Along with the fast development of blue economy, wireless communication in oceans has received extensive attention in recent years, and opportunistic networks without any aid from fixed infrastructure or centralized management are expected to play an important role in such highly dynamic environments. Here, link prediction can help nodes to select proper links for data forwarding to reduce transmission failure. The existing prediction schemes are mainly based on analytical models with no adaptability, and consider relatively simple and small terrestrial wireless networks. In this paper, we propose a new link prediction algorithm based on machine learning, which is composed of an extractor of convolutional layers and an estimator of long short-term memory to extract useful representations of time-series data and identify effective long-term dependencies. The experiments manifest that the proposed scheme is more effective and flexible compared with the other link prediction schemes.

This paper presents a driver status monitoring (DSM) system with body channel communication (BCC) technology to acquire the driver's physiological condition. Specifically, a conductive thread, the receiving electrode, is sewn to the surface of the seat so that the acquired signal can be continuously detected. As a signal transmission medium, body channel characteristics using the conductive thread electrode were investigated according to the driver's pose and the material of the driver's pants. Based on this, a BCC transceiver was implemented using an analog frequency modulation (FM) scheme to minimize the additional circuitry and system cost. We analyzed the heart rate variability (HRV) from the driver's electrocardiogram (ECG) and displayed the heart rate and Root Mean Square of Successive Differences (RMSSD) values together with the ECG waveform in real-time. A prototype of the DSM system with commercial-off-the-shelf (COTS) technology was implemented and tested. We verified that the proposed approach was robust to the driver's movements, showing the feasibility and validity of the DSM with BCC technology using a conductive thread electrode.

This paper introduces a high-resolution and compact CMOS switch-type phase shifter (STPS) for the 5th generation mobile network (5G) n260 band. In this work, totally four coarse phase shifting stages and a high-resolution tuning stage are included. The coarse stages based on the bridged-T topology is capable of providing 202.5° phase coverage with a 22.5° tuning step. To further improve the phase shifting resolution, a compact fine-tuning stage covering 23° is also integrated with the coarse stages. Sub-degree phase shifting resolution is realized for supporting the fine beam-steering and high-accuracy phase calibration in the 5G new radio. Simplified phase control algorithm and suppressed insertion loss can also be maintained by the proposed fine-tuning stage. In the measurement, the achieved RMS gain errors at 39 GHz are 0.1 dB and 0.4 dB for the coarse stages and fine stage, respectively. The achieved RMS phase errors at 39 GHz are 3.1° for the coarse stages and 0.1° for the fine stage. Within 37 GHz to 40 GHz, the measured return loss within all phase-tuning states is always better than -14 dB. The proposed phase shifter consumes a core area of only 0.12mm2 with 65-nm CMOS process, which is area-efficient.

High reliability is required, even in Internet of things (IoT) communications, which are sometimes used for crucial control such as automatic driving devices. Hence, both the uplink (UL) and downlink (DL) communication quality must be improved in the physical layer. In this study, we focus on the communication quality of broadcast DL, which is configured using orthogonal frequency-division multiplexing (OFDM) as a multiplexing scheme and turbo code as forward error correction (FEC). To reduce the frame-error rate (FER) in the DL, we consider two transmit-diversity (TD) techniques that use space-time block code (STBC) or cyclic-delay diversity (CDD). The purpose of this paper is to evaluate the TD performance and to enhance FER performance of CDD up to that of STBC. To achieve this goal, a channel estimation method is proposed to improve FER for CDD. For this purpose, we first evaluate the FER performance of STBC and CDD by performing computer simulations and conducting hardware tests using a fading emulator. Then, we conduct field experiments in the 2.5GHz band. From the results of these evaluations, we confirm that STBC and CDD improved FER compared with single antenna transmission. CDD with the proposed channel estimation method achieved almost the same performance as STBC by accurately estimating the channel frequency response (CFR) and appropriately adjusting the amount of cyclic shift (ACS). When moving a received device around Yokosuka Research Park, STBC and CDD, using spatial diversity with omni antennas for TD, improved the FER from 3.84×10-2 to 1.42×10-2 and 1.19×10-2, respectively.

