Within information hiding technology, digital watermarking is one of the most important technologies for copyright protection of digital content. Many digital watermarking schemes have been proposed in academia. However, these schemes are not used, because they are not practical; one reason for this is that the evaluation criteria are loosely defined. To make the evaluation more concrete and improve the practicality of digital watermarking, watermarking schemes must use common evaluation criteria. To realize such criteria, we organized the Information Hiding and its Criteria for Evaluation (IHC) Committee to create useful, globally accepted evaluation criteria for information hiding technology. The IHC Committee improves their evaluation criteria every year, and holds a competition for digital watermarking based on state-of-the-art evaluation criteria. In this paper, we describe the activities of the IHC Committee and its evaluation criteria for digital watermarking of still images, videos, and audio.

We propose a digital image watermarking method satisfying information hiding criteria (IHC) for robustness against JPEG compression, cropping, scaling, and rotation. When a stego-image is cropped, the marking positions of watermarks are unclear. To detect the position in a cropped stego-image, a marker or synchronization code is embedded with the watermarks in a lattice pattern. Attacks by JPEG compression, scaling, and rotation cause errors in extracted watermarks. Against such errors, the same watermarks are repeatedly embedded in several areas. The number of errors in the extracted watermarks can be reduced by using a weighted majority voting (WMV) algorithm. To correct residual errors in output of the WMV algorithm, we use a high-performance error-correcting code: a low-density parity-check (LDPC) code constructed by progressive edge-growth (PEG). In computer simulations using the IHC ver. 4 the proposed method could a bit error rate of 0, the average PSNR was 41.136 dB, and the computational time for synchronization recovery was less than 10 seconds. The proposed method can thus provide high image quality and fast synchronization recovery.

In these days, we can see digital signages in many places, for example, inside stations or trains with the distribution of attractive promotional video clips. Users can easily get additional information related to such video clips via mobile devices such as smartphone by using some websites for retrieval. However, such retrieval is time-consuming and sometimes leads users to incorrect information. Therefore, it is desirable that the additional information can be directly obtained from the video clips. We implement a suitable digital watermarking method on smartphone to extract watermarks from video clips on signages in real-time. The experimental results show that the proposed method correctly extracts watermarks in a second on smartphone.

Video data mining based on topic models as an emerging technique recently has become a very popular research topic. In this paper, we present a novel topic model named sequential correspondence hierarchical Dirichlet processes (Seq-cHDP) to learn the hidden structure within video data. The Seq-cHDP model can be deemed as an extended hierarchical Dirichlet processes (HDP) model containing two important features: one is the time-dependency mechanism that connects neighboring video frames on the basis of a time dependent Markovian assumption, and the other is the correspondence mechanism that provides a solution for dealing with the multimodal data such as the mixture of visual words and speech words extracted from video files. A cascaded Gibbs sampling method is applied for implementing the inference task of Seq-cHDP. We present a comprehensive evaluation for Seq-cHDP through experimentation and finally demonstrate that Seq-cHDP outperforms other baseline models.

Quick Response (QR) code is a two dimensional barcode widely used in many applications. A standard QR code consists of black and white square modules, and it appears randomized patterns. By modifying the modules using certain rule, it is possible to display a logo image on the QR code. Such a QR code is called an aesthetic QR code. In this paper, we change the encoding method of the Reed-Solomon (RS) code to produce an aesthetic QR code without sacrificing its error correcting capability. The proposed method randomly produces candidates of RS blocks and finds the best one during encoding. Considering an image to be displayed, we also introduce a weighting function during random selection that classifies the visually important regions in the image. We further investigate the shape of modules which represents the image and consider the trade-off between the visual quality and its readability. As a result, we can produce a beautiful aesthetic QR code, which still can be decoded by standard QR code reader.

In many multimedia applications, image encryption has to be conducted prior to image compression. This letter proposes an Encryption-then-Compression system using JPEG XR/JPEG-LS friendly perceptual encryption method, which enables to be conducted prior to the JPEG XR/JPEG-LS standard used as an international standard lossless compression method. The proposed encryption scheme can provides approximately the same compression performance as that of the lossless compression without any encryption. It is also shown that the proposed system consists of four block-based encryption steps, and provides a reasonably high level of security. Existing conventional encryption methods have not been designed for international lossless compression standards, but for the first time this letter focuses on applying the standards.

