This paper presents a pivot-set generation algorithm for accelerating exact similarity search in a large-scale data set. To deal with the large-scale data set, it is important to efficiently construct a search index offline as well as to perform fast exact similarity search online. Our proposed algorithm efficiently generates competent pivots with two novel techniques: hierarchical data partitioning and fast pivot optimization techniques. To make effective use of a small number of pivots, the former recursively partitions a data set into two subsets with the same size depending on the rank order from each of two assigned pivots, resulting in a complete binary tree. The latter calculates a defined objective function for pivot optimization with a low computational cost by skillfully operating data objects mapped into a pivot space. Since the generated pivots provide the tight lower bounds on distances between a query object and the data objects, an exact similarity search algorithm effectively avoids unnecessary distance calculations. We demonstrate that the search algorithm using the pivots generated by the proposed algorithm reduces distance calculations with an extremely high rate regarding a range query problem for real large-scale image data sets.

The illegal parking of bicycles is a serious urban problem in Tokyo. The purpose of this study was to sustainably build Linked Open Data (LOD) to assist in solving the problem of illegally parked bicycles (IPBs) by raising social awareness, in cooperation with the Office for Youth Affairs and Public Safety of the Tokyo Metropolitan Government (Tokyo Bureau). We first extracted information on the problem factors and designed LOD schema for IPBs. Then we collected pieces of data from the Social Networking Service (SNS) and the websites of municipalities to build the illegally parked bicycle LOD (IPBLOD) with more than 200,000 triples. We then estimated the temporal missing data in the LOD based on the causal relations from the problem factors and estimated spatial missing data based on geospatial features. As a result, the number of IPBs can be inferred with about 70% accuracy, and places where bicycles might be illegally parked are estimated with about 31% accuracy. Then we published the complemented LOD and a Web application to visualize the distribution of IPBs in the city. Finally, we applied IPBLOD to large social activity in order to raise social awareness of the IPB issues and to remove IPBs, in cooperation with the Tokyo Bureau.

Strategic Dual Image method (SDI) for three-dimensional magnetic field problems is proposed. The basic idea of the SDI method is that the open boundary solution is in-between the Dirichlet and Neumann solutions. The relationship between the specific topology (e.g. sphere, and ellipsoid) of the boundary and the averaging weight has been discussed in the previous literature, however no discussions on the arbitrary topology. In this paper, combined with “the perturbation approach using equivalence theorem”, the methodology to derive the averaging weight of Dirichlet and Neumann solutions on the arbitrary topology has been proposed. Some numerical examples are also demonstrated.

The security of current public-key cryptosystems relies on the hardness of factoring large integers or solving discrete logarithm problems. However, these mathematical problems can be solved in polynomial time using a quantum computer. This vulnerability has prompted research into post-quantum cryptography using alternative mathematical problems that are secure in the era of quantum computers. In this regard, the National Institute of Standards and Technology (NIST) began to standardize post-quantum cryptography in 2016. In this expository article, we give an overview of recent research on post-quantum cryptography. In particular, we describe the construction and security of multivariate polynomial cryptosystems and lattice-based cryptosystems, which are the main candidates of post-quantum cryptography.

This letter investigates the performance of a legitimate surveillance system, where a wireless powered legitimate monitor aims to eavesdrop a suspicious communication link. Power splitting technique is adopted at the monitor for simultaneous information eavesdropping and energy harvesting. In order to maximize the successful eavesdropping probability, the power splitting ratio is optimized under the minimum harvested energy constraint. Assuming that perfect channel state information (CSI) or only the channel distribution information (CDI) is available, the closed-form maximum successful eavesdropping probability is obtained in Rayleigh fading channels. It is shown that the minimum harvested energy constraint has no impact on the eavesdropping performance if the minimum harvested energy constraint is loose. It is also shown that the eavesdropping performance loss due to partial knowledge of CSI is negligible when the eavesdropping link channel condition is much better than that of the suspicious communication link channel.

