Random network topologies have been proposed as a low-latency network for parallel computers. Although multicast is a common collective-communication operation, multicast algorithms each of which consists of a large number of unicasts are not well optimized for random network topologies. In this study, we firstly apply a two-opt algorithm for building efficient multicast on random network topologies. The two-opt algorithm creates a skilled ordered list of visiting nodes to minimize the total path hops or the total possible contention counts of unicasts that form the target multicast. We secondly extend to apply the two-opt algorithm for the other collective-communication operations, e.g., allreduce and allgather. The SimGrid discrete-event simulation results show that the two-opt multicast outperforms that in typical MPI implementation by up to 22% of the execution time of an MPI program that repeats the MPI_Bcast function. The two-opt allreduce and the two-opt allgather operations also improve by up to 15% and 14% the execution time when compared to those used in typical MPI implementations, respectively.

With the rapid development of multimedia, violent video can be easily accessed in games, movies, websites, and so on. Identifying violent videos and rating violence extent is of great importance to media filtering and children protection. Many previous studies only address the problems of violence scene detection and violent action recognition, yet violence rating problem is still not solved. In this paper, we present a novel video-level rating prediction method to estimate violence extent automatically. It has two main characteristics: (1) a two-stream network is fine-tuned to construct effective representations of violent videos; (2) a violence rating prediction machine is designed to learn the strength relationship among different videos. Furthermore, we present a novel violent video dataset with a total of 1,930 human-involved violent videos designed for violence rating analysis. Each video is annotated with 6 fine-grained objective attributes, which are considered to be closely related to violence extent. The ground-truth of violence rating is given by pairwise comparison method. The dataset is evaluated in both stability and convergence. Experiment results on this dataset demonstrate the effectiveness of our method compared with the state-of-art classification methods.

Recently, visual trackers based on the framework of kernelized correlation filter (KCF) achieve the robustness and accuracy results. These trackers need to learn information on the object from each frame, thus the state change of the object affects the tracking performances. In order to deal with the state change, we propose a novel KCF tracker using the filter response map, namely a confidence map, and adaptive model. This method firstly takes a skipped scale pool method which utilizes variable window size at every two frames. Secondly, the location of the object is estimated using the combination of the filter response and the similarity of the luminance histogram at multiple points in the confidence map. Moreover, we use the re-detection of the multiple peaks of the confidence map to prevent the target drift and reduce the influence of illumination. Thirdly, the learning rate to obtain the model of the object is adjusted, using the filter response and the similarity of the luminance histogram, considering the state of the object. Experimentally, the proposed tracker (CFCA) achieves outstanding performance for the challenging benchmark sequence (OTB2013 and OTB2015).

Since wild animals are causing more accidents and damages, it is important to safely detect them as early as possible. In this paper, we propose two battery-powered wild animal detection nodes based on deep learning that can automatically detect wild animals; the detection information is notified to the people concerned immediately. To use the proposed nodes outdoors where power is not available, we devise power saving techniques for the proposed nodes. For example, deep learning is used to save power by avoiding operations when wild animals are not detected. We evaluate the operation time and the power consumption of the proposed nodes. Then, we evaluate the energy consumption of the proposed nodes. Also, we evaluate the detection range of the proposed nodes, the accuracy of deep learning, and the success rate of communication through field tests to demonstrate that the proposed nodes can be used to detect wild animals outdoors.

In this paper, we propose L0 norm optimization in a scrambled sparse representation domain and its application to an Encryption-then-Compression (EtC) system. We design a random unitary transform that conserves L0 norm isometry. The resulting encryption method provides a practical orthogonal matching pursuit (OMP) algorithm that allows computation in the encrypted domain. We prove that the proposed method theoretically has exactly the same estimation performance as the nonencrypted variant of the OMP algorithm. In addition, we demonstrate the security strength of the proposed secure sparse representation when applied to the EtC system. Even if the dictionary information is leaked, the proposed scheme protects the privacy information of observed signals.

Network throughput has become an important issue for big-data analysis on Warehouse-Scale Computing (WSC) systems. It has been reported that randomly-connected inter-switch networks can enlarge the network throughput. For irregular networks, a multi-path routing method called k-shortest path routing is conventionally utilized. However, it cannot efficiently exploit longer-than-shortest paths that would be detour paths to avoid bottlenecks. In this work, a novel routing method called k-optimized path routing to achieve high throughput is proposed for irregular networks. We introduce a heuristic to select detour paths that can avoid bottlenecks in the network to improve the average-case network throughput. Experimental results by network simulation show that the proposed k-optimized path routing can improve the saturation throughput by up to 18.2% compared to the conventional k-shortest path routing. Moreover, it can reduce the computation time required for optimization to 1/2760 at a minimum compared to our previously proposed method.

