The network load is increasing due to the spread of content distribution services. Caching is known as a technique to reduce a peak network load by prefetching popular contents into memories of users. Coded caching is a new caching approach based on a carefully designed content placement in order to create coded multicasting opportunities. Recent works have discussed single-layer caching systems, but many networks consist of multiple layers of cache. In this paper, we discuss a coded caching problem for a hierarchical network that has a different number of layers of cache. The network has users who connect to an origin server via a mirror server and users who directly connect to the origin server. We provide lower bounds of the rates for this problem setting based on the cut-set bound. In addition, we propose three basic coded caching schemes and characterize these schemes. Also, we propose a new coded caching scheme by combining two basic schemes and provide achievable rates of the combination coded caching scheme. Finally, we show that the proposed combination scheme demonstrates a good performance by a numerical result.

We normally hold a lot of confidential information in hard disk drives and solid-state drives. When we want to erase such information to prevent the leakage, we have to overwrite the sequence of information with a sequence of symbols independent of the information. The overwriting is needed only at places where overwritten symbols are different from original symbols. Then, the cost of overwrites such as the number of overwritten symbols to erase information is important. In this paper, we clarify the minimum cost such as the minimum number of overwrites to erase information under weak and strong independence criteria. The former (resp. the latter) criterion represents that the mutual information between the original sequence and the overwritten sequence normalized (resp. not normalized) by the length of the sequences is less than a given desired value.

In this paper, we investigate how to obtain binary locally repairable codes (LRCs) with good locality and availability from binary Simplex codes. We first propose a Combination code having the generator matrix with all the columns of positive weights less than or equal to a given value. Such a code can be also obtained by puncturing all the columns of weights larger than a given value from a binary Simplex Code. We call by block-puncturing such puncturing method. Furthermore, we suggest a heuristic puncturing method, called subblock-puncturing, that punctures a few more columns of the largest weight from the Combination code. We determine the minimum distance, locality, availability, joint information locality, joint information availability of Combination codes in closed-form. We also demonstrate the optimality of the proposed codes with certain choices of parameters in terms of some well-known bounds.

As energy efficiency has become a major design constraint or objective, heterogeneous manycore architectures have emerged as mainstream target platforms not only in server systems but also in embedded systems. Manycore accelerators such as GPUs are getting also popular in embedded domains, as well as the heterogeneous CPU cores. However, as the number of cores in an embedded GPU is far less than that of a server GPU, it is important to utilize both heterogeneous multi-core CPUs and GPUs to achieve the desired throughput with the minimal energy consumption. In this paper, we present a case study of mapping LBP-based face detection onto a recent CPU-GPU heterogeneous embedded platform, which exploits both task parallelism and data parallelism to achieve maximal energy efficiency with a real-time constraint. We first present the parallelization technique of each task for the GPU execution, then we propose performance and energy models for both task-parallel and data-parallel executions on heterogeneous processors, which are used in design space exploration for the optimal mapping. The design space is huge since not only processor heterogeneity such as CPU-GPU and big.LITTLE, but also various data partitioning ratios for the data-parallel execution on these heterogeneous processors are considered. In our case study of LBP face detection on Exynos 5422, the estimation error of the proposed performance and energy models were on average -2.19% and -3.67% respectively. By systematically finding the optimal mappings with the proposed models, we could achieve 28.6% less energy consumption compared to the manual mapping, while still meeting the real-time constraint.

In this paper, we propose a novel method to extrinsically calibrate a camera to a 3D reference object that is not directly visible from the camera. We use a human cornea as a spherical mirror and calibrate the extrinsic parameters from the reflections of the reference points. The main contribution of this paper is to present a cornea-reflection-based calibration algorithm with a simple configuration: five reference points on a single plane and one mirror pose. In this paper, we derive a linear equation and obtain a closed-form solution of extrinsic calibration by introducing two ideas. The first is to model the cornea as a virtual sphere, which enables us to estimate the center of the cornea sphere from its projection. The second is to use basis vectors to represent the position of the reference points, which enables us to deal with 3D information of reference points compactly. We demonstrate the performance of the proposed method with qualitative and quantitative evaluations using synthesized and real data.

