Adaptive Volterra filters (AVFs) are usually used to identify nonlinear systems, such as loudspeaker systems, and ordinary adaptive algorithms can be used to update the filter coefficients of AVFs. However, AVFs require huge computational complexity even if the order of the AVF is constrained to the second order. Improving calculation efficiency is therefore an important issue for the real-time implementation of AVFs. In this paper, we propose a novel sub-band AVF with high calculation efficiency for second-order AVFs. The proposed sub-band AVF consists of four parts: input signal transformation for a single sub-band AVF, tap length determination to improve calculation efficiency, switching the number of sub-bands while maintaining the estimation accuracy, and an automatic search for an appropriate number of sub-bands. The proposed sub-band AVF can improve calculation efficiency for which the dominant nonlinear components are concentrated in any frequency band, such as loudspeakers. A simulation result demonstrates that the proposed sub-band AVF can realize higher estimation accuracy than conventional efficient AVFs.

Hand-dorsa vein recognition is solved based on the convolutional activations of the pre-trained deep convolutional neural network (DCNN). In specific, a novel task-specific cross-convolutional-layer pooling is proposed to obtain the more representative and discriminative feature representation. Rigorous experiments on the self-established database achieves the state-of-the-art recognition result, which demonstrates the effectiveness of the proposed model.

We propose a novel hue-preserving tone mapping scheme. Various tone mapping operations have been studied so far, but there are very few works on color distortion caused in image tone mapping. First, LDR images produced from HDR ones by using conventional tone mapping operators (TMOs) are pointed out to have some distortion in hue values due to clipping and rounding quantization processing. Next,we propose a novel method which allows LDR images to have the same maximally saturated color values as those of HDR ones. Generated LDR images by the proposed method have smaller hue degradation than LDR ones generated by conventional TMOs. Moreover, the proposed method is applicable to any TMOs. In an experiment, the proposed method is demonstrated not only to produce images with small hue degradation but also to maintain well-mapped luminance, in terms of three objective metrics: TMQI, hue value in CIEDE2000, and the maximally saturated color on the constant-hue plane in the RGB color space.

In ID-based user authentications, a privacy problem can occur, since the service provider (SP) can accumulate the user's access history from the user ID. As a solution to that problem, group signatures have been researched. One of important issues in the group signatures is the user revocation. Previously, an efficient revocable scheme with signing/verification of constant complexity was proposed by Libert et al. In this scheme, users are managed by a binary tree, and a list of data for revoked users, called a revocation list (RL), is used for revocation. However, the scheme suffers from the large RL. Recently, an extended scheme has been proposed by Sadiah and Nakanishi, where the RL size is reduced by compressing RL. On the other hand, there is a problem that some overhead occurs in the authentication as a price for reducing the size of RL. In this paper, we propose an extended scheme where the authentication is speeded up by reducing the number of Groth-Sahai (GS) proofs. Furthermore, we implemented it on a PC to show the effectiveness. The verification time is about 30% shorter than that of the previous scheme by Sadiah and Nakanishi.

In the large-scale multi-UAV systems, the direct link may be invalid for two remote nodes on account of the constrained power or complex communication environment. Idle UAVs may work as relays between the sources and destinations to enhance communication quality. In this letter, we investigate the opportunistic relay selection for the UAVs dynamic network. On account of the time-varying channel states and the variable numbers of sources and relays, relay selection becomes much more difficult. In addition, information exchange among all nodes may bring much cost and it is difficult to implement in practice. Thus, we propose a decentralized relay selection approach based on mood-driven mechanism to combat the dynamic characteristics, aiming to maximize the total capacity of the network without information exchange. With the proposed approach, the sources can make decisions only according to their own current states and update states according to immediate rewards. Numerical results show that the proposed approach has attractive properties.

Free/libre open source software (FLOSS) are being rapidly employed in several companies and organizations, because it can be modified and used for free. Hence, the use of FLOSS could contribute to its originally intended benefits and to the competence of its users. In this study, we analyzed the effect of using FLOSS on related competences. We investigated the change in the competences through an empirical study before and after the use of FLOSS among project participants. Consequently, it was confirmed that the competences of the participants improved after utilizing FLOSS.

Indexing is one of the fields where the non-volatile memory (NVM) has the advantages of byte-addressable characteristics and fast read/write speed. The existing index structures for NVM have been developed based on the fact that the size of cache line and the atomicity guarantee unit of NVM are different and they tried to overcome the weakness of consistency from the difference. To overcome the weakness, an expensive flush operation is required which results in a lower performance than a basic B+tree index. Recent studies have shown that the I/O units of the NVM can be matched with the atomicity guarantee units under limited circumstances. In this paper, we propose a Cache line sized Atomic Write B+tree (CAWBT), which is a minimal B+tree structure that shows higher performance than a basic b+ tree and designed for NVM. CAWBT has almost same performance compared to basic B+tree without consistency guarantee and shows remarkable performance improvement compared to other B+tree indexes for NVM.