The efficient implementation strategy for speeding up high-quality clustering algorithms is developed on the basis of general purpose graphic processing units (GPGPUs) in this work. Among various clustering algorithms, a sophisticated Gaussian mixture model (GMM) by estimating parameters through variational Bayesian (VB) mechanism is conducted due to its superior performances. Since the VB-GMM methodology is computation-hungry, the GPGPU is employed to carry out massive matrix-computations. To efficiently migrate the conventional CPU-oriented schemes of VB-GMM onto GPGPU platforms, an entire migration-flow with thirteen stages is presented in detail. The CPU-GPGPU co-operation scheme, execution re-order, and memory access optimization are proposed for optimizing the GPGPU utilization and maximizing the clustering speed. Five types of real-world applications along with relevant data-sets are introduced for the cross-validation. From the experimental results, the feasibility of implementing VB-GMM algorithm by GPGPU is verified with practical benefits. The proposed GPGPU migration achieves 192x speedup in maximum. Furthermore, it succeeded in identifying the proper number of clusters, which is hardly conducted by the EM-algotihm.

Android operating system occupies a high share in the mobile terminal market. It promotes the rapid development of Android applications (apps). However, the emergence of Android malware greatly endangers the security of Android smartphone users. Existing research works have proposed a lot of methods for Android malware detection, but they did not make the utilization of apps' functional category information so that the strong similarity between benign apps in the same functional category is ignored. In this paper, we propose an Android malware detection scheme based on the functional classification. The benign apps in the same functional category are more similar to each other, so we can use less features to detect malware and improve the detection accuracy in the same functional category. The aim of our scheme is to provide an automatic application functional classification method with high accuracy. We design an Android application functional classification method inspired by the hyperlink induced topic search (HITS) algorithm. Using the results of automatic classification, we further design a malware detection method based on app similarity in the same functional category. We use benign apps from the Google Play Store and use malware apps from the Drebin malware set to evaluate our scheme. The experimental results show that our method can effectively improve the accuracy of malware detection.

The usefulness and usability of existing knowledge graphs (KGs) are mostly limited because of the incompleteness of knowledge compared to the growing number of facts about the real world. Most existing ontology-based KG completion methods are based on the closed-world assumption, where KGs are fixed. In these methods, entities and relations are defined, and new entity information cannot be easily added. In contrast, in open-world assumptions, entities and relations are not previously defined. Thus there is a vast scope to find new entity information. Despite this, knowledge acquisition under the open-world assumption is challenging because most available knowledge is in a noisy unstructured text format. Nevertheless, Open Information Extraction (OpenIE) systems can extract triples, namely (head text; relation text; tail text), from raw text without any prespecified vocabulary. Such triples contain noisy information that is not essential for KGs. Therefore, to use such triples for the KG completion task, it is necessary to identify competent triples for KGs from the extracted triple set. Here, competent triples are the triples that can contribute to add new information to the existing KGs. In this paper, we propose the Competent Triple Identification (CTID) model for KGs. We also propose two types of feature, namely syntax- and semantic-based features, to identify competent triples from a triple set extracted by a state-of-the-art OpenIE system. We investigate both types of feature and test their effectiveness. It is found that the performance of the proposed features is about 20% better compared to that of the ReVerb system in identifying competent triples.

Virtual Adversarial Training (VAT) has shown impressive results among recently developed regularization methods called consistency regularization. VAT utilizes adversarial samples, generated by injecting perturbation in the input space, for training and thereby enhances the generalization ability of a classifier. However, such adversarial samples can be generated only within a very small area around the input data point, which limits the adversarial effectiveness of such samples. To address this problem we propose LVAT (Latent space VAT), which injects perturbation in the latent space instead of the input space. LVAT can generate adversarial samples flexibly, resulting in more adverse effect and thus more effective regularization. The latent space is built by a generative model, and in this paper we examine two different type of models: variational auto-encoder and normalizing flow, specifically Glow. We evaluated the performance of our method in both supervised and semi-supervised learning scenarios for an image classification task using SVHN and CIFAR-10 datasets. In our evaluation, we found that our method outperforms VAT and other state-of-the-art methods.

