Almost all existing password-based authenticated key exchange (PAKE) schemes achieve concurrent security in the standard model by relying on the common reference string (CRS) model. A drawback of the CRS model is to require a centralized trusted authority in the setup phase; thus, passwords of parties may be revealed if the authority ill-uses trapdoor information of the CRS. There are a few secure PAKE schemes in the plain model, but, these are not achievable in a constant round (i.e., containing a linear number of rounds). In this paper, we discuss how to relax the setup assumption for (constant round) PAKE schemes. We focus on the multi-string (MS) model that allows a number of authorities (including malicious one) to provide some reference strings independently. The MS model is a more relaxed setup assumption than the CRS model because we do not trust any single authority (i.e., just assuming that a majority of authorities honestly generate their reference strings). Though the MS model is slightly restrictive than the plain model, it is very reasonable assumption because it is very easy to implement. We construct a (concurrently secure) three-move PAKE scheme in the MS model (justly without random oracles) based on the Groce-Katz PAKE scheme. The main ingredient of our scheme is the multi-string simulation-extractable non-interactive zero-knowledge proof that provides both the simulation-extractability and the extraction zero-knowledge property even if minority authorities are malicious. This work can be seen as a milestone toward constant round PAKE schemes in the plain model.

This paper introduces the maximization version of the k-path vertex cover problem, called the MAXIMUM K-PATH VERTEX COVER problem (MaxPkVC for short): A path consisting of k vertices, i.e., a path of length k-1 is called a k-path. If a k-path Pk includes a vertex v in a vertex set S, then we say that v or S covers Pk. Given a graph G=(V, E) and an integer s, the goal of MaxPkVC is to find a vertex subset S⊆V of at most s vertices such that the number of k-paths covered by S is maximized. The problem MaxPkVC is generally NP-hard. In this paper we consider the tractability/intractability of MaxPkVC on subclasses of graphs. We prove that MaxP3VC remains NP-hard even for split graphs. Furthermore, if the input graph is restricted to graphs with constant bounded treewidth, then MaxP3VC can be solved in polynomial time.

Advances in deep reinforcement learning have demonstrated its effectiveness in a wide variety of domains. Deep neural networks are capable of approximating value functions and policies in complex environments. However, deep neural networks inherit a number of drawbacks. Lack of interpretability limits their usability in many safety-critical real-world scenarios. Moreover, they rely on huge amounts of data to learn efficiently. This may be suitable in simulated tasks, but restricts their use to many real-world applications. Finally, their generalization capability is low, the ability to determine that a situation is similar to one encountered previously. We present a method to combine external knowledge and interpretable reinforcement learning. We derive a rule-based variant version of the Sarsa(λ) algorithm, which we call Sarsa-rb(λ), that augments data with prior knowledge and exploits similarities among states. We demonstrate that our approach leverages small amounts of prior knowledge to significantly accelerate the learning in multiple domains such as trading or visual navigation. The resulting agent provides substantial gains in training speed and performance over deep q-learning (DQN), deep deterministic policy gradients (DDPG), and improves stability over proximal policy optimization (PPO).

This paper investigates the closure properties of multi-inkdot nondeterministic Turing machines with sublogarithmic space. We show that the class of sets accepted by the Turing machines is not closed under concatenation with regular set, Kleene closure, length-preserving homomorphism, and intersection.

With the rapid evolution and increase of cyberthreats in recent years, it is necessary to detect and understand it promptly and precisely to reduce the impact of cyberthreats. A darknet, which is an unused IP address space, has a high signal-to-noise ratio, so it is easier to understand the global tendency of malicious traffic in cyberspace than other observation networks. In this paper, we aim to capture global cyberthreats in real time. Since multiple hosts infected with similar malware tend to perform similar behavior, we propose a system that estimates a degree of synchronizations from the patterns of packet transmission time among the source hosts observed in unit time of the darknet and detects anomalies in real time. In our evaluation, we perform our proof-of-concept implementation of the proposed engine to demonstrate its feasibility and effectiveness, and we detect cyberthreats with an accuracy of 97.14%. This work is the first practical trial that detects cyberthreats from in-the-wild darknet traffic regardless of new types and variants in real time, and it quantitatively evaluates the result.

