This paper describes a novel harmonic-based robust voice activity detection (H-RVAD) method with harmonic spectral local peak (HSLP) feature. HSLP is extracted by spectral amplitude analysis between the adjacent formants, and such characteristic can be used to identify and verify audio stream containing meaningful human speech accurately in low SNR environment. And, an enhanced low SNR noisy speech recognition system framework with wakeup module, speech recognition module and confirmation module is proposed. Users can determine or reject the system feedback while a recognition result was given in the framework, to prevent any chance that the voiced noise misleads the recognition result. The H-RVAD method is evaluated by the AURORA2 corpus in eight types of noise and three SNR levels and increased overall average performance from 4% to 20%. In home noise, the performance of H-RVAD method can be performed from 4% to 14% sentence recognition rate in average.

This paper introduces amplitude-only images to image trading systems in which not only the copyright of images but also the privacy of consumers are protected. In the latest framework for image trading systems, an image is divided into an unrecognizable piece and a recognizable but distorted piece to simultaneously protect the privacy of a consumer and the copyright of the image. The proposed scheme uses amplitude-only images which are completely unrecognizable as the former piece, whereas the conventional schemes leave recognizable parts to the piece which degrades privacy protection performance. Moreover, the proposed scheme improves the robustness against copyright violation regardless of the used digital fingerprinting technique, because an amplitude-only image is larger than the piece in the conventional scheme. In addition, phase-only image is used as the second piece in the proposed scheme, the consumer can confirm what he/she bought. Experimental results show the effectiveness of the proposed scheme.

In this paper, a compression-friendly copyright- and privacy-protected image trading system is proposed. In the image trading system, the copyright of the image and the consumer's privacy is important. In addition, it should preserve existing image compression standards. In the proposed method, for privacy protection, the content provider (CP) multiplies random signs to the discrete wavelet transformed (DWTed) coefficients of an image to generate the visually encrypted image. The proposed visually protected images can be efficiently compressed by using JPEG 2000 which compresses the image in the DWTed domain as well. For copyright protection, the trusted third party (TTP) applies digital fingerprinting to the image in the encrypted domain. While in the conventional system, the amplitude-only image (AOI) which is the inversely transformed amplitude spectra of an image is used for privacy protection. Since, the AOI consists of real numbers, to store and transmit the AOI, it has to be quantized before compression. Therefore, quantization errors cannot be avoided in the conventional system. On the other hand, the proposed method applies the digital fingerprint in the DWTed domain, so clipping errors in decoding the image by the TTP is avoided. In addition, only a seed number which is input to a pseudo random number generator is shared between the CP and the consumer, whereas an extra image is shared in the conventional systems. Experimental results show that the proposed system is efficient in terms of privacy protection, compression performance, quality of fingerprinted images, and correct fingerprint extracting performance.

This paper proposes a visual secret sharing (VSS) scheme with efficient pixel expansion which prevents malicious share holders from deceiving an honest share holder. A VSS scheme encrypts a secret image into pieces referred to as shares where each party keeps a share so that stacking a sufficient number of shares recovers the secret image. A cheat prevention VSS scheme gives another piece to each party for verifying whether the share presented by another party is genuine. The proposed scheme improves the contrast of the recovered image and cheat-prevention functionality by introducing randomness in producing pieces for verification. Experimental results show the effectiveness of the proposed scheme.

Since the dark channel prior (DCP)-based dehazing method is ineffective in the sky area and will cause the problem of too dark and color distortion of the image, we propose a novel dehazing method based on sky area segmentation and image fusion. We first segment the image according to the characteristics of the sky area and non-sky area of the image, then estimate the atmospheric light and transmission map according to the DCP and correct them, and then fuse the original image after the contrast adaptive histogram equalization to improve the details information of the image. Experiments illustrate that our method performs well in dehazing and can reduce image distortion.

