Zero-knowledge arguments allows one party to prove that a statement is true, without leaking any other information than the truth of the statement. In many applications such as verifiable shuffle (as a practical application) and circuit satisfiability (as a theoretical application), zero-knowledge arguments for mathematical statements related to linear algebra are essentially used. Groth proposed (at CRYPTO 2009) an elegant methodology for zero-knowledge arguments for linear algebraic relations over finite fields. He obtained zero-knowledge arguments of the sub-linear size for linear algebra using reductions from linear algebraic relations to equations of the form z=x*'y, where x, y ∈ Fnp are committed vectors, z ∈ Fp is a committed element, and *': FnpFnpFp is a bilinear map. These reductions impose additional rounds on zero-knowledge arguments of the sub-linear size. The round complexity of interactive zero-knowledge arguments is an important measure along with communication and computational complexities. We focus on minimizing the round complexity of sub-linear zero-knowledge arguments for linear algebra. To reduce round complexity, we propose a general transformation from a t-round zero-knowledge argument, satisfying mild conditions, to a (t-2)-round zero-knowledge argument; this transformation is of independent interest.

This paper presents a novel method for improving the readability of automatic speech recognition (ASR) results for classroom lectures. Because speech in a classroom is spontaneous and contains many ill-formed utterances with various disfluencies, the ASR result should be edited to improve the readability before presenting it to users, by applying some operations such as removing disfluencies, determining sentence boundaries, inserting punctuation marks and repairing dropped words. Owing to the presence of many kinds of domain-dependent words and casual styles, even state-of-the-art recognizers can only achieve a 30-50% word error rate for speech in classroom lectures. Therefore, a method for improving the readability of ASR results is needed to make it robust to recognition errors. We can use multiple hypotheses instead of the single-best hypothesis as a method to achieve a robust response to recognition errors. However, if the multiple hypotheses are represented by a lattice (or a confusion network), it is difficult to utilize sentence-level knowledge, such as chunking and dependency parsing, which are imperative for determining the discourse structure and therefore imperative for improving readability. In this paper, we propose a novel algorithm that infers clean, readable transcripts from spontaneous multiple hypotheses represented by a confusion network while integrating sentence-level knowledge. Automatic and manual evaluations showed that using multiple hypotheses and sentence-level knowledge is effective to improve the readability of ASR results, while preserving the understandability.

Recent developments of electronic devices containing Josephson junctions (JJ) with high-Tc superconductors (HTS) are reported. In particular, the fabrication process and the properties of superconducting quantum interference devices (SQUIDs) with a multilayer structure and ramp-edge-type JJs are described. The JJs were fabricated by recrystallization of an artificially deposited Cu-poor precursory layer. The formation mechanism of the junction barrier is discussed. We have fabricated various types of gradiometers and magnetometers. They have been actually utilized for several application systems, such as a non-destructive evaluation (NDE) system for deep-lying defects in a metallic plate and a reel-to-reel testing system for striated HTS-coated conductors.

The k-edge-connectivity augmentation problem with bipartition constraints (kECABP, for short) is defined by “Given an undirected graph G=(V, E) and a bipartition π = {VB, VW} of V with VB ∩ VW = ∅, find an edge set Ef of minimum cardinality, consisting of edges that connect VB and VW, such that G'=(V, E ∪ Ef) is k-edge-connected.” The problem has applications for security of statistical data stored in a cross tabulated table, and so on. In this paper we propose a fast algorithm for finding an optimal solution to (σ + 1)ECABP in O(|V||E| + |V2|log |V|) time when G is σ-edge-connected (σ > 0), and show that the problem can be solved in linear time if σ ∈ {1, 2}.

We study the problem of reconfiguring one list edge-coloring of a graph into another list edge-coloring by changing only one edge color assignment at a time, while at all times maintaining a list edge-coloring, given a list of allowed colors for each edge. Ito, Kamiski and Demaine gave a sufficient condition so that any list edge-coloring of a tree can be transformed into any other. In this paper, we give a new sufficient condition which improves the known one. Our sufficient condition is best possible in some sense. The proof is constructive, and yields a polynomial-time algorithm that finds a transformation between two given list edge-colorings of a tree with n vertices via O(n2) recoloring steps. We remark that the upper bound O(n2) on the number of recoloring steps is tight, because there is an infinite family of instances on paths that satisfy our sufficient condition and whose reconfiguration requires Ω(n2) recoloring steps.

We propose a motion detection model, which is suitable for higher speed operation than the video rate, inspired by the neuronal propagation in the hippocampus in the brain. The model detects motion of edges, which are extracted from monocular image sequences, on specified 2D maps without image matching. We introduce gating units into a CA3-CA1 model, where CA3 and CA1 are the names of hippocampal regions. We use the function of gating units to reduce mismatching for applying our model in complicated situations. We also propose a map-division method to achieve accurate detection. We have evaluated the performance of the proposed model by using artificial and real image sequences. The results show that the proposed model can run up to 1.0 ms/frame if using a resolution of 6460 units division of 320240 pixels image. The detection rate of moving edges is achieved about 99% under a complicated situation. We have also verified that the proposed model can achieve accurate detection of approaching objects at high frame rate (>100 fps), which is better than conventional models, provided we can obtain accurate positions of image features and filter out the origins of false positive results in the post-processing.

