With the rapid spread of smart devices, such as smartphones and tablet PCs, user authentication is becoming increasingly important because various kinds of data concerning user privacy are processed within them. At present, in the case of smart devices, password-based authentication is frequently used; however, biometric authentication has attracted more attention as a user authentication technology. A smart device is equipped with various sensors, such as cameras, microphones, and touch panels, many of which enable biometric information to be obtained. While the function of biometric authentication is available in many smart devices, there remain some problems to be addressed for more secure and convenient user authentication. In this paper, we summarize the current problems with user authentication on smart devices and propose a novel user authentication system based on the concept of context awareness to resolve these problems. We also present our evaluation of the performance of the system by using biometric information that was acquired from smart devices. The evaluation demonstrates the effectiveness of our system.

In this paper, an improved lattice reduction (LR)-aided soft-output multiple-input multiple-output (MIMO) detector is proposed. Conventional LR-aided soft-output MIMO detectors involve the empty set problem (ESP), in which an entry with a particular bit in the candidate list might not exist. To overcome the performance degradation resulting from this ESP, a post-processing algorithm that modifies the candidate list is proposed. The proposed algorithm efficiently resolves the ESP by utilizing the near-orthogonality of the lattice-reduced system model so that the bit error rate (BER) performance is enhanced. In addition, as the complexity of the candidate list generation is reduced with the aid of the post-processing algorithm, the overall complexity is also reduced. Simulation results and the complexity comparisons demonstrate that our proposed method lowers the required Eb/No by 4-5 dB at the BER of 10-5 and the complexity by 13%-55%, compared to the conventional method.

Locally repairable code (LRC) can recover any codeword symbol failure by accessing a small number of other symbols, which can increase the efficiency during the repair process. In a distributed storage system with locally repairable codes, any node failure can be rebuilt by accessing other fixed nodes. It is a promising prospect for the application of LRC. In this paper, some methods of constructing matrices which can generate codes with small locality will be proposed firstly. By analyzing the parameters, we construct the generator matrices of the best-known ternary linear codes of dimension 6, using methods such as shortening, puncturing and expansion. After analyzing the linear dependence of the column vectors in the generator matrices above, we find out the locality of the codes they generate. Many codes with small locality have been found.

This paper presents a fast and large-scale optical circuit-switch architecture for intra-datacenter applications that uses a combination of space switches and wavelength-routing switches are utilized. A 1,440 × 1,440 optical switch is designed with a fast-tunable laser, 8×8 delivery-and-coupling switch, and a 180×180 wavelength-routing switch. We test the bit-error-ratio characteristics of all ports of the wavelength-routing switch using 180-wavelength 10-Gbps signals in the full C-band. The worst switching time, 498 microseconds, is confirmed and all bit-error ratios are acceptable.

In this letter, an effective algorithm is proposed to improve the performance of relay enhanced cellular networks, which is to allocate appropriate resources to each access point with quality of service constraint. First we derive the ergodic rate for backhaul link based on a poison point process model, and then allocate resources to each link according to the quality of service requirements and ergodic rate of links. Numerical results show that the proposed algorithm can not only improve system throughput but also improve the rate distribution of user equipment.

This paper deals with a novel, interesting problem of detecting frauds in comparison-shopping services (CSS). In CSS, there exist frauds who perform excessive clicks on a target item. They aim at making the item look very popular and subsequently ranked high in the search and recommendation results. As a result, frauds may distort the quality of recommendations and searches. We propose an approach of detecting such frauds by analyzing click behaviors of users in CSS. We evaluate the effectiveness of the proposed approach on a real-world clickstream dataset.

The breadth-first tree searching (BFTS) detection algorithm such as the QR decomposition with M algorithm (QRD-M) which is the generally K-best detection algorithm is suboptimal, but has high complexity. In this letter, the K-best BFTS detection algorithm having reduced complexity is proposed. The proposed detection algorithm calculates the channel condition to decide the thresholds for regulating complexity and performance and from the simulation results, it has good error performance with very low complexity.

