ICT development progresses, and many cryptographic algorithms are used. The most of cryptographic algorithms require assumptions to guarantee their security, but it is sometimes not clearly written. This causes many problems. This paper shows previous cases, and suggests to concede cryptographers and system developer each other from an industrial cryptographers viewpoint.

The separating redundancy is an important property in the analysis of the error-and-erasure decoding of a linear block code. In this work, we investigate the separating redundancy of the duals of first-order generalized Reed-Muller (GRM) codes, a class of nonbinary linear block codes that have nice algebraic properties. The dual of a first-order GRM code can be specified by two positive integers m and q and denoted by R(m,q), where q is the power of a prime number and q≠2. We determine the first separating redundancy value of R(m,q) for any m and q. We also determine the second separating redundancy values of R(m,q) for any q and m=1 and 2. For m≥3, we set up a binary integer linear programming problem, the optimum of which gives a lower bound on the second separating redundancy of R(m,q).

In 2017, Shirase proposed a variant of Elliptic Curve Method combined with Complex Multiplication method for generating certain special kinds of elliptic curves. His algorithm can efficiently factorize a given composite integer when it has a prime factor p of the form 4p=1+Dv2 for some integer v, where -D is an auxiliary input integer called a discriminant. However, there is a disadvantage that the previous method works only for restricted cases where the class polynomial associated to -D has degree at most two. In this paper, we propose a generalization of the previous algorithm to the cases of class polynomials having arbitrary degrees, which enlarges the class of composite integers factorizable by our algorithm. We also extend the algorithm to more various cases where we have 4p=t2+Dv2 and p+1-t is a smooth integer.

Frequency-hopping sequence (FHS) sets with low-hit-zone (LHZ) have Hamming correlations maintained at a low level as long as the relative time delay between different sequences are limited in a zone around the origin, and thus can be well applied in quasi-synchronous (QS) frequency-hopping multiple-access (FHMA) systems to reduce the mutual interference between different users. Moreover, the periodic partial Hamming correlation (PPHC) properties of employed LHZ-FHS sets usually act as evaluation criterions for the performances of QS-FHMA systems in practice. In this letter, a new class of LHZ-FHS sets is constructed via interleaving techniques. Furthermore, these new LHZ-FHS sets also possess optimal PPHC properties and parameters not included in the related literature.

State-of-the-art parallel systems employ a huge number of computing nodes that are connected by an interconnection network. An interconnection network (ICN) plays an important role in a parallel system, since it is responsible to communication capability. In general, an ICN shows non-linear phenomena in its communication performance, most of them are caused by congestion. Thus, designing a large-scale parallel system requires sufficient discussions through repetitive simulation runs. This causes another problem in simulating large-scale systems within a reasonable cost. This paper shows a promising solution by introducing the cellular automata concept, which is originated in our prior work. Assuming 2D-torus topologies for simplification of discussion, this paper discusses fundamental design of router functions in terms of cellular automata, data structure of packets, alternative modeling of a router function, and miscellaneous optimization. The proposed models have a good affinity to GPGPU technology and, as representative speed-up results, the GPU-based simulator accelerates simulation upto about 1264 times from sequential execution on a single CPU. Furthermore, since the proposed models are applicable in the shared memory model, multithread implementation of the proposed methods achieve about 162 times speed-ups at the maximum.

The traffic of the future metro network will dynamically change not only in volume but also in destination to support the application of virtualization technology to network edge equipment such as cloud edges to achieve cost-effectiveness. Therefore, the future metro network will have to accommodate traffic cost-effectively, even though both the traffic volume and the traffic destination will change dynamically. To handle to this trend, in this paper, we propose a future metro network architecture based on Next-Generation Passive Optical Network Stage 2 systems that offers cost-effectiveness while supporting virtual machine migration of cloud edges. The basic idea of the proposed method is sharing a burst-mode receiver between the continuous-mode transmitters and burst-mode transmitters. In this paper, we show the feasibility and effectiveness of the proposed method with experiments on prototype systems, and simulations for the preliminary evaluation of network capital expenditure.

Conventional target recognition methods usually suffer from information-loss and target-aspect sensitivity when applied to radar high resolution range profile (HRRP) recognition. Thus, Effective establishment of robust and discriminatory feature representation has a significant performance improvement of practical radar applications. In this work, we present a novel feature extraction method, based on modified collaborative auto-encoder, for millimeter-wave radar HRRP recognition. The latent frame-specific weight vector is trained for samples in a frame, which contributes to retaining local information for different targets. Experimental results demonstrate that the proposed algorithm obtains higher target recognition accuracy than conventional target recognition algorithms.

This paper presents various Iterative Progressive Numerical Methods (IPNMs) for the computation of electromagnetic (EM) wave scattering from many objects. We previously modified the original IPNM from the standpoint of the classical and the IDR-based linear iterative solvers. We demonstrate the performance of the IDR(s)-based IPNMs through some numerical examples of EM wave scattering from regularly placed 27 perfectly electric conducting spheres.

