S-shaped nonlinearity is found in the electrical resistance-length relationship in an electroactive supercoiled polymer artificial muscle. The modulation of the electrical resistance is mainly caused by the change in the contact condition of coils in the artificial muscle upon deformation. A mathematical model based on logistic function fairly reproduces the experimental data of electrical resistance-length relationship.

Software-Defined Networking (SDN) enables flexible deployment and innovation of new networking applications by decoupling and abstracting the control and data planes. It has radically changed the concept and way of building and managing networked systems, and reduced the barriers to entry for new players in the service markets. It is considered to be a promising solution providing the scale and versatility necessary for IoT. However, SDN may also face many challenges, i.e., the centralized control plane would be a single point of failure. With the advent of blockchain technology, blockchain-based SDN has become an emerging architecture for securing a distributed network environment. Motivated by this, in this work, we summarize the generic framework of blockchain-based SDN, discuss security challenges and relevant solutions, and provide insights on the future development in this field.

Research on adversarial examples for machine learning has received much attention in recent years. Most of previous approaches are white-box attacks; this means the attacker needs to obtain before-hand internal parameters of a target classifier to generate adversarial examples for it. This condition is hard to satisfy in practice. There is also research on black-box attacks, in which the attacker can only obtain partial information about target classifiers; however, it seems we can prevent these attacks, since they need to issue many suspicious queries to the target classifier. In this paper, we show that a naive defense strategy based on surveillance of number query will not suffice. More concretely, we propose to generate not pixel-wise but block-wise adversarial perturbations to reduce the number of queries. Our experiments show that such rough perturbations can confuse the target classifier. We succeed in reducing the number of queries to generate adversarial examples in most cases. Our simple method is an untargeted attack and may have low success rates compared to previous results of other black-box attacks, but needs in average fewer queries. Surprisingly, the minimum number of queries (one and three in MNIST and CIFAR-10 dataset, respectively) is enough to generate adversarial examples in some cases. Moreover, based on these results, we propose a detailed classification for black-box attackers and discuss countermeasures against the above attacks.

As one type of machine-learning model, a “decision-tree ensemble model” (DTEM) is represented by a set of decision trees. A DTEM is mainly known to be valid for structured data; however, like other machine-learning models, it is difficult to train so that it returns the correct output value (called “prediction value”) for any input value (called “attribute value”). Accordingly, when a DTEM is used in regard to a system that requires reliability, it is important to comprehensively detect attribute values that lead to malfunctions of a system (failures) during development and take appropriate countermeasures. One conceivable solution is to install an input filter that controls the input to the DTEM and to use separate software to process attribute values that may lead to failures. To develop the input filter, it is necessary to specify the filtering condition for the attribute value that leads to the malfunction of the system. In consideration of that necessity, we propose a method for formally verifying a DTEM and, according to the result of the verification, if an attribute value leading to a failure is found, extracting the range in which such an attribute value exists. The proposed method can comprehensively extract the range in which the attribute value leading to the failure exists; therefore, by creating an input filter based on that range, it is possible to prevent the failure. To demonstrate the feasibility of the proposed method, we performed a case study using a dataset of house prices. Through the case study, we also evaluated its scalability and it is shown that the number and depth of decision trees are important factors that determines the applicability of the proposed method.

Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered neural network consists of a complex nonlinear relationship between large number of parameters, we failed to understand how they could achieve input-output mappings with a given data set. In this paper, we propose the non-negative task matrix decomposition method, which applies non-negative matrix factorization to a trained layered neural network. This enables us to decompose the inference mechanism of a trained layered neural network into multiple principal tasks of input-output mapping, and reveal the roles of hidden units in terms of their contribution to each principal task.

In this paper, we introduce conditional decisions for enforcing forcible events in the decentralized supervisory control framework for timed discrete event systems. We first present sufficient conditions for the existence of a decentralized supervisor with conditional decisions. These sufficient conditions are weaker than the necessary and sufficient conditions for the existence of a decentralized supervisor without conditional decisions. We next show that the presented sufficient conditions are also necessary under the assumption that if the occurrence of the event tick, which represents the passage of one time unit, is illegal, then a legal forcible event that should be forced to occur uniquely exists. In addition, we develop a method for verifying the presented conditions under the same assumption.

This paper proposes statistical analysis of phase-only correlation functions between two signals with stochastic phase-spectra following bivariate circular probability distributions based on directional statistics. We give general expressions for the expectation and variance of phase-only correlation functions in terms of joint characteristic functions of the bivariate circular probability density function. In particular, if we assume bivariate wrapped distributions for the phase-spectra, we obtain exactly the same results between in case of a bivariate linear distribution and its corresponding bivariate wrapped distribution.