Audio hashing has been successfully employed for protection, management, and indexing of digital music archives. For a reliable audio hashing system, improving hash matching accuracy is crucial. In this paper, we try to improve a binary audio hash matching performance by utilizing auxiliary information, resilience mask, which is obtained while constructing hash DB. The resilience mask contains reliability information of each hash bit. We propose a new type of resilience mask by considering spectrum scaling and additive noise distortions. Experimental results show that the proposed resilience mask is effective in improving hash matching performance.

It is crucial to provide Internet videos with the best possible content value (or quality) to users. To adapt to network fluctuations, existing solutions provide various client-based heuristics to change video versions without considering the actual quality. In this work, we present for the first time the use of a quality model in making adaptation decisions to improve the overall quality. The proposed method also estimates the buffer level in the near future to prevent the client from buffer underflows. Experiment results show that the proposed method is able to provide high and consistent video quality under strongly fluctuating bandwidths.

Dynamic voltage and frequency scaling (DVFS) is an essential mechanism for power saving in smartphones and mobile devices. Central processing unit (CPU) load based DVFS algorithms are widely used due to their simplicity of implementation. However, such algorithms often lead to a poor response time, which is one of the most important factors of user experience, especially for interactive applications. In this paper, the response time is mathematically modeled by considering the CPU frequency and characteristics of the running applications based on the Linux kernel's completely fair scheduler (CFS), and a Response time constrained Frequency & Priority (RFP) control scheme for improved power efficiency of smartphones is proposed. In the RFP algorithm, the CPU frequency and priority of the interactive applications are adaptively adjusted by estimating the response time in real time. The experimental results show that RFP can save energy up to 24.23% compared to the ondemand governor and up to 7.74% compared to HAPPE while satisfying the predefined threshold of the response time in Android-based smartphones.

The write operations on emerging Non-Volatile Memory (NVM), such as NAND Flash and Phase Change Memory (PCM), usually incur high access latency, and are required to be optimized. In this paper, we propose Asymmetric Read-Write (ARW) policies to minimize the write traffic sent to NVM. ARW policies exploit the asymmetry costs of read and write operations, and make adjustments on the insertion policy and hit-promotion policy of the replacement algorithm. ARW can reduce the write traffic to NVM by preventing dirty data blocks from frequent evictions. We evaluate ARW policies on systems with PCM as main memory and NAND Flash as disk. Simulation results on an 8-core multicore show that ARW adopted on the last-level cache (LLC) can reduce write traffic by more than 15% on average compared to LRU baseline. When used on both LLC and DRAM cache, ARW policies achieve an impressive reduction of 40% in write traffic without system performance degradation. When employed on the on-disk buffer of the Solid State Drive (SSD), ARW demonstrates significant reductions in both write traffic and overall access latency. Moreover, ARW policies are lightweight, easy to implement, and incur negligible storage and runtime overhead.

Single-instruction multiple-data (SIMD) extension provides an energy-efficient platform to scale the performance of media and scientific applications while still retaining post-programmability. However, the major challenge is to translate the parallel resources of the SIMD hardware into real application performance. Currently, all the slots in the vector register are used when compilers exploit SIMD parallelism of programs, which can be called sufficient vectorization. Sufficient vectorization means all the data in the vector register is valid. Because all the slots which vector register provides must be used, the chances of vectorizing programs with low SIMD parallelism are abandoned by sufficient vectorization method. In addition, the speedup obtained by full use of vector register sometimes is not as great as that obtained by partial use. Specifically, the length of vector register provided by SIMD extension becomes longer, sufficient vectorization method cannot exploit the SIMD parallelism of programs completely. Therefore, insufficient vectorization method is proposed, which refer to partial use of vector register. First, the adaptation scene of insufficient vectorization is analyzed. Second, the methods of computing inter-iteration and intra-iteration SIMD parallelism for loops are put forward. Furthermore, according to the relationship between the parallelism and vector factor a method is established to make the choice of vectorization method, in order to vectorize programs as well as possible. Finally, code generation strategy for insufficient vectorization is presented. Benchmark test results show that insufficient vectorization method vectorized more programs than sufficient vectorization method by 107.5% and the performance achieved by insufficient vectorization method is 12.1% higher than that achieved by sufficient vectorization method.