File systems based on persistent memory deploy Copy-on-Write (COW) or logging to guarantee data consistency. However, COW has a write amplification problem and logging has a double write problem. Both COW and logging increase write traffic on persistent memory. In this work, we present adaptive differential logging and zero-copy logging for persistent memory. Adaptive differential logging applies COW or logging selectively to each block. If the updated size of a block is smaller than or equal to half of the block size, we apply logging to the block. If the updated size of a block is larger than half of the block size, we apply COW to the block. Zero-copy logging treats an user buffer on persistent memory as a redo log. Zero-copy logging does not incur any additional data copy. We implement adaptive differential logging and zero-copy logging on both NOVA and PMFS file systems. Our measurement on real workloads shows that adaptive differential logging and zero-copy logging get 150.6% and 149.2% performance improvement over COW, respectively.

Detecting phishing websites is imperative. Among several detection schemes, the promising ones are the visual similarity-based approaches. In those, targeted legitimate website's visual features referred to as signatures are stored in SDB (Signature Database) by the system administrator. They can only detect phishing websites whose signatures are highly similar to SDB's one. Thus, the system administrator has to register multiple signatures to detect various phishing websites and that cost is very high. This incurs the vulnerability of zero-day phishing attack. In order to address this issue, an auto signature update mechanism is needed. The naive way of auto updating SDB is expanding the scope of detection by adding detected phishing website's signature to SDB. However, the previous approaches are not suitable for auto updating since their similarity can be highly different among targeted legitimate website and subspecies of phishing website targeting that legitimate website. Furthermore, the previous signatures can be easily manipulated by attackers. In order to overcome the problems mentioned above, in this paper, we propose a hue signature auto update system for visual similarity-based phishing detection with tolerance to zero-day attack. The phishing websites targeting certain legitimate website tend to use the targeted website's theme color to deceive users. In other words, the users can easily distinguish phishing website if it has highly different hue information from targeted legitimate one (e.g. red colored Facebook is suspicious). Thus, the hue signature has a common feature among the targeted legitimate website and subspecies of phishing websites, and it is difficult for attackers to change it. Based on this notion, we argue that the hue signature fulfills the requirements about auto updating SDB and robustness for attackers' manipulating. This commonness can effectively expand the scope of detection when auto updating is applied to the hue signature. By the computer simulation with a real dataset, we demonstrate that our system achieves high detection performance compared with the previous scheme.

Recently, social relation analysis receives an increasing amount of attention from text to image data. However, social relation analysis from video is an important problem, which is lacking in the current literature. There are still some challenges: 1) it is hard to learn a satisfactory mapping function from low-level pixels to high-level social relation space; 2) how to efficiently select the most relevant information from noisy and unsegmented video. In this paper, we present an Attentive Sequences Recurrent Network model, called ASRN, to deal with the above challenges. First, in order to explore multiple clues, we design a Multiple Feature Attention (MFA) mechanism to fuse multiple visual features (i.e. image, motion, body, and face). Through this manner, we can generate an appropriate mapping function from low-level video pixels to high-level social relation space. Second, we design a sequence recurrent network based on Global and Local Attention (GLA) mechanism. Specially, an attention mechanism is used in GLA to integrate global feature with local sequence feature to select more relevant sequences for the recognition task. Therefore, the GLA module can better deal with noisy and unsegmented video. At last, extensive experiments on the SRIV dataset demonstrate the performance of our ASRN model.

Detecting community structures and analyzing temporal evolution in dynamic networks are challenging tasks to explore the inherent characteristics of the complex networks. In this paper, we propose a semi-supervised evolutionary clustering model based on symmetric nonnegative matrix factorization to detect communities in dynamic networks, named sEC-SNMF. We use the results of community partition at the previous time step as the priori information to modify the current network topology, then smooth-out the evolution of the communities and reduce the impact of noise. Furthermore, we introduce a community transition probability matrix to track and analyze the temporal evolutions. Different from previous algorithms, our approach does not need to know the number of communities in advance and can deal with the situation in which the number of communities and nodes varies over time. Extensive experiments on synthetic datasets demonstrate that the proposed method is competitive and has a superior performance.

We propose an efficient two-layer near-lossless coding method using an extended histogram packing technique with backward compatibility to the legacy JPEG standard. The JPEG XT, which is the international standard to compress HDR images, adopts a two-layer coding method for backward compatibility to the legacy JPEG standard. However, there are two problems with this two-layer coding method. One is that it does not exhibit better near-lossless performance than other methods for HDR image compression with single-layer structure. The other problem is that the determining the appropriate values of the coding parameters may be required for each input image to achieve good compression performance of near-lossless compression with the two-layer coding method of the JPEG XT. To solve these problems, we focus on a histogram-packing technique that takes into account the histogram sparseness of HDR images. We used zero-skip quantization, which is an extension of the histogram-packing technique proposed for lossless coding, for implementing the proposed near-lossless coding method. The experimental results indicate that the proposed method exhibits not only a better near-lossless compression performance than that of the two-layer coding method of the JPEG XT, but also there are no issue regarding the combination of parameter values without losing backward compatibility to the JPEG standard.

