We present an interactive modeling system for Japanese castles. We develop an user interface that can generate the fundamental structure of the castle tower consisting of stone walls, turrets, and roofs. By clicking on the screen displaying the 3D space with the mouse, relevant parameters are calculated automatically to generate 3D models of Japanese-style castles. We use characteristic curves that often appear in ancient Japanese architecture for the realistic modeling of the castles. We evaluate the effectiveness of our method by comparing the castle generated by our method with a commercially-available 3D mode of a castle.

Intelligent reconfigurable surfaces (IRS) have attracted much attention from both industry and academia due to their performance improving capability and low complexity for 6G wireless communication systems. In this letter, we introduce an IRS-assisted space-time line code (STLC) technique. The STLC was introduced as a promising technique to acquire the optimal diversity gain in 1×2 single-input multiple-output (SIMO) channel without channel state information at receiver (CSIR). Using the cosine similarity theorem, we propose a novel phase-steering technique for the proposed IRS-assisted STLC technique. We also mathematically characterize the proposed IRS-assisted STLC technique in terms of outage probability and bit-error rate (BER). Based on computer simulations, it is shown that the results of analysis shows well match with the computer simulation results for various communication scenarios.

When a disaster hits a network, network service disruptions can occur even if the network facilities have survived and battery and power generators are provided. This is because in the event of a disaster, the power supply will not be restarted within the lifetime of the battery or oil transportation will not be restarted before running out of oil and power will be running out. Therefore, taking a power grid into account is important. This paper proposes a polynomial-time algorithm to identify the critical location C*D of a communications network Nc when a disaster hits. Electrical power grid Np supplies power to the nodes of Nc, and a link in Nc is disconnected when a node or a link in Nc or Np fails. Here, the disaster area is modeled as co-centric disks and the failure probability is higher in the inner disk than the outer one. The location of the center of the disaster with the greatest expected number of disconnected links in Nc is taken as the critical location C*D.

Recently, deep learning for image generation with a guide for the generation has been progressing. Many methods have been proposed to generate the animation of facial expression change from a single face image by transferring some facial expression information to the face image. In particular, the method of using facial landmarks as facial expression information can generate a variety of facial expressions. However, most methods do not focus on anime characters but humans. Moreover, we attempted to apply several existing methods to anime characters by training the methods on an anime character face dataset; however, they generated images with noise, even in regions where there was no change. The first order motion model (FOMM) is an image generation method that takes two images as input and transfers one facial expression or pose to the other. By explicitly calculating the difference between the two images based on optical flow, FOMM can generate images with low noise in the unchanged regions. In the following, we focus on the aspect of the face image generation in FOMM. When we think about the employment of facial landmarks as targets, the performance of FOMM is not enough because FOMM cannot use a facial landmark as a facial expression target because the appearances of a face image and a facial landmark are quite different. Therefore, we propose an advanced FOMM method to use facial landmarks as a facial expression target. In the proposed method, we change the input data and data flow to use facial landmarks. Additionally, to generate face images with expressions that follow the target landmarks more closely, we introduce the landmark estimation loss, which is computed by comparing the landmark detected from the generated image with the target landmark. Our experiments on an anime character face image dataset demonstrated that our method is effective for landmark-guided face image generation for anime characters. Furthermore, our method outperformed other methods quantitatively and generated face images with less noise.

At ToSC 2021, Sakamoto et al. proposed Rocca, an AES-based encryption scheme, for Beyond 5G applications. They presented a class of round functions that achieved impressive performance in software by improving the design strategy for constructing an efficient AES-based round function that was proposed by Jean and Nikolić at FSE 2016. In this paper, we revisit their design strategy for finding more efficient round functions. We add new requirements further to improve speed of Rocca. Specifically, we focus on the number of temporary registers for updating the round function and search for round functions with the minimum number of required temporary registers. As a result, we find a class of round functions with only one required temporary register, while round function of Rocca requires two temporary registers. We show that new round functions are significantly faster than that of Rocca on the latest Ice Lake and Tiger Lake architectures. We emphasize that, regarding speed, our round functions are optimal among the Rocca class of round functions because the search described in this paper covers all candidates that satisfy the requirements of Rocca.