Packages are re-usable components for faster and effective software maintenance. To promote the re-use in object-oriented systems and maintenance tasks easier, packages should be organized to depict compact design. Therefore, understanding and assessing package organization is primordial for maintenance tasks like Re-usability and Changeability. We believe that additional investigations of prevalent basic design principles such as defined by R.C. Martin are required to explore different aspects of package organization. In this study, we propose package-organization framework based on reachable components that measures re-usability index. Package re-usability index measures common effect of change taking place over dependent elements of a package in an object-oriented design paradigm. A detailed quality assessment on different versions of open source software systems is presented which evaluates capability of the proposed package re-usability index and other traditional package-level metrics to predict fault-proneness in software. The experimental study shows that proposed index captures different aspects of package-design which can be practically integrated with best practices of software development. Furthermore, the results provide insights on organization of feasible software design to counter potential faults appearing due to complex package dependencies.

Research on intrusion-tolerant systems (ITSs) is being conducted to protect critical systems which provide useful information services. To provide services reliably, these critical systems must not have even a single point of failure (SPOF). Therefore, most ITSs employ redundant components to eliminate the SPOF problem and improve system reliability. However, systems that include identical components have common vulnerabilities that can be exploited to attack the servers. Attackers prefer to exploit these common vulnerabilities rather than general vulnerabilities because the former might provide an opportunity to compromise several servers. In this study, we analyze software vulnerability data from the National Vulnerability Database (NVD). Based on the analysis results, we present a scheme that finds software combinations that minimize the risk of common vulnerabilities. We implement this scheme with CSIM20, and simulation results prove that the proposed scheme is appropriate for a recovery-based intrusion tolerant architecture.

Due to outsourcing of numerous stages of the IC manufacturing process to different foundries, the security risk, such as hardware Trojan becomes a potential threat. In this paper, we present a layout aware localized hardware Trojan detection method that magnifies the detection sensitivity for small Trojan in power-based side-channel analysis. A scan segmentation approach with a modified launch-on-capture (LoC) transition delay fault test pattern application technique is proposed so as to maximize the dynamic power consumption of any target region. The new architecture allows activating any target region and keeping others quiet, which reduces total circuit toggling activity. We evaluate our approach on ISCAS89 benchmark and two practical circuits to demonstrate its effectiveness in side-channel analysis.

We address the problem of measuring matching similarity in terms of template matching. A novel method called two-side agreement learning (TAL) is proposed which learns the implicit correlation between two sets of multi-dimensional data points. TAL learns from a matching exemplar to construct a symmetric tree-structured model. Two points from source set and target set agree to form a two-side agreement (TA) pair if each point falls into the same leaf cluster of the model. In the training stage, unsupervised weak hyper-planes of each node are learned at first. After then, tree selection based on a cost function yields final model. In the test stage, points are propagated down to leaf nodes and TA pairs are observed to quantify the similarity. Using TAL can reduce the ambiguity in defining similarity which is hard to be objectively defined and lead to more convergent results. Experiments show the effectiveness against the state-of-the-art methods qualitatively and quantitatively.

The sparse representation models have been widely applied in image super-resolution. The certain optimization problem is supposed and can be solved by the iterative shrinkage algorithm. During iteration, the update of dictionaries and similar patches is necessary to obtain prior knowledge to better solve such ill-conditioned problem as image super-resolution. However, both the processes of iteration and update often spend a lot of time, which will be a bottleneck in practice. To solve it, in this paper, we present the concept of image quality difference based on generalized Gaussian distribution feature which has the same trend with the variation of Peak Signal to Noise Ratio (PSNR), and we update dictionaries or similar patches from the termination strategy according to the adaptive threshold of the image quality difference. Based on this point, we present two sparse representation algorithms for image super-resolution, one achieves the further improvement in image quality and the other decreases running time on the basis of image quality assurance. Experimental results also show that our quantitative results on several test datasets are in line with exceptions.

In this paper, we propose a filtering approach based on global motion estimation (GME) and global motion compensation (GMC) for pre- and postprocessing of video codecs. For preprocessing a video codec, group of pictures (GOP), which is a basic unit for GMC, and reference frames are first defined for an input video sequence. Next, GME and GMC are sequentially performed for every frame in each GOP. Finally, a block-based adaptive temporal filter is applied between the GMC frames before video encoding. For postprocessing a video codec at the decoder end, every decoded frame is inversely motion-compensated using the transmitted global motion information. The holes generated during inverse motion compensation can be filled with the reference frames. The experimental results show that the proposed algorithm provides higher Bjontegaard-delta peak signal-to-noise ratios (BD-PSNRs) of 0.63 and 0.57 dB on an average compared with conventional H.264 and HEVC platforms, respectively.

