We demonstrate an all-optical non-return-to-zero differential phase shift keying (NRZ-DPSK) to return-to-zero differential phase shift keying (RZ-DPSK) format conversion with wavelength-shift-free and pulsewidth tunable operations by using a semiconductor optical amplifier (SOA)-based switch. An NRZ-DPSK signal is injected into the SOA-based switch with an RZ clock, and is converted to RZ-DPSK signal owing to the nonlinear effects inside the SOA. In this scheme, the wavelength of the converted RZ-DPSK signal is maintained as the original wavelength of the input NRZ-DPSK signal during the format conversion. Moreover, the pulsewidth of the converted signal is tunable in a wider operating range from 30 to 60 ps. The format conversion with pulsewidth tunability is based on cross-phase modulation (XPM) and cross-gain modulation (XGM) effects in the SOA. The clear eye diagrams, optical spectra and the bit-error-rate (BER) characteristics show high conversion performance with the wide pulsewidth tuning range. For all cases of the converted RZ-DPSK signal with different pulsewidths, the receiver sensitivities at a BER of 10$^{-9}$ for the converted RZ-DPSK signal were 0.7 to 1.5 dB higher than the receiver sensitivity of the input NRZ-DPSK signal.

We investigate resolution improvement in optical quantization with keeping high sampling rate performance in optical sampling. Since our optical quantization approach uses power-to-wavelength conversion based on soliton self-frequency shift, a spectral compression can improve resolution in exchange for sampling rate degradation. In this work, we propose a different approach for resolution improvement by parallel use of dispersion devices so as to avoid sampling rate degradation. Additional use of different dispersion devices can assist the wavelength separation ability of an original dispersion device. We demonstrate the principle of resolution improvement in 3 bit optical quantization. Simulation results based on experimental evaluation of 3 bit optical quantization system shows 4 bit optical quantization is achieved by parallel use of dispersion devices in 3 bit optical quantization system. The maximum differential non-linearity (DNL) and integral non-linearity (INL) are 0.49 least significant bit (LSB) and 0.50 LSB, respectively. The effective number of bits (ENOB) estimated to 3.62 bit.

Trivium is a synchronous stream cipher using three shift registers. It is designed to have a simple structure and runs at high speed. A scan-based side-channel attack retrieves secret information using scan chains, one of design-for-test techniques. In this paper, a scan-based side-channel attack method against Trivium using scan signatures is proposed. In our method, we reconstruct a previous internal state in Trivium one by one from the internal state just when a ciphertext is generated. When we retrieve the internal state, we focus on a particular 1-bit position in a collection of scan chains and then we can attack Trivium even if the scan chain includes other registers than internal state registers in Trivium. Experimental results show that our proposed method successfully retrieves a plaintext from a ciphertext generated by Trivium.

We analyze the defects of window-based TCP algorithm in datacenter networks and propose Rate-based Datacenter TCP (RDT) algorithm in this paper. The RDT algorithm combines rate-based congestion control technology with ECN (Explicit Congestion Notification) mechanism of DCTCP. The experiments in NS2 show that RDT has a potential to completely avoid TCP incast collapse in datacenters and inherit the low latency advantages of DCTCP.

Smart city services are implemented using various data collected from houses and infrastructure within a city. As the volume and variety of the smart city data becomes huge, individual services have suffered from expensive computation effort and large processing time. In order to reduce the effort and time, this paper proposes a concept of Materialized View as a Service (MVaaS). Using the MVaaS, every application can easily and dynamically construct its own materialized view, in which the raw data is converted and stored in a convenient format with appropriate granularity. Thus, once the view is constructed, the application can quickly access necessary data. In this paper, we design a framework of MVaaS specifically for large-scale house log, managed in a smart-city data platform. In the framework, each application first specifies how the raw data should be filtered, grouped and aggregated. For a given data specification, MVaaS dynamically constructs a MapReduce batch program that converts the raw data into a desired view. The batch is then executed on Hadoop, and the resultant view is stored in HBase. We present case studies using house log in a real home network system. We also conduct an experimental evaluation to compare the response time between cases with and without MVaaS.

This paper presents a novel test data compression scheme for SoCs based on block merging and compatibility. The technique exploits the properties of compatibility and inverse compatibility between consecutive blocks, consecutive merged blocks, and two halves of the encoding merged block itself to encode the pre-computed test data. The decompression circuit is simple to be implemented and has advantage of test-independent. In addition, the proposed scheme is applicable for IP cores in SoCs since it compresses the test data without requiring any structural information of the circuit under test. Experimental results demonstrate that the proposed technique can achieve an average compression ratio up to 68.02% with significant low test application time.

