The performance of secret-sharing (SS)-based multiparty computation (MPC) has recently increased greatly, and several efforts to implement and use it have been put into practice. Authentication of clients is one critical mechanism for implementing SS-based MPC successfully in practice. We propose a password-based authentication protocol for SS-based MPC. Our protocol is secure in the presence of secure channels, and it is optimized for practical use with SS-based MPC in the following ways. Threshold security: Our protocol is secure in the honest majority, which is necessary and sufficient since most practical results on SS-based MPC are secure in the same environment. Establishing distinct channels: After our protocol, a client has distinct secure and two-way authenticated channels to each server. Ease of implementation: Our protocol consists of SS, operations involving SS, and secure channels, which can be reused from an implementation of SS-based MPC. Furthermore, we implemented our protocol with an optimization for the realistic network. A client received the result within 2 sec even when the network delay was 200 ms, which is almost the delay that occurs between Japan and Europe.

We study an authentication method using secret figures of Pattern Lock, called pass patterns. In recent years, it is important to prevent the leakage of personal and company information on mobile devices. Android devices adopt a login authentication called Pattern Lock, which achieves both high resistance to Brute Force Attack and usability by virtue of pass pattern. However, Pattern Lock has a problem that pass patterns directly input to the terminal can be easily remembered by shoulder-surfing attack. In this paper, we propose a shoulder-surfing resistant authentication using pass pattern of Pattern Lock, which adopts a challenge & response authentication and also uses users' short-term memory. We implement the proposed method as an Android application and measure success rate, authentication time and the resistance against shoulder surfing. We also evaluate security and usability in comparison with related work.

In this work we utilize feature transformations that are common in supervised learning without having prior supervision, with the goal to improve Dirichlet process Gaussian mixture model (DPGMM) based acoustic unit discovery. The motivation of using such transformations is to create feature vectors that are more suitable for clustering. The need of labels for these methods makes it difficult to use them in a zero resource setting. To overcome this issue we utilize a first iteration of DPGMM clustering to generate frame based class labels for the target data. The labels serve as basis for learning linear discriminant analysis (LDA), maximum likelihood linear transform (MLLT) and feature-space maximum likelihood linear regression (fMLLR) based feature transformations. The novelty of our approach is the way how we use a traditional acoustic model training pipeline for supervised learning to estimate feature transformations in a zero resource scenario. We show that the learned transformations greatly support the DPGMM sampler in finding better clusters, according to the performance of the DPGMM posteriorgrams on the ABX sound class discriminability task. We also introduce a method for combining posteriorgram outputs of multiple clusterings and demonstrate that such combinations can further improve sound class discriminability.

Regular expression matching is essential in network and big-data applications; however, it still has a serious performance bottleneck. The state-of-the-art schemes use a multi-pattern exact string-matching algorithm as a filtering module placed before a heavy regular expression engine. We design a new approximate string-matching filter using multiple q-grams; this filter not only achieves better space compactness, but it also has higher throughput than the existing filters.

The radio wave shadowing by a two-dimensional human body is examined numerically as the scattering problem by using the Method of Moments (MoM) in order to verify the equivalent human body diameter. Three human body models are examined: (1) a circular cylinder, (2) an elliptical cylinder, and (3) an elliptical cylinder with two circular cylinders are examined. The scattered fields yields by the circular cylinder are compared with measured data. Since the angle of the model to an incident wave affects scattered fields in models other than a circular cylinder, the models of an elliptical cylinder and an elliptical cylinder with two circular cylinders are converted into a circular cylinder of equivalent diameter. The frequency characteristics for the models are calculated by using the equivalent diameter.

Among various indoor positioning technologies, impulse-radio UWB is a promising technique to provide indoor positioning and tracking services with high precision. Because UWB regulations turned to imposing restrictions on UWB low band, UWB high band becomes attractive for enabling simple and low cost implementation. However, UWB high band endures much larger propagation loss than UWB low band. In this paper, we propose two separated methods to compensate the deficiency of high band in propagation. With the first method, we bundle several IR-UWB modules to increase the average transmission power, while an adaptive detection threshold is introduced at the receiver to raise receiving sensitivity with the second method. We respectively implement each of these two proposed methods and evaluate their performance through measurements in laboratory. The results show that each of them achieves about 7dB gains in signal power. Furthermore, positioning performance of these two proposed methods are evaluated and compared through field measurements in an indoor sports land.

