We propose a new hidden vector encryption (HVE) scheme that we call a ciphertext-policy delegatable hidden vector encryption (CP-dHVE) scheme. Several HVE schemes have been proposed and their properties have been analyzed extensively. Nonetheless, the definition of the HVE has been left unchanged. We therefore reconsider it, and point out that the conventional HVE should be categorized as the key-policy HVE, because the vectors corresponding to the secret keys can contain wildcards (which specify an access policy) whereas those corresponding to the ciphertexts cannot contain them. We then formalize its dual concept, the ciphertext-policy HVE, and propose a concrete scheme. Then, as an application of our scheme, we propose a public-key encryption with conjunctive keyword search scheme that can be used in the hierarchical user systems. Our scheme is novel in that the ciphertext size grows logarithmically to the number of uses in the system, while that of a conventional scheme grows linearly.

Context awareness is one of the ultimate goals of ubiquitous computing, and spatial information plays an important role in building context awareness. In this paper, we propose a new interoperable spatial information model, which is based on ucode relation (ucR) and Place Identifier (PI), for realizing ubiquitous spatial infrastructure. In addition, we propose a design environment for spatial information database using our model. Our model is based on ucode and its relation. ucode is 128 bits number and the number itself has no meaning. Hence, it is difficult to manage the relation between ucodes without using a tool. Our design environment provides to describe connection between each ucode visually and is able to manipulate data using the target space map interactively. To evaluate the proposed model and environment, we designed three spaces using our tool. In addition, we developed a web application using our spatial model. From evaluation, we have been showed that our model is effective and our design environment is useful to develop our spatial information model.

This paper presents a new span-based dependency chart parsing algorithm that models the relations between the left and right dependents of a head. Such relations cannot be modeled in existing span-based algorithms, despite their popularity in dependency corpora. We address this problem through ternary-span combination during the subtree derivation. By modeling the relations between the left and right dependents of a head, our proposed algorithm provides a better capability of coordination disambiguation when the conjunction is annotated as the head of the left and right conjuncts. This eventually leads to state-of-the-art performance of dependency parsing on the Chinese data of the CoNLL shared task.

In this paper, we propose a geographic location-based distributed routing (GDR) system. The GDR system provides information lookup based on latitude and longitude coordinates. Each node of the GDR system utilizes the coordinates as an identifier (ID), and manages an overlay routing table. An ID is generated to reflect the geographical location without using Space Filling Curve (SFC). The ID is in cartesian format (x, y), which represents the longitude x and latitude y. In a system with N nodes, each node has a routing table of size log N and a search is possible in O(log N). We evaluate the routing performance of GDR and other systems based on Chord, Kademlia and CAN. We show that in both the ID is in cartesian format and the ID is generated by using SFC, GDR, Chord and Kademlia have the same mean and the same variance of the path length, while the mean and the variance of the relay length of GDR are smaller than those of Chord and Kademlia. Furthermore, while GDR and CAN have the same mean and the same variance of the relay length, the mean and the variance of the path length of GDR are smaller than those of CAN.

Given an integer n-dimensional lattice basis, the random sampling reduction was proven to find a short vector in arithmetic steps with an integer k, which is freely chosen by users. This paper introduces new random sampling reduction using precomputation techniques. The computation cost is almost independent of the lattice dimension number. The new method is therefore especially advantageous to find a short lattice vector in higher dimensions. The arithmetic operation number of our new method is about 20% of the random sampling reduction with 200 dimensions, and with 1000 dimensions it is less than 1% ( 1/130) of that of the random sampling reduction with representative parameter settings under reasonable assumptions.

This paper provides a sufficient condition to construct timed-release public-key encryption (TRPKE), where the constructed TRPKE scheme guarantees strong security against malicious time servers, proposed by Chow et al., and strong security against malicious receivers, defined by Cathalo et al., in the random oracle model if the component IBE scheme is IND-ID-CPA secure, the component PKE scheme is IND-ID-CPA secure, and the PKE scheme satisfies negligible γ-uniformity for every public key. Although Chow et al. proposed a strongly secure TRPKE scheme, which is concrete in the standard model, to the best of our knowledge, the proposed construction is the first generic one for TRPKE that guarantees strong security even in the