This paper addresses the task of assigning labels of fine-grained named entity (NE) types to Wikipedia articles. Information of NE types are useful when extracting knowledge of NEs from natural language text. It is common to apply an approach based on supervised machine learning to named entity classification. However, in a setting of classifying into fine-grained types, one big challenge is how to alleviate the data sparseness problem since one may obtain far fewer instances for each fine-grained types. To address this problem, we propose two methods. First, we introduce a multi-task learning framework, in which NE type classifiers are all jointly trained with a neural network. The neural network has a hidden layer, where we expect that effective combinations of input features are learned across different NE types. Second, we propose to extend the input feature set by exploiting the hyperlink structure of Wikipedia. While most of previous studies are focusing on engineering features from the articles' contents, we observe that the information of the contexts the article is mentioned can also be a useful clue for NE type classification. Concretely, we propose to learn article vectors (i.e. entity embeddings) from Wikipedia's hyperlink structure using a Skip-gram model. Then we incorporate the learned article vectors into the input feature set for NE type classification. To conduct large-scale practical experiments, we created a new dataset containing over 22,000 manually labeled articles. With the dataset, we empirically show that both of our ideas gained their own statistically significant improvement separately in classification accuracy. Moreover, we show that our proposed methods are particularly effective in labeling infrequent NE types. We've made the learned article vectors publicly available. The labeled dataset is available if one contacts the authors.

Transient scattering by a metal cylinder covered with inhomogeneous lossy material is analyzed for application of radar systems to nondestructive testing of reinforced concrete structures. First, inhomogeneous lossy material that is a model of corrosion by rust is approximated by a cylindrical multilayered medium, and analytic solution of a scattered field in frequency domain is derived. Next, time domain scattering response is calculated from the frequency domain data by using the inverse Fourier transform. Numerical results of pulse responses indicate that corrosion rate of the concrete can be evaluated by checking the waveform distortion of the pulse responses.

Surveillance with multiple cameras and microphones is promising to trace activities of suspicious persons for security purposes. When these sensors are connected to the Internet, they might also jeopardize innocent people's privacy because, as a result of human error, signals from sensors might allow eavesdropping by malicious persons. This paper presents a proposal for exploiting super-resolution to address this problem. Super-resolution is a signal processing technique by which a high-resolution version of a signal can be reproduced from a low-resolution version of the same signal source. Because of this property, an intelligible speech signal is reconstructed from multiple sensor signals, each of which is completely unintelligible because of its sufficiently low sampling rate. A method based on Bayesian linear regression is proposed in comparison with one based on maximum likelihood. Computer simulations using a simple sinusoidal input demonstrate that the methods restore the original signal from those which are actually measured. Moreover, results show that the method based on Bayesian linear regression is more robust than maximum likelihood under various microphone configurations in noisy environments and that this advantage is remarkable when the number of microphones enrolled in the process is as small as the minimum required. Finally, listening tests using speech signals confirmed that mean opinion score (MOS) of the reconstructed signal reach 3, while those of the original signal captured at each single microphone are almost 1.

To support the rapid increase of mobile traffic, the LTE-based air interface is expected to be employed in the unlicensed spectrum known as “Licensed-Assisted Access (LAA).” The LAA terminal, which employs an LTE-based air interface, suffers from interference from WiFi access points as well as the LAA base station. The interference rejection combining (IRC) receiver, which employs a linear minimum mean square error (MMSE) filter, can suppress this interference from WiFi access points in addition to that of the LAA base station. The IRC receiver is effective, since it requires no knowledge of the interference, which is generally difficult to obtain for different systems. In this paper, we use a link-level simulation to evaluate the performance of the IRC receiver in suppressing the interference from WiFi access points, and show that the IRC receiver can effectively cancel the interference from WiFi systems as well as LTE systems, although we observed a slight performance degradation due to the covariance matrix estimation error caused by the WiFi interference fluctuation in the frequency-domain.