The Kobayashi potential in electromagnetic theory is reviewed. As an illustration we consider two problems, diffraction of plane wave by disk and rectangular plate of perfect conductor. Some numerical results are compared with approximated and experimental results when they are available to verify the validity of the present method. We think the present method can be used as reference solutions of the related problems.

The social network model has been regarded as a promising mechanism to defend against Sybil attack. This model assumes that honest peers and Sybil peers are connected by only a small number of attack edges. Detection of the attack edges plays a key role in restraining the power of Sybil peers. In this paper, an attack-resisting, distributed algorithm, named Random walk and Social network model-based clustering (RSC), is proposed to detect the attack edges. In RSC, peers disseminate random walk packets to each other. For each edge, the number of times that the packets pass this edge reflects the betweenness of this edge. RSC observes that the betweennesses of attack edges are higher than those of the non-attack edges. In this way, the attack edges can be identified. To show the effectiveness of RSC, RSC is integrated into an existing social network model-based algorithm called SOHL. The results of simulations with real world social network datasets show that RSC remarkably improves the performance of SOHL.

This paper proposes a high-accuracy sub-pixel registration framework based on phase correlation for noisy images. First we introduce a denoising module, where the edge-preserving filter is adopted. This strategy not only filters off the noise but also preserves most of the original image signal. A confidence-weighted optimization module is then proposed to fit the linear phase plane discriminately and to achieve sub-pixel shifts. Experiments demonstrate the effectiveness of the combination of our modules and improvements of the accuracy and robustness against noise compared to other sub-pixel phase correlation methods in the Fourier domain.

Previous studies of pattern recognition have shown that classifiers ensemble approaches can lead to better recognition results. In this paper, we apply the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts. Six machine learning-based systems are combined through three different voting schemes. We demonstrate the effectiveness of classifiers ensemble approaches and compare the performance of three different voting schemes for hedge cue and their scope detection. Experiments on the CoNLL-2010 evaluation data show that our best system achieves an F-score of 87.49% on hedge detection task and 60.87% on scope finding task respectively, which are significantly better than those of the previous systems.

This letter presents a novel edge-based blur metric that averages the ratios between the slopes and heights of edges. The metric computes the edge slopes more carefully, i.e., by averaging the edge gradients. The effectiveness of the proposed metric is confirmed by experiments with motion or Gaussian blurred real images and comparison with existing edge-based blur metrics.

In this paper, we propose a Network Knowledge Layer (NKL) that is a service platform overlaid onto the existing networks to provide network knowledge for user-centric services in the Next Generation Network (NGN). Most traditional networks lack capabilities for accommodating user-centric service paradigm. Taking this into consideration, the proposed NKL has capabilities to acquire contextual information from various sources, to evolve this information with legacy information into high-level knowledge, and to expose the high-level knowledge to entities outside the network. For easy knowledge exposure, we specify a set of abstracted application programming interfaces (APIs). For efficient handling of network knowledge accessed by the APIs, we design and compare the three different network knowledge models. With the proposed APIs and the three knowledge models, we accomplish the experimental performance evaluation of NKL to verify its feasibility. The results of the various tests on knowledge models and APIs give good guidelines for efficient design and exposing network knowledge in developing a user-centric service platform. Finally, we expect NKL can support the effective development and execution of user-centric services by providing rich network knowledge with the APIs.

An ability to assess similarity lies close to the core of cognition. Its understanding support the comprehension of human success in tasks like problem solving, categorization, memory retrieval, inductive reasoning, etc, and this is the main reason that it is a common research topic. In this paper, we introduce the idea of semantic differences and commonalities between words to the similarity computation process. Five new semantic similarity metrics are obtained after applying this scheme to traditional WordNet-based measures. We also combine the node based similarity measures with a corpus-independent way of computing the information content. In an experimental evaluation of our approach on two standard word pairs datasets, four of the measures outperformed their classical version, while the other performed as well as their unmodified counterparts.

We have studied the transport property of the Vertical MOSFET (V-MOSFET) with an impurity from the viewpoint of quantum electron dynamics. In order to obtain the position dependence of impurity for the electron transmission property through the channel of the V-MOSFET, we solve the time-dependent Shrodinger equation in real space mesh technique We reveal that the impurity in the source edge can assist the electron transmission from the source to drain working as a wave splitter. In addition, we also reveal the effect of an impurity in the surface of pillar is limited because of its dimensionality. Furthermore, we obtained that the electron injection from the source to the channel becomes difficult due to the energy difference between the subbands of the source and the channel. These results enable us to obtain the guiding principle to design the V-MOSFET in the 10 nm pillar. The results enable us to obtain the guiding principle to design the V-MOSFET beyond 20 nm design rule.