This paper introduces a user study regarding the effects of hand- and ocular-dominances to pointing gestures. The result of this study is applicable for designing new gesture interfaces which are close to a user's cognition, intuitive, and easy to use. The user study investigates the relationship between the participant's dominances and pointing gestures. Four participant groups—right-handed right-eye dominant, right-handed left-eye dominant, left-handed right-eye dominant and left-handed left-eye dominant—were prepared, and participants were asked to point at the targets on a screen by their left and right hands. The pointing errors among the different participant groups are calculated and compared. The result of this user study shows that using dominant eyes produces better results than using non-dominant eyes and the accuracy increases when the targets are located at the same side of dominant eye. Based on these interesting properties, a method to find the dominant eye for pointing gestures is proposed. This method can find the dominant eye of an individual with more than 90% accuracy.

Modern deep learning has significantly improved performance and has been used in a wide variety of applications. Since the amount of computation required for the inference process of the neural network is large, it is processed not by the data acquisition location like a surveillance camera but by the server with abundant computing power installed in the data center. Edge computing is getting considerable attention to solve this problem. However, edge computing can provide limited computation resources. Therefore, we assumed a divided/distributed neural network model using both the edge device and the server. By processing part of the convolution layer on edge, the amount of communication becomes smaller than that of the sensor data. In this paper, we have evaluated AlexNet and the other eight models on the distributed environment and estimated FPS values with Wi-Fi, 3G, and 5G communication. To reduce communication costs, we also introduced the compression process before communication. This compression may degrade the object recognition accuracy. As necessary conditions, we set FPS to 30 or faster and object recognition accuracy to 69.7% or higher. This value is determined based on that of an approximation model that binarizes the activation of Neural Network. We constructed performance and energy models to find the optimal configuration that consumes minimum energy while satisfying the necessary conditions. Through the comprehensive evaluation, we found that the optimal configurations of all nine models. For small models, such as AlexNet, processing entire models in the edge was the best. On the other hand, for huge models, such as VGG16, processing entire models in the server was the best. For medium-size models, the distributed models were good candidates. We confirmed that our model found the most energy efficient configuration while satisfying FPS and accuracy requirements, and the distributed models successfully reduced the energy consumption up to 48.6%, and 6.6% on average. We also found that HEVC compression is important before transferring the input data or the feature data between the distributed inference processes.

Nurimisaki and Sashigane are Nikoli's pencil puzzles. We study the computational complexity of Nurimisaki and Sashigane puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.

This paper proposes a technique that increases the lifetime of large scale integration (LSI) devices. As semiconductor technology improves at miniaturizing transistors, aging effects due to bias temperature instability (BTI) seriously affects their lifetime. BTI increases the threshold voltage of transistors thereby also increasing the delay of an electronics device, resulting in failures due to timing violations. To compensate for aging-induced timing violations, we exploit configurable approximate computing. Assuming that target circuits have exact and approximate modes, they are configured for the approximate mode if an aging sensor predicts violations. Experiments using an example circuit revealed an increase in its lifetime to >10 years.

Approximate multiplier design is an effective technique to improve hardware performance at the cost of accuracy loss. The current approximate multipliers are mostly ASIC-based and are dedicated for one particular application. In contrast, FPGA has been an attractive choice for many applications because of its high performance, reconfigurability, and fast development round. This paper presents a novel methodology for designing approximate multipliers by employing the FPGA-based fabrics (primarily look-up tables and carry chains). The area and latency are significantly reduced by applying approximation on carry results and cutting the carry propagation path in the multiplier. Moreover, we explore higher-order multipliers on architectural space by using our proposed small-size approximate multipliers as elementary modules. For different accuracy-hardware requirements, eight configurations for approximate 8×8 multiplier are discussed. In terms of mean relative error distance (MRED), the error of the proposed 8×8 multiplier is as low as 1.06%. Compared with the exact multiplier, our proposed design can reduce area by 43.66% and power by 24.24%. The critical path latency reduction is up to 29.50%. The proposed multiplier design has a better accuracy-hardware tradeoff than other designs with comparable accuracy. Moreover, image sharpening processing is used to assess the efficiency of approximate multipliers on application.

