Public auditing is a new technique to protect the integrity of outsourced data in the remote cloud. Users delegate the ability of auditing to a third party auditor (TPA), and assume that each result from the TPA is correct. However, the TPA is not always trustworthy in reality. In this paper, we consider a scenario in which the TPA may lower the reputation of the cloud server by cheating users, and propose a novel public auditing scheme to address this security issue. The analyses and the evaluation prove that our scheme is both secure and efficient.

A novel auto-tuning digital DC--DC converter is presented. In order to reduce the recovery time and undershoot, the auto-tuning control combines LnL, conventional PID and a predictive PID with a configurable predictive coefficient. A switch module is used to select an algorithm from the three control algorithms, according to the difference between the error signal and the two initially predefined thresholds. The detection and control logic is designed for both window delay line ADC and $Sigma Delta$ DPWM to correct the delay deviation. When the output of the converter exceeds the quantization range, the digital output of ADC is set at 0 or 1, and the delay line stops working to reduce power consumption. Theoretical analysis and simulations in the CSMC CMOS 0.5,$mu$m process are carried out to verify the proposed DC--DC converter. It is found that the converter achieves a power efficiency of more than 90% at heavy load, and reduces the recovery time and undershoot.

Since the Content Distribution Network (CDN) and IP multicast have heavy infrastructure requirements, their deployment is quite restricted. In contrast, peer-to-peer (P2P) streaming applications are independent on infrastructures and thus have been widely deployed. Emerging wireless ad-hoc networks are poised to enable a variety of streaming applications. However, many potential problems, that are trivial in wired networks, will emerge when deploying existing P2P streaming applications directly into wireless ad-hoc networks. In this paper, we propose a goodput optimization framework for P2P streaming over wireless ad-hoc networks. A two-level buffer architecture is proposed to reassign the naive streaming systems' data requests. The framework adopts a chunk size-varying transmission algorithm to obtain smooth playback experience and acceptable overhead and utilize limited bandwidth resources efficiently. The distinguishing features of our implementation are as follows: first, the framework works as a middleware and is independent on the streaming service properties; existing P2P streaming application can be deployed in wireless ad-hoc networks with minimum modifications and development cost; second, the proposed algorithm can reduce unnecessary communication overheads compared with traditional algorithms which gain high playback continuity with small chunk size; finally, our scheme can utilize low bandwidth transmission paths rather than discarding them, and thus improve overall performance of the wireless network. We also present a set of experiments to show the effectiveness of the proposed mechanism.

For cross-corpus speech emotion recognition (SER), how to obtain effective feature representation for the discrepancy elimination of feature distributions between source and target domains is a crucial issue. In this paper, we propose a Target-adapted Subspace Learning (TaSL) method for cross-corpus SER. The TaSL method trys to find a projection subspace, where the feature regress the label more accurately and the gap of feature distributions in target and source domains is bridged effectively. Then, in order to obtain more optimal projection matrix, ℓ1 norm and ℓ2,1 norm penalty terms are added to different regularization terms, respectively. Finally, we conduct extensive experiments on three public corpuses, EmoDB, eNTERFACE and AFEW 4.0. The experimental results show that our proposed method can achieve better performance compared with the state-of-the-art methods in the cross-corpus SER tasks.

In cloud computing, multiple users coexist in one datacenter infrastructure and the network is always shared using VMs. Network bandwidth allocation is necessary for security and performance guarantees in the datacenter. InfiniBand (IB) is more widely applied in the construction of datacenter cluster and attracts more interest from the academic field. In this paper, we propose an IB dynamic bandwidth allocation mechanism IBShare to achieve different Weight-proportional and Min-guarantee requirements of allocation entities. The differentiated IB Congestion Control (CC) configuration is proven to offer the proportional throughput characteristic at the flow level. IBShare leverages distributed congestion detection, global congestion computation and configuration to dynamically provide predictable bandwidth division. The real IB experiment results showed IBShare can promptly adapt to the congestion variation and achieve the above two allocation demands through CC reconfiguration. IBShare improved the network utilization than reservation and its computation/configuration overhead was low.

Inspired from the idea of data representation in manifold learning, we derive a novel model which combines the original training images and their tangent vectors to represent each image in the testing set. Different from the previous methods, the L1 norm is used to control the reconstruction error. Considering the fact that the objective function in the proposed model is non-smooth, we utilize the majorization minimization (MM) method to solve the proposed optimization model. It is interesting to note that at each iteration a quadratic optimization problem is formulated and its analytical solution can be achieved, thereby making the proposed algorithm effective. Extensive experiments on face images demonstrate that our method achieves better performance than some previous methods.