This paper proposes a simple and practical scheme to decide the direction of a phased array antenna beam in wireless access systems using Radio-over-Fiber (RoF) technique. The feasibility of the proposed scheme is confirmed by the optical and wireless transmission experiments using 2GHz RoF signals. In addition, two-dimensional steering operation in the millimeter-wave band is demonstrated for targeting future high-speed wireless communication systems. The required system parameters for practical use are also provided by investigating the induced transmission penalties. The proposed detection scheme is applicable to two-dimensional antenna beam steering in the millimeter-wave band by properly designing the fiber length and wavelength variable range.

This paper proposes a novel approach for integrating spectral feature extraction and acoustic modeling in hidden Markov model (HMM) based speech synthesis. The statistical modeling process of speech waveforms is typically divided into two component modules: the frame-by-frame feature extraction module and the acoustic modeling module. In the feature extraction module, the statistical mel-cepstral analysis technique has been used and the objective function is the likelihood of mel-cepstral coefficients for given speech waveforms. In the acoustic modeling module, the objective function is the likelihood of model parameters for given mel-cepstral coefficients. It is important to improve the performance of each component module for achieving higher quality synthesized speech. However, the final objective of speech synthesis systems is to generate natural speech waveforms from given texts, and the improvement of each component module does not always lead to the improvement of the quality of synthesized speech. Therefore, ideally all objective functions should be optimized based on an integrated criterion which well represents subjective speech quality of human perception. In this paper, we propose an approach to model speech waveforms directly and optimize the final objective function. Experimental results show that the proposed method outperformed the conventional methods in objective and subjective measures.

In this paper, we present a differential dual-modulus prescaler based on an injection-locked frequency divider (ILFD) for satellite low-noise block (LNB) down-converters. We fabricated three-stage differential latches using an ILFD and a cascaded differential divider in a 130-nm CMOS process. The prototype chip core area occupies 40µm × 20µm. The proposed prescaler achieved the locking range of 2.1-10GHz with both divide-by-10 and divide-by-11 operations at a supply voltage of 1.4V. Normalized energy consumptions are 0.4pJ (=mW/GHz) at a 1.4-V supply voltage and 0.24pJ at a 1.2-V supply voltage. To evaluate the tolerance of phase-difference deviation of the input differential pair from the perfect differential phase-difference, 180 degrees, we measured the operational frequencies for various phase-difference inputs. The proposed prescaler achieved the operational frequency range of 2.1-10GHz with an input phase-difference deviation of less than 90 degrees. However, the range of operational frequency decreases as the phase-difference deviation increases beyond 90 degrees and reaches 3.9-7.9GHz for the phase-difference deviation of 180 degrees (i.e. no phase difference). In addition, to confirm the fully locking operation, we measured the spurious noise and the phase noise degradation while reducing the supply voltage. The sensitivity analysis of the prescaler for various supply voltages can explain the above degradation of spectral purity. Spurious noise arises and the phase noise degrades with decreasing supply voltage due to the quasi- and non-locking operations. We verified the fully-locking operation for the LNB down-converter at a 1.4-V supply voltage.

Parametric resonance based solutions for sub-gigahertz radio frequency transceiver with 0.3V supply voltage are proposed in this paper. As an implementation example, a 0.3V 720µW variation-tolerant injection-locked frequency multiplier is developed in 90nm CMOS. It features a parametric resonance based multi-phase synthesis scheme, thereby achieving the lowest supply voltage with -110dBc@ 600kHz phase noise and 873MHz-1.008GHz locking range in state-of-the-art frequency synthesizers.

Multi-task learning is an important area of machine learning that tries to learn multiple tasks simultaneously to improve the accuracy of each individual task. We propose a new tree-based ensemble multi-task learning method for classification and regression (MT-ExtraTrees), based on Extremely Randomized Trees. MT-ExtraTrees is able to share data between tasks minimizing negative transfer while keeping the ability to learn non-linear solutions and to scale well to large datasets.

In phrase-based statistical machine translation, long distance reordering problem is one of the most challenging issues when translating syntactically distant language pairs. In this paper, we propose a novel reordering model to solve this problem. In our model, reordering is affected by the overall structures of sentences such as listings, reduplications, and modifications as well as the relationships of adjacent phrases. To this end, we reflect global syntactic contexts including the parts that are not yet translated during the decoding process.

