Zero-shot learning (ZSL) aims to classify images of unseen classes by learning relationship between visual and semantic features. Existing works have been improving recognition accuracy from various approaches, but they employ computationally intensive algorithms that require iterative optimization. In this work, we revisit the primary approach of the pattern recognition, ı.e., nearest neighbor classifiers, to solve the ZSL task by an extremely simple and fast way, called SimpleZSL. Our algorithm consists of the following three simple techniques: (1) just averaging feature vectors to obtain visual prototypes of seen classes, (2) calculating a pseudo-inverse matrix via singular value decomposition to generate visual features of unseen classes, and (3) inferring unseen classes by a nearest neighbor classifier in which cosine similarity is used to measure distance between feature vectors. Through the experiments on common datasets, the proposed method achieves good recognition accuracy with drastically small computational costs. The execution time of the proposed method on a single CPU is more than 100 times faster than those of the GPU implementations of the existing methods with comparable accuracies.

The Proof of Stake (PoS) protocol is one of the consensus algorithms for blockchain, in which the integrity of a new block is validated according to voting by nodes called validators. However, due to validator-oriented voting, voting results are likely to be false when the number of validators with wrong votes increases. In the PoS protocol, validators are motivated to vote correctly by reward and penalty mechanisms. With such mechanisms, validators who contribute to correct consensuses are rewarded, while those who vote incorrectly are penalized. In this paper, we consider an incentivization mechanism based on the voting profile of a validator, which is estimated from the voting history of the validator. In this mechanism, the stake collected due to the penalties are redistributed to validators who vote correctly, improving the incentive of validators to contribute to the system. We evaluate the performance of the proposed mechanism by computer simulations, investigating the impacts of system parameters on the estimation accuracy of the validator profile and the amount of validator's stake. Numerical results show that the proposed mechanism can estimate the voting profile of a validator accurately even when the voting profile dynamically changes. It is also shown that the proposed mechanism gives more reward to validators who vote correctly with high voting profile.

We have utilized single-pixel imaging and deep-learning to solve the privacy-preserving problem in gesture recognition for interactive display. Silhouette images of hand gestures were acquired by use of a display panel as an illumination. Reconstructions of gesture images have been performed by numerical experiments on single-pixel imaging by changing the number of illumination mask patterns. For the training and the image restoration with deep learning, we prepared reconstructed data with 250 and 500 illuminations as datasets. For each of the 250 and 500 illuminations, we prepared 9000 datasets in which original images and reconstructed data were paired. Of these data, 8500 data were used for training a neural network (6800 data for training and 1700 data for validation), and 500 data were used to evaluate the accuracy of image restoration. Our neural network, based on U-net, was able to restore images close to the original images even from reconstructed data with greatly reduced number of illuminations, which is 1/40 of the single-pixel imaging without deep learning. Compared restoration accuracy between cases using shadowgraph (black on white background) and negative-positive reversed images (white on black background) as silhouette image, the accuracy of the restored image was lower for negative-positive-reversed images when the number of illuminations was small. Moreover, we found that the restoration accuracy decreased in the order of rock, scissor, and paper. Shadowgraph is suitable for gesture silhouette, and it is necessary to prepare training data and construct neural networks, to avoid the restoration accuracy between gestures when further reducing the number of illuminations.

In this letter, we derive a novel and accurate closed-form bit error rate (BER) approximation of the optical wireless communications (OWC) systems for the sub-carrier intensity modulation (SIM) employing binary phase-shift keying (BPSK) with multiple transmit and single receive apertures over strong atmospheric turbulence channels, which makes it possible to effectively investigate and predict the BER performance for various system configurations. Furthermore, we also derive a concise asymptotic BER formula to quantitatively evaluate the asymptotically achievable error performance (i.e., asymptotic diversity and combining gains) in the high signal-to-noise (SNR) regimes. Some numerical results are provided to corroborate the accuracy and effectiveness of our theoretical expressions.