This letter proposes an efficient compression scheme for the copyright- and privacy-protected image trading system. The proposed scheme multiplies pseudo random signs to amplitude components of discrete cosine transformed coefficients before the inverse transformation is applied. The proposed scheme efficiently compresses amplitude-only image which is the inversely transformed amplitude components, and the scheme simultaneously improves the compression efficiency of phase-only image which is the inversely transformed phase components, in comparison with the conventional systems.

Detecting anomalies, such as network failure or intentional attack in Internet, is a vital but challenging task. Although numerous techniques have been developed based on Internet traffic, detecting anomalies from the perspective of Internet topology structure is going to be possible because the anomaly detection of structured datasets based on complex network theory has become a focus of attention recently. In this paper, an anomaly detection method for the large-scale Internet topology is proposed to detect local structure crashes caused by the cascading failure. In order to quantify the dynamic changes of Internet topology, the network path changes coefficient (NPCC) is put forward which highlights the Internet abnormal state after it is attacked continuously. Furthermore, inspired by Fibonacci Sequence, we proposed the decision function that can determine whether the Internet is abnormal or not. That is the current Internet is abnormal if its NPCC is out of the normal domain calculated using the previous k NPCCs of Internet topology. Finally the new Internet anomaly detection method is tested against the topology data of three Internet anomaly events. The results show that the detection accuracy of all events are over 97%, the detection precision for three events are 90.24%, 83.33% and 66.67%, when k=36. According to the experimental values of index F1, larger values of k offer better detection performance. Meanwhile, our method has better performance for the anomaly behaviors caused by network failure than those caused by intentional attack. Compared with traditional anomaly detection methods, our work is more simple and powerful for the government or organization in items of detecting large-scale abnormal events.

Image denoising is an indispensable process of manifold high level tasks in image processing and computer vision. However, the traditional low-rank minimization-based methods suffer from a biased problem since only the noisy observation is used to estimate the underlying clean matrix. To overcome this issue, a new low-rank minimization-based method, called nuclear norm minus Frobenius norm rank residual minimization (NFRRM), is proposed for image denoising. The propose method transforms the ill-posed image denoising problem to rank residual minimization problems through excavating the nonlocal self-similarity prior. The proposed NFRRM model can perform an accurate estimation to the underlying clean matrix through treating each rank residual component flexibly. More importantly, the global optimum of the proposed NFRRM model can be obtained in closed-form. Extensive experiments demonstrate that the proposed NFRRM method outperforms many state-of-the-art image denoising methods.

Single-image dehazing is a challenging task in computer vision research. Aiming at the limitations of traditional convolutional neural network representation capabilities and the high computational overhead of the self-attention mechanism in recent years, we proposed image attention and designed a single image dehazing network based on the image attention: IAD-Net. The proposed image attention is a plug-and-play module with the ability of global modeling. IAD-Net is a parallel network structure that combines the global modeling ability of image attention and the local modeling ability of convolution, so that the network can learn global and local features. The proposed network model has excellent feature learning ability and feature expression ability, has low computational overhead, and also improves the detail information of hazy images. Experiments verify the effectiveness of the image attention module and the competitiveness of IAD-Net with state-of-the-art methods.

Image dehazing is of great significance in computer vision and other fields. The performance of dehazing mainly relies on the precise computation of transmission map. However, the computation of the existing transmission map still does not work well in the sky area and is easily influenced by noise. Hence, the dark channel prior (DCP) and luminance model are used to estimate the coarse transmission in this work, which can deal with the problem of transmission estimation in the sky area. Then a novel weighted variational regularization model is proposed to refine the transmission. Specifically, the proposed model can simultaneously refine the transmittance and restore clear images, yielding a haze-free image. More importantly, the proposed model can preserve the important image details and suppress image noise in the dehazing process. In addition, a new Gaussian Adaptive Weighted function is defined to smooth the contextual areas while preserving the depth discontinuity edges. Experiments on real-world and synthetic images illustrate that our method has a rival advantage with the state-of-art algorithms in different hazy environments.

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.