStrOut algorithm is efficient and effective compared with existing outlier detection algorithms, and more suitable for data streams.

Sparsity estimation matching pursuit algorithm based on restricted isometry property for signal reconstruction

Abstract Achievement of good reconstruction performance by most of existing greedy algorithms is possible only when signal sparsity has been known well in advance. However, it is difficult in practice to ensure signal sparsity making the reconstruction performance of the greedy algorithms stable. Moreover, some greedy algorithms with previous unknown signal sparsity are time-consuming in the process of adaptive adjustment of signal sparsity, and thereby making the reconstruction time too long. To address these concerns, the greedy algorithm from signal sparsity estimation proposed in this paper. Based on the restricted isometry property criterion, signal sparsity is estimated before atoms selection and the step size of atoms selection adjusted adaptively based on the relations between of the signal residuals in each iteration. The research which solves the problem of sparsity estimation in the greedy algorithm provides the compressed sensing available to the applications where the signal sparsity is un-known. It has important academic and practical values. Experimental results demonstrate the superiority of the performance of proposed algorithm to the greedy algorithms with previous unknown signal sparsity, no matter on the performance stability and reconstruction precision.

Video segmentation algorithm based on superpixel link weight model

Based on the traditional segmentation algorithms, this paper proposes unsupervised video segmentation approach. The proposed algorithm applies superpixel to indicate the movement foreground and uses the static features of current frame and the relevant features of adjacent frames to compute the weight. It also brings in the mechanism of superpixel color features match restriction and motion relevance match restriction. The experiment result shows this algorithm can achieve the segmentation of video pictures and effectively solve the problem of over-segmentation.

Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications

Abstract Particle Swarm Optimization (PSO) algorithms often face premature convergence problem, specially in multimodal problems as it may get stuck in specific point. In this paper, we have enhanced Dynamic-PSO i.e. and an extention of our earlier research work. This newly proposed algorithm Guided Dynamic-PSO (GDPSO) also targets the particles whose personal best get stuck i.e. their personal best does not improve for fixed number of iterations similar to DPSO, however a new approach is proposed for replacing personal bests of such particles. The replacement of this new personal best is done on the basis of sharing fitness so that better diversity can be provided to avoid the problem. The performance of GDPSO has been compared with PSO and its variants including DPSO over 24 benchmark functions provided by Black-Box Optimization Benchmarking (BBOB 2015). Results show that the performance of GDPSO is better in comparison with other peer algorithms. Further effectiveness of GDPSO is demonstrated in digital image watermarking. Digital image watermarking schemes primarily focus on providing good tradeoff between imperceptibility and robustness along with reliability in watermarked images produced for wide variety of applications. To support watermarking scheme in achieving this tradeoff, suitable watermark strength is identified in the form of scaling factor using GDPSO for colored images. Results achieved through GDPSO are compared with PSO and other widely accepted variants of PSO over different combination of host and watermark images. Experiment results demonstrate that performance of underline watermarking scheme when used with GDPSO, in terms of imperceptibility and robustness, is better than other variants of PSO.

An efficient and provably secure time-limited key management scheme for outsourced data: An efficient and provably secure time-limited key management scheme for outsourced data

A time‐limited data access control scheme allows a users access to the data files only for a specified time period. A cryptographic solution to the time‐limited access control problem is by encrypting each data group associated with a time period with a distinct key. The data is encrypted by the data owner. The respective decryption keys are then distributed to authorized users by the data owner. A user requires one secret decryption key storage for each authorized time period. To reduce the secret key storage with each user, time‐limited hierarchical key management schemes are generally used. Many such schemes are proposed in the recent years. The objective of these schemes is system efficiency and data security. Construction of such schemes become more challenging when data is outsourced to an untrusted third party service provider. In current work, an efficient and secure time‐limited hierarchical key assignment scheme is proposed for key management suitable for data outsourcing scenario. We compare it with the other recent similar schemes. The scheme is formally proved against the modern stronger security notion called keyu2009indistinguishability.