Silver electrical contacts were separated at constant opening speed in a 200V-500VDC/10A resistive circuit. Break arcs were extinguished by magnetic blowing-out with transverse magnetic field of a permanent magnet. The permanent magnet was appropriately located to simplify the lengthened shape of the break arcs. Magnetic flux density of the transverse magnetic field was varied from 20 to 140mT. Images of the break arcs were observed from the horizontal and vertical directions using two high speed cameras simultaneously. Arc length just before extinction was analyzed from the observed images. It was shown that shapes of the break arcs were simple enough to trace the most part of paths of the break arcs for all experimental conditions owing to simplification of the shapes of the break arcs by appropriate arrangement of the magnet. The arc length increased with increasing supply voltage and decreased with increasing magnetic flux density. These results will be discussed in the view points of arc lengthening time and arc lengthening velocity.

Machine learning models inherently memorize significant amounts of information, and thus hiding not only prediction processes but also trained models, i.e., model obliviousness, is desirable in the cloud setting. Several works achieved model obliviousness with the MNIST dataset, but datasets that include complicated samples, e.g., CIFAR-10 and CIFAR-100, are also used in actual applications, such as face recognition. Secret sharing-based secure prediction for CIFAR-10 is difficult to achieve. When a deep layer architecture such as CNN is used, the calculation error when performing secret calculation becomes large and the accuracy deteriorates. In addition, if detailed calculations are performed to improve accuracy, a large amount of calculation is required. Therefore, even if the conventional method is applied to CNN as it is, good results as described in the paper cannot be obtained. In this paper, we propose two approaches to solve this problem. Firstly, we propose a new protocol named Batch-normalizedActivation that combines BatchNormalization and Activation. Since BatchNormalization includes real number operations, when performing secret calculation, parameters must be converted into integers, which causes a calculation error and decrease accuracy. By using our protocol, calculation errors can be eliminated, and accuracy degradation can be eliminated. Further, the processing is simplified, and the amount of calculation is reduced. Secondly, we explore a secret computation friendly and high accuracy architecture. Related works use a low-accuracy, simple architecture, but in reality, a high accuracy architecture should be used. Therefore, we also explored a high accuracy architecture for the CIFAR10 dataset. Our proposed protocol can compute prediction of CIFAR-10 within 15.05 seconds with 87.36% accuracy while providing model obliviousness.

This paper shows design and implementation of an image-based vibration detection system on a field-programmable gate array (FPGA), aiming at application to tremor suppression for microsurgery assistance systems. The system can extract a vibration component within a user-specified frequency band from moving images in real-time. For fast and robust detection, we employ a statistical approach using dense optical flow to derive vibration component, and design a custom hardware based on the Lucas-Kanade (LK) method to compute optical flow. And for band-pass filtering without phase delay, we implement the band-limited multiple Fourier linear combiner (BMFLC), a sort of adaptive band-pass filter which can recompose an input signal as a mixture of sinusoidal signals with multiple frequencies within the specified band, with no phase delay. The whole system is implemented as a deep pipeline on a Xilinx Kintex-7 XC7K325T FPGA without using any external memory. We employ fixed-point arithmetic to reduce resource utilization while maintaining accuracy close to double-precision floating-point arithmetic. Empirical experiments reveal that the proposed system extracts a high-frequency tremor component from hand motions, with intentional low-frequency motions successfully filtered out. The system can process VGA moving images at 60fps, with a delay of less than 1 µs for the BMFLC, suggesting effectiveness of the deep pipelined architecture. In addition, we are planning to integrate a CNN-based segmentation system for improving detection accuracy, and show preliminary software evaluation results.

We propose a novel hue-correction scheme for multi-exposure image fusion (MEF). Various MEF methods have so far been studied to generate higher-quality images. However, there are few MEF methods considering hue distortion unlike other fields of image processing, due to a lack of a reference image that has correct hue. In the proposed scheme, we generate an HDR image as a reference for hue correction, from input multi-exposure images. After that, hue distortion in images fused by an MEF method is removed by using hue information of the HDR one, on the basis of the constant-hue plane in the RGB color space. In simulations, the proposed scheme is demonstrated to be effective to correct hue-distortion caused by conventional MEF methods. Experimental results also show that the proposed scheme can generate high-quality images, regardless of exposure conditions of input multi-exposure images.

In the realization of convolutional neural networks (CNNs) in resource-constrained embedded hardware, the memory footprint of weights is one of the primary problems. Pruning techniques are often used to reduce the number of weights. However, the distribution of nonzero weights is highly skewed, which makes it more difficult to utilize the underlying parallelism. To address this problem, we present SENTEI*, filter-wise pruning with distillation, to realize hardware-aware network architecture with comparable accuracy. The filter-wise pruning eliminates weights such that each filter has the same number of nonzero weights, and retraining with distillation retains the accuracy. Further, we develop a zero-weight skipping inter-layer pipelined accelerator on an FPGA. The equalization enables inter-filter parallelism, where a processing block for a layer executes filters concurrently with straightforward architecture. Our evaluation of semantic-segmentation tasks indicates that the resulting mIoU only decreased by 0.4 points. Additionally, the speedup and power efficiency of our FPGA implementation were 33.2× and 87.9× higher than those of the mobile GPU. Therefore, our technique realizes hardware-aware network with comparable accuracy.