A new adaptive binarization method is proposed for the vehicle state images obtained from the intelligent operation and maintenance system of rail transit. The method can check the corresponding vehicle status information in the intelligent operation and maintenance system of rail transit more quickly and effectively, track and monitor the vehicle operation status in real time, and improve the emergency response ability of the system. The advantages of the proposed method mainly include two points. For decolorization, we use the method of contrast preserving decolorization[1] obtain the appropriate ratio of R, G, and B for the grayscale of the RGB image which can retain the color information of the vehicle state images background to the maximum, and maintain the contrast between the foreground and the background. In terms of threshold selection, the mean value and standard deviation of gray value corresponding to multi-color background of vehicle state images are obtained by using major cluster estimation[2], and the adaptive threshold is determined by the 2 sigma principle for binarization, which can extract text, identifier and other target information effectively. The experimental results show that, regarding the vehicle state images with rich background color information, this method is better than the traditional binarization methods, such as the global threshold Otsu algorithm[3] and the local threshold Sauvola algorithm[4],[5] based on threshold, Mean-Shift algorithm[6], K-Means algorithm[7] and Fuzzy C Means[8] algorithm based on statistical learning. As an image preprocessing scheme for intelligent rail transit data verification, the method can improve the accuracy of text and identifier recognition effectively by verifying the optical character recognition through a data set containing images of different vehicle statuses.

Fine-grained image classification is a difficult problem, and previous studies mainly overcome this problem by locating multiple discriminative regions in different scales and then aggregating complementary information explored from the located regions. However, locating discriminative regions introduces heavy overhead and is not suitable for real-world application. In this paper, we propose the recursive multi-scale channel-spatial attention module (RMCSAM) for addressing this problem. Following the experience of previous research on fine-grained image classification, RMCSAM explores multi-scale attentional information. However, the attentional information is explored by recursively refining the deep feature maps of a convolutional neural network (CNN) to better correspond to multi-scale channel-wise and spatial-wise attention, instead of localizing attention regions. In this way, RMCSAM provides a lightweight module that can be inserted into standard CNNs. Experimental results show that RMCSAM can improve the classification accuracy and attention capturing ability over baselines. Also, RMCSAM performs better than other state-of-the-art attention modules in fine-grained image classification, and is complementary to some state-of-the-art approaches for fine-grained image classification. Code is available at https://github.com/Dichao-Liu/Recursive-Multi-Scale-Channel-Spatial-Attention-Module.

This paper presents a novel cooperative recording scheme in networked PVRs based on P2P networks to increase storage efficiency compared with when PVRs operate independently of each other, while maintaining program availability to a similar degree. We employ an erasure coding technique to guarantee data availability of recorded programs in P2P networks. We determine the data redundancy degree of recorded programs so that the system can support all the concurrent streaming requests for them and maintain as much availability as needed. We also present how to assign recording tasks to PVRs and playback the recorded programs without performance degradation. We show that our proposed scheme improves the storage efficiency significantly, compared with when PVRs do not cooperate with each other, while keeping the playbackability of each request similarly.

Various electromagnetic (EM) wave applications have become commonplace, and humans are frequently exposed to EM waves. Therefore, the effect of EM waves on the human body should be evaluated. In this study, we focused on the specific absorption rate (SAR) due to the EM waves emitted from smartphones, developed high-resolution numerical smartphone models, and studied the SAR variation by changing the position and tilt angle (the angle between the display of the smartphone model and horizontal plane) of the smartphone models vis-à-vis the human abdomen, assuming the use of the smartphone at various tilt angles in front of the abdomen. The calculations showed that the surface shape of the human model influenced the SAR variation.