Information Security Management Systems (ISMSs) play important roles in helping organizations to manage their information securely. However, establishing, managing, and maintaining ISMSs is not an easy task for most organizations because an ISMS has many participants and tasks, and requires many kinds of documents. Therefore, organizations with ISMSs demand tools that can support them to perform all tasks in ISMS lifecycle processes consistently and continuously. To realize such support tools, a database system that manages ISO/IEC 27000 series, which are international standards for ISMSs, and ISMS documents, which are the products of tasks in ISMS lifecycle processes, is indispensable. The database system should manage data of the standards and documents for all available versions and translations, relationship among the standards and documents, authorization to access the standards and documents, and metadata of the standards and documents. No such database system has existed until now. This paper presents an information security management database system (ISMDS) that manages ISO/IEC 27000 series and ISMS documents. ISMDS is a meta-database system that manages several databases of standards and documents. ISMDS is used by participants in ISMS as well as tools supporting the participants to perform tasks in ISMS lifecycle processes. The users or tools can retrieve data from all versions and translations of the standards and documents. The paper also presents some use cases to show the effectiveness of ISMDS.

In this paper, an automatic and unsupervised method using context-dependent hidden Markov models (CD-HMMs) is proposed for the prosodic labeling of speech synthesis databases. This method consists of three main steps, i.e., initialization, model training and prosodic labeling. The initial prosodic labels are obtained by unsupervised clustering using the acoustic features designed according to the characteristics of the prosodic descriptor to be labeled. Then, CD-HMMs of the spectral parameters, F0s and phone durations are estimated by a means similar to the HMM-based parametric speech synthesis using the initial prosodic labels. These labels are further updated by Viterbi decoding under the maximum likelihood criterion given the acoustic feature sequences and the trained CD-HMMs. The model training and prosodic labeling procedures are conducted iteratively until convergence. The performance of the proposed method is evaluated on Mandarin speech synthesis databases and two prosodic descriptors are investigated, i.e., the prosodic phrase boundary and the emphasis expression. In our implementation, the prosodic phrase boundary labels are initialized by clustering the durations of the pauses between every two consecutive prosodic words, and the emphasis expression labels are initialized by examining the differences between the original and the synthetic F0 trajectories. Experimental results show that the proposed method is able to label the prosodic phrase boundary positions much more accurately than the text-analysis-based method without requiring any manually labeled training data. The unit selection speech synthesis system constructed using the prosodic phrase boundary labels generated by our proposed method achieves similar performance to that using the manual labels. Furthermore, the unit selection speech synthesis system constructed using the emphasis expression labels generated by our proposed method can convey the emphasis information effectively while maintaining the naturalness of synthetic speech.

This paper describes the design and evaluation of a method for developing a chat-oriented dialog system by utilizing real human-to-human conversation examples from movie scripts and Twitter conversations. The aim of the proposed method is to build a conversational agent that can interact with users in as natural a fashion as possible, while reducing the time requirement for database design and collection. A number of the challenging design issues we faced are described, including (1) constructing an appropriate dialog corpora from raw movie scripts and Twitter data, and (2) developing an multi domain chat-oriented dialog management system which can retrieve a proper system response based on the current user query. To build a dialog corpus, we propose a unit of conversation called a tri-turn (a trigram conversation turn), as well as extraction and semantic similarity analysis techniques to help ensure that the content extracted from raw movie/drama script files forms appropriate dialog-pair (query-response) examples. The constructed dialog corpora are then utilized in a data-driven dialog management system. Here, various approaches are investigated including example-based (EBDM) and response generation using phrase-based statistical machine translation (SMT). In particular, we use two EBDM: syntactic-semantic similarity retrieval and TF-IDF based cosine similarity retrieval. Experiments are conducted to compare and contrast EBDM and SMT approaches in building a chat-oriented dialog system, and we investigate a combined method that addresses the advantages and disadvantages of both approaches. System performance was evaluated based on objective metrics (semantic similarity and cosine similarity) and human subjective evaluation from a small user study. Experimental results show that the proposed filtering approach effectively improve the performance. Furthermore, the results also show that by combing both EBDM and SMT approaches, we could overcome the shortcomings of each.

