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The analysis for social networks, such as the sensor-networks in socially networked industries, has shown a deep influence of intelligent information processing technology on industrial systems. The large amounts of data on these networks raise the urgent demands of analyzing the topological content effectively and efficiently in Industrial Internet of Things. One of the ways to locate important information amongst such large troves of data is to recognize influential nodes. In this article, we examine an intelligent way to recognize the influence of such nodes automatically. Motivated by the concepts of system controllability and observability from control theory, we introduce a novel method to evaluate nodes from two different aspects, namely, the ability of “observe” information on the network (i.e., observability), and the ability to propagate information to other nodes (i.e., controllability). We propose a unified data mining framework that incorporates content analysis with nodes behavioral tendencies, and show that it is able to outperform competitive baselines in recognizing influential nodes in networks. We also show that it is important to detect the presence of spammer nodes within networks, which might otherwise be wrongly recognized as influential nodes. The experimental results demonstrate the superiority of the proposed approach in comparison with baseline methods.

Internet of Things and the related computing paradigms, such as cloud computing and fog computing, provide solutions for various applications and services with massive and high-dimensional data, while producing threats to the personal privacy. Differential privacy is a promising privacy-preserving definition for various applications and is enforced by injecting random noise into each query result such that the adversary with arbitrary background knowledge cannot infer sensitive input from the noisy results. Nevertheless, existing differentially private mechanisms have poor utility and high-computation complexity on high-dimensional data because the necessary noise in queries is proportional to the size of the data domain, which is exponential to the dimensionality. To address these issues, we develop a compressed sensing mechanism (CSM) that enforces differential privacy on the basis of the compressed sensing (CS) framework while providing accurate results to linear queries. We derive the utility guarantee of CSM theoretically. An extensive experimental evaluation on real-world data sets over multiple fields demonstrates that our proposed mechanism consistently outperforms several state-of-the-art mechanisms under differential privacy.

The early diagnosis of pulmonary cancer can significantly improve the survival rate of patients, where pulmonary nodules detection in computed tomography images plays an important role. In this article, we propose a novel pulmonary nodule detection system based on convolutional neural networks (CNN). Our system consists of two stages, pulmonary nodule candidate detection and false positive reduction. For candidate detection, we introduce Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to Faster Region-based Convolutional Neural Network (Faster R-CNN) model. For false positive reduction, a three-dimensional convolutional neural network (3D-CNN) is employed to completely utilize the three-dimensional nature of CT images. In this network, Focal Loss is used to solve the class imbalance problem in this task. Experiments were conducted on LUNA16 dataset. The results show the preferable performance of the proposed system and the effectiveness of using ISODATA and Focal loss in pulmonary nodule detection is proved.

The wireless video transmission environment of unmanned aerial vehicles (UAVs) is complex and unstable given the high mobility and changeable working conditions of UAVs, which lead to burst and consecutive errors and high error rates. A compressed video stream is extremely sensitive to transmission errors, such that even a single bit error sharply degrades the video quality. Hence, we propose an intraframe pixel-row-interleaved error concealment algorithm that interleaves pixel rows to generate high similarity in different parts of a frame, thereby achieving intraframe error resilience. Subsequently, we suggest an interframe time-field-interleaved alternative motion-compensated prediction that allows for automatic error elimination and recovers at least four consecutive frames in wireless video communications. The experiments demonstrate that the proposed algorithms recover frames with excellent subjective and objective effects. Moreover, these algorithms can provide reliable and robust video transmission for UAVs.

The advancement of data communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the communication perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in communication field.

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.

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.

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.

A key challenge in computer networking is how to organize network topology effectively among a large number of servers in the cloud storage system. In a cloud environment, the topology, which is different from the underlying topology, may be established in any form at any potential edge peers. The cloud content delivery network (CDN) always faces problems of complex distributed path creation, cache update, load balancing, etc. To address the problem as a static content delivery, we propose an Improved Heuristic Genetic Algorithm for Static Content Delivery in Cloud Storage (IHGA-SCDCS) based on a resource management model and cost model. The static content delivery in cloud storage is abstracted into mathematical model for set solving problem, which is then solved by an improved Genetic Algorithm (GA). Finally, the optimal solution is reduced to an optimal content delivery program. The simulation experiment, based on CloudSim, shows that IHGA-SCDCS can effectively obtain optimal solution while reducing delivery cost.

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.

A weighted fusion method of D-S evidence theory in decision making is proposed to aim at the problem of lacking in the distribution of trust, data processing and precision in D-S evidential theory. The method of gesture recognition based on Kinect and sEMG signal are established. Weighted D-S evidence theory is used to fuse Kinect and sEMG signals and the simulation experiment is made respectively. The stimulation results show that comparing with other experimental methods, the decision fusion method based on weighted D-S evidence theory has higher utilization efficiency and recognition rate.

In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modeled as graph processing. Graph processing, especially the processing of the large-scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large-scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping, and specific GPU programming. In this article, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in detail, and explore the research opportunities for the future.