In integrated circuit design of advanced technology nodes, layout density uniformity significantly influences the manufacturability due to the CMP variability. In analog design, especially, designers are suffering from passing the density checking since there are few useful tools. To tackle this issue, we focus a transistor-array(TA)-style analog layout, and propose a density optimization algorithm consistent with complicated design rules. Based on TA-style, we introduce a density-aware layout format to explicitly control the layout pattern density, and provide the mathematical optimization approach. Hence, a design flow incorporating our density optimization can drastically reduce the design time with fewer iterations. In a design case of an OPAMP layout in a 65nm CMOS process, the result demonstrates that the proposed approach achieves more than 48× speed-up compared with conventional manual layout, meanwhile it shows a good circuit performance in the post-layout simulation.

This paper proposes a novel interaction technique to transfer data across various types of digital devices in uniform a manner and to allow specifying what kind of data should be sent. In our framework, when users tap multiple devices rhythmically, data corresponding to the rhythm (transfer type) are transferred from a device tapped in the first tap (source device) to the other (target device). It is easy to operate, applicable to a wide range of devices, and extensible in a sense that we can adopt new transfer types by adding new rhythms. Through a subjective evaluation and a simulation, we had a prospect that our approach would be feasible. We also discuss suggestions and limitation to implement the technique.

For image restoration, an image prior that is obtained from the morphological gradient has been proposed. In the field of mathematical morphology, the optimization of the structuring element (SE) used for this morphological gradient using a genetic algorithm (GA) has also been proposed. In this paper, we introduce a new image prior that is the sum of the morphological gradients and total variation for an image restoration problem to improve the restoration accuracy. The proposed image prior makes it possible to almost match the fitness to a quantitative evaluation such as the mean square error. It also solves the problem of the artifact due to the unsuitability of the SE for the image. An experiment shows the effectiveness of the proposed image restoration method.

Hadamard matrix is defined as a square matrix where any components are -1 or +1, and where any pairs of rows are mutually orthogonal. In this work, we consider the similar matrix on finite field GF(p) where p is an odd prime. In such a matrix, every component is one of the integers on GF(p){0}, that is, {1,2,...,p-1}. Any additions and multiplications should be executed under modulo p. In this paper, a method to generate such matrices is proposed. In addition, the paper includes the applications to generate n-shift orthogonal sequences and complete complementary codes. The generated complete complementary code is a family of multi-valued sequences on GF(p){0}, where the number of sequence sets, the number of sequences in each sequence set and the sequence length depend on the various divisors of p-1. Such complete complementary codes with various parameters have not been proposed in previous studies.

Key encapsulation mechanism (KEM) combiners, recently formalized by Giacon, Heuer, and Poettering (PKC'18), enable hedging against insecure KEMs or weak parameter choices by combining ingredient KEMs into a single KEM that remains secure assuming just one of the underlying ingredient KEMs is secure. This seems particularly relevant when considering quantum-resistant KEMs which are often based on arguably less well-understood hardness assumptions and parameter choices. We propose a new simple KEM combiner based on a one-time secure message authentication code (MAC) and two-time correlated input secure hash. Instantiating the correlated input secure hash with a t-wise independent hash for an appropriate value of t, yields a KEM combiner based on a strictly weaker additional primitive than the standard model construction of Giaon et al. and furthermore removes the need to do n full passes over the encapsulation, where n is the number of ingredient KEMs, which Giacon et al. highlight as a disadvantage of their scheme. However, unlike Giacon et al., our construction requires the public key of the combined KEM to include a hash key, and furthermore requires a MAC tag to be added to the encapsulation of the combined KEM.

Linear complexity and the k-error linear complexity of periodic sequences are the important security indices of stream cipher systems. This paper focuses on the distribution of p-error linear complexity of p-ary sequences with period pn. For p-ary sequences of period pn with linear complexity pn-p+1, n≥1, we present all possible values of the p-error linear complexity, and derive the exact formulas to count the number of the sequences with any given p-error linear complexity.

SPHINCS+, an updated version of SPHINCS, is a post-quantum hash-based signature scheme submitted to the NIST post-quantum cryptography standardization project. To evaluate its performance, SPHINCS+ gives the theoretical number of function calls and the actual runtime of a reference implementation. We show that the theoretical number of function calls for SPHINCS+ verification is inconsistent with the runtime and then present the correct number of function calls.

To implement the parallel acceleration of convolution operation of Convolutional Neural Networks (CNNs) on field programmable gate array (FPGA), large quantities of the logic resources will be consumed, expecially DSP cores. Many previous researches fail to make a well balance between DSP and LUT6. For better resource efficiency, a typical convolution structure is implemented with LUT6s in this paper. Besides, a novel convolution structure is proposed to further reduce the LUT6 resource consumption by modifying the typical convolution structure. The equations to evaluate the LUT6 resource consumptions of both structures are presented and validated. The theoretical evaluation and experimental results show that the novel structure can save 3.5-8% of LUT6s compared with the typical structure.