Recently, IEEE 802.15.4 has been standardized for WSNs (Wireless Sensor Networks). This paper proposes an enhanced CCA scheme which involves the data transmission device sending a notifyBusyChannel (nBC) signal in the backoff period when the Channel Using Quotient (CUQ) exceeds 0.5. The CUQ stands for the rate of channel utilization in the previous slot duration. In a single CCA operation, the device nodes are made aware of the busy status of the channel by the nBC signal. We implement the ECCA scheme in a hardware chip for a performance evaluation. The results show that the proposed scheme has short queuing times and less energy consumption than IEEE 802.15.4 CCA. And the scheme is compatible with conventional IEEE 802.15.4 devices.

Improving quality of healthcare for people with chronic conditions requires informed and knowledgeable healthcare providers and patients. Decision support and clinical information system are two of the main components to support improving chronic care. In this paper, we describe an ontology-based information and knowledge management framework that is important for chronic disease care management. Ontology-based knowledge acquisition and modeling based on knowledge engineering approach provides an effective mechanism in capturing expert opinion in form of clinical practice guidelines. The framework focuses on building of healthcare ontology and clinical reminder system that link clinical guideline knowledge with patient registries to support evidenced-based healthcare. We describe implementation and approaches in integrating clinical reminder services to existing healthcare provider environment by focusing on augmenting decision making and improving quality of patient care services.

In the text summarization field, a table-of-contents is a type of indicative summary that is especially suited for locating information in a long document, or a set of documents. It is also a useful summary for a reader to quickly get an overview of the entire contents. The current models for generating a table-of-contents produced relatively low quality output with many meaningless titles, or titles that have no overlapping meaning with the corresponding contents. This problem may be due to the lack of semantic information and topic information in those models. In this research, we propose to integrate supportive knowledge into the learning models to improve the quality of titles in a generated table-of-contents. The supportive knowledge is derived from a hierarchical clustering of words, which is built from a large collection of raw text, and a topic model, which is directly estimated from the training data. The relatively good results of the experiments showed that the semantic and topic information supplied by supportive knowledge have good effects on title generation, and therefore, they help to improve the quality of the generated table-of-contents.

Let C be a set of colors, and let ω(c) be an integer cost assigned to a color c in C. An edge-coloring of a graph G is to color all the edges of G so that any two adjacent edges are colored with different colors in C. The cost ω(f) of an edge-coloring f of G is the sum of costs ω(f(e)) of colors f(e) assigned to all edges e in G. An edge-coloring f of G is optimal if ω(f) is minimum among all edge-colorings of G. In this paper, we show that the problem of finding an optimal edge-coloring of a tree T can be simply reduced in polynomial time to the minimum weight perfect matching problem for a new bipartite graph constructed from T. The reduction immediately yields an efficient simple algorithm to find an optimal edge-coloring of T in time O(n1.5Δlog(nNω)), where n is the number of vertices in T, Δ is the maximum degree of T, and Nω is the maximum absolute cost |ω(c)| of colors c in C. We then show that our result can be extended for multitrees.

The diffraction by a composite wedge composed of a perfect conductor and a lossy dielectric is investigated using the hidden rays of diffraction (HRD) method. The usual principle of geometrical optics is employed to trace not only ordinary rays incident on the lit boundary but also hidden rays incident on the shadow boundary. The modified propagation constants are adopted to represent the non-uniform plane wave transmission through the lossy dielectric. The HRD diffraction coefficients are constructed routinely by the sum of the cotangent functions, which have one-to-one correspondence with both ordinary and hidden rays. The angular period of the cotangent functions is adjusted to satisfy the edge condition at the tip of the composite wedge. The accuracy of the HRD diffraction coefficients in the physical region is checked by showing how closely the diffraction coefficients in the complementary region satisfy the null-field condition.

We present a new methodology for constructing an efficient identification scheme, and based on it, we propose a lightweight identification scheme whose computational and storage costs are sufficiently low even for cheap devices such as RFID tags. First, we point out that the efficiency of a scheme with statistical zero-knowledgeness can be significantly improved by enhancing its zero-knowledgeness to perfect zero-knowledge. Then, we apply this technique to the Girault-Poupard-Stern (GPS) scheme which has been standardized by ISO/IEC. The resulting scheme shows a perfect balance between communication cost, storage cost, and circuit size (computational cost), which are crucial factors for implementation on RFID tags. Compared to GPS, the communication and storage costs are reduced, while the computational cost is kept sufficiently low so that it is implementable on a circuit nearly as small as GPS. Under standard parameters, the prover's response is shortened 80 bits from 275 bits to 195 bits and in application using coupons, storage for one coupon is also reduced 80 bits, whereas the circuit size is estimated to be larger by only 335 gates. Hence, we believe that the new scheme is a perfect solution for fast authentication of RFID tags.