Recently, the face hallucination method based on deep learning understands the mapping between low-resolution (LR) and high-resolution (HR) facial patterns by exploring the priors of facial structure. However, how to maintain the face structure consistency after the reconstruction of face images at different scales is still a challenging problem. In this letter, we propose a novel multi-scale structure prior learning (MSPL) for face hallucination. First, we propose a multi-scale structure prior block (MSPB). Considering the loss of high-frequency information in the LR space, we mainly process the input image in three different scale ascending dimensional spaces, and map the image to the high dimensional space to extract multi-scale structural prior information. Then the size of feature maps is recovered by downsampling, and finally the multi-scale information is fused to restore the feature channels. On this basis, we propose a local detail attention module (LDAM) to focus on the local texture information of faces. We conduct extensive face hallucination reconstruction experiments on a public face dataset (LFW) to verify the effectiveness of our method.

In this letter, we consider a global stabilization problem for a class of feedforward systems by an event-triggered control. This is an extended work of [10] in a way that there are uncertain feedforward nonlinearity and time-varying input delay in the system. First, we show that the considered system is globally asymptotically stabilized by a proposed event-triggered controller with a gain-scaling factor. Then, we also show that the interexecution times can be enlarged by adjusting a gain-scaling factor. A simulation example is given for illustration.

Multi-core fiber (MCF) is one of the most promising candidates for achieving ultra-wideband optical transmission in the near future. To build a network using MCF, a high-performance and reliable MCF connector is indispensable. We have developed an SC-type optical connector for MCF and confirmed its excellent optical performance, mechanical durability, and environmental reliability. To put the communication system using MCF into practical use, it is necessary to establish a procedure for measuring the initial connection characteristics. Fan-in / fan-out (FIFO) devices are indispensable for measuring the connection characteristics of MCF connectors. To measure the return loss of the MCF connector, it is necessary to remove the influence of reflection at the FIFO itself and at the connection points with the FIFO. In this paper, we compare four types of return loss measurement procedures (three usual method and a new method we proposed) and find that most stable measurement method involves using our new method, the OCWR method without FIFO. The OCWR method without FIFO is considered to be the most advantageous when used for outgoing inspection of connectors. The reason is that it eliminates the measurement uncertainty caused by the FIFO and enables speedy measurement.

In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.

Enterprises have paid attention to consortium blockchains like Hyperledger Fabric, which is one of the most promising platforms, for efficient decentralized transactions without depending on any particular organization. A consortium blockchain-based system will be typically built across multiple organizations. In such blockchain-based systems, system operations across multiple organizations in a decentralized manner are essential to maintain the value of introducing consortium blockchains. Decentralized system operations have recently been becoming realistic with the evolution of consortium blockchains. For instance, the release of Hyperledger Fabric v2.x, in which individual operational tasks for a blockchain network, such as command execution of configuration change of channels (Fabric's sub-networks) and upgrade of chaincodes (Fabric's smart contracts), can be partially executed in a decentralized manner. However, the operations workflows also include the preceding procedure of pre-sharing, coordinating, and pre-agreeing the operational information (e.g., configuration parameters) among organizations, after which operation executions can be conducted, and this preceding procedure relies on costly manual tasks. To realize efficient decentralized operations workflows for consortium blockchain-based systems in general, we propose a decentralized inter-organizational operations method that we call Operations Smart Contract (OpsSC), which defines an operations workflow as a smart contract. Furthermore, we design and implement OpsSC for blockchain network operations with Hyperledger Fabric v2.x. This paper presents OpsSC for operating channels and chaincodes, which are essential for managing the blockchain networks, through clarifying detailed workflows of those operations. A cost evaluation based on an estimation model shows that the total operational cost for executing a typical operational scenario to add an organization to a blockchain network having ten organizations could be reduced by 54 percent compared with a conventional script-based method. The implementation of OpsSC has been open-sourced and registered as one of Hyperledger Labs projects, which hosts experimental projects approved by Hyperledger.