With an annual growth of billions of sensor-based devices, it is an urgent need to do stream mining for the massive data streams produced by these devices. Cloud computing is a competitive choice for this, with powerful computational capabilities. However, it sacrifices real-time feature and energy efficiency. Application-specific integrated circuit (ASIC) is with high performance and efficiency, which is not cost-effective for diverse applications. The general-purpose microcontroller is of low performance. Therefore, it is a challenge to do stream mining on these low-cost devices with scalability and efficiency. In this paper, we introduce an FPGA-based scalable and parameterized architecture for stream mining.Particularly, Dynamic Time Warping (DTW) based k-Nearest Neighbor (kNN) is adopted in the architecture. Two processing element (PE) rings for DTW and kNN are designed to achieve parameterization and scalability with high performance. We implement the proposed architecture on an FPGA and perform a comprehensive performance evaluation. The experimental results indicate thatcompared to the multi-core CPU-based implementation, our approach demonstrates over one order of magnitude on speedup and three orders of magnitude on energy-efficiency.

In this paper, we propose a received signal interpolation method for enhancing the performance of multiple signal classification (MUSIC) algorithm. In general, the performance of the conventional MUSIC algorithm is very sensitive to signal-to-noise ratio (SNR) of the received signal. When array elements receive the signals with nonuniform SNR values, the resolution performance is degraded compared to elements receiving the signals with uniform SNR values. Hence, we propose a signal calibration technique for improving the resolution of the algorithm. First, based on original signals, rough direction of arrival (DOA) estimation is conducted. In this stage, using frequency-domain received signals, SNR values of each antenna element in the array are estimated. Then, a deteriorated element that has a relatively lower SNR value than those of the other elements is selected by our proposed scheme. Next, the received signal of the selected element is spatially interpolated based on the signals received from the neighboring elements and the DOA information extracted from the rough estimation. Finally, fine DOA estimation is performed again with the calibrated signal. Simulation results show that the angular resolution of the proposed method is better than that of the conventional MUSIC algorithm. Also, we apply the proposed scheme to actual data measured in the testing ground, and it gives us more enhanced DOA estimation result.

Let G be a graph such that each edge has its list of available colors, and assume that each list is a subset of the common set consisting of k colors. Suppose that we are given two list edge-colorings f0 and fr of G, and asked whether there exists a sequence of list edge-colorings of G between f0 and fr such that each list edge-coloring can be obtained from the previous one by changing a color assignment of exactly one edge. This problem is known to be PSPACE-complete for every integer k ≥ 6 and planar graphs of maximum degree three, but any computational hardness was unknown for the non-list variant in which every edge has the same list of k colors. In this paper, we first improve the known result by proving that, for every integer k ≥ 4, the problem remains PSPACE-complete even for planar graphs of bounded bandwidth and maximum degree three. Since the problem is known to be solvable in polynomial time if k ≤ 3, our result gives a sharp analysis of the complexity status with respect to the number k of colors. We then give the first computational hardness result for the non-list variant: for every integer k ≥ 5, the non-list variant is PSPACE-complete even for planar graphs of bandwidth quadratic in k and maximum degree k.

A new method is proposed for the construction of pairing-friendly elliptic curves. For any fixed embedding degree, it can transform the problem to solving equation systems instead of exhaustive searching, thus it's more targeted and efficient. Via this method, we obtain various families including complete families, complete families with variable discriminant and sparse families. Specifically, we generate a complete family with important application prospects which has never been given before as far as we know.

In this paper, we focus on two-layer wireless sensor networks (WSNs) that consist of sensor-concentrator and inter-concentrator networks. In order to collect as much data as possible from a wide area, improving of network capacity is essential because data collection applications often require to gather data within a limited period, i.e., acceptable collection delay. Therefore, we propose a two-stage scheduling method for inter-concentrator networks. The proposed method first strictly schedules time slots of links with heavy interference and congestion by exploiting the combination metric of interference and traffic demand. After that, it simply schedules time slots of the remaining sinks to mitigate complexity. Simulation-based evaluations show our proposal offers much larger capacity than conventional scheduling algorithms. In particular, our proposal improves up to 70% capacity compared with the conventional methods in situations where the proportion of one- and two-hop links is small.

The generalized spatial modulation (GSM) is a new transmission technique that can realize high-performance multiple-input multiple-output (MIMO) communication systems with a low RF complexity. This paper presents an efficient sphere decoding method used to perform the symbol detection for the generalized spatial modulation (GSM) multiple-input multiple-output (MIMO) systems. In the proposed method, the cost metric is modified so that it does not include the cancellation of the nonexistent interference. The modified cost metric can be computed by formulating a detection tree that has a regular structure representing the transmit antenna combinations as well as the symbol vectors, both of which are detected efficiently by finding the shortest path on the basis of an efficient tree search algorithm. As the tree search algorithm is performed for the regular detection tree to compute the modified but mathematically-equivalent cost metric, the efficiency of the sphere decoding is improved while the bit-error rate performance is not degraded. The simulation results show that the proposed method reduces the complexity significantly when compared with the previous method: for the 6×6 64QAM GSM-MIMO system with two active antennas, the average reduction rate of the complexity is as high as 45.8% in the count of the numerical operations.

