Danmaku commenting has become popular for co-viewing on video-sharing platforms, such as Nicovideo. However, many irrelevant comments usually contaminate the quality of the information provided by videos. Such an information pollutant problem can be solved by a comment classifier trained with an abstention option, which detects comments whose video categories are unclear. To improve the performance of this classification task, this paper presents Nicovideo-specific language representations. Specifically, we used sentences from Nicopedia, a Japanese online encyclopedia of entities that possibly appear in Nicovideo contents, to pre-train a bidirectional encoder representations from Transformers (BERT) model. The resulting model named Nicopedia BERT is then fine-tuned such that it could determine whether a given comment falls into any of predefined categories. The experiments conducted on Nicovideo comment data demonstrated the effectiveness of Nicopedia BERT compared with existing BERT models pre-trained using Wikipedia or tweets. We also evaluated the performance of each model in an additional sentiment classification task, and the obtained results implied the applicability of Nicopedia BERT as a feature extractor of other social media text.

Online social networks have increased their impact on the real world, which motivates information senders to control the propagation process of information to promote particular actions of online users. However, the existing works on information provisioning seem to oversimplify the users' decision-making process that involves information reception, internal actions of social networks, and external actions of social networks. In particular, characterizing the best practices of information provisioning that promotes the users' external actions is a complex task due to the complexity of the propagation process in OSNs, even when the variation of information is limited. Therefore, we propose a new information diffusion model that distinguishes user behaviors inside and outside of OSNs, and formulate an optimization problem to maximize the number of users who take the external actions by providing information over multiple rounds. Also, we define a robust provisioning policy for the problem, which selects a message sequence to maximize the expected number of desired users under the probabilistic uncertainty of OSN settings. Our experiment results infer that there could exist an information provisioning policy that achieves nearly-optimal solutions in different types of OSNs. Furthermore, we empirically demonstrate that the proposed robust policy can be such a universally optimal solution.

To protect user privacy and establish self-information control rights, service providers must notify users of their privacy policies and obtain their consent in advance. The frameworks that impose these requirements are mandatory. Although originally designed to protect user privacy, obtaining user consent in advance has become a mere formality. These problems are induced by the gap between service providers' privacy policies, which prioritize the observance of laws and guidelines, and user expectations which are to easily understand how their data will be handled. To reduce this gap, we construct a tool supporting users in reading privacy policies in Japanese. We designed the tool to present users with separate unique expressions containing relevant information to improve the display format of the privacy policy and render it more comprehensive for Japanese users. To accurately extract the unique expressions from privacy policies, we created training data for machine learning for the constructed tool. The constructed tool provides a summary of privacy policies for users to help them understand the policies of interest. Subsequently, we assess the effectiveness of the constructed tool in experiments and follow-up questionnaires. Our findings reveal that the constructed tool enhances the users' subjective understanding of the services they read about and their awareness of the related risks. We expect that the developed tool will help users better understand the privacy policy content and and make educated decisions based on their understanding of how service providers intend to use their personal data.

Microservice architecture has been widely adopted for large-scale applications because of its benefits of scalability, flexibility, and reliability. However, microservice architecture also proposes new challenges in diagnosing root causes of performance degradation. Existing methods rely on labeled data and suffer a high computation burden. This paper proposes MicroState, an unsupervised and lightweight method to pinpoint the root cause with detailed descriptions. We decompose root cause diagnosis into element location and detailed reason identification. To mitigate the impact of element heterogeneity and dynamic invocations, MicroState generates elements' invoked states, quantifies elements' abnormality by warping-based state comparison, and infers the anomalous group. MicroState locates the root cause element with the consideration of anomaly frequency and persistency. To locate the anomalous metric from diverse metrics, MicroState extracts metrics' trend features and evaluates metrics' abnormality based on their trend feature variation, which reduces the reliance on anomaly detectors. Our experimental evaluation based on public data of the Artificial intelligence for IT Operations Challenge (AIOps Challenge 2020) shows that MicroState locates root cause elements with 87% precision and diagnoses anomaly reasons accurately.