With the recent advances in e-commerce, it has become important to recommend not only mass-produced daily items, such as books, but also items that are not mass-produced. In this study, we present an algorithm for real estate recommendations. Automatic property recommendations are a highly difficult task because no identical properties exist in the world, occupied properties cannot be recommended, and users rent or buy properties only a few times in their lives. For the first step of property recommendation, we predict users' preferences for properties by combining content-based filtering and Multi-Layer Perceptron (MLP). In the MLP, we use not only attribute data of users and properties, but also deep features extracted from property floor plan images. As a result, we successfully predict users' preference with a Matthews Correlation Coefficient (MCC) of 0.166.

This paper presents the excitation coefficient optimization of slot array antennas for increasing channel capacity in 2×2-mode two-dimensional ROM (rectangular coordinate orthogonal) transmission. Because the ROM transmission is for non-far region communication, the transmission between Tx (transmission) and Rx (reception) antennas increases when the antennas radiate beams inwardly. At first, we design the excitation coefficients of the slot arrays in order to enhance the transmission rate for a given transmission distance. Then, we fabricate monopulse corporate-feed waveguide slot array antennas that have the designed excitation amplitude and phase in the 60-GHz band for the 2×2-mode two-dimensional ROM transmission. The measured transmission between the fabricated Tx and Rx antennas increases at the given propagation distance and agrees with the simulation.

Secret key generation based on channel characteristics is an effective physical-layer security method for 5G wireless networks. The issues of how to ensure the high key generation rate and correlation of the secret key under active attack are needed to be addressed. In this paper, a new practical secret key generation scheme with high rate and correlation is proposed. In our proposed scheme, Alice and Bob transmit independent random sequences instead of known training sequences or probing signals; neither Alice nor Bob can decode these random sequences or estimate the channel. User's random sequences together with the channel effects are used as common random source to generate the secret key. With this solution, legitimate users are able to share secret keys with sufficient length and high security under active attack. We evaluate the proposed scheme through both analytic and simulation studies. The results show that our proposed scheme achieves high key generation rate and key security, and is suitable for 5G wireless networks with resource-constrained devices.

Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.

Argumentation is a process of reaching a consensus through premises and rebuttals. If an artificial dialogue system can perform argumentation, it can improve users' decisions and ability to negotiate with the others. Previously, researchers have studied argumentative dialogue systems through a structured database regarding argumentation structure and evaluated the logical consistency of the dialogue. However, these systems could not change its response based on the user's agreement or disagreement to its last utterance. Furthermore, the persuasiveness of the generated dialogue has not been evaluated. In this study, a method is proposed to generate persuasive arguments through a hierarchical argumentation structure that considers human agreement and disagreement. Persuasiveness is evaluated through a crowd sourcing platform wherein participants' written impressions of shown dialogue texts are scored via a third person Likert scale evaluation. The proposed method was compared to the baseline method wherein argument response texts were generated without consideration of the user's agreement or disagreement. Experiment results suggest that the proposed method can generate a more persuasive dialogue than the baseline method. Further analysis implied that perceived persuasiveness was induced by evaluations of the behavior of the dialogue system, which was inherent in the hierarchical argumentation structure.

In the theory of orthogonal arrays, an orthogonal array (OA) is called schematic if its rows form an association scheme with respect to Hamming distances. In this paper, we study the Hamming distances of any two rows in an OA, construct some schematic OAs of strength two and give the positive solution to the open problem for classifying all schematic OAs. Some examples are given to illustrate our methods.

In the context of compressed sensing (CS), simultaneous orthogonal matching pursuit (SOMP) algorithm is an important iterative greedy algorithm for multiple measurement matrix vectors sharing the same non-zero locations. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the convergence of CS algorithms. Based on the RIP of measurement matrix, this paper shows that for the K-row sparse recovery, the restricted isometry constant (RIC) is improved to $delta_{K+1}<rac{sqrt{4K+1}-1}{2K}$ for SOMP algorithm. In addition, based on this RIC, this paper obtains sufficient conditions that ensure the convergence of SOMP algorithm in noisy case.

Sorting is one of the most fundamental problems in mathematics and computer science. Because high-throughput and flexible sorting is a key requirement in modern databases, this paper presents efficient techniques for designing a high-throughput sorting matrix that supports continuous data sequences. There have been numerous studies on the optimization of sorting circuits on FPGA (field-programmable gate array) platforms. These studies focused on attaining high throughput for a single command with fixed data width. However, the architectures proposed do not meet the requirement of diversity for database data types. A sorting matrix architecture is thus proposed to overcome this problem. Our design consists of a matrix of identical basic sorting cells. The sorting cells work in a pipeline and in parallel, and the matrix can simultaneously process multiple data streams, which can be combined into a high-width single-channel data stream or low-width multiple-channel data streams. It can handle continuous sequences and allows for sorting variable-length data sequences. Its maximum throughput is approximately 1.4 GB/s for 32-bit sequences and approximately 2.5 GB/s for 64-bit sequences on our platform.