Accurate registration is crucial for medical image analysis. In this letter, we proposed an improved Demons technique (IDT) for medical image registration. The IDT improves registration quality using orthogonal gradient information. The advantage of the proposed IDT is assessed using 14 medical image pairs. Experimental results show that the proposed technique provides about 8% improvement over existing Demons-based techniques in terms of registration accuracy.

To overcome the challenge in micro-expression recognition that it only emerge in several small facial regions with low intensity, some researchers proposed facial region partition mechanisms and introduced group sparse learning methods for feature selection. However, such methods have some shortcomings, including the complexity of region division and insufficient utilization of critical facial regions. To address these problems, we propose a novel Group Sparse Reduced Rank Tensor Regression (GSRRTR) to transform the fearure matrix into a tensor by laying blocks and features in different dimensions. So we can process grids and texture features separately and avoid interference between grids and features. Furthermore, with the use of Tucker decomposition, the feature tensor can be decomposed into a product of core tensor and a set of matrix so that the number of parameters and the computational complexity of the scheme will decreased. To evaluate the performance of the proposed micro-expression recognition method, extensive experiments are conducted on two micro expression databases: CASME2 and SMIC. The experimental results show that the proposed method achieves comparable recognition rate with less parameters than state-of-the-art methods.

In order to reduce the iterative decoding delay of convolutional turbo codes, this paper presents a concurrent decoding algorithm for the hardware implementation of turbo convolutional decoders. Different than a general turbo code, the hardware turbo decoder based on the proposed algorithm can update the priori information of message for each component code in a bit-by-bit manner as soon as it is generated by the other component code. The two component codes in a turbo code can thus be decoded concurrently, by using a single MAP decoder, subsequently reducing the decoding latency by approximately half while maintaining the bit error rate performance and a comparable hardware complexity, as a general turbo decoder.

Root cause analysis of BGP updates is the key to debug and troubleshoot BGP routing problems. However, it is a challenge to precisely diagnose the cause and the origin of routing instability. In this paper, we are the first to distinguish link failure events from policy change events based on BGP updates from single vantage points by analyzing the relationship of the closed loops formed through intersecting all the transient paths during instability and the length variation of the stable paths after instability. Once link failure events are recognized, their origins are precisely inferred with 100% accuracy. Through simulation, our method is effective to distinguish link failure events from link restoration events and policy related events, and reduce the size of candidate set of origins.

With the continuous refinement of Deep Neural Networks (DNNs), a series of deep and complex networks such as Residual Networks (ResNets) show impressive prediction accuracy in image classification tasks. Unfortunately, the structural complexity and computational cost of residual networks make hardware implementation difficult. In this paper, we present the quantized and reconstructed deep neural network (QR-DNN) technique, which first inserts batch normalization (BN) layers in the network during training, and later removes them to facilitate efficient hardware implementation. Moreover, an accurate and efficient residual network accelerator (RNA) is presented based on QR-DNN with batch-normalization-free structures and weights represented in a logarithmic number system. RNA employs a systolic array architecture to perform shift-and-accumulate operations instead of multiplication operations. QR-DNN is shown to achieve a 1∼2% improvement in accuracy over existing techniques, and RNA over previous best fixed-point accelerators. An FPGA implementation on a Xilinx Zynq XC7Z045 device achieves 804.03 GOPS, 104.15 FPS and 91.41% top-5 accuracy for the ResNet-50 benchmark, and state-of-the-art results are also reported for AlexNet and VGG.

In this letter, we propose a novel deep domain-adaptive convolutional neural network (DDACNN) model to handle the challenging cross-corpus speech emotion recognition (SER) problem. The framework of the DDACNN model consists of two components: a feature extraction model based on a deep convolutional neural network (DCNN) and a domain-adaptive (DA) layer added in the DCNN utilizing the maximum mean discrepancy (MMD) criterion. We use labeled spectrograms from source speech corpus combined with unlabeled spectrograms from target speech corpus as the input of two classic DCNNs to extract the emotional features of speech, and train the model with a special mixed loss combined with a cross-entrophy loss and an MMD loss. Compared to other classic cross-corpus SER methods, the major advantage of the DDACNN model is that it can extract robust speech features which are time-frequency related by spectrograms and narrow the discrepancies between feature distribution of source corpus and target corpus to get better cross-corpus performance. Through several cross-corpus SER experiments, our DDACNN achieved the state-of-the-art performance on three public emotion speech corpora and is proved to handle the cross-corpus SER problem efficiently.