Crack detection is a vital task to maintain a bridge's health and safety condition. Traditional computer-vision based methods easily suffer from disturbance of noise and clutters for a real bridge inspection. To address this limitation, we propose a two-stage crack detection approach based on Convolutional Neural Networks (CNN) in this letter. A predictor of small receptive field is exploited in the first detection stage, while another predictor of large receptive field is used to refine the detection results in the second stage. Benefiting from data fusion of confidence maps produced by both predictors, our method can predict the probability belongs to cracked areas of each pixel accurately. Experimental results show that the proposed method is superior to an up-to-date method on real concrete surface images.

In the past two decades, many generalized cyclotomic sequences have been constructed and they have been used in cryptography and communication systems for their high linear complexity and low autocorrelation. But there are a few of papers focusing on the 2-adic complexities of such sequences. In this paper, we first give a property of a class of Gaussian periods based on Whiteman's generalized cyclotomic classes of order 4. Then, as an application of this property, we study the 2-adic complexity of a class of Whiteman's generalized cyclotomic sequences constructed from two distinct primes p and q. We prove that the 2-adic complexity of this class of sequences of period pq is lower bounded by pq-p-q-1. This lower bound is at least greater than one half of its period and thus it shows that this class of sequences can resist against the rational approximation algorithm (RAA) attack.

Dai-Yuan (DY) conjugate gradient method is an effective method for solving large-scale unconstrained optimization problems. In this paper, a new DY method, possessing a spectral conjugate parameter βk, is presented. An attractive property of the proposed method is that the search direction generated at each iteration is descent, which is independent of the line search. Global convergence of the proposed method is also established when strong Wolfe conditions are employed. Finally, comparison experiments on impulse noise removal are reported to demonstrate the effectiveness of the proposed method.

In this paper, we propose a generation method of new mutually zero-correlation zone set of optical orthogonal sequences (MZCZ-OOS) consisting of binary and bi-phase sequence pairs based on the optical zero-correlation zone (ZCZ) sequence set. The MZCZ-OOS is composed of several small orthogonal sequence sets. The sequences that belong to same subsets are orthogonal, and there is a ZCZ between the sequence that belong to different subsets. The set is suitable for the M-ary quasi-synchronous optical code-division multiple access (M-ary/QS-OCDMA) system. The product of set size S and family size M of proposed MMZCZ-OOS is more than the upper bound of optical ZCZ sequence set, and is fewer than the that of optical orthogonal sequence set.

In this paper, a perceptual distortion based rate-distortion optimized video coding scheme for High Efficiency Video Coding (HEVC) is proposed. Structural Similarity Index (SSIM) in transform domain, which is known as distortion metric to better reflect human's perception, is derived for the perceptual distortion model to be applied for hierarchical coding block structure of HEVC. A SSIM-quantization model is proposed using the properties of DCT and high resolution quantization assumption. The SSIM model is obtained as the sum of SSIM in each Coding Unit (CU) depth of HEVC, which precisely predict SSIM values for the hierarchical quadtree structure of CU in HEVC. The rate model is derived from the entropy, based on Laplacian distributions of transform residual coefficients and is jointly combined with the SSIM-based distortion model for rate-distortion optimization in an HEVC video codec and can be compliantly applied to HEVC. The experimental results demonstrate that the proposed method achieves 8.1% and 4.0% average bit rate reductions in rate-SSIM performance for low-delay and random access configurations respectively, outperforming other existing methods. The proposed method provides better visual quality than the conventional mean square error (MSE)-based RDO coding scheme.

The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.

Defects in spacecraft software may result in loss of life and serious economic damage. To avoid such consequences, the software development process incorporates code review activity. A code review conducted by a third-party organization independently of a software development team can effectively identify defects in software. However, such review activity is difficult for third-party reviewers, because they need to understand the entire structure of the code within a limited time and without prior knowledge. In this study, we propose a tool to visualize inter-module dataflow for source code of spacecraft software systems. To evaluate the method, an autonomous rover control program was reviewed using this visualization. While the tool does not decreases the time required for a code review, the reviewers considered the visualization to be effective for reviewing code.

Boolean functions used in stream ciphers and block ciphers should have high second-order nonlinearity to resist several known attacks and some potential attacks which may exist but are not yet efficient and might be improved in the future. The second-order nonlinearity of Boolean functions also plays an important role in coding theory, since its maximal value equals the covering radius of the second-order Reed-Muller code. But it is an extremely hard task to calculate and even to bound the second-order nonlinearity of Boolean functions. In this paper, we present a lower bound on the second-order nonlinearity of the generalized Maiorana-McFarland Boolean functions. As applications of our bound, we provide more simpler and direct proofs for two known lower bounds on the second-order nonlinearity of functions in the class of Maiorana-McFarland bent functions. We also derive a lower bound on the second-order nonlinearity of the functions which were conjectured bent by Canteaut and whose bentness was proved by Leander, by further employing our bound.

In terms of spatial online aggregation, traditional stand-alone serial methods gradually become limited. Although parallel computing is widely studied nowadays, there scarcely has research conducted on the index-based parallel online aggregation methods, specifically for spatial data. In this letter, a parallel multilevel indexing method is proposed to accelerate spatial online aggregation analyses, which contains two steps. In the first step, a parallel aR tree index is built to accelerate aggregate query locally. In the second step, a multilevel sampling data pyramid structure is built based on the parallel aR tree index, which contribute to the concurrent returned query results with certain confidence degree. Experimental and analytical results verify that the methods are capable of handling billion-scale data.

The number of infected hosts on enterprise networks has been increased by drive-by download attacks. In these attacks, users of compromised popular websites are redirected toward websites that exploit vulnerabilities of a browser and its plugins. To prevent damage, detection of infected hosts on the basis of proxy logs rather than blacklist-based filtering has started to be researched. This is because blacklists have become difficult to create due to the short lifetime of malicious domains and concealment of exploit code. To detect accesses to malicious websites from proxy logs, we propose a system for detecting malicious URL sequences on the basis of three key ideas: focusing on sequences of URLs that include artifacts of malicious redirections, designing new features related to software other than browsers, and generating new training data with data augmentation. To find an effective approach for classifying URL sequences, we compared three approaches: an individual-based approach, a convolutional neural network (CNN), and our new event de-noising CNN (EDCNN). Our EDCNN reduces the negative effects of benign URLs redirected from compromised websites included in malicious URL sequences. Evaluation results show that only our EDCNN with proposed features and data augmentation achieved a practical classification performance: a true positive rate of 99.1%, and a false positive rate of 3.4%.

Encryption-then-Compression (EtC) systems have been considered for the user-controllable privacy protection of social media like Twitter. The aim of this paper is to evaluate the security of block scrambling-based encryption schemes, which have been proposed to construct EtC systems. Even though this scheme has enough key spaces against brute-force attacks, each block in encrypted images has almost the same correlation as that of original images. Therefore, it is required to consider the security from different viewpoints from number theory-based encryption methods with provable security such as RSA and AES. In this paper, we evaluate the security of encrypted images including JPEG distortion by using automatic jigsaw puzzle solvers.

Forward Error Correction (FEC) schemes have played an important role in intensity-modulation direct-detection (IM/DD) Visible Light Communication (VLC) systems. While hard-decision FEC schemes are inferior to soft-decision FEC codes in terms of decoding performance, they are widely used in these VLC systems because receivers are only capable of recognizing logical values 0 and 1. In this letter, we propose a method to calculate the log-likelihood ratios (LLR) values which are used as input of soft-decision FEC decoders. Simulation results show that Polar decoder using proposed method performs better than that of using the hard-decision technique.

This letter proposes a novel speech enhancement system based on the ‘L’ shaped triple-microphone. The modified coherence-based algorithm and the first-order differential beamforming are combined to filter the spatial distributed noise. The experimental results reveal that the proposed algorithm achieves significant performance in spatial filtering under different noise scenarios.

The Domain Name System (DNS) maps domain names to IP addresses. It is an important infrastructure in the Internet. Recently, DNS has experienced various security threats. DNS resolvers experience the security threats most frequently, since they interact with clients and they are the largest group of domain name servers. In order to eliminate security threats against DNS resolvers, it is essential to improve their “health status”. Since DNS resolvers' owners are not clear which DNS resolvers should be improved and how to improve “health status”, the evaluation of “health status” for DNS resolvers has become vital. In this paper, we emphasize five indicators describing “health status” for DNS resolvers, including security, integrity, availability, speed and stability. We also present nine metrics measuring the indicators. Based on the measurement of the metrics, we present a “health status” evaluation method with factor analysis. To validate our method, we measured and evaluated more than 30,000 DNS resolvers in China and Japan. The results showed that the proposed “health status” evaluation method could describe “health status” well. We also introduce instructions for evaluating a small number of DNS resolvers. And we discuss DNSSEC and its effects on resolution speed. At last, we make suggestions for inspecting and improving “health status” of DNS resolvers.