It is an important research area to construct a cryptosystem that satisfies the security for multi-user setting. In addition, it is desirable that such a cryptosystem is tightly secure and the ciphertext size is small. For IND-CCA public key encryption schemes for multi-user setting with constant-size ciphertexts tightly secure under the DH assumptions, in 2020, Y. Sakai and G. Hanaoka firstly proposed such a scheme (implicitly based on hybrid encryption paradigm) under the DDH assumption. More recently, Y. Lee et al. proposed such a hybrid encryption scheme (with slightly stronger security) where the assumption for the KEM part is weakened to the CDH assumption. In this paper, we revisit the twin-DH hashed ElGamal KEM with even shorter ciphertexts than those schemes, and prove that its IND-CCA security for multi-user setting is in fact tightly reducible to the CDH assumption.

Due to the property of supporting arbitrary operation over the encrypted data, fully homomorphic encryption (FHE) has drawn considerable attention since it appeared. Some FHE schemes have been constructed based on the general approximate common divisor (GACD) problem, which is widely believed intractable. Therefore, studying the GACD problem's hardness can provide proper security parameters for these FHE schemes and their variants. This paper aims to study an orthogonal lattice algorithm introduced by Ding and Tao (Ding-Tao algorithm) to solve the GACD problem. We revisit the condition that Ding-Tao algorithm works and obtain a new bound of the GACD samples' number based on geometric series assumption. Simultaneously, we also give an analysis of the bound given in the previous work. To further verify the theoretical results, we conduct experiments on Ding-Tao algorithm under our bound. We show a comparison with the experimental results under the previous bound, which indicates the success probability under our bound is higher than that of the previous bound with the growth of the bound.

In secure multiparty computation (MPC), floating-point numbers should be handled in many potential applications, but these are basically expensive. In particular, for MPC based on secret sharing (SS), the floating-point addition takes many communication rounds though the addition is the most fundamental operation. In this paper, we propose an SS-based two-party protocol for floating-point addition with 13 rounds (for single/double precision numbers), which is much fewer than the milestone work of Aliasgari et al. in NDSS 2013 (34 and 36 rounds, respectively) and also fewer than the state of the art in the literature. Moreover, in contrast to the existing SS-based protocols which are all based on “roundTowardZero” rounding mode in the IEEE 754 standard, we propose another protocol with 15 rounds which is the first result realizing more accurate “roundTiesToEven” rounding mode. We also discuss possible applications of the latter protocol to secure Validated Numerics (a.k.a. Rigorous Computation) by implementing a simple example.

Generalized Feistel Network (GFN) is widely used in block ciphers. CLEFIA is one of the GFN type-2 block ciphers. CLEFIA employs Diffusion Switching Mechanism (DSM) in its diffusion layer. DSM improves CLEFIA's security by increasing its number of active S-boxes, which is an indicator of security against differential and linear cryptanalyses. However, two matrices in DSM increase implementational cost. In this paper, we pursue the research question whether it is possible to achieve the same security as original CLEFIA with only one matrix without overhead in hardware. Our idea to answer the research question is applying byte-shuffling technique to CLEFIA. Byte-shuffling is an operation to shuffle 8-bit bytes. On the other hand, traditional GFN ciphers rotate 32-bit or larger words in their permutation layer. Since implementation of byte-shuffling is considered as cost-free in hardware, it adds no overhead in comparison with word rotation. Byte-shuffling has numerous shuffle patterns whereas word rotation has a few patterns. In addition, security property varies among the shuffle patterns. So, we have to find the optimal shuffle pattern(s) on the way to pursue the research question. Although one way to find the optimal shuffle pattern is evaluating all possible shuffle patterns, it is impractical to evaluate them since the evaluation needs much time and computation. We utilize even-odd byte-shuffling technique to narrow the number of shuffle patterns to be searched. Among numerous shuffle patterns, we found 168 shuffle patterns as the optimal shuffle patterns. They achieved full diffusion in 5 rounds. This is the same security as original CLEFIA. They achieved enough security against differential and linear cryptanalyses at 13th and 14th round, respectively, by active S-box evaluations. It is just one and two rounds longer than original CLEFIA. However, it is three and two rounds earlier than CLEFIA without DSM.