We have developed a digital watermarking method that use neural networks to learn embedding and extraction processes that are robust against rotation and JPEG compression. The proposed neural networks consist of a stego-image generator, a watermark extractor, a stego-image discriminator, and an attack simulator. The attack simulator consists of a rotation layer and an additive noise layer, which simulate the rotation attack and the JPEG compression attack, respectively. The stego-image generator can learn embedding that is robust against these attacks, and also, the watermark extractor can extract watermarks without rotation synchronization. The quality of the stego-images can be improved by using the stego-image discriminator, which is a type of adversarial network. We evaluated the robustness of the watermarks and image quality and found that, using the proposed method, high-quality stego-images could be generated and the neural networks could be trained to embed and extract watermarks that are robust against rotation and JPEG compression attacks. We also showed that the robustness and image quality can be adjusted by changing the noise strength in the noise layer.

As one of the most popular intelligent optimization algorithms, Simulated Annealing (SA) faces two key problems, the generation of perturbation solutions and the control strategy of the outer loop (cooling schedule). In this paper, we introduce the Gaussian Cloud model to solve both problems and propose a novel cloud annealing algorithm. Its basic idea is to use the Gaussian Cloud model with decreasing numerical character He (Hyper-entropy) to generate new solutions in the inner loop, while He essentially indicates a heuristic control strategy to combine global random search of the outer loop and local tuning search of the inner loop. Experimental results in function optimization problems (i.e. single-peak, multi-peak and high dimensional functions) show that, compared with the simple SA algorithm, the proposed cloud annealing algorithm will lead to significant improvement on convergence and the average value of obtained solutions is usually closer to the optimal solution.

The major weakness of global navigation satellite system receivers is their vulnerability to intentional and unintentional interference. Frequency domain interference suppression (FDIS) technology is one of the most useful countermeasures. The pseudo-range measurement is unbiased after FDIS filtering given an ideal analog channel. However, with the influence of the analog modules used in RF front-end, the amplitude response and phase response of the channel equivalent filter are non-ideal, which bias the pseudo-range measurement after FDIS filtering and the bias varies along with the frequency of the interference. This paper proposes an unbiased interference suppression method based on signal estimation and spectrum compensation. The core idea is to use the parameters calculated from the tracking loop to estimate and reconstruct the desired signal. The estimated signal is filtered by the equivalent filter of actual channel, then it is used for compensating the spectrum loss caused by the FDIS method in the frequency domain. Simulations show that the proposed algorithm can reduce the pseudo-range measurement bias significantly, even for channels with asymmetrical group delay and multiple interference sources at any location.

Multi-modal semantic trajectory prediction has become a new challenge due to the rapid growth of multi-modal semantic trajectories with text message. Traditional RNN trajectory prediction methods have the following problems to process multi-modal semantic trajectory. The distribution of multi-modal trajectory samples shifts gradually with training. It leads to difficult convergency and long training time. Moreover, each modal feature shifts in different directions, which produces multiple distributions of dataset. To solve the above problems, MNERM (Mode Normalization Enhanced Recurrent Model) for multi-modal semantic trajectory is proposed. MNERM embeds multiple modal features together and combines the LSTM network to capture long-term dependency of trajectory. In addition, it designs Mode Normalization mechanism to normalize samples with multiple means and variances, and each distribution normalized falls into the action area of the activation function, so as to improve the prediction efficiency while improving greatly the training speed. Experiments on real dataset show that, compared with SERM, MNERM reduces the sensitivity of learning rate, improves the training speed by 9.120 times, increases HR@1 by 0.03, and reduces the ADE by 120 meters.

As Internet-connected service is emerged, there has been a need for use cases where a lightweight cryptographic primitive meets both of a constrained hardware implementation requirement and a constrained embedded software requirement. One of the examples of these use cases is the PKES (Passive Keyless Entry and Start) system in an automotive domain. From the perspective on these use cases, one interesting direction is to investigate how small the memory (RAM/ROM) requirement of ARM-implementations of hardware-oriented stream ciphers can be. In this paper, we propose implementation techniques for memory-optimized implementations of lightweight hardware-oriented stream ciphers including Grain-128a specified in ISO/IEC 29167-13 for RFID protocols. Our techniques include data-dependency analysis to take a close look at how and in which timing certain variables are updated and also the way taking into account the structure of registers on the target micro-controller. In order to minimize RAM size, we reduce the number of general purpose registers for computation of Grain-128a's update and pre-output values. We present results of our memory-optimized implementations of Grain-128a, one of which requires 84 RAM bytes on ARM Cortex-M3.