The flying ad hoc network (FANET) is a new paradigm of wireless communication that governs the autonomous movement of UAVs and supports UAV-to-UAV communication. A FANET can provide an effective real-time communication solution for the multiple UAV systems considering each flying UAV as a router. However, existing mobile ad hoc protocols cannot meet the needs of FANETs due to high-speed mobility and frequent topology change. In addition, the complicated flight environment and varied flight tasks lead to the traditional built-in-rules protocols no longer meeting the demands of autonomy. Hence, we have proposed adaptive hybrid communication protocols including a novel position-prediction-based directional MAC protocol (PPMAC) and a self-learning routing protocol based on reinforcement learning (RLSRP). The performance results show that the proposed PPMAC overcomes the directional deafness problem with directional antennas, and RLSRP provides an automatically evolving and more effective routing scheme. Our proposed hybrid adaptive communication protocols have the potential to provide an intelligent and highly autonomous communication solution for FANETs, and indicate the main research orientation of FANET protocols.

The access security of wireless devices is a serious challenge in present wireless network security. Radio frequency (RF) fingerprint recognition technology as an important non-password authentication technology attracts more and more attention, because of its full use of radio frequency characteristics that cannot be imitated to achieve certification. In this paper, a RF fingerprint identification method based on dimensional reduction and machine learning is proposed as a component of intrusion detection for resolving authentication security issues. We compare three kinds of dimensional reduction methods, which are the traditional PCA, RPCA and KPCA. And we take random forests, support vector machine, artificial neural network and grey correlation analysis into consideration to make decisions on the dimensional reduction data. Finally, we obtain the recognition system with the best performance. Using a combination of RPCA and random forests, we achieve 90% classification accuracy is achieved at SNR $$\ge $$ 10 dB when reduced dimension is 76. The proposed method improves wireless device authentication and improves security protection due to the introduction of RF fingerprinting.

Detection plays an important role in improving the performance of multi-object tracking (MOT), but most recently MOT works mainly focus on association algorithm and usually ignore the detections. To assist in associating object detections and to overcome detection failures, in this paper, we explore the low-rank-based foreground detection method to refine the detections and show it can significantly lead a better tracking result in online multi-object tracking. Firstly, the low-level pixel information from low-rank foreground segmentation and high-level detection responses from object detector are combined to form an overcomplete detections set, which serves as input for the tracking-by-detection-based multi-object tracking. Then, the predicted object location in online tracking as a prior to feedback for the foreground segmentation in sparse approximation for future frames can improve the foreground detection performance. Finally, to effectively solve the data association problem in online MOT, two-step data association relies on tracklet confidence is used to associate the detections and generate long trajectories since the existing trajectories provide a reliable history to support their presence in current frame. The experimental results in public pedestrian tracking datasets show that our detection optimization strategy can help to improve the tracking performance compared with several state-of-the-art multi-object trackers, with improved recall, precision, FP, FN and MOTA, MOTP results.

As the concept of smart cities is evolving, the need for automation and effective delivery of services is essential. Over the last few decades, need for safety and security of travelers on the road has been a major concern. Vehicular Ad Hoc Networks (VANETs) can play an influential role in recognizing and implementing such concept, by supporting safety, comfort and infotainment services. However, the complex and inflexible architecture of VANETs faced a set of challenges such as high mobility, intermittent connectivity, heterogeneity of applications. In this context, Software-defined networking (SDN) has emerged as a programmable and flexible network, which has recently gained attention from research communities, businesses, and industries, in both wired network management and heterogeneous wireless communication. This paper aims at examining and classifying a number of related SDN- based research works on wireless networks specially VANETs. Firstly, a brief on the requirements of SDN over traditional networking is provided, followed by an elaboration on basic architecture and its layers. Thereafter, SDN applications in various wireless network areas such as mobile network and VANETs are described along with a focus on analyzing and comparing the current SDN-related research on different parameters. Furthermore, the paper presents a review of current research initiatives to solve challenges of vehicular environment. The impact of SDN paradigm along with implementation issues in vehicular communication and explore likely use cases based on SDN paradigm.

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.

IEEE Pulmonary cancer is one of the most dangerous cancers with a high incidence and mortality. An early accurate diagnosis and treatment of pulmonary cancer can observably increase the survival rates, where computer-aided diagnosis systems can largely improve the efficiency of radiologists. In this paper, we propose a deep automated lung nodule diagnosis system based on three-dimensional convolutional neural network (3D-CNN) and support vector machine (SVM) with multiple kernel learning (MKL) algorithms. The system not only explores the computed tomography (CT) scans, but also the clinical information of patients like age, smoking history and cancer history. To extract deeper image features, a 34-layers 3D Residual Network (3D-ResNet) is employed. Heterogeneous features including the extracted image features and the clinical data are learned with MKL. The experimental results prove the effectiveness of the proposed image feature extractor and the combination of heterogeneous features in the task of lung nodule diagnosis.

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.

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.

