In historical learning, to grasp the causal relationship between historical events and to understand factors that bring about important events are significant for fostering the historical thinking. However, some students are not able to find historical events that have causal relationships. The view of observing the historical events is different among individuals, so it is not appropriate to define the historical events that have causal relationships and impose students to remember them. The students need to understand the definition of the causal relationships and find the historical events that satisfy the definition according to their viewpoints. The objective of this paper is to develop a support system for understanding the meaning of a causal relationship and creating causal relation graphs that represent the causal relationships between historical events. When historical events have a causal relationship, a state change caused by one event becomes the cause of the other event. To consider these state changes is critically important to connect historical events. This paper proposes steps for considering causal relationships between historical events by arranging the state changes of historical people along with them. It also develops the system that supports students to create the causal relation graph according to the state changes. In our system, firstly, the interface for arranging state changes of historical people according to the historical events is given. Then, the interface for drawing the causal relation graph of historical events is provided in which state changes are automatically indicated on the created links in the causal relation graph. By observing the indicated state changes on the links, students are able to check by themselves whether their causal relation graphs correctly represent the causal relationships between historical events.

This letter describes a method that characterizes and improves the performance of a time-interleaved (TI) digital-to-analog converter (DAC) system by using multiport signal-flow graphs at microwave frequencies. A commercial signal generator with two TI DACs was characterized through s-parameter measurements and was compared to the conventional method. Moreover, prefilters were applied to correct the response, resulting in an error-vector magnitude improvement of greater than 8 dB for a 64-quadrature-amplitude-modulated signal of 4.8 Gbps. As a result, the bandwidth limitation and the complex post processing of the conventional method could be minimized.

Both placing responsibility of message sending on every IoT object and obfuscating the object's location from other objects are essential to realize a secure and privacy-preserved communication service. Two or more short-lived link identifiers (or pseudonyms) authorized by a trustable authority are often used in related studies, instead of a persistent or long-term use link identifier (i.e. vendor assigned MAC address). However, related studies have limitations in terms of frequently changing pseudonyms to enhance location privacy because the cryptographic algorithms used in them fixedly couple object's identifiers with its security keys. To overcome those limitations, we present a new pseudonym and key management scheme that enables dynamic coupling of pseudonym and key pairs without incurring any adverse impacts. Furthermore, we propose two lightweight pseudonym allocation protocols to effectively reduce the volume of message carrying the allocation parameters. Through qualitative analyses, we verify that the proposed scheme is more scalable than related approaches as it can efficiently allocate enough number of pseudonym/key pairs by reducing the control message overhead by more than 90%.

This paper presents an adaptive least-significant-bit (LSB) steganography for spatial color images on smartphones. For each red, green, and blue color component, the combinations of All-4bit, One-4bit+Two-2bit, and Two-3bit+One-2bit LSB replacements are proposed for content-adaptivity and natural histograms. The high capacity of 8.4bpp with the average peak signal noise ratio (PSNR) 43.7db and fast processing times on smartphones are also demonstrated

A vertex subset F ⊆ V(G) is called a cyclic vertex-cut set of a connected graph G if G-F is disconnected such that at least two components in G-F contain cycles. The cyclic vertex connectivity is the cardinality of a minimum cyclic vertex-cut set. In this paper, we show that the cyclic vertex connectivity of the trivalent Cayley graphs TGn is equal to eight for n ≥ 4.

A frequently occurring subcircuit consists of a loop of a resistor (R), a field-effect transistor (FET), and a capacitor (C). The FET acts as a switch, controlled at its gate terminal by a clock voltage. This subcircuit may be acting as a sample-and-hold (S/H), as a passive mixer (P-M), or as a bandpass filter or bandpass impedance. In this work, we will present a useful analysis that leads to a simple signal flow graph (SFG), which captures the FET-R-C circuit's action completely across a wide range of design parameters. The SFG dissects the circuit into three filtering functions and ideal sampling. This greatly simplifies analysis of frequency response, noise, input impedance, and conversion gain, and leads to guidelines for optimum design. This paper focuses on the analysis of a single-path FET-R-C circuit's signal transfer characteristics including the reconstruction of the complete waveform from the discrete-time sampled voltage.