In this study, a computational method is proposed for acoustic field analysis tasks that require lengthy observation times. The acoustic fields at a given observation time are obtained using a fast inverse Laplace transform with a finite-difference complex-frequency-domain. The transient acoustic field can be evaluated at arbitrary sampling intervals by obtaining the instantaneous acoustic field at the desired observation time using the proposed method.

Aggregate signature schemes enable us to aggregate multiple signatures into a single short signature. One of its typical applications is sensor networks, where a large number of users and devices measure their environments, create signatures to ensure the integrity of the measurements, and transmit their signed data. However, if an invalid signature is mixed into aggregation, the aggregate signature becomes invalid, thus if an aggregate signature is invalid, it is necessary to identify the invalid signature. Furthermore, we need to deal with a situation where an invalid sensor generates invalid signatures probabilistically. In this paper, we introduce a model of aggregate signature schemes with interactive tracing functionality that captures such a situation, and define its functional and security requirements and propose aggregate signature schemes that can identify all rogue sensors. More concretely, based on the idea of Dynamic Traitor Tracing, we can trace rogue sensors dynamically and incrementally, and eventually identify all rogue sensors of generating invalid signatures even if the rogue sensors adaptively collude. In addition, the efficiency of our proposed method is also sufficiently practical.

To facilitate the reuse of environmental waste heat in our society, we have developed high-efficiency flexible thermoelectric power generators (TEPGs). In this study, we investigated the thermoelectromotive force (TEMF) and output power of a prototype device with 50 pairs of Π-type structures using a homemade measurement system for flexible TEPGs in order to evaluate their characteristics along the thickness direction. The prototype device consisted of C fabrics (CAFs) used as p-type materials, NiCu fabrics (NCFs) used as n-type materials, and Ag fabrics (AGFs) used as metal electrodes. Applying a temperature difference of 5K, we obtained a TEMF of 150μV and maximum output power of 6.4pW. The obtained TEMF was smaller than that expected from the Seebeck coefficients of each fabric, which is considered to be mainly because of the influence of contact thermal resistance at the semiconductor-fabric/AGF interfaces.

This brief presents a slope analog-digital converter (ADC)-based supply voltage monitor (SVM) for biofuel-cell-powered supply-sensing systems operating in a supply voltage range of 0.18-0.35V. The proposed SVM is designed to utilize the output of energy harvester extracting power from biological reactions, realizing energy-autonomous sensor interfaces. A burst pulse generator uses a dynamic leakage suppression logic oscillator to generate a stable clock signal under the sub-threshold region for pulse counting. A slope-based voltage-to-time converter is employed to generate a pulse width proportional to the supply voltage with high linearity. The test chip of the proposed SVM is implemented in 180-nm CMOS technology with an active area of 0.018mm2. It consumes 2.1nW at 0.3V and achieves a conversion time of 117-673ms at 0.18-0.35V with a nonlinearity error of -5.5/+8.3mV, achieving an energy-efficient biosensing frontend.

Stance prediction on social media aims to infer the stances of users towards a specific topic or event, which are not expressed explicitly. It is of great significance for public opinion analysis to extract and determine users' stances using user-generated content on social media. Existing research makes use of various signals, ranging from text content to online network connections of users on these platforms. However, it lacks joint modeling of the heterogeneous information for stance prediction. In this paper, we propose a self-supervised heterogeneous graph contrastive learning framework for stance prediction in online debate forums. Firstly, we perform data augmentation on the original heterogeneous information network to generate an augmented view. The original view and augmented view are learned from a meta-path based graph encoder respectively. Then, the contrastive learning among the two views is conducted to obtain high-quality representations of users and issues. Finally, the stance prediction is accomplished by matrix factorization between users and issues. The experimental results on an online debate forum dataset show that our model outperforms other competitive baseline methods significantly.

