In this study, we discuss a structure that realizes a wideband polarization splitter comprising fiber 1 with a single core and fiber 2 with circular pits, which touch the top and bottom of a single core. The refractive index profile of the W type was adopted in the core of fiber 1 to realize the wideband. We compared the maximum bandwidth of BW-15 (bandwidth at an extinction ratio of -15dB) for the W type obtained in this study with those (our previous results) of BW-15 for the step and graded types with cores and pits at the same location; this comparison clarified that the maximum bandwidth of BW-15 for the W type is 5.22 and 4.96 times wider than those of step and graded types, respectively. Furthermore, the device length at the maximum bandwidth improved, becoming slightly shorter. The main results of the FPS in this study are all obtained by numerical analysis based on our proposed MM-DM (a method that combines the multipole method and the difference method for the inhomogeneous region). Our MM-DM is a quite reliable method for high accuracy analysis of the FPS composed of inhomogeneous circular regions.

An approach to dedicated computers is discussed in this study as a possibility for portable, low-cost, and low-power consumption high-performance computing technologies. Particularly, dataflow architecture dedicated computer of the finite integration technique (FIT) for 2D magnetostatic field simulation is considered for use in industrial applications. The dataflow architecture circuit of the BiCG-Stab matrix solver of the FIT matrix calculation is designed by the very high-speed integrated circuit hardware description language (VHDL). The operation of the dedicated computer's designed circuit is considered by VHDL logic circuit simulation.

This article presents a 28GHz high-accuracy phase and amplitude detection circuit for dual-polarized phased-array calibration. With dual-polarized calibration scheme, external LO signal is not required for calibration. The proposed detection circuit detects phase and amplitude independently, using PDC and ADC. By utilizing a 28GHz-to-140kHz downconversion scheme, the phase and amplitude are detected more accurately. In addition, reference signal for PDC and ADC is generated from 28GHz LO signal with divide-by-6 dual-step-mixing injection locked frequency divider (ILFD). This ILFD achieves 24.5-32.5GHz (28%) locking range with only 3.0mW power consumption and 0.01mm2 area. In the measurement, the detection circuit achieves phase and amplitude detections with RMS errors of 0.17degree and 0.12dB, respectively. The total power consumption of the proposed circuit is 59mW with 1-V supply voltage.

As the Internet has become prevalent, the popularity of net media has been growing, to a point that it has taken over conventional mass media. However, TWtrends, the Twitter trends visualization system operated by our research team since 2019, indicates that many topics on TV programs frequently appear on Twitter trendlines. This study investigates the relationship between Twitter and TV programs by collecting information on Twitter trends and TV programs simultaneously. Although this study provides a rough estimation of the volume of tweets that mention TV programs, the results show that several tweets mention TV programs at a constant rate, which tends to increase on the weekend. This tendency of TV-related tweets stems from the audience rating survey results. Considering the study outcome, and the fact that many TV programs introduce topics popular in social media, implies codependency between Internet media (social media) and mass media.

In recent years, with the aging of society, many kinds of research have been actively conducted to recognize human activity in a home to watch over the elderly. Multiple sensors for activity recognition are used. However, we need to consider privacy when using these sensors. One of the candidates of the sensors that keep privacy is a sound sensor. MFCC (Mel-Frequency Cepstral Coefficient) is widely used as a feature extraction algorithm for voice recognition. However, it is not suitable to apply conventional MFCC to activity recognition by sounds of daily life. We denote “sounds of daily life” as “life sounds” simply in this paper. The reason is that conventional MFCC does not extract well several features of life sounds that appear at high frequencies. This paper proposes the improved MFCC and reports the evaluation results of activity recognition by machine learning SVM (Support Vector Machine) using features extracted by improved MFCC.

Animating 3D characters using motion capture data requires basic expertise and manual labor. To support the creativity of animation design and make it easier for common users, we present a sketch-based interface DualMotion, with rough sketches as input for designing daily-life animations of characters, such as walking and jumping. Our approach enables to combine global motions of lower limbs and the local motion of the upper limbs in a database by utilizing a two-stage design strategy. Users are allowed to design a motion by starting with drawing a rough trajectory of a body/lower limb movement in the global design stage. The upper limb motions are then designed by drawing several more relative motion trajectories in the local design stage. We conduct a user study and verify the effectiveness and convenience of the proposed system in creative activities.