With the increase of IoT devices, P2P-based IoT platforms have been attracting attention because of their capabilities of building and maintaining their networks autonomously in a decentralized way. In particular, Skip Graph, which has a low network rebuilding cost and allows range search, is suitable for the platform. However, when data observed at geographically close points have similar values (i.e. when data have strong spatial autocorrelation), existing types of Skip Graph degrade their search performances. In this paper, we propose a query transfer method that enables efficient search even for spatially autocorrelated data by adaptively using two-types of Skip Graph depending on the key-distance to the target key. Simulation results demonstrate that the proposed method can reduce the query transfer distance compared to the existing method even for spatially autocorrelated data.

This paper presents examination result of possibility for automatic unclear region detection in the CAD system for colorectal tumor with real time endoscopic video image. We confirmed that it is possible to realize the CAD system with navigation function of clear region which consists of unclear region detection by YOLO2 and classification by AlexNet and SVMs on customizable embedded DSP cores. Moreover, we confirmed the real time CAD system can be constructed by a low power ASIC using customizable embedded DSP cores.

In this letter, we study low-density parity-check (LDPC) codes for noisy channels with insertion and deletion (ID) errors. We first propose a design method of irregular LDPC codes for such channels, which can be used to simultaneously obtain degree distributions for different noise levels. We then show the asymptotic/finite-length decoding performances of designed codes and compare them with the symmetric information rates of cascaded ID-noisy channels. Moreover, we examine the relationship between decoding performance and a code structure of irregular LDPC codes.

This letter proposes an adaptive beamforming switch algorithm for realistic massive multiple-input multiple-output (MIMO) systems through prototypes. It is analyzed and identified that a rigid single-mode beamforming regime is hard to maintain superior performance all the time due to no adaption to the inevitable channel variation in practice. In order to cope with this practical issue, the proposed systematic beamforming mechanism is investigated to enable dynamic selection between minimum mean-squared error and grid-of-beams beamforming algorithms, which improves system downlink performance, including throughput and block error rate. The significant performance benefits and realistic feasibility have been validated through the field tests in live networks and theoretical analyses. Meanwhile, the adaptive beamforming switch algorithm is applicable to both fourth and fifth generation time-division duplexing cellular communication system using massive-MIMO technology.

In this letter, we propose a novel transferable sparse regression (TSR) method, for cross-database facial expression recognition (FER). In TSR, we firstly present a novel regression function to regress the data into a latent representation space instead of a strict binary label space. To further alleviate the influence of outliers and overfitting, we impose a row sparsity constraint on the regression term. And a pairwise relation term is introduced to guide the feature transfer learning. Secondly, we design a global graph to transfer knowledge, which can well preserve the cross-database manifold structure. Moreover, we introduce a low-rank constraint on the graph regularization term to uncover additional structural information. Finally, several experiments are conducted on three popular facial expression databases, and the results validate that the proposed TSR method is superior to other non-deep and deep transfer learning methods.

The Canonical Polyadic Decomposition (CPD) is the tensor analog of the Singular Value Decomposition (SVD) for a matrix and has many data science applications including signal processing and machine learning. For the CPD, the Alternating Least Squares (ALS) algorithm has been used extensively. Although the ALS algorithm is simple, it is sensitive to a noise of a data tensor in the applications. In this paper, we propose a novel strategy to realize the noise suppression for the CPD. The proposed strategy is decomposed into two steps: (Step 1) denoising the given tensor and (Step 2) solving the exact CPD of the denoised tensor. Step 1 can be realized by solving a structured low-rank approximation with the Douglas-Rachford splitting algorithm and then Step 2 can be realized by solving the simultaneous diagonalization of a matrix tuple constructed by the denoised tensor with the DODO method. Numerical experiments show that the proposed algorithm works well even in typical cases where the ALS algorithm suffers from the so-called bottleneck/swamp effect.