Skeletal bone age assessment is a widely used standard procedure in both disease detection and growth prediction for children in endocrinology. Conventional manual assessment methods mainly rely on personal experience in observing X-ray images of left hand and wrist to calculate bone age, which show some intrinsic limitations from low efficiency to unstable accuracy. To address these problems, some automated methods based on image processing or machine learning have been proposed, while their performances are not satisfying enough yet in assessment accuracy. Motivated by the remarkable success of deep learning (DL) techniques in the fields of image classification and speech recognition, we develop a deep automated skeletal bone age assessment model based on convolutional neural networks (CNNs) and support vector regression (SVR) using multiple kernel learning (MKL) algorithm to process heterogeneous features in this paper. This deep framework has been constructed, not only exploring the X-ray images of hand and twist but also some other heterogeneous information like race and gender. The experiment results prove its better performance with higher bone age assessment accuracy on two different data sets compared with the state of the art, indicating that the fused heterogeneous features provide a better description of the degree of bones' maturation.

In general, dynamic traffic signal control or smooth driving benefits energy saving and CO2 emissions. However, most of studies in the past lack the integrated optimization of traffic signals control and instantaneous vehicle motion states simultaneously on energy saving and CO2 emission reduction. In this paper, a bi-level optimization model is proposed to minimize fuel consumption and CO2 emissions by considering real-time traffic signal control in road side unit (RSU) and instantaneous vehicle motion states optimization in on-board unit (OBU) synthetically. The RSU communicates with OBUs by vehicle wireless communication networks. Then, the traffic signal scheme in RSU is optimized with the received vehicle motion data sent from OBUs, and the system in vehicles optimize their speed and acceleration with the received traffic signal scheme sent from RSU. The simulation results indicate that the proposed model outperforms the existing Maximize Throughput Model (MaxTM) up to 10% in reducing fuel consumption and CO2 emissions especially when the traffic is heavy.

Axial and visibility graph analysis models are combined with GIS and Space Syntax to study the corresponding relationship between street network and specific city life in Hankou, China, based on three scales - city, district, and community, so as to interpret the hidden structure out of complex urban form through the spatial logic of street network. In this paper, a quantitative analysis is made on selected parameters in Space Syntax, including Integration, Choice, density of road, and Ht index. The result indicates that there is certain correlation among these parameters. Moreover, these parameters also present certain changing patterns along with the increase of analysis radius. The results reveal that the street network of Hankou presents a multi-hierarchical structure and spatial wholeness from all these three scales. This characteristic creates vitality and diversity while maintaining the feature of wholeness for the urban space. Research has turned out that community-scale street network performs a positive role to keep neighborhoods alive. Therefore, it is an important strategy for maintaining the vitality of urban space and realizing coordination and unification between parts and the whole. It is proposed in this paper that a good street network is a key factor for carrying forward urban context of Hankou. Undoubtedly, it is worthy of reflecting on large-scale demolition of existing urban space, especially in historical areas. Afterwards, the paper proposes a hierarchically synergetic planning strategy based on the analysis. More attention should be paid to street network to preserve diversity, continuity, and integrity, and finally archive holistically sustainable development within a city.

In order to save the computer data effectively, we should not only check the integrity for the data integrity, but also check storage system to recover data in a timely manner to reduce losses to a minimum, to prevent the recover fails when the fault occurred. The viability and integrity of the system to recover the data is determined by the recovery algorithm, so the key to recover the data is how to design the recovery algorithm. This paper presented an advanced recovery techniques based on the traditional recover algorithm which based on the log, then the specific data structure and algorithm implementation is given at the last of this paper.

Intrusion Detection is an important component of the security of the Network, through the critical information of the network and host system, it can determine the user’s invasion of illegal and legal acts of the user misuse of resources, and make an adequate response. According to the problem, which machine learning anomaly detection effect is not ideal when the user behavior changes and a separate anomaly detection. Based on the Bayesian inference anomaly detection, applying the Bayesian inference of statistical methods to machine learning anomaly detection, this paper established a decision tree corresponding to the method. This method overcomes the satisfactory of the anomaly detection individual test, and improves the machine learning in the predictive ability of anomaly detection and anomaly detection efficiency.

At present, the vascular tissue disease has become the major cause to against people’s health, vascular graft is an effective means for the treatment of vascular tissue disease, but the preservation of blood vessels has always been a big challenge. This paper based on the conduct of porcining aortic cryofreeze-drying, with using micro-CT to track the whole drying process, expect to reveal what vascular morphology changes have taken place during the process of cryofreeze-drying, and take a comparative analysis of porcine aortic mechanical properties between freeze-dried after the restoration of water and fresh porcine aortic by the Texture analyzer, so that uncover the effect the process of freeze-dried making on pig arteries mechanical properties. Test results showed that the pig aorta vascular occur stratification in the process of cryofreeze-drying, blood vessels freeze-dried following the resumption of water comparing the mechanical properties with the fresh blood vessels, its maximum axial tensile stress reduced by about 40%, and the largest peripheral tensile stress increase by about 45%, and the biggest puncture stress is about 75 percent of fresh blood vessels.Copyright © 2009 by ASME