We propose an appearance-based proficiency evaluation methodology based on fine-motion analysis. We consider the effects of individual habit in evaluating proficiency and analyze the fine motion of guitar-picking. We first extract multiple features on a large number of dense trajectories of fine motion. To facilitate analysis, we then generate a histogram of motion features using a bag-of-words model and change the number of visual words as appropriate. To remove the effects of individual habit, we extract the common principal histogram elements corresponding to experts or beginners according to discrimination's contribution rates using random forests. We finally calculate the similarity of the histograms to evaluate the proficiency of a guitar-picking motion. By optimizing the number of visual words for proficiency evaluation, we demonstrate that our method distinguishes experts from beginners with an accuracy of about 86%. Moreover, we verify experimentally that our proposed methodology can evaluate proficiency while removing the effects of individual habit.

Many hemodialysis patients undergo plasitc surgery to form the arterio-venous fistula (AVF) in their forearm to improve the vascular access by shunting blood flows. The issue of AVF is the stenosis caused by the disturbance of blood flows; therefore the auscultation system to assist the stenosis diagnosis has been developed. Although the system is intended to be used as a steady monitoring for stenosis assessment, its efficiency was not always high because it cannot estimate where the stenosis locates. In this study, for extracting and estimating the stenosis signal, the shunt murmurs captured by many microphones were decomposed by the principal component analysis (PCA). Furthermore, applying the hierarchical categorization of the recursive subdivision self-organizing map (rs-SOM), the modelling of the stenosis signal was proposed to realise the effective stenosis assessment. The false-positive rate of the stenosis assessment was significantly reduced by using the improved auscultation system.

The purpose of this study was to identify the key variables that determine the quality of the auditory environment, for the purposes of workplace auditory design and assessment. To this end, we characterized changes in oscillatory neural activity in electroencephalographic (EEG) data recorded from subjects who performed an intellectual activity while exposed to fluctuating ambient noise. Seven healthy men participated in the study. Subjects performed a verbal and spatial task that used the 3-back task paradigm to study working memory. During the task, subjects were presented with auditory stimuli grouped by increasing high-frequency content: (1) a sound with frequencies similar to Brownian noise and no modulation; (2) an amplitude-modulated sound with frequencies similar to white noise; (3) amplitude-modulated pink noise; and (4) amplitude-modulated Brownian noise. Upon presentation, we observed a characteristic change in three EEG bands: theta (4-8Hz), alpha (8-13Hz), and beta (13-30Hz). In particular, a frequency-dependent enhancement and reduction of power was observed in the theta and beta bands, respectively.

With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

This paper addresses the issue of source retrieval in plagiarism detection. The task of source retrieval is retrieving all plagiarized sources of a suspicious document from a source document corpus whilst minimizing retrieval costs. The classification-based methods achieved the best performance in the current researches of source retrieval. This paper points out that it is more important to cast the problem as ranking and employ learning to rank methods to perform source retrieval. Specially, it employs RankBoost and Ranking SVM to obtain the candidate plagiarism source documents. Experimental results on the dataset of PAN@CLEF 2013 Source Retrieval show that the ranking based methods significantly outperforms the baseline methods based on classification. We argue that considering the source retrieval as a ranking problem is better than a classification problem.

All the existing sender-based message logging (SBML) protocols share a well-known limitation that they cannot tolerate concurrent failures. In this paper, we analyze the cause for this limitation in a unicast network environment, and present an enhanced SBML protocol to overcome this shortcoming while preserving the strengths of SBML. When the processes on different nodes execute a distributed application together in a broadcast network, this new protocol replicates the log information of each message to volatile storages of other processes within the same broadcast network. It may reduce the communication overhead for the log replication by taking advantage of the broadcast nature of the network. Simulation results show our protocol performs better than the traditional one modified to tolerate concurrent failures in terms of failure-free execution time regardless of distributed application communication pattern.