Active network monitoring based on Boolean network tomography is a promising technique to localize link failures instantly in transport networks. However, the required set of monitoring trails must be recomputed after each link failure has occurred to handle succeeding link failures. Existing heuristic methods cannot compute the required monitoring trails in a sufficiently short time when multiple-link failures must be localized in the whole of large-scale managed networks. This paper proposes an approach for computing the required monitoring trails within an allowable expected period specified beforehand. A random walk-based analysis estimates the number of monitoring trails to be computed in the proposed approach. The estimated number of monitoring trails are computed by a lightweight method that only guarantees partial localization within restricted areas. The lightweight method is repeatedly executed until a successful set of monitoring trails achieving unambiguous localization in the entire managed networks can be obtained. This paper demonstrates that the proposed approach can compute a small number of monitoring trails for localizing all independent dual-link failures in managed networks made up of thousands of links within a given expected short period.

This paper proposes an analytical method to design septum-type polarizers by assuming a polarizer as a series of four septum elements with a short ridge-waveguide approximation. We determine parameters of respective elements in such a manner that, at the center frequency, the reflection coefficient of the first element is equal to that of the second one, the reflection of the third one equals to that of the forth, and the electrical lengths of the first, second and third elements are 90 deg. We name this method the Short Ridge-waveguide Approximation Method (SRAM). We fabricated an X-band polarizer, which achieves a cross polarization discrimination (XPD) value of 40.7-64.1 dB over 8.0-8.4 GHz, without any numerical optimization.

In this paper, we propose a novel user selection scheme based on jointly combining channel gain (CG) and signal to interference plus noise ratio (SINR) to improve the sum-rate as well as to reduce the computation complexity of multi-user massive multi-input multi-output (MU-massive MIMO) downlink transmission through a block diagonalization (BD) precoding technique. By jointly considering CG and SINR based user sets, sum-rate performance improvement can be achieved by selecting higher gain users with better SINR conditions as well as by eliminating the users who cause low sum-rate in the system. Through this approach, the number of possible outcomes for the user selection scheme can be reduced by counting the common users for every pair of user combinations in the selection process since the common users of CG-based and SINR-based sets possess both higher channel gains and better SINR conditions. The common users set offers not only sum-rate performance improvements but also computation complexity reduction in the proposed scheme. It is shown by means of computer simulation experiments that the proposed scheme can increase the sum-rate with lower computation complexity for various numbers of users as compared to conventional schemes requiring the same or less computational complexity.

The solution of the ordinary kernel ridge regression, based on the squared loss function and the squared norm-based regularizer, can be easily interpreted as a stochastic linear estimator by considering the autocorrelation prior for an unknown true function. As is well known, a stochastic affine estimator is one of the simplest extensions of the stochastic linear estimator. However, its corresponding kernel regression problem is not revealed so far. In this paper, we give a formulation of the kernel regression problem, whose solution is reduced to a stochastic affine estimator, and also give interpretations of the formulation.

It has become common practice for software projects to adopt third-party dependencies. Developers are encouraged to update any outdated dependency to remain safe from potential threats of vulnerabilities. In this study, we present an approach to aid developers show whether or not a vulnerable code is reachable for JavaScript projects. Our prototype, SōjiTantei, is evaluated in two ways (i) the accuracy when compared to a manual approach and (ii) a larger-scale analysis of 780 clients from 78 security vulnerability cases. The first evaluation shows that SōjiTantei has a high accuracy of 83.3%, with a speed of less than a second analysis per client. The second evaluation reveals that 68 out of the studied 78 vulnerabilities reported having at least one clean client. The study proves that automation is promising with the potential for further improvement.

Maximum run-length limited codes are constraint codes used in communication and data storage systems. Insertion/deletion correcting codes correct insertion or deletion errors caused in transmitted sequences and are used for combating synchronization errors. This paper investigates the maximum run-length limited single insertion/deletion correcting (RLL-SIDC) codes. More precisely, we construct efficiently encodable and decodable RLL-SIDC codes. Moreover, we present its encoding and decoding algorithms and show the redundancy of the code.