Non-volatile memories are a promising alternative to memory design but data stored in them still may be destructed due to crosstalk and radiation. The data stored in them can be restored by using error-correcting codes but they require extra bits to correct bit errors. One of the largest problems in non-volatile memories is that they consume ten to hundred times more energy than normal memories in bit-writing. It is quite necessary to reduce writing bits. Recently, a REC code (bit-write-reducing and error-correcting code) is proposed for non-volatile memories which can reduce writing bits and has a capability of error correction. The REC code is generated from a linear systematic error-correcting code but it must include the codeword of all 1's, i.e., 11…1. The codeword bit length must be longer in order to satisfy this condition. In this letter, we propose a method to generate a relaxed REC code which is generated from a relaxed error-correcting code, which does not necessarily include the codeword of all 1's and thus its codeword bit length can be shorter. We prove that the maximum flipping bits of the relaxed REC code is still limited theoretically. Experimental results show that the relaxed REC code efficiently reduce the number of writing bits.

Modeling user activities on the Web is a key problem for various Web services, such as news article recommendation and ad click prediction. In our work-in-progress paper[1], we introduced an approach that summarizes each sequence of user Web page visits using Paragraph Vector[3], considering users and URLs as paragraphs and words, respectively. The learned user representations are used among the user-related prediction tasks in common. In this paper, on the basis of analysis of our Web page visit data, we propose Backward PV-DM, which is a modified version of Paragraph Vector. We show experimental results on two ad-related data sets based on logs from Web services of Yahoo! JAPAN. Our proposed method achieved better results than those of existing vector models.

Utilizing software architecture patterns is important for reducing maintenance costs. However, maintaining code according to the constraints defined by the architecture patterns is time-consuming work. As described herein, we propose a technique to detect code fragments that are incompliant to the architecture as fine-grained architectural violations. For this technique, the dependence graph among code fragments extracted from the source code and the inference rules according to the architecture are the inputs. A set of candidate components to which a code fragment can be affiliated is attached to each node of the graph and is updated step-by-step. The inference rules express the components' responsibilities and dependency constraints. They remove candidate components of each node that do not satisfy the constraints from the current estimated state of the surrounding code fragment. If the inferred role of a code fragment does not include the component that the code fragment currently belongs to, then it is detected as a violation. We have implemented our technique for the Model-View-Controller for Web Application architecture pattern. By applying the technique to web applications implemented using Play Framework, we obtained accurate detection results. We also investigated how much does each inference rule contribute to the detection of violations.

The microfabrication technique based on SR (Synchrotron Radiation) direct etching process has recently been applied to construct PTFE microstructures. This paper attempts to fabricate an integrated PTFE-filled waveguide Butler matrix for short millimeter-wave by SR direct etching. First, a cruciform 3-dB directional coupler and an intersection circuit (0-dB coupler) are designed at 180 GHz. Then, a 4×4 Butler matrix with horn antennas is designed and fabricated. Finally, the measured radiation patterns of the Butler matrix are shown.

In this paper, we propose the first identity-based dynamic multi-cast key distribution (ID-DMKD) protocol which is secure against maximum exposure of secret information (e.g., secret keys and session-specific randomness). In DMKD protocols, users share a common session key without revealing any information of the session key to the semi-honest server, and can join/leave to/from the group at any time even after establishing the session key. Most of the known DMKD protocols are insecure if some secret information is exposed. Recently, an exposure resilient DMKD protocol was introduced, however, each user must manage his/her certificate by using the public-key infrastructure. We solve this problem by constructing the DMKD protocol authenticated by user's ID (i.e., without certificate). We introduce a formal security definition for ID-DMKD by extending the previous definition for DMKD. We must carefully consider exposure of the server's static secret key in the ID-DMKD setting because exposure of the server's static secret key causes exposure of all users' static secret keys. We prove that our protocol is secure in our security model in the standard model. Another advantage of our protocol is scalability: communication and computation costs of each user are independent from the number of users. Furthermore, we show how to extend our protocol to achieve non-interactive join by using certificateless encryption. Such an extension is useful in applications that the group members frequently change like group chat services.

Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion. So far, the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph. Among them, translation models obtain excellent performance. However, the proposed translation models do not adequately consider the indirect relationships among entities, affecting the precision of the representation. Based on the long short-term memory neural network and existing translation models, we propose a multiple-module hybrid neural network model called TransP. By modeling the entity paths and their relationship paths, TransP can effectively excavate the indirect relationships among the entities, and thus, improve the quality of knowledge graph completion tasks. Experimental results show that TransP outperforms state-of-the-art models in the entity prediction task, and achieves the comparable performance with previous models in the relationship prediction task.

The elastic optical network (EON) is a promising new optical technology that uses spectrum resources much more efficiently than does traditional wavelength division multiplexing (WDM). This paper focuses on the routing, modulation level, spectrum and transceiver allocation (RMSTA) problems of the EON. In contrast to previous works that consider only the routing and spectrum allocation (RSA) or routing, modulation level and spectrum allocation (RMSA) problems, we additionally consider the transceiver allocation problem. Because transceivers can be used to regenerate signals (by connecting two transceivers back-to-back) along a transmission path, different regeneration sites on a transmission path result in different spectrum and transceiver usage. Thus, the RMSTA problem is both more complex and more challenging than are the RSA and RMSA problems. To address this problem, we first propose an integer linear programming (ILP) model whose objective is to optimize the balance between spectrum usage and transceiver usage by tuning a weighting coefficient to minimize the cost of network operations. Then, we propose a novel virtual network-based heuristic algorithm to solve the problem and present the results of experiments on representative network topologies. The results verify that, compared to previous works, the proposed algorithm can significantly reduce both resource consumption and time complexity.

The isomorphism of polynomials with two secret (IP2S) problem is one candidate of computational assumptions for post-quantum cryptography. The idea of identification scheme based on IP2S is firstly introduced in 1996 by Patarin. However, the scheme was not described concretely enough and no more details are provided on how to transcribe the idea into a real-world implementation. Moreover, the security of the scheme has not been formally proven and the originally proposed security parameters are no longer secure based on the most recent research. In this paper, we propose a concrete identification scheme based on IP2S with the idea of Patarin as the starting point. We provide formal security proof of the proposed scheme against impersonation under passive attack, sequential active attack, and concurrent active attack. We also propose techniques to reduce the implementation cost such that we are able to cut the storage cost and average communication cost to an extent that under parameters for the standard 80-bit security, the scheme is implementable even on the lightweight devices in the current market.

Direct-binary search method has been used for converting complex holograms into binary format. However, this algorithm is optimized to reconstruct monochromatic digital holograms and is accurate only in a narrow-depth range. In this paper, we proposed an advanced direct-binary search method to increase the depth of field of 3D scenes reconstructed in RGB by binary holograms.

Video reranking is an effective way for improving the retrieval performance of text-based video search engines. This paper proposes a graph-based Web video search reranking method with local and global consistency analysis. Generally, the graph-based reranking approach constructs a graph whose nodes and edges respectively correspond to videos and their pairwise similarities. A lot of reranking methods are built based on a scheme which regularizes the smoothness of pairwise relevance scores between adjacent nodes with regard to a user's query. However, since the overall consistency is measured by aggregating only the local consistency over each pair, errors in score estimation increase when noisy samples are included within query-relevant videos' neighbors. To deal with the noisy samples, the proposed method leverages the global consistency of the graph structure, which is different from the conventional methods. Specifically, in order to detect this consistency, the propose method introduces a spectral clustering algorithm which can detect video groups, in which videos have strong semantic correlation, on the graph. Furthermore, a new regularization term, which smooths ranking scores within the same group, is introduced to the reranking framework. Since the score regularization is performed by both local and global aspects simultaneously, the accurate score estimation becomes feasible. Experimental results obtained by applying the proposed method to a real-world video collection show its effectiveness.