In order to improve crop production and efficiency of farming operations, an IoT (Internet of Things) system for remote monitoring has been attracting a lot of attention. The existing studies have proposed agricultural sensing systems such that environmental information is collected from many sensor nodes installed in farmland through wireless communications (e.g., Wi-Fi, ZigBee). Especially, Low-Power Wide-Area (LPWA) is a focus as a candidate for wireless communication that enables the support of vast farmland for a long time. However, it is difficult to achieve long distance communication even when using the LPWA because a clear line of sight is difficult to keep due to many obstacles such as crops and agricultural machinery in the farmland. In addition, a sensor node cannot run permanently on batteries because the battery capacity is not infinite. On the other hand, an Unmanned Aerial Vehicle (UAV) that can move freely and stably in the sky has been leveraged for agricultural sensor network systems. By utilizing a UAV as the gateway of the sensor network, the gateway can move to the appropriate location to ensure a clear line of sight from the sensor nodes. In addition, the coverage area of the sensor network can be expanded as the UAV travels over a wide area even when short-range and ultra-low-power wireless communication (e.g., Bluetooth Low Energy (BLE)) is adopted. Furthermore, various wireless technologies (e.g., wireless power transfer, wireless positioning) that have the possibility to improve the coverage area and the lifetime of the sensor network have become available. Therefore, in this study, we propose and develop two kinds of new agricultural sensing systems utilizing a UAV and various wireless technologies. The objective of the proposed system is to provide the solution for achieving the wide-area and long-term sensing for the vast farmland. Depending on which problem is in a priority, the proposed system chooses one of two designs. The first design of the system attempts to achieve the wide-area sensing, and so it is based on the LPWA for wireless communication. In the system, to efficiently collect the environmental information, the UAV autonomously travels to search for the locations to maintain the good communication properties of the LPWA to the sensor nodes dispersed over a wide area of farmland. In addition, the second design attempts to achieve the long-term sensing, so it is based on BLE, a typical short-range and ultra-low-power wireless communication technology. In this design, the UAV autonomously flies to the location of sensor nodes and supplies power to them using a wireless power transfer technology for achieving a battery-less sensor node. Through experimental evaluations using a prototype system, it is confirmed that the combination of the UAV and various wireless technologies has the possibility to achieve a wide-area and long-term sensing system for monitoring vast farmland.

QoS of applications is essential for content providers, and it is required to improve the end-to-end communication quality from a content provider to users. Generally, a content provider's data center network is connected to multiple ASes and has multiple egress paths to reach the content user's network. However, on the Internet, the communication quality of network paths outside of the provider's administrative domain is a black box, so multiple egress paths cannot be quantitatively compared. In addition, it is impossible to determine a unique egress path within a network domain because the parameters that affect the QoS of the content are different for each network. We propose a “Performance Aware Egress Path Discovery” method to improve QoS for content providers. The proposed method uses two techniques: Egress Peer Engineering with Segment Routing over IPv6 and Passive End-to-End Measurement. The method is superior in that it allows various metrics depending on the type of content and can be used for measurements without affecting existing systems. To evaluate our method, we deployed the Performance Aware Egress Path Discovery System in an existing content provider network and conducted experiments to provide production services. Our findings from the experiment show that, in this network, 15.9% of users can expect a 30Mbps throughput improvement, and 13.7% of users can expect a 10ms RTT improvement.

Let us consider a regular expression r of length m and a text string T of length n over an alphabet Σ. Then, the RE minimal substring search problem is to find all minimal substrings of T matching r. Yamamoto proposed O(mn) time and O(m) space algorithm using a Thompson automaton. In this paper, we improve Yamamoto's algorithm by introducing parallelism. The proposed algorithm runs in O(mn) time in the worst case and in O(mn/p) time in the best case, where p denotes the number of processors. Besides, we show a parameter related to the parallel time of the proposed algorithm. We evaluate the algorithm experimentally.

Many modern regular expression engines employ various extensions to give more expressive support for real-world usages. Among the major extensions employed by many of the modern regular expression engines are backreferences and lookaheads. A question of interest about these extended regular expressions is their expressive power. Previous works have shown that (i) the extension by lookaheads does not enhance the expressive power, i.e., the expressive power of regular expressions with lookaheads is still regular, and that (ii) the extension by backreferences enhances the expressive power, i.e., the expressive power of regular expressions with backreferences (abbreviated as rewb) is no longer regular. This raises the following natural question: Does the extension of regular expressions with backreferences by lookaheads enhance the expressive power of regular expressions with backreferences? This paper answers the question positively by proving that adding either positive lookaheads or negative lookaheads increases the expressive power of rewb (the former abbreviated as rewblp and the latter as rewbln). A consequence of our result is that neither the class of finite state automata nor that of memory automata (MFA) of Schmid[2] (which corresponds to regular expressions with backreferenes but without lookaheads) corresponds to rewblp or rewbln. To fill the void, as a first step toward building such automata, we propose a new class of automata called memory automata with positive lookaheads (PLMFA) that corresponds to rewblp. The key idea of PLMFA is to extend MFA with a new kind of memories, called positive-lookahead memory, that is used to simulate the backtracking behavior of positive lookaheads. Interestingly, our positive-lookahead memories are almost perfectly symmetric to the capturing-group memories of MFA. Therefore, our PLMFA can be seen as a natural extension of MFA that can be obtained independently of its original intended purpose of simulating rewblp.