We propose GConvLoc, a WiFi fingerprinting-based indoor localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.

Annealing machines such as quantum annealing machines and semiconductor-based annealing machines have been attracting attention as an efficient computing alternative for solving combinatorial optimization problems. They solve original combinatorial optimization problems by transforming them into a data structure called an Ising model. At that time, the bit-widths of the coefficients of the Ising model have to be kept within the range that an annealing machine can deal with. However, by reducing the Ising-model bit-widths, its minimum energy state, or ground state, may become different from that of the original one, and hence the targeted combinatorial optimization problem cannot be well solved. This paper proposes an effective method for reducing Ising model's bit-widths. The proposed method is composed of two processes: First, given an Ising model with large coefficient bit-widths, the shift method is applied to reduce its bit-widths roughly. Second, the spin-adding method is applied to further reduce its bit-widths to those that annealing machines can deal with. Without adding too many extra spins, we efficiently reduce the coefficient bit-widths of the original Ising model. Furthermore, the ground state before and after reducing the coefficient bit-widths is not much changed in most of the practical cases. Experimental evaluations demonstrate the effectiveness of the proposed method, compared to existing methods.

Memory-efficient Internet Protocol (IP) lookup with high speed is essential to achieve link-speed packet forwarding in IP routers. The rapid growth of Internet traffic and the development of optical link technologies have made IP lookup a major performance bottleneck in core routers. In this paper, we propose a new IP route lookup architecture based on hardware called Prefix-Route Trie (PR-Trie), which supports both IPv4 and IPv6 addresses. In PR-Trie, we develop a novel structure called Overlapping Hybrid Trie (OHT) to perform fast longest-prefix-matching (LPM) based on Multibit-Trie (MT), and a hash-based level matching query used to achieve only one off-chip memory access per lookup. In addition, the proposed PR-Trie also supports fast incremental updates. Since the memory complexity in MT-based IP lookup schemes depends on the level-partitioning solution and the data structure used, we develop an optimization algorithm called Bitmap-based Prefix Partitioning Optimization (BP2O). The proposed BP2O is based on a heuristic search using Ant Colony Optimization (ACO) algorithms to optimize memory efficiency. Experimental results using real-life routing tables prove that our proposal has superior memory efficiency. Theoretical performance analyses show that PR-Trie outperforms the classical Trie-based IP lookup algorithms.

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%.

In this work, we propose a new automatic speech recognition (ASR) system based on feature learning and an end-to-end training procedure for air traffic control (ATC) systems. The proposed model integrates the feature learning block, recurrent neural network (RNN), and connectionist temporal classification loss to build an end-to-end ASR model. Facing the complex environments of ATC speech, instead of the handcrafted features, a learning block is designed to extract informative features from raw waveforms for acoustic modeling. Both the SincNet and 1D convolution blocks are applied to process the raw waveforms, whose outputs are concatenated to the RNN layers for the temporal modeling. Thanks to the ability to learn representations from raw waveforms, the proposed model can be optimized in a complete end-to-end manner, i.e., from waveform to text. Finally, the multilingual issue in the ATC domain is also considered to achieve the ASR task by constructing a combined vocabulary of Chinese characters and English letters. The proposed approach is validated on a multilingual real-world corpus (ATCSpeech), and the experimental results demonstrate that the proposed approach outperforms other baselines, achieving a 6.9% character error rate.

Multimodal named entity recognition (MNER) is the task of recognizing named entities in multimodal context. Existing methods focus on utilizing co-attention mechanism to discover the relationships between multiple modalities. However, they still have two deficiencies: First, current methods fail to fuse the multimodal representations in a fine-grained way, which may bring noise of visual modalities. Second, current methods ignore bridging the semantic gap between heterogeneous modalities. To solve the above issues, we propose a novel MNER method with bottleneck fusion and contrastive learning (BFCL). Specifically, we first incorporate the transformer-based bottleneck fusion mechanism, subsequently, information between different modalities can only be exchanged through several bottleneck tokens, thus reducing the noise propagation. Then we propose two decoupled image-text contrastive losses to align the unimodal representations, making the representations of semantically similar modalities closer, while the representations of semantically different modalities farther away. Experimental results demonstrate that our method is competitive to the state-of-the-art models, and achieves 74.54% and 85.70% F1-scores on Twitter-2015 and Twitter-2017 datasets, respectively.