The present paper proposes orthogonal variable spreading factor codes over finite fields for multi-rate communications. The proposed codes have layered structures that combine sequences generated by discrete Fourier transforms over finite fields, and have various code lengths. The design method for the proposed codes and examples of the codes are shown.

Utilization data (a kind of incomplete data) is defined as the fraction of a fixed period in which the system is busy. In computer systems, utilization data is very common and easily observable, such as CPU utilization. Unlike inter-arrival times and waiting times, it is more significant to consider the parameter estimation of transaction-based systems with utilization data. In our previous work [7], a novel parameter estimation method using utilization data for an Mt/M/1/K queueing system was presented to estimate the parameters of a non-homogeneous Poisson process (NHPP). Since NHPP is classified as a simple counting process, it may not fit actual arrival streams very well. As a generalization of NHPP, Markovian arrival process (MAP) takes account of the dependency between consecutive arrivals and is often used to model complex, bursty, and correlated traffic streams. In this paper, we concentrate on the parameter estimation of an MAP/M/1/K queueing system using utilization data. In particular, the parameters are estimated by using maximum likelihood estimation (MLE) method. Numerical experiments on real utilization data validate the proposed approach and evaluate the effective traffic intensity of the arrival stream of MAP/M/1/K queueing system. Besides, three kinds of utilization datasets are created from a simulation to assess the effects of observed time intervals on both estimation accuracy and computational cost. The numerical results show that MAP-based approach outperforms the exiting method in terms of both the estimation accuracy and computational cost.

Multi-FPGA systems have gained attention because of their high performance and power efficiency. A multi-FPGA system called Flow-in-Cloud (FiC) is currently being developed as an accelerator of multi-access edge computing (MEC). FiC consists of multiple mid-range FPGAs tightly connected by high-speed serial links. Since time-critical jobs are assumed in MEC, a circuit-switched network with static time-division multiplexing (STDM) switches has been implemented on FiC. This paper investigates techniques of enhancing the interconnection performance of FiC. Unlike switching fabrics for Network on Chips or parallel machines, economical multi-FPGA systems, such as FiC, use Xilinx Aurora IP and FireFly cables with multiple lanes. We adopted the link aggregation and the slot distribution for using multiple lanes. To mitigate the bottleneck between an STDM switch and user logic, we also propose a multi-ejection STDM switch. We evaluated various combinations of our techniques by using three practical applications on an FiC prototype with 24 boards. When the number of slots is large and transferred data size is small, the slot distribution was sometimes more effective, while the link aggregation was superior for other most cases. Our multi-ejection STDM switch mitigated the bottleneck in ejection ports and successfully reduced the number of time slots. As a result, by combining the link aggregation and multi-ejection STDM switch, communication performance improved up to 7.50 times with few additional resources. Although the performance of the fast Fourier transform with the highest communication ratio could not be enhanced by using multiple boards when a lane was used, 1.99 times performance improvement was achieved by using 8 boards with four lanes and our multi-ejection switch compared with a board.

Multisignatures enable multiple users to sign a message interactively. Many instantiations are proposed for multisignatures, however, most of them are quantum-insecure, because these are based on the integer factoring assumption or the discrete logarithm assumption. Although there exist some constructions based on the lattice problems, which are believed to be quantum-secure, their security reductions are loose. In this paper, we aim to improve the security reduction of lattice-based multisignature schemes concerning tightness. Our basic strategy is combining the multisignature scheme proposed by El Bansarkhani and Sturm with the lattice-based signature scheme by Abdalla, Fouque, Lyubashevsky, and Tibouchi which has a tight security reduction from the Ring-LWE (Ring Learning with Errors) assumption. Our result shows that proof techniques for standard signature schemes can be applied to multisignature schemes, then we can improve the polynomial loss factor concerning the Ring-LWE assumption. Our second result is to address the problem of security proofs of existing lattice-based multisignature schemes pointed out by Damgård, Orlandi, Takahashi, and Tibouchi. We employ a new cryptographic assumption called the Rejected-Ring-LWE assumption, to complete the security proof.