In wireless sensor networks, the on-off attacker nodes can present good behaviors and then opportunistically and selectively behave badly to compromise the network. Such misbehaving nodes are usually difficult to be spotted by the network system in a short term. To address this issue, in this study, we propose a reputation scheme to mitigate the on-off attack. In addition, a penalty module is properly designed so that the reputation scheme can effectively respond to the on-off misbehaviors and make such nodes quickly detected by the system, hence the minimization of their influence. We confirm the feasibility and effectiveness of the proposed scheme through simulation tests.

There have been several recent reports that botnet communication between bot-infected computers and Command and Control servers (C&C servers) using the Domain Name System (DNS) protocol has been used by many cyber attackers. In particular, botnet communication based on the DNS TXT record type has been observed in several kinds of botnet attack. Unfortunately, the DNS TXT record type has many forms of legitimate usage, such as hostname description. In this paper, in order to detect and block out botnet communication based on the DNS TXT record type, we first differentiate between legitimate and suspicious usages of the DNS TXT record type and then analyze real DNS TXT query data obtained from our campus network. We divide DNS queries sent out from an organization into three types — via-resolver, and indirect and direct outbound queries — and analyze the DNS TXT query data separately. We use a 99-day dataset for via-resolver DNS TXT queries and an 87-day dataset for indirect and direct outbound DNS TXT queries. The results of our analysis show that about 30%, 8% and 19% of DNS TXT queries in via-resolver, indirect and direct outbound queries, respectively, could be identified as suspicious DNS traffic. Based on our analysis, we also consider a comprehensive botnet detection system and have designed a prototype system.

Measuring program execution time is a much-used technique for performance evaluation in computer science. Without proper care, however, timed results may vary a lot, thus making it hard to trust their validity. We propose a novel timing protocol to significantly reduce such variability by eliminating executions involving infrequent, long-running daemons.

To aim to achieve a high-performance computation for microwave simulations with low cost, small size machine and low energy consumption, a method of the FDTD dedicated computer has been investigated. It was shown by VHDL logical circuit simulations that the FDTD dedicated computer with a dataflow architecture has much higher performance than that of high-end PC and GPU. Then the remaining task of this work is large scale computations by the dedicated computer, since microwave simulations for only 18×18×Z grid space (Z is the number of girds for z direction) can be executed in a single FPGA at most. To treat much larger numerical model size for practical applications, this paper considers an implementation of a domain decomposition method operation of the FDTD dedicated computer in a single FPGA.

This research aims to find the best practice of prototyping a smart Lingzhi mushroom farm in Thailand. This research applied the use of NodeMCU with a humidity sensor and IOT platform to measure and monitor the humidity in the Lingzhi mushroom farm. The humidity data proceeds through NETPIE was developed and provided by NECTEC, Thailand as a free service for IOT. The humidity data was stored into a NET FEED (a sub service from NETPIE) and displayed on mobile devices and computers through NET FREEBOARD (another sub service of NETPIE). This technology also automatically controlled the sprinkler, fog pumps, and the functional status (switching on and off periodically) of push notifications through LINE API on the LINE Application. The equipment and tools used in this research were NodeMCU, humidity sensor, RTC (real time clock), relay module, sprinkler and fog pumps. C++ and Node.JS were used for programming. The services and protocol used were NETPIE (Network Platform for internet of everything) with subservices such as NETPIE FEED, NETPIE FREEBOARD, and NETPIE REST API. The LINE API was also included. The results of the research show that using NodeMCU with the humidity sensor and IOT platform demonstrates the best practice of smart farming.

Space optical communication has been considered one of the major candidates for high-rate data transmission and it reaches the practical stage to operate as a high-rate data transmission system. In this paper, the author reports the latest situation of space optical communication around the world, flight demonstrations, technological research and standardization. Research and development activities at Japan aerospace exploration agency (JAXA) are also presented.