To overcome the challenge in micro-expression recognition that it only emerge in several small facial regions with low intensity, some researchers proposed facial region partition mechanisms and introduced group sparse learning methods for feature selection. However, such methods have some shortcomings, including the complexity of region division and insufficient utilization of critical facial regions. To address these problems, we propose a novel Group Sparse Reduced Rank Tensor Regression (GSRRTR) to transform the fearure matrix into a tensor by laying blocks and features in different dimensions. So we can process grids and texture features separately and avoid interference between grids and features. Furthermore, with the use of Tucker decomposition, the feature tensor can be decomposed into a product of core tensor and a set of matrix so that the number of parameters and the computational complexity of the scheme will decreased. To evaluate the performance of the proposed micro-expression recognition method, extensive experiments are conducted on two micro expression databases: CASME2 and SMIC. The experimental results show that the proposed method achieves comparable recognition rate with less parameters than state-of-the-art methods.

Identity-based encryption with equality test (IBEET) is a generalization of the traditional identity-based encryption (IBE) and public key searchable encryption, where trapdoors enable users to check whether two ciphertexts of distinct identities are encryptions of the same plaintext. By definition, IBEET cannot achieve indistinguishability security against insiders, i.e., users who have trapdoors. To address this issue, IBEET against insider attacks (IBEETIA) was later introduced as a dual primitive. While all users of IBEETIA are able to check whether two ciphertexts are encryptions of the same plaintext, only users who have tokens are able to encrypt plaintexts. Hence, IBEETIA is able to achieve indistinguishability security. On the other hand, the definition of IBEETIA weakens the notion of IBE due to its encryption inability. Nevertheless, known schemes of IBEETIA made use of rich algebraic structures such as bilinear groups and lattices. In this paper, we propose a generic construction of IBEETIA without resorting to rich algebraic structures. In particular, the only building blocks of the proposed construction are symmetric key encryption and pseudo-random permutations in the standard model. If a symmetric key encryption scheme satisfies CCA security, our proposed IBEETIA scheme also satisfies CCA security.

We present a forward-secure public-key encryption (PKE) scheme without key update, i.e. both public and private keys are immutable. In contrast, prior forward-secure PKE schemes achieve forward security by constantly updating the secret keys. Our scheme is based on witness encryption by Garg et al. (STOC 2013) and a proof-of-stake blockchain with the distinguishable forking property introduced by Goyal et al. (TCC 2017), and ensures a ciphertext cannot be decrypted more than once, thereby rendering a compromised secret key useless with respect to decryption of past ciphertext the legitimate user has already decrypted. In this work, we formalize the notion of blockchain-based forward-secure PKE, show the feasibility of constructing a forward-secure PKE scheme without key update, and discuss interesting properties of our scheme such as post-compromise security.

We propose a short signature scheme under the ring-SIS assumption in the standard model. Specifically, by revisiting an existing construction [Ducas and Micciancio, CRYPTO 2014], we demonstrate lattice-based signatures with improved reduction loss. As far as we know, there are no ways to use multiple tags in the signature simulation of security proof in the lattice tag-based signatures. We address the tag-collision possibility in the lattice setting, which improves reduction loss. Our scheme generates tags from messages by constructing a scheme under a mild security condition that is existentially unforgeable against random message attack with auxiliary information. Thus our scheme can reduce the signature size since it does not need to send tags with the signatures. Our scheme has short signature sizes of O(1) and achieves tighter reduction loss than that of Ducas et al.'s scheme. Our proposed scheme has two variants. Our scheme with one property has tighter reduction and the same verification key size of O(log n) as that of Ducas et al.'s scheme, where n is the security parameter. Our scheme with the other property achieves much tighter reduction loss of O(Q/n) and verification key size of O(n), where Q is the number of signing queries.