Deep detection network for real-life traffic sign in vehicular networks

Abstract The challenge for real-life traffic sign detection lies in recognizing small targets in a large and complex background, making state-of-the-art general object detection methods not work well in both detection speed and precision. The existing deep learning models for traffic signs detection fail to use the fixed feature of the targets. This paper proposes a novel end-to-end deep network that extracts region proposals by a two-stages adjusting strategy. Firstly, we introduce an AN (Attention Network) to Faster-RCNN for finding all potential RoIs (Regions of Interest) and roughly classifying them into three categories according to colour feature of the traffic signs. Then the FRPN (Fine Region Proposal Network) produces the final region proposals from a set of anchors per feature map location extracted by the AN. We also modify the model by (1) adding a deconvolutional structure to convolutional layers to fit the small size of targets, and (2) replacing the classifier with three softmax corresponding to three coarse categories obtained by the AN. Our method is evaluated on two publicly available traffic sign benchmarks which are collected in real road condition. The experiments show our method generates only 1/14 of the anchors generated by Faster-R-CNN so the detection speed is increased by about 2 fps with ZF-Net and it reaches an average mAP of 80.31% and 94.95% in two benchmarks, 9.69% and 7.88% higher than Faster-R-CNN with VGG16, respectively.

The optimization for recurring queries in big data analysis system with MapReduce

Abstract As data-intensive cluster computing systems like MapReduce grow in popularity, there is a strong need to promote the efficiency. Recurring queries, repeatedly being executed for long periods of time on rapidly evolving data-intensive workloads, have become a bedrock component in big data analytic applications. Consequently, this paper presents optimization strategies for recurring queries for big data analysis. Firstly, it analyzes the impact of recurring queries efficiency by MapReduce recurring queries model. Secondly, it proposes the MapReduce consistent window slice algorithm, which can not only create more opportunities for reuse of recurring queries, but also greatly reduce redundant data while loading input data by the fine-grained scheduling. Thirdly, in terms of data scheduling, it designs the MapReduce late scheduling strategy that improve data processing and optimize computation resource scheduling in MapReduce cluster. Finally, it constructs the efficient data reuse execution plans by MapReduce recurring queries reuse strategy. The experimental results on a variety of workloads show that the algorithms outperform the state-of-the-art approaches.

Providing security and privacy to huge and vulnerable songs repository using visual cryptography

In the today’s scenario, the number of online song repositories such as iTunes, Hungama.com, etc. is increasing day-by-day. The reason for this can be attributed to the exponential growth in the Internet users in the past few years. These song repositories store huge number of songs (mostly in millions) and charge their users for listening and downloading them. With increased number of users requires more enhanced security measures to protect such vulnerable songs repository. Any breach in security of such song repositories would not only cause huge financial loss but also copyright infringement for the owners. Therefore, in this paper we have presented a novel and efficient approach for providing security and privacy to huge and vulnerable songs repository using visual cryptography. Presented approach not only provides confidentiality to the songs but also provides integrity verification with access control to the songs repository. We have also removed various basic security constraints of (2, 2) visual cryptography existed in most of the state of art approaches like meaningless pattern of the shares, explicit codebook requirement, contrast loss, lossy recovery etc which are eliminated in the proposed approach.

A novel deep learning method for aircraft landing speed prediction based on cloud-based sensor data

Abstract The combination of artificial intelligence methods and IoT based sensor data will play a critical and crucial role in various environments. Flight landing safety is a research hotspot of aviation field for a long time. Accurately predicting the landing speed is conducive to reducing the landing accidents. In this paper, we proposed an accurate aircraft landing speed prediction model based on the long-short term memory (LSTM) with flight sensor data. Firstly, we analyze and pre-process the dataset with statistical method including randomness tests and stationary tests. Secondly, we design the features by random forest algorithm and reduce the dimensionality of features with principal component analysis. Thirdly, we develop a deep architecture based on long-short term memory to predict the aircraft landing speed. Experiment results prove that it has better performance with higher prediction accuracy compared with the state of the art, indicating that the proposed model is accurate and effective. The findings are expected to be applied into flight operation practice for further preventing of landing accidents and improving the air management for air traffic controllers.

An efficient joint compression and sparsity estimation matching pursuit algorithm for artificial intelligence application

Abstract Measurement matrix and signal reconstruction algorithm are the key factors influencing the performance of signal reconstruction. However, the reconstruction performance of the existing matching pursuit algorithms, the most popular reconstruction algorithms, is closely related to the signal sparsity, which is hard to determinate apriori. As well, the researches on the reconstruction algorithms are developed independently with the design of measurement matrix. So, in this paper, we originally conduct a joint study of design of measurement matrix and signal reconstruction algorithm. RIP criterion is used to quantitatively analyze the relationship between the signal sparsity and the measurement matrix, and then an efficient joint compression and sparsity estimation matching pursuit (JCSEMP) algorithm is proposed. JCSEMP algorithm constructs a chaotic measurement matrix, a sparsity estimation algorithm based on the chaotic measurement matrix, and a variable atom selection criterion which use the variation between the residuals to adaptively adjust the number of atoms to select. Experimental results demonstrate that this algorithm can provide a better reconstruction performance and a lower reconstruction period.