RESTful web APIs have become ubiquitous with most modern web applications embracing the micro-service architecture. A RESTful API provides data over the network using HTTP probably interacting with databases and other services and must preserve its security properties. However, REST is not a protocol but rather a set of guidelines on how to design resources accessed over HTTP endpoints. There are guidelines on how related resources should be structured with hierarchical URIs as well as how the different HTTP verbs should be used to represent well-defined actions on those resources. Whereas security has always been critical in the design of RESTful APIs, there are few or no clear model driven engineering techniques utilizing a secure-by-design approach that interweaves both the functional and security requirements. We therefore propose an approach to specifying APIs functional and security requirements with the practical Structured-Object-oriented Formal Language (SOFL). Our proposed approach provides a generic methodology for designing security aware APIs by utilizing concepts of domain models, domain primitives, Ecore metamodel and SOFL. We also describe a case study to evaluate the effectiveness of our approach and discuss important issues in relation to the practical applicability of our method.

Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.

The literature presents a digitized pension system based on a consortium blockchain, with the aim of overcoming existing pension system challenges such as multiparty collaboration, manual intervention, high turnaround time, cost transparency, auditability, etc. In addition, the adoption of hyperledger fabric and the introduction of smart contracts aim to transform multi-organizational workflow into a synchronized, automated, modular, and error-free procedure.

Cross-modal hashing technology has attracted much attention for its favorable retrieval performance and low storage cost. However, for existing cross-modal hashing methods, the heterogeneity of data across modalities is still a challenge and how to fully explore and utilize the intra-modality features has not been well studied. In this paper, we propose a novel cross-modal hashing approach called Modality-fused Graph Network (MFGN). The network architecture consists of a text channel and an image channel that are used to learn modality-specific features, and a modality fusion channel that uses the graph network to learn the modality-shared representations to reduce the heterogeneity across modalities. In addition, an integration module is introduced for the image and text channels to fully explore intra-modality features. Experiments on two widely used datasets show that our approach achieves better results than the state-of-the-art cross-modal hashing methods.

The MPEG Immersive Video (MIV) standard for immersive video coding provides users with an immersive sense of 6 degrees of freedom (6DoF) of view position and orientation by efficiently compressing multiview video acquired from different positions in a limited 3D space. In the MIV reference software called Test Model for Immersive Video (TMIV), the number of pixels to be compressed and transmitted is reduced by removing inter-view redundancy. Therefore, the occupancy information that indicates whether each pixel is valid or invalid must also be transmitted to the decoder for viewport rendering. The occupancy information is embedded in a geometry atlas and transmitted to the decoder side. At this time, to prevent occupancy errors that may occur during the compression of the geometry atlas, a guard band is set in the depth dynamic range. Reducing this guard band can improve the rendering quality by allowing a wider dynamic range for depth representation. Therefore, in this paper, based on the analysis of occupancy error of the current TMIV, two methods of occupancy error correction which allow depth dynamic range extension in the case of computer-generated (CG) sequences are presented. The experimental results show that the proposed method gives an average 2.2% BD-rate bit saving for CG compared to the existing TMIV.

A new zero-forcing block diagonalization (ZF-BD) scheme that enables both a more simplified ZF-BD and further increase in sum rate of MU-MIMO channels is proposed in this paper. The proposed scheme provides the improvement in BER performance for equivalent SU-MIMO channels. The proposed scheme consists of two components. First, a permuted channel matrix (PCM), which is given by moving the submatrix related to a target user to the bottom of a downlink MIMO channel matrix, is newly defined to obtain a precoding matrix for ZF-BD. Executing QR decomposition alone for a given PCM provides null space for the target user. Second, a partial MSQRD (PMSQRD) algorithm, which adopts MSQRD only for a target user to provide improvement in bit rate and BER performance for the user, is proposed. Some numerical simulations are performed, and the results show improvement in sum rate performance of the total system. In addition, appropriate bit allocation improves the bit error rate (BER) performance in each equivalent SU-MIMO channel. A successive interference cancellation is applied to achieve further improvement in BER performance of user terminals.