In this letter, a novel and highly efficient haze removal algorithm is proposed for haze removal from only a single input image. The proposed algorithm is built on the atmospheric scattering model. Firstly, global atmospheric light is estimated and coarse atmospheric veil is inferred based on statistics of dark channel prior. Secondly, the coarser atmospheric veil is refined by using a fast Tri-Gaussian filter based on human retina property. To avoid halo artefacts, we then redefine the scene albedo. Finally, the haze-free image is derived by inverting the atmospheric scattering model. Results on some challenging foggy images demonstrate that the proposed method can not only improve the contrast and visibility of the restored image but also expedite the process.

Speaker verification is the task of determining whether two utterances represent the same person. After representing the utterances in the i-vector space, the crucial problem is only how to compute the similarity of two i-vectors. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Restricted Boltzmann Machine network. The proposed method is evaluated on the NIST SRE 2008 dataset. Since the proposed method has a deep learning architecture, the evaluation results show superior performance than some state-of-the-art methods.

This paper presents a novel method for unsupervised segmentation of objects with large displacements in high speed video sequences. Our general framework introduces a new foreground object predicting method that finds object hypotheses by encoding both spatial and temporal features via a semantic motion signature scheme. More specifically, temporal cues of object hypotheses are captured by the motion signature proposed in this paper, which is derived from sparse saliency representation imposed on magnitude of optical flow field. We integrate semantic scores derived from deep networks with location priors that allows us to directly estimate appearance potentials of foreground hypotheses. A unified MRF energy functional is proposed to simultaneously incorporate the information from the motion signature and semantic prediction features. The functional enforces both spatial and temporal consistency and impose appearance constancy and spatio-temporal smoothness constraints directly on the object hypotheses. It inherently handles the challenges of segmenting ambiguous objects with large displacements in high speed videos. Our experiments on video object segmentation benchmarks demonstrate the effectiveness of the proposed method for segmenting high speed objects despite the complicated scene dynamics and large displacements.

Correlation filter-based approaches achieve competitive results in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the videos. To address this issue, we propose a novel tracker combined with color features in a correlation filter framework, which extracts not only gray but also color information as the feature maps to compute the maximum response location via multi-channel correlation filters. In particular, we modify the label function of the conventional classifier to improve positioning accuracy and employ a discriminative correlation filter to handle scale variations. Experiments are performed on 35 challenging benchmark color sequences. And the results clearly show that our method outperforms state-of-the-art tracking approaches while operating in real-time.

Object detection is the first step in the object recognition. According to the detection results, its following works are affected. However, object detection has a heavy resource requirement in terms of, computing power and memory. If an image is enlarged, the computational load required for object detection is also increased. An-integral-image-based method guarantees fast object detection. Once an integral image is generated, the speed of the object detection procedure remains fixed, regardless of the pattern region size. However, this becomes an even greater issue if the image is enlarged. In this paper, we propose the use of directional integral image based object detection. A directional integral image gives direction to an integral image, which can then be calculated from various directions. Furthermore, many unnecessary calculations, which typically occur when a partial integral image is used for object detection, can be avoided. Therefore, the amount of computation is reduced, compared with methods using integral images. In experiments comparing methods, the proposed method required 40% fewer computations.

This letter proposes a Light Space Partitioned Shadow Maps (LSPSMs) algorithm which implements shadow rendering based on a novel partitioning scheme in light space. In stead of splitting the view frustum like traditional Z-partitioning methods, we split partitions from the projection of refined view frustum in light space. The partitioning scheme is performed dual-directionally while limiting the wasted space. Partitions are created in dynamic number corresponding to the light and view directions. Experiments demonstrate that high quality shadows can be rendered in high efficiency with our algorithm.

We previously proposed an unsupervised model using the inclusion-exclusion principle to compute sentence information content. Though it can achieve desirable experimental results in sentence semantic similarity, the computational complexity is more than O(2ⁿ). In this paper, we propose an efficient method to calculate sentence information content, which employs the thinking of the difference set in hierarchical network. Impressively, experimental results show that the computational complexity decreases to O(n). We prove the algorithm in the form of theorems. Performance analysis and experiments are also provided.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique, suitable for measurement during motor learning. However, effects of contamination by systemic artifacts derived from the scalp layer on learning-related fNIRS signals remain unclear. Here we used fNIRS to measure activity of sensorimotor regions while participants performed a visuomotor task. The comparison of results using a general linear model with and without systemic artifact removal shows that systemic artifact removal can improve detection of learning-related activity in sensorimotor regions, suggesting the importance of removal of systemic artifacts on learning-related cerebral activity.