The problem with distributed representations generated by neural networks is that the meaning of the features is difficult to understand. We propose a new method that gives a specific meaning to each node of a hidden layer by introducing a manually created word semantic vector dictionary into the initial weights and by using paragraph vector models. We conducted experiments to test the hypotheses using a single domain benchmark for Japanese Twitter sentiment analysis and then evaluated the expandability of the method using a diverse and large-scale benchmark. Moreover, we tested the domain-independence of the method using a Wikipedia corpus. Our experimental results demonstrated that the learned vector is better than the performance of the existing paragraph vector in the evaluation of the Twitter sentiment analysis task using the single domain benchmark. Also, we determined the readability of document embeddings, which means distributed representations of documents, in a user test. The definition of readability in this paper is that people can understand the meaning of large weighted features of distributed representations. A total of 52.4% of the top five weighted hidden nodes were related to tweets where one of the paragraph vector models learned the document embeddings. For the expandability evaluation of the method, we improved the dictionary based on the results of the hypothesis test and examined the relationship of the readability of learned word vectors and the task accuracy of Twitter sentiment analysis using the diverse and large-scale benchmark. We also conducted a word similarity task using the Wikipedia corpus to test the domain-independence of the method. We found the expandability results of the method are better than or comparable to the performance of the paragraph vector. Also, the objective and subjective evaluation support each hidden node maintaining a specific meaning. Thus, the proposed method succeeded in improving readability.

We develop a technique for representing variable names and name binding which is a mechanism of associating a name with an entity in many formal systems including logic, programming languages and mathematics. The idea is to use a general form of graph links (or edges) called hyperlinks to represent variables, graph nodes as constructors of the formal systems, and a graph type called hlground to define substitutions. Our technique is based on simple notions of graph theory in which graph types ensure correct substitutions and keep bound variables distinct. We encode strong reduction of the untyped λ-calculus to introduce our technique. Then we encode a more complex formal system called System F<:, a polymorphic λ-calculus with subtyping that has been one of important theoretical foundations of functional programming languages. The advantage of our technique is that the representation of terms, definition of substitutions, and implementation of formal systems are all straightforward. We formalized the graph type hlground, proved that it ensures correct substitutions in the λ-calculus, and implemented hlground in HyperLMNtal, a modeling language based on hypergraph rewriting. Experiments were conducted to test this technique. By this technique, one can implement formal systems simply by following the steps of their definitions as described in papers.

Early reviews, posted on online review sites shortly after products enter the market, are useful for estimating long-term evaluations of those products and making decisions. However, such reviews can be influenced easily by anomalous reviewers, including malicious and fraudulent reviewers, because the number of early reviews is usually small. It is therefore challenging to detect anomalous reviewers from early reviews and estimate long-term evaluations by reducing their influences. We find that two characteristics of heterogeneity on actual review sites such as Amazon.com cause difficulty in detecting anomalous reviewers from early reviews. We propose ideas for consideration of heterogeneity, and a methodology for computing reviewers' degree of anomaly and estimating long-term evaluations simultaneously. Our experimental evaluations with actual reviews from Amazon.com revealed that our proposed method achieves the best performance in 19 of 20 tests compared to state-of-the-art methodologies.

In this paper, we study a variant of the MINIMUM DOMINATING SET problem. Given an unweighted undirected graph G=(V,E) of n=|V| vertices, the goal of the MINIMUM SINGLE DOMINATING CYCLE problem (MinSDC) is to find a single shortest cycle which dominates all vertices, i.e., a cycle C such that for the set V(C) of vertices in C and the set N(V(C)) of neighbor vertices of C, V(G)=V(C)∪N(V(C)) and |V(C)| is minimum over all dominating cycles in G [6], [17], [24]. In this paper we consider the (in)approximability of MinSDC if input graphs are restricted to some special classes of graphs. We first show that MinSDC is still NP-hard to approximate even when restricted to planar, bipartite, chordal, or r-regular (r≥3). Then, we show the (lnn+1)-approximability and the (1-ε)lnn-inapproximability of MinSDC on split graphs under P≠NP. Furthermore, we explicitly design a linear-time algorithm to solve MinSDC for graphs with bounded treewidth and estimate the hidden constant factor of its running time-bound.