Plane wave scattering from a circular conducting cylinder and a circular conducting strip has been formulated by equivalent surface currents which are postulated from the scattering geometrical optics (GO) field. Thus derived radiation far fields are found to be the same as those formulated by a conventional physical optics (PO) approximation for both E and H polarizations.

A polyphase sequence set with orthogonality consisting complex elements with unit magnitude, can be expressed by a unitary matrix corresponding to the complex Hadamard matrix or the discrete Fourier transform (DFT) matrix, whose rows are orthogonal to each other. Its matched filter bank (MFB), which can simultaneously output the correlation between a received symbol and any sequence in the set, is effective for constructing communication systems flexibly. This paper discusses the compact design of the MFB of a polyphase sequence set, which can be applied to any sequence set generated by the given logic function. It is primarily focused on a ZCZ code with q-phase or more elements expressed as A(N=qn+s, M=qn-1, Zcz=qs(q-1)), where q, N, M and Zcz respectively denote, a positive integer, sequence period, family size, and a zero correlation zone, since the compact design of the MFB becomes difficult when Zcz is large. It is shown that the given logic function on the ring of integers modulo q generating the ZCZ code gives the matrix representation of the MFB that M-dimensional output vector can be represented by the product of the unitary matrix of order M and an M-dimensional input vector whose elements are written as the sum of elements of an N-dimensional input vector. Since the unitary matrix (complex Hadamard matrix) can be factorized into n-1 unitary matrices of order M with qM nonzero elements corresponding to fast unitary transform, a compact MFB with a minimum number of circuit elements can be designed. Its hardware complexity is reduced from O(MN) to O(qM log q M+N).

In this paper, we propose a filter-and-forward relay scheme with energy harvesting for single-carrier transmission in frequency-selective channels. The relay node harvests energy from both the source node transmit signal and its own transmit signal by self-energy recycling. The signal received by the relay node is filtered to suppress the inter-symbol interference and then forwarded to the destination node using the harvested energy. We consider a filter design method based on the signal-to-interference-plus-noise power ratio maximization, subject to a constraint that limits the relay transmit power. In addition, we provide a golden-section search based algorithm to optimize the power splitting ratio of the power splitting protocol. The simulation results show that filtering and self-energy recycling of the proposed scheme are effective in improving performance. It is also shown that the proposed scheme is useful even when only partial channel state information is available.

This paper presents a method for many-to-one voice conversion using phonetic posteriorgrams (PPGs) based on an adversarial training of deep neural networks (DNNs). A conventional method for many-to-one VC can learn a mapping function from input acoustic features to target acoustic features through separately trained DNN-based speech recognition and synthesis models. However, 1) the differences among speakers observed in PPGs and 2) an over-smoothing effect of generated acoustic features degrade the converted speech quality. Our method performs a domain-adversarial training of the recognition model for reducing the PPG differences. In addition, it incorporates a generative adversarial network into the training of the synthesis model for alleviating the over-smoothing effect. Unlike the conventional method, ours jointly trains the recognition and synthesis models so that they are optimized for many-to-one VC. Experimental evaluation demonstrates that the proposed method significantly improves the converted speech quality compared with conventional VC methods.

This paper investigates the performance of interleave division multiple access (IDMA)-based random access with various interference canceller structures in order to support massive machine-type communications (mMTC) in the fifth generation (5G) mobile communication system. To support massive connectivity in the uplink, a grant-free and contention-based multiple access scheme is essential to reduce the control signaling overhead and transmission latency. To suppress the packet loss due to collision and to achieve multi-packet reception, non-orthogonal multiple access (NOMA) with interference cancellation at the base station receiver is essential. We use IDMA and compare various interference canceller structures such as the parallel interference canceller (PIC), successive interference canceller (SIC), and their hybrid from the viewpoints of the error rate and decoding delay time. Based on extensive computer simulations, we show that IDMA-based random access is a promising scheme for supporting mMTC and the PIC-SIC hybrid achieves a good tradeoff between the error rate and decoding delay time.