The hardness of the syndrome decoding problem (SDP) is the primary evidence for the security of code-based cryptosystems, which are one of the finalists in a project to standardize post-quantum cryptography conducted by the U.S. National Institute of Standards and Technology (NIST-PQC). Information set decoding (ISD) is a general term for algorithms that solve SDP efficiently. In this paper, we conducted a concrete analysis of the time complexity of the latest ISD algorithms under the limitation of memory using the syndrome decoding estimator proposed by Esser et al. As a result, we present that theoretically nonoptimal ISDs, such as May-Meurer-Thomae (MMT) and May-Ozerov, have lower time complexity than other ISDs in some actual SDP instances. Based on these facts, we further studied the possibility of multiple parallelization for these ISDs and proposed the first GPU algorithm for MMT, the multiparallel MMT algorithm. In the experiments, we show that the multiparallel MMT algorithm is faster than existing ISD algorithms. In addition, we report the first successful attempts to solve the 510-, 530-, 540- and 550-dimensional SDP instances in the Decoding Challenge contest using the multiparallel MMT.

Ramp secret sharing is a variant of secret sharing which can achieve better information ratio than perfect schemes by allowing some partial information on a secret to leak out. Strongly secure ramp schemes can control the amount of leaked information on the components of a secret. In this paper, we reduce the construction of strongly secure ramp secret sharing for general access structures to a linear algebraic problem. As a result, we show that previous results on strongly secure network coding imply two linear transformation methods to make a given linear ramp scheme strongly secure. They are explicit or provide a deterministic algorithm while the previous methods which work for any linear ramp scheme are non-constructive. In addition, we present a novel application of strongly secure ramp schemes to symmetric PIR in a multi-user setting. Our solution is advantageous over those based on a non-strongly secure scheme in that it reduces the amount of communication between users and servers and also the amount of correlated randomness that servers generate in the setup.

An Oblivious Priority Queue (OPQ) is a cryptographic primitive that enables a client to outsource its data to a dishonest server, and also to securely manage the data according to a priority queue algorithm. Though the first OPQ achieves perfect security, it supports only two operations; Inserting an element and extracting the top-priority element, which are the minimal requirement for a priority queue. In addition, this OPQ allows an adversary to observe operations in progress, which leaks the exact number of elements in the data structure. On the other hand, there are many subsequent works for OPQs that implement additional operations of a priority queue, hide the running operations, and improve efficiency. Though the recent works realize optimal efficiency, all of them achieve only statistical or computational security. Aiming to reconcile perfect security of the first OPQ with all functions (including the operation hiding) supported by recent OPQs, we construct a novel perfectly secure OPQ that can simulate the following operations while hiding which one is in progress; Inserting an element, extracting the top-priority one, deleting an element, and modifying the priority of an element. The efficiency of our scheme is O(log2 N), which is larger than that of the best known statistically secure OPQ but is the same as the known perfectly secure scheme.

Card-based cryptography realizes secure multiparty computation using physical cards. In 2018, Watanabe et al. proposed a card-based three-input majority voting protocol using three cards. In a card-based cryptographic protocol with n-bit inputs, it is known that a protocol using shuffles requires at least 2n cards. In contrast, as Watanabe et al.'s protocol, a protocol using private permutations can be constructed with fewer cards than the lower bounds above. Moreover, an n-input protocol using private permutations would not even require n cards in principle since a private permutation depending on an input can represent the input without using additional cards. However, there are only a few protocols with fewer than n cards. Recently, Abe et al. extended Watanabe et al.'s protocol and proposed an n-input majority voting protocol with n cards and n + ⌊n/2⌋ + 1 private permutations. This paper proposes an n-input majority voting protocol with ⌈n/2⌉ + 1 cards and 2n-1 private permutations, which is also obtained by extending Watanabe et al.'s protocol. Compared with Abe et al.'s protocol, although the number of private permutations increases by about n/2, the number of cards is reduced by about n/2. In addition, unlike Abe et al.'s protocol, our protocol includes Watanabe et al.'s protocol as a special case where n=3.