In this paper, we propose a non-orthogonal multiple access with adaptive resource allocation. The proposed non-orthogonal multiple access assigns multiple frequency resources for each device to send packets. Even if the number of devices is more than that of the available frequency resources, the proposed non-orthogonal access allows all the devices to transmit their packets simultaneously for high capacity massive machine-type communications (mMTC). Furthermore, this paper proposes adaptive resource allocation algorithms based on factor graphs that adaptively allocate the frequency resources to the devices for improvement of the transmission performances. This paper proposes two allocation algorithms for the proposed non-orthogonal multiple access. This paper shows that the proposed non-orthogonal multiple access achieves superior transmission performance when the number of the devices is 50% greater than the amount of the resource, i.e., the overloading ratio of 1.5, even without the adaptive resource allocation. The adaptive resource allocation enables the proposed non-orthogonal access to attain a gain of about 5dB at the BER of 10-4.

Given a set of n disjoint intervals on a line and an integer k, we want to find k points in the intervals so that the minimum pairwise distance of the k points is maximized. Intuitively, given a set of n disjoint time intervals on a timeline, each of which is a time span we are allowed to check something, and an integer k, which is the number of times we will check something, we plan k checking times so that the checks occur at equal time intervals as much as possible, that is, we want to maximize the minimum time interval between the k checking times. We call the problem the k-dispersion problem on intervals. If we need to choose exactly one point in each interval, so k=n, and the disjoint intervals are given in the sorted order on the line, then two O(n) time algorithms to solve the problem are known. In this paper we give the first O(n) time algorithm to solve the problem for any constant k. Our algorithm works even if the disjoint intervals are given in any (not sorted) order. If the disjoint intervals are given in the sorted order on the line, then, by slightly modifying the algorithm, one can solve the problem in O(log n) time. This is the first sublinear time algorithm to solve the problem. Also we show some results on the k-dispersion problem on disks, including an FPTAS.

This paper presents an improved YOLOv3 network, named MSFF-YOLOv3, for precisely detecting variable surface defects of aluminum profiles in practice. First, we introduce a larger prediction scale to provide detailed information for small defect detection; second, we design an efficient attention-guided block to extract more features of defects with less overhead; third, we design a bottom-up pyramid and integrate it with the existing feature pyramid network to construct a twin-tower structure to improve the circulation and fusion of features of different layers. In addition, we employ the K-median algorithm for anchor clustering to speed up the network reasoning. Experimental results showed that the mean average precision of the proposed network MSFF-YOLOv3 is higher than all conventional networks for surface defect detection of aluminum profiles. Moreover, the number of frames processed per second for our proposed MSFF-YOLOv3 could meet real-time requirements.

As a kind of marine vehicles, Unmanned Surface Vehicles (USV) are widely used in military and civilian fields because of their low cost, good concealment, strong mobility and high speed. High-precision detection of obstacles plays an important role in USV autonomous navigation, which ensures its subsequent path planning. In order to further improve obstacle detection performance, we propose an encoder-decoder architecture named Fusion Refinement Network (FRN). The encoder part with a deeper network structure enables it to extract more rich visual features. In particular, a dilated convolution layer is used in the encoder for obtaining a large range of obstacle features in complex marine environment. The decoder part achieves the multiple path feature fusion. Attention Refinement Modules (ARM) are added to optimize features, and a learnable fusion algorithm called Feature Fusion Module (FFM) is used to fuse visual information. Experimental validation results on three different datasets with real marine images show that FRN is superior to state-of-the-art semantic segmentation networks in performance evaluation. And the MIoU and MPA of the FRN can peak at 97.01% and 98.37% respectively. Moreover, FRN could maintain a high accuracy with only 27.67M parameters, which is much smaller than the latest obstacle detection network (WaSR) for USV.

Fault localization analyzes the runtime information of two classes of test cases (i.e., passing test cases and failing test cases) to identify suspicious statements potentially responsible for a failure. However, the failing test cases are always far fewer than passing test cases in reality, and the class imbalance problem will affect fault localization effectiveness. To address this issue, we propose a data augmentation approach using conditional variational auto-encoder to synthesize new failing test cases for FL. The experimental results show that our approach significantly improves six state-of-the-art fault localization techniques.