Query autocompletion is an important and practical technique when users want to search for desirable information. As mobile devices become more and more popular, one of the main applications is location-aware service, such as Web mapping. In this paper, we propose a new solution to location-aware query autocompletion. We devise a trie-based index structure and integrate spatial information into trie nodes. Our method is able to answer both range and top-k queries. In addition, we discuss the extension of our method to support the error tolerant feature in case user's queries contain typographical errors. Experiments on real datasets show that the proposed method outperforms existing methods in terms of query processing performance.

Accurate stereo matching remains a challenging problem in case of weakly-textured areas, discontinuities and occlusions. In this letter, a novel stereo matching method, consisting of leveraging feature ensemble network to compute matching cost, error detection network to predict outliers and priority-based occlusion disambiguation for refinement, is presented. Experiments on the Middlebury benchmark demonstrate that the proposed method yields competitive results against the state-of-the-art algorithms.

Symbol-pair read channels output overlapping pairs of symbols in storage applications. Pair distance and pair error are used in the channels. In this paper, we discuss error-trapping decoding for cyclic codes over symbol-pair read channels. By putting some restrictions on the correctable pair error patterns, we propose a novel error-trapping decoding algorithm over the channels and show a circuitry for implementing the decoding algorithm. In addition, we discuss how to modify the restrictions on the correctable pair error patterns.

This paper deals with the constrained DAG shortest path problem (CDSP), which finds the shortest path on a given directed acyclic graph (DAG) under any logical constraints posed on taken edges. There exists a previous work that uses binary decision diagrams (BDDs) to represent the logical constraints, and traverses the input DAG and the BDD simultaneously. The time and space complexity of this BDD-based method is derived from BDD size, and tends to be fast only when BDDs are small. However, since it does not prioritize the search order, there is considerable room for improvement, particularly for large BDDs. We combine the well-known A* search with the BDD-based method synergistically, and implement several novel heuristic functions. The key insight here is that the ‘shortest path’ in the BDD is a solution of a relaxed problem, just as the shortest path in the DAG is. Experiments, particularly practical machine learning applications, show that the proposed method decreases search time by up to 2 orders of magnitude, with the specific result that it is 2,000 times faster than a commercial solver. Moreover, the proposed method can reduce the peak memory usage up to 40 times less than the conventional method.

One of the problems associated with voice conversion from a nonparallel corpus is how to find the best match or alignment between the source and the target vector sequences without linguistic information. In a previous study, alignment was achieved by minimizing the distance between the source vector and the transformed vector. This method, however, yielded a sequence of feature vectors that were not well matched with the underlying speaker model. In this letter, the vectors were selected from the candidates by maximizing the overall likelihood of the selected vectors with respect to the target model in the HMM context. Both objective and subjective evaluations were carried out using the CMU ARCTIC database to verify the effectiveness of the proposed method.

A quantized index for evaluating the pattern similarity of two different datasets is designed by calculating the number of correlated dictionary atoms. Guided by this theory, task-specific biometric recognition model transferred from state-of-the-art DNN models is realized for both face and vein recognition.

Hardware implementation of neural networks usually have high computational complexity that increase exponentially with the size of a circuit, leading to more uncertain and unreliable circuit performance. This letter presents a novel Radial Basis Function (RBF) neural network based on parallel fault tolerant stochastic computing, in which number is converted from deterministic domain to probabilistic domain. The Gaussian RBF for middle layer neuron is implemented using stochastic structure that reduce the hardware resources significantly. Our experimental results from two pattern recognition tests (the Thomas gestures and the MIT faces) show that the stochastic design is capable to maintain equivalent performance when the stream length set to 10Kbits. The stochastic hidden neuron uses only 1.2% hardware resource compared with the CORDIC algorithm. Furthermore, the proposed algorithm is very flexible in design tradeoff between computing accuracy, power consumption and chip area.

Photo Diode Array (PDA) is the key semiconductor component expected to produce specified output voltage in photo couplers and photo sensors when the light is on. PDA partitioning problem, which is to design PDA, is: Given die area, anode and cathode points, divide the area into N cells, with identical areas, connected in series from anode to cathode. In this paper, we first make restrictions for the problem and reveal the underlying properties of necessary and sufficient conditions for the existence of solutions when the restrictions are satisfied. Then, we propose a method to solve the problem using recursive algorithm, which can be guaranteed to obtain a solution in polynomial time.