Smart education environment, that is a learning environment utilizing the Information Communication Technology (ICT), has attracted a great deal of attention. In order to expand this environment, we need a system that can establish the learning environment armed cloud systems to reduce a significant strain on teaching staff. The important issue for such system is extensibility because the system should be adapted to many kinds of original digital learning material with minimum modification. Therefore, this paper proposes “An Application Framework for Smart Education System: SES Framework”. In this Smart Education System, multi-aspect information concerning to a technical term embedded in the original digital learning material can be retrieved from different social media automatically. They can be also displayed on multi-screen devices according to user's operation. It is implemented based on “Transforming Model” which enables the migration of the original digital learning material to the smart education environment. It also has an easy operation flow for trainees named “three-step selection flow”. SES Framework derived from Model-View-Controller (MVC) pattern is based on the system architecture that enables triple mashup against the original digital learning material, external social media, and screen devices in front of users. All these functionalities have been implemented on cloud systems. We show SES Framework through the implementation example. We also demonstrate the effectiveness of SES Framework by indicating the system modification case study.

We surveyed employees who use personal devices for work. Residual analysis for cross-tabulation was carried out for three groups classified based on company rules and regulations established for mobile work. We show that the behavior of employees working with personal devices to process business data changes due to the presence or absence of the company rules and regulations.

With the increasing demand of Internet-of-Things applicability in various devices and location-based services (LBSs) with positioning capabilities, we proposed simple and effective post-processing techniques to reduce positioning error and provide more precise navigation to users in a pedestrian environment in this letter. The proposed positioning error reduction techniques (Technique 1-minimum range securement and bounce elimination, Technique 2-direction vector-based error correction) were studied considering low complexity and wide applicability to various types of positioning systems, e.g., global positioning system (GPS). Through the real field tests in urban areas, we have verified that an average positioning error of the proposed techniques is significantly decreased compared to that of a GPS-only environment.

In this paper, we study partial key exposure attacks on RSA where the number of unexposed blocks of the private key is greater than or equal to one. This situation, called generalized framework of partial key exposure attack, was first shown by Sarkar [22] in 2011. Under a certain condition for the values of exposed bits, we present a new attack which needs fewer exposed bits and thus improves the result in [22]. Our work is a generalization of [28], and the approach is based on Coppersmith's method and the technique of unravelled linearization.

This paper describes optical access networks focusing on passive optical network (PON) technologies from a technical point of view. Optical access networks have been applied to fiber-to-the-home as a driving force of broadband services and their use will continue growing in the near future. They will be applied as an aggregate component of broadband wireless networks. This paper also addresses solutions for their application.

This paper presents a novel speaking aid system to help laryngectomees produce more naturally sounding electrolaryngeal (EL) speech. An electrolarynx is an external device to generate excitation signals, instead of vibration of the vocal folds. Although the conventional EL speech is quite intelligible, its naturalness suffers from the unnatural fundamental frequency (F0) patterns of the mechanically generated excitation signals. To improve the naturalness of EL speech, we have proposed EL speech enhancement methods using statistical F0 pattern prediction. In these methods, the original EL speech recorded by a microphone is presented from a loudspeaker after performing the speech enhancement. These methods are effective for some situation, such as telecommunication, but it is not suitable for face-to-face conversation because not only the enhanced EL speech but also the original EL speech is presented to listeners. In this paper, to develop an EL speech enhancement also effective for face-to-face conversation, we propose a method for directly controlling F0 patterns of the excitation signals to be generated from the electrolarynx using the statistical F0 prediction. To get an "actual feel” of the proposed system, we also implement a prototype system. By using the prototype system, we find latency issues caused by a real-time processing. To address these latency issues, we furthermore propose segmental continuous F0 pattern modeling and forthcoming F0 pattern modeling. With evaluations through simulation, we demonstrate that our proposed system is capable of effectively addressing the issues of latency and those of electrolarynx in term of the naturalness.

The history of optical fiber and optical transmission technologies has been described in many publications. However, the history of other technologies designed to support the physical layer of optical transmission has not been described in much detail. I would like to highlight those technologies in addition to optical fibers. Therefore, this paper describes the history of the development of optical fiber related technologies such as fusion splicers, optical fiber connectors, ribbon fiber, and passive components based on changes in optical fibers and optical fiber cables. Moreover, I describe technologies designed to support multi-core fibers such as fan-in/fan-out devices.