Fresh Tea Shoot Maturity Estimation (FTSME) is the basement of automatic tea picking technique, determines whether the shoot can be picked. Unfortunately, the ambiguous information among single labels and uncontrollable imaging condition lead to a low FTSME accuracy. A novel Fresh Tea Shoot Maturity Estimating method via multispectral imaging and Deep Label Distribution Learning (FTSME-DLDL) is proposed to overcome these issues. The input is 25-band images, and the output is the corresponding tea shoot maturity label distribution. We utilize the multiple VGG-16 and auto-encoding network to obtain the multispectral features, and learn the label distribution by minimizing the Kullback-Leibler divergence using deep convolutional neural networks. The experimental results show that the proposed method has a better performance on FTSME than the state-of-the-art methods.

This paper describes designing a new pedestrian navigation system using a comprehensive framework called the pedestrian navigation concept reference model (PNCRM). We implement this system as a publicly-available smartphone application and evaluate its positioning performance near Omiya station's western entrance. We also evaluate users' subjective impressions of the system using a questionnaire. In both cases, promising results are obtained, showing that the PNCRM can be used as a tool for designing pedestrian navigation systems, allowing such systems to be created systematically.

In the age of big data, recommendation systems provide users with fast access to interesting information, resulting to a significant commercial value. However, the extreme sparseness of user assessment data is one of the key factors that lead to the poor performance of recommendation algorithms. To address this problem, we propose a spectral clustering recommendation scheme with low-rank matrix completion and spectral clustering. Our scheme exploits spectral clustering to achieve the division of a similar user group. Meanwhile, the low-rank matrix completion is used to effectively predict un-rated items in the sub-matrix of the spectral clustering. With the real dataset experiment, the results show that our proposed scheme can effectively improve the prediction accuracy of un-rated items.

The sidelobe level at tilts around 30-40 degrees in both the E and H planes due to a tapered excitation of units of 2×2 radiation slots is suppressed by introducing slit layers over a corporate-feed waveguide slot array antenna. The slit layers act as averaging the excitation of the adjacent radiating slots for sidelobe suppression in both planes. A 16×16-element array in the 70GHz band is fabricated. At the design frequency, the sidelobe levels at tilts around 30-40 degrees are suppressed from -25.4dB to -31.3dB in the E-plane and from -27.1dB to -38.9dB in the H-plane simultaneously as confirmed by measurements. They are suppressed over the desired range of 71.0-76.0GHz frequencies, compared to the conventional antenna.

Routing problems derived from silicon-interposer and etc. are often formulated as a set-pair routing problem where the combination of pin-pairs to be connected is flexible. In this routing problem, a length matching routing pattern is often required due to the requirement of the signal propagation delays be the same. We propose a fast length matching routing method for the set-pair routing problem. The existing algorithm generates a good length matching routing pattern in practical time. However, due to the limited searching range, there are length matching routing patterns that cannot find due to the limited searching range of the algorithm. Also, it needs heavy iterative steps to improve a solution, and the computation time is practical but not fast. In the set-pair routing, although pin-pairs to be connected is flexible, it is expected that combinations of pin-pairs which realize length matching are restricted. In our method, such a combination of pin-pairs is selected in advance, then routing is performed to realize the connection of the selected pin-pairs. Heavy iterative steps are not used for both the selection and the routing, then a routing pattern is generated in a short time. In the experiments, we confirm that the quality of routing patterns generated by our method is almost equivalent to the existing algorithm. Furthermore, our method finds length matching routing patterns that the existing algorithm cannot find. The computation time is about 360 times faster than the existing algorithm.