This paper proposes an ultra-low-power 5.5-GHz PLL which employs the new divide-by-4 injection-locked frequency divider (ILFD) and a class-C VCO with linearity-compensated varactor for low supply voltage operation. A forward-body-biasing (FBB) technique can decrease threshold voltage of MOS transistors, which can improve operation frequency and can widen the lock range of the ILFD. The FBB is also employed for linear-frequency-tuning of VCO under low supply voltage of 0.5V. The double-switch injection technique is also proposed to widen the lock range of the ILFD. The digital calibration circuit is introduced to control the lock-range of ILFD automatically. The proposed PLL was fabricated in a 65nm CMOS process. With a 34.3-MHz reference, it shows a 1-MHz-offset phase noise of -106dBc/Hz at 5.5GHz output. The supply voltage is 0.54V for divider and 0.5V for other components. Total power consumption is 0.95mW.

This paper proposes a generic construction of hierarchical identity-based identification (HIBI) protocols secure against impersonation under active and concurrent attacks in the standard model. The proposed construction converts a digital signature scheme existentially unforgeable against chosen message attacks, where the scheme has a protocol for showing possession of a signing key, not a signature. Our construction is based on the so-called certificate-based construction of hierarchical identity-based cryptosystems, and utilizes a variant of the well-known OR-proof technique to ensure the security against impersonation under active and concurrent attacks. We also present several concrete examples of our construction employing the Waters signature (EUROCRYPT 2005), and other signatures. As results, its concurrent security of each instantiation is proved under the computational Diffie-Hellman (CDH) assumption, the RSA assumption, or their variants in the standard model. Chin, Heng, and Goi proposed an HIBI protocol passively and concurrently secure under the CDH and one-more CDH assumption, respectively (FGIT-SecTech 2009). However, its security is proved in the random oracle model.

ID-based authenticated key exchange (ID-AKE) is a cryptographic tool to establish a common session key between parties with authentication based on their IDs. If IDs contain some hierarchical structure such as an e-mail address, hierarchical ID-AKE (HID-AKE) is especially suitable because of scalability. However, most of existing HID-AKE schemes do not satisfy advanced security properties such as forward secrecy, and the only known strongly secure HID-AKE scheme is inefficient. In this paper, we propose a new HID-AKE scheme which achieves both strong security and efficiency. We prove that our scheme is eCK-secure (which ensures maximal-exposure-resilience including forward secrecy) without random oracles, while existing schemes is proved in the random oracle model. Moreover, the number of messages and pairing operations are independent of the hierarchy depth; that is, really scalable and practical for a large-system.

In this paper, we present a lattice based method on small secret exponent attack on the RSA scheme. Boneh and Durfee reduced the attack to finding the small roots of the bivariate modular equation: x(N+1+y)+1 ≡ 0 (mod e), where N is an RSA modulus and e is the RSA public key and proposed a lattice based algorithm for solving the problem. When the secret exponent d is less than N0.292, their method breaks the RSA scheme. Since the lattice used in the analysis is not full-rank, the analysis is not easy. Blömer and May proposed an alternative algorithm that uses a full-rank lattice, even though it gives a bound (d≤N0.290) that is worse than Boneh-Durfee. However, the proof for their bound is still complicated. Herrmann and May, however, have given an elementary proof for the Boneh-Durfee's bound: d≤N0.292. In this paper, we first give an elementary proof for achieving Blömer-May's bound: d≤N0.290. Our proof employs the unravelled linearization technique introduced by Herrmann and May and is rather simpler than that of Blömer-May's proof. We then provide a unified framework — which subsumes the two previous methods, the Herrmann-May and the Blömer-May methods, as a special case — for constructing a lattice that can be are used to solve the problem. In addition, we prove that Boneh-Durfee's bound: d≤N0.292 is still optimal in our unified framework.

The research reported in this paper is an attempt to elucidate the predictors of pause duration in read-aloud discourse. Through simple linear regression analysis and stepwise multiple linear regression, we examined how different factors (namely, syntactic structure, discourse hierarchy, topic structure, preboundary length, and postboundary length) influenced pause duration both separately and jointly. Results from simple regression analysis showed that discourse hierarchy, syntactic structure, topic structure, and postboundary length had significant impacts on boundary pause duration. However, when these factors were tested in a stepwise regression analysis, only discourse hierarchy, syntactic structure, and postboundary length were found to have significant impacts on boundary pause duration. The regression model that best predicted boundary pause duration in discourse context was the one that first included syntactic structure, and then included discourse hierarchy and postboundary length. This model could account for about 80% of the variance of pause duration. Tests of mediation models showed that the effects of topic structure and discourse hierarchy were significantly mediated by syntactic structure, which was most closely correlated with pause duration. These results support an integrated model combining the influence of several factors and can be applied to text-to-speech systems.