In spite of the research for a linear layer of Type-2 Generalized Feistel Network (Type-2 GFN) over more than 10 years, finding a good 32-branch permutation for Type-2 GFN is still a very hard task due to a huge search space. In terms of the diffusion property, Suzaki and Minematsu investigated the required number of rounds to achieve the full diffusion when the branch number is up to 16. After that, Derbez et al. presented a class of 32-branch permutations that achieves the 9-round full diffusion and they prove that this is optimal. However, this class is not suitable to be used in Type-2 GFN because it requires a large number of rounds to ensure a sufficient number of active S-boxes. In this paper, we present how to find a good class of 32-branch permutations for Type-2 GFN. To achieve this goal, we convert Type-2 GFN into a LBlock-like structure, and then we evaluate the diffusion property and the resistance against major attacks, such as differential, linear, impossible differential and integral attacks by an MILP. As a result, we present a good class of 32-branch permutations that achieves the 10-round full diffusion, ensures differentially/linearly active S-boxes of 66 at 19 round, and has the 18/20-round impossible differential/integral distinguisher, respectively. The 32-branch permutation used in WARP was chosen among this class.

Autonomous robots are controlled using physical information acquired by various sensors. The sensors are susceptible to physical attacks, which tamper with the observed values and interfere with control of the autonomous robots. Recently, sensor spoofing attacks targeting subsequent algorithms which use sensor data have become large threats. In this paper, we introduce a new attack against the LiDAR-based simultaneous localization and mapping (SLAM) algorithm. The attack uses an adversarial LiDAR scan to fool a pose graph and a generated map. The adversary calculates a falsification amount for deceiving pose estimation and physically injects the spoofed distance against LiDAR. The falsification amount is calculated by gradient method against a cost function of the scan matching algorithm. The SLAM algorithm generates the wrong map from the deceived movement path estimated by scan matching. We evaluated our attack on two typical scan matching algorithms, iterative closest point (ICP) and normal distribution transform (NDT). Our experimental results show that SLAM can be fooled by tampering with the scan. Simple odometry sensor fusion is not a sufficient countermeasure. We argue that it is important to detect or prevent tampering with LiDAR scans and to notice inconsistencies in sensors caused by physical attacks.

Integer division is one of the most fundamental arithmetic operators and is ubiquitously used. However, the existing division protocols in secure multi-party computation (MPC) are inefficient and very complex, and this has been a barrier to applications of MPC such as secure machine learning. We already have some secure division protocols working in Z2n. However, these existing results have drawbacks that those protocols needed many communication rounds and needed to use bigger integers than in/output. In this paper, we improve a secure division protocol in two ways. First, we construct a new protocol using only the same size integers as in/output. Second, we build efficient constant-round building blocks used as subprotocols in the division protocol. With these two improvements, communication rounds of our division protocol are reduced to about 36% (87 rounds → 31 rounds) for 64-bit integers in comparison with the most efficient previous one.

In the user identification (UI) scheme for a multiple-access fading channel based on a randomly generated (0, 1, -1)-signature code, previous studies used the signature code over a noisy multiple-access adder channel, and only the user state information (USI) was decoded by the signature decoder. However, by considering the communication model as a compressed sensing process, it is possible to estimate the channel coefficients while identifying users. In this study, to improve the efficiency of the decoding process, we propose an iterative deep neural network (DNN)-based decoder. Simulation results show that for the randomly generated (0, 1, -1)-signature code, the proposed DNN-based decoder requires less computing time than the classical signal recovery algorithm used in compressed sensing while achieving higher UI and channel estimation (CE) accuracies.