This article proposes a dynamic linkage service which is a specific service model of integrated broadcast — broadband services based ATSC 3.0. The dynamic linkage service is useful to the viewer who wants to continue watching programs using TV or their personal devices, even after the terrestrial broadcast ends due to the start of the next regular programming. In addition, we verify the feasibility of the proposed extended dynamic linkage service through developed emulation system based on ATSC 3.0. In consideration of the personal network capabilities of the viewer environment, the service was tested with 4K/2K Ultra HD and receiving the service was finished within 4 second over intranet.

Designing an optimum quantizer can be treated as the optimization problem of finding the quantization indices that minimize the quantization error. One solution to the optimization problem, DP quantization, is based on dynamic programming. Some applications, such as bit-depth scalable codec and tone mapping, require the construction of multiple quantizers with different quantization levels, for example, from 12bit/channel to 10bit/channel and 8bit/channel. Unfortunately, the above mentioned DP quantization optimizes the quantizer for just one quantization level. That is, it is unable to simultaneously optimize multiple quantizers. Therefore, when DP quantization is used to design multiple quantizers, there are many redundant computations in the optimization process. This paper proposes an extended DP quantization with a complexity reduction algorithm for the optimal design of multiple quantizers. Experiments show that the proposed algorithm reduces complexity by 20.8%, on average, compared to conventional DP quantization.

Gaussian integer has a potential to enhance the safety of elliptic curve cryptography (ECC) on system under the condition fixing bit length of integral and floating point types, in viewpoint of the order of a finite field. However, there seems to have been no algorithm which makes Gaussian integer ECC safer under the condition. We present the algorithm to enhance the safety of ECC under the condition. Then, we confirm our Gaussian integer ECC is safer in viewpoint of the order of finite field than rational integer ECC or Gaussian integer ECC of naive methods under the condition.

In this work, we introduce notions of quantum frequency arrangements consisting of quantum frequency squares, cubes, hypercubes and a notion of orthogonality between them. We also propose a notion of quantum mixed orthogonal array (QMOA). By using irredundant mixed orthogonal array proposed by Goyeneche et al. we can obtain k-uniform states of heterogeneous systems from quantum frequency arrangements and QMOAs. Furthermore, some examples are presented to illustrate our method.

This paper provides theoretical analyses for maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing techniques in cognitive radio networks. The MCAS-based spectrum sensing techniques are low computational complexity spectrum sensing in comparison with some cyclostationary detection. However, MCAS-based spectrum sensing characteristics have never been theoretically derived. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the values match well with each other.

In this paper we analyze the interval algorithm for random number generation proposed by Han and Hoshi in the case of Markov coin tossing. Using the expression of real numbers on the interval [0,1), we first establish an explicit representation of the interval algorithm with the representation of real numbers on the interval [0,1) based one number systems. Next, using the expression of the interval algorithm, we give a rigorous analysis of the interval algorithm. We discuss the difference between the expected number of the coin tosses in the interval algorithm and their upper bound derived by Han and Hoshi and show that it can be characterized explicitly with the established expression of the interval algorithm.

We propose a reversible data hiding (RDH) method in compressible encrypted images called the encryption-then-compression (EtC) images. The proposed method allows us to not only embed a payload in encrypted images but also compress the encrypted images containing the payload. In addition, the proposed RDH method can be applied to both plain images and encrypted ones, and the payload can be extracted flexibly in the encrypted domain or from the decrypted images. Various RDH methods have been studied in the encrypted domain, but they are not considered to be two-domain data hiding, and the resultant images cannot be compressed by using image coding standards, such as JPEG-LS and JPEG 2000. In our experiment, the proposed method shows high performance in terms of lossless compression efficiency by using JPEG-LS and JPEG 2000, data hiding capacity, and marked image quality.

Aggregate signature (AS) schemes enable anyone to compress signatures under different keys into one. In sequential aggregate signature (SAS) schemes, the aggregate signature is computed incrementally by the sighers. Several trapdoor-permutation-based SAS have been proposed. In this paper, we give a constructions of SAS based on the first SAS scheme with lazy verification proposed by Brogle et al. in ASIACRYPT 2012. In Brogle et al.'s scheme, the size of the aggregate signature is linear of the number of the signers. In our scheme, the aggregate signature has constant length which satisfies the original ideal of compressing the size of signatures.

This paper proposes a novel compressed sensing (CS) framework for reconstructing electroencephalogram (EEG) signals. A feature of this framework is the application of independent component analysis (ICA) to remove the interference from artifacts after undersampling in a data processing unit. Therefore, we can remove the ICA processing block from the sensing unit. In this framework, we used a random undersampling measurement matrix to suppress the Gaussian. The developed framework, in which the discrete cosine transform basis and orthogonal matching pursuit were used, was evaluated using raw EEG signals with a pseudo-model of an eye-blink artifact. The normalized mean square error (NMSE) and correlation coefficient (CC), obtained as the average of 2,000 results, were compared to quantitatively demonstrate the effectiveness of the proposed framework. The evaluation results of the NMSE and CC showed that the proposed framework could remove the interference from the artifacts under a high compression ratio.