Recent research on the assessment of low probability of interception (LPI) radar waveforms is mainly based on limiting spectral properties of Wigner matrices. As the dimension of actual operating data is constrained by the sampling frequency, it is very urgent and necessary to research the finite theory of Wigner matrices. This paper derives a closed-form expression of the spectral cumulative distribution function (CDF) for Wigner matrices of finite sizes. The expression does not involve any derivatives and integrals, and therefore can be easily computed. Then we apply it to quantifying the LPI performance of radar waveforms, and the Kullback-Leibler divergence (KLD) is also used in the process of quantification. Simulation results show that the proposed LPI metric which considers the finite sample size and signal-to-noise ratio is more effective and practical.

With the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., “enemy's enemy is a friend” rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser_RecSBT+CF. During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser_RecSBT+CF approach outperforms other up-to-date approaches.

An energy-efficient nonvolatile FPGA with assuring highly-reliable backup operation using a self-terminated power-gating scheme is proposed. Since the write current is automatically cut off just after the temporal data in the flip-flop is successfully backed up in the nonvolatile device, the amount of write energy can be minimized with no write failure. Moreover, when the backup operation in a particular cluster is completed, power supply of the cluster is immediately turned off, which minimizes standby energy due to leakage current. In fact, the total amount of energy consumption during the backup operation is reduced by 66% in comparison with that of a conventional worst-case-based approach where the long time write current pulse is used for the reliable write.

Distributed Denial of Service (DDoS) attacks based on HTTP and HTTPS (i.e., HTTP(S)-DDoS) are increasingly popular among attackers. Overlay-based mitigation solutions attract small and medium-sized enterprises mainly for their low cost and high scalability. However, conventional overlay-based solutions assume content inspection to remotely mitigate HTTP(S)-DDoS attacks, prompting trust concerns. This paper reports on a new overlay-based method which practically adds a third level of client identification (to conventional per-IP and per-connection). This enhanced identification enables remote mitigation of more complex HTTP(S)-DDoS categories without content inspection. A novel behavior-based reputation and penalty system is designed, then a simplified proof of concept prototype is implemented and deployed on DeterLab. Among several conducted experiments, two are presented in this paper representing a single-vector and a multi-vector complex HTTP(S)-DDoS attack scenarios (utilizing LOIC, Slowloris, and a custom-built attack tool for HTTPS-DDoS). Results show nearly 99.2% reduction in attack traffic and 100% chance of legitimate service. Yet, attack reduction decreases, and cost in service time (of a specified file) rises, temporarily during an approximately 2 minutes mitigation time. Collateral damage to non-attacking clients sharing an attack IP is measured in terms of a temporary extra service time. Only the added identification level was utilized for mitigation, while future work includes incorporating all three levels to mitigate switching and multi-request per connection attack categories.

This paper proposes Deep Neural Network (DNN)-based Voice Conversion (VC) using input-to-output highway networks. VC is a speech synthesis technique that converts input features into output speech parameters, and DNN-based acoustic models for VC are used to estimate the output speech parameters from the input speech parameters. Given that the input and output are often in the same domain (e.g., cepstrum) in VC, this paper proposes a VC using highway networks connected from the input to output. The acoustic models predict the weighted spectral differentials between the input and output spectral parameters. The architecture not only alleviates over-smoothing effects that degrade speech quality, but also effectively represents the characteristics of spectral parameters. The experimental results demonstrate that the proposed architecture outperforms Feed-Forward neural networks in terms of the speech quality and speaker individuality of the converted speech.

We present the first demonstration of an electro-optic modulator based on a photolithographically fabricated photonic crystal (PhC) nanocavity with a p-i-n junction with SiO2 cladding. We show that the device exhibits an ultrahigh quality factor (Q∼105) and allow us to demonstrate electro-optic modulation through the integrated p-i-n diode structure. We demonstrate an electro-optic modulation based on the carrier injection.