In mobile communication systems, the channel state information (CSI) is severely affected by the noise effect of the receiver. The adaptive subcarrier grouping (ASG) for sample matrix inversion (SMI) based minimum mean square error (MMSE) adaptive array has been previously proposed. Although it can reduce the additive noise effect by increasing samples to derive the array weight for co-channel interference suppression, it needs to know the signal-to-noise ratio (SNR) in advance to set the threshold for subcarrier grouping. This paper newly proposes adaptive zero padding (AZP) in the time domain to improve the weight accuracy of the SMI matrix. This method does not need to estimate the SNR in advance, and even if the threshold is always constant, it can adaptively identify the position of zero-padding to eliminate the noise interference of the received signal. Simulation results reveal that the proposed method can achieve superior bit error rate (BER) performance under various Rician K factors.

To accelerate research on Beyond 5G (B5G) technologies in Japan, we propose an algorithm that designs mesh-type metropolitan area network (MAN) models based on a priori Japanese regional railway information, because ground-truth communication network information is unavailable. Instead, we use the information of regional railways, which is expected to express the necessary geometric structure of our metropolitan cities while remaining strongly correlated with their population densities and demographic variations. We provide an additional compression algorithm for use in reducing a small-scale network model from the original MAN model designed using the proposed algorithm. Two Tokyo MAN models are created, and we provide day and night variants for each while highlighting the number of passengers alighting/boarding at each station and the respective population densities. The validity of the proposed algorithm is verified through comparisons with the Japan Photonic Network model and another model designed using the communication network information, which is not ground-truth. Comparison results show that our proposed algorithm is effective for designing MAN models and that our result provides a valid Tokyo MAN model.

Direction of arrival (DOA) estimation has been a primary focus of research for many years. Research on DOA estimation continues to be immensely popular in the fields of the internet of things, radar, and smart driving. In this paper, a simple new two-dimensional DOA framework is proposed in which a triangular array is used to receive wideband linear frequency modulated continuous wave signals. The mixed echo signals from various targets are separated into a series of single-tone signals. The unwrapping algorithm is applied to the phase difference function of the single-tone signals. By using the least-squares method to fit the unwrapped phase difference function, the DOA information of each target is obtained. Theoretical analysis and simulation demonstrate that the framework has the following advantages. Unlike traditional phase goniometry, the framework can resolve the trade-off between antenna spacing and goniometric accuracy. The number of detected targets is not limited by the number of antennas. Moreover, the framework can obtain highly accurate DOA estimation results.

Clipping is an efficient and simple method that can reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. However, clipping causes in-band distortion referred to as clipping noise. To resolve this problem, a novel iterative estimation and cancellation (IEC) scheme for clipping noise is one of the most popular schemes because it can significantly improve the performance of clipped OFDM systems. However, IEC exploits detected symbols at the receiver to estimate the clipping noise in principle and the detected symbols are not the sufficient statistic in terms of estimation theory. In this paper, we propose the post-processing technique of IEC, which fully exploits given sufficient statistic at the receiver and thus further enhances the performance of a clipped OFDM system as verified by simulations.

In recent years, physical layer security (PLS), which is based on information theory and whose strength does not depend on the eavesdropper's computing capability, has attracted much attention. We have proposed a chaos modulation method as one PLS method that offers channel coding gain. One alternative is based on polar codes. They are robust error-correcting codes, have a nested structure in the encoder, and the application of this mechanism to PLS encryption (PLS-polar) has been actively studied. However, most conventional studies assume the application of conventional linear modulation such as BPSK, do not use encryption modulation, and the channel coding gain in the modulation is not achieved. In this paper, we propose a PLS-polar method that can realize high-quality transmission and encryption of a modulated signal by applying chaos modulation to a polar-coding system. Numerical results show that the proposed method improves the performance compared to the conventional PLS-polar method by 0.7dB at a block error rate of 10-5. In addition, we show that the proposed method is superior to conventional chaos modulation concatenated with low-density parity-check codes, indicating that the polar code is more suitable for chaos modulation. Finally, it is demonstrated that the proposed method is secure in terms of information theoretical and computational security.

High-frequency wireless communication is drawing attention because of its potential to actualize huge transmission capacity in the next generation wireless system. The use of high-frequency bands requires dense deployment of access points to compensate for significant distance attenuation and diffraction loss. Dense deployment of access points in a mobility environment triggers an increase in the frequency of handover because the number of candidate access points increases. Therefore, simple handover schemes are needed. High-frequency wireless systems enable station position to be determined using their wideband and highly directional communication signals. Thus, simple handover based on position information estimated using the communication signal is possible. Interruptions caused by handover are also a huge barrier to actualizing stable high-frequency wireless communications. This paper proposes a seamless handover scheme using multiple radio units. This paper evaluates the combination of simple handover and the proposed scheme based on experiments using a formula racing car representing the fastest high-speed mobility environment. Experimental results show that seamless handover and high-speed wireless transmission over 200Mbps are achieved over a 400-m area even at station velocities of greater than 200km/h.