Software defect prediction (SDP) plays a significant part in allocating testing resources reasonably, reducing testing costs, and ensuring software quality. One of the most widely used algorithms of SDP models is Naive Bayes (NB) because of its simplicity, effectiveness and robustness. In NB, when a data set has continuous or numeric attributes, they are generally assumed to follow normal distributions and incorporate the probability density function of normal distribution into their conditional probabilities estimates. However, after conducting a Kolmogorov-Smirnov test, we find that the 21 main software metrics follow non-normal distribution at the 5% significance level. Therefore, this paper proposes an improved NB approach, which estimates the conditional probabilities of NB with kernel density estimation of training data sets, to help improve the prediction accuracy of NB for SDP. To evaluate the proposed method, we carry out experiments on 34 software releases obtained from 10 open source projects provided by PROMISE repository. Four well-known classification algorithms are included for comparison, namely Naive Bayes, Support Vector Machine, Logistic Regression and Random Tree. The obtained results show that this new method is more successful than the four well-known classification algorithms in the most software releases.

Sparse representation has been successfully applied to visual tracking. Recent progresses in sparse tracking are mainly made within the particle filter framework. However, most sparse trackers need to extract complex feature representations for each particle in the limited sample space, leading to expensive computation cost and yielding inferior tracking performance. To deal with the above issues, we propose a novel sparse tracking method based on the circulant reverse lasso model. Benefiting from the properties of circulant matrices, densely sampled target candidates are implicitly generated by cyclically shifting the base feature descriptors, and then embedded into a reverse sparse reconstruction model as a dictionary to encode a robust appearance template. The alternating direction method of multipliers is employed for solving the reverse sparse model and the optimization process can be efficiently solved in the frequency domain, which enables the proposed tracker to run in real-time. The calculated sparse coefficient map represents the similarity scores between the template and circular shifted samples. Thus the target location can be directly predicted according to the coordinates of the peak coefficient. A scale-aware template updating strategy is combined with the correlation filter template learning to take into account both appearance deformations and scale variations. Both quantitative and qualitative evaluations on two challenging tracking benchmarks demonstrate that the proposed algorithm performs favorably against several state-of-the-art sparse representation based tracking methods.

We propose a method for accurately identifying people using temporal and spatial changes in local movements measured from video sequences of body sway. Existing methods identify people using gait features that mainly represent the large swinging of the limbs. The use of gait features introduces a problem in that the identification performance decreases when people stop walking and maintain an upright posture. To extract informative features, our method measures small swings of the body, referred to as body sway. We extract the power spectral density as a feature from local body sway movements by dividing the body into regions. To evaluate the identification performance using our method, we collected three original video datasets of body sway sequences. The first dataset contained a large number of participants in an upright posture. The second dataset included variation over the long term. The third dataset represented body sway in different postures. The results on the datasets confirmed that our method using local movements measured from body sway can extract informative features for identification.

We propose a T-junction OMT consisting of an offset stepped post. The offset stepped post contributes to the matching of two rectangular ports at the short circuit, situated at the opposite side walls. The structure without conventional ridges is simple and makes it possible to achieve robust performance. We fabricated a proposed T-junction OMT in a single piece of an aluminum alloy, using a commercial metal 3D-printer. The simple and compact structure with robust performance is proposed to overcome the disadvantages of a 3D-printer, such as fabrication tolerance and surface roughness. The measured results demonstrated a return loss of 22dB and an insertion loss of 0.3dB, with a bandwidth of 8% in the K-band.

360-degree video is an important component of the emerging Virtual Reality. In this paper, we propose a new adaptation method for tiling-based viewport adaptive streaming of 360-degree video. The proposed method is able to dynamically select the best tiling scheme given the network conditions and user status. Experiments show that our proposed method can improve the viewport quality by up to 2.3 dB compared to a conventional fixed tiling method.

The security analysis of Keccak, the winner of SHA-3, has attracted considerable interest. Recently, some attention has been paid to distinguishing Keccak sponge function from random permutation. In EUROCRYPT'17, Huang et al. proposed conditional cube tester to recover the key of Keccak-MAC and Keyak and to construct practical distinguishing attacks on Keccak sponge function up to 7 rounds. In this paper, we improve the conditional cube tester model by refining the formulation of cube variables. By classifying cube variables into three different types and working the candidates of these types of cube variable carefully, we are able to establish a new theoretical distinguisher on 8-round Keccak sponge function. Our result is more efficient and greatly improves the existing results. Finally we remark that our distinguishing attack on the the reduced-round Keccak will not threat the security margin of the Keccak sponge function.

In this paper, we consider full-duplex (FD) relay networks with filter-and-forward (FF)-based multiple relays (FD-FF), where relay filters jointly mitigate self-interference (SI), inter-relay interference (IRI), and inter-symbol interference. We consider the filter design problem based on signal-to-noise-plus-interference ratio maximization subject to a total relay transmit power constraint. To make the problem tractable, we propose two methods: one that imposes an additional constraint whereby the filter responses to SI and IRI are nulled, and the other that makes i.i.d. assumptions on the relay transmit signals. Simulation results show that the proposed FD-FF scheme outperforms a conventional FF scheme in half-duplex mode. We also consider the filter design when only second-order statistics of channel path gains are available.

Road generalization is a method for thinning out road networks to allow easy viewing according to the size of the map. Most conventional road generalization methods mainly focus on the length of a stroke, which is a chain of links with good continuity based on the principle of perceptual grouping applied to network data such as roads and rivers. However, in the case of facility search in a web map service, for example, a “restaurant guide map,” a road generalization mechanism can be more effective if it depends not only on the stroke length but also on the facility search results. Accordingly, in this study, we implement an on-demand road generalization method that adapts to both the facility search results and the stroke length. Moreover, a sufficiently fast response speed is achieved for practical use in web map services. In particular, this study proposes a fat-stroke model that links facility information to individual strokes and implements a road generalization method that uses this model to improve the response time. In addition, we develop a prototype based on the proposed system. The system evaluation results are based on three indicators, namely, response time of the road generalization system, connectivity between strokes, and connectivity between stroke and facilities. Our experimental results suggest that the proposed method can yield improved response times by a factor of 100 or more while affording higher connectivity.

This paper presents a radio-based real-time moving object tracking method based on Kalman filtering using a phase-difference compensation technique and a non-uniform pulse transmission scheme. Conventional Kalman-based tracking methods often require time, amplitude, phase information and their derivatives for each receiver antenna; however, their location estimation accuracy does not become good even with many transmitting pulses. The presented method employs relative phase-difference information and a non-uniform pulse generation scheme, which can greatly reduce the number of transmitting pulses while preserving the tracking accuracy. Its performance is evaluated in comparison with that of conventional methods.

We introduce a notion of watermarking for cryptographic functions and propose a concrete scheme for watermarking cryptographic functions. Informally speaking, a digital watermarking scheme for cryptographic functions embeds information, called a mark, into functions such as one-way functions and decryption functions of public-key encryption. There are two basic requirements for watermarking schemes. A mark-embedded function must be functionally equivalent to the original function. It must be difficult for adversaries to remove the embedded mark without damaging the original functionality. In spite of its importance and usefulness, there have only been a few theoretical works on watermarking for functions (or programs). Furthermore, we do not have rigorous definitions of watermarking for cryptographic functions and concrete constructions. To solve the problem above, we introduce a notion of watermarking for cryptographic functions and define its security. Furthermore, we present a lossy trapdoor function (LTF) based on the decisional bilinear Diffie-Hellman problem problem and a watermarking scheme for the LTF. Our watermarking scheme is secure under the symmetric external Diffie-Hellman assumption in the standard model. We use techniques of dual system encryption and dual pairing vector spaces (DPVS) to construct our watermarking scheme. This is a new application of DPVS.

In the model of no-dictionary searchable symmetric encryption (SSE) schemes, the client does not need to keep the list of keywords W. In this paper, we first show a generic method to transform any passively secure SSE scheme to a no-dictionary SSE scheme such that the client can verify search results even if w ∉ W. In particular, it takes only O(1) time for the server to prove that w ∉ W. We next present a no-dictionary SSE scheme such that the client can hide even the search pattern from the server.

Homomorphic encryption (HE) is useful to analyze encrypted data without decrypting it. However, by using ordinary HE, a user who can decrypt a ciphertext that is generated by executing homomorphic operations, can also decrypt ciphertexts on which homomorphic evaluations have not been performed, since homomorphic operations cannot be executed among ciphertexts which are encrypted under different public keys. To resolve the above problem, we introduce a new cryptographic primitive called Homomorphic Proxy Re-Encryption (HPRE) combining the “key-switching” property of Proxy Re-Encryption (PRE) and the homomorphic property of HE. In our HPRE, original ciphertexts (which have not been re-encrypted) guarantee CCA2 security (and in particular satisfy non-malleability). On the other hand, re-encrypted ciphertexts only guarantee CPA security, so that homomorphic operations can be performed on them. We define the functional/security requirements of HPRE, and then propose a specific construction supporting the group operation (over the target group in bilinear groups) based on the PRE scheme by Libert and Vergnaud (PKC 2008) and the CCA secure public key encryption scheme by Lai et al. (CT-RSA 2010), and prove its security in the standard model. Additionally, we show two extensions of our HPRE scheme for the group operation: an HPRE scheme for addition and an HPRE scheme for degree-2 polynomials (in which the number of degree-2 terms is constant), by using the technique of the recent work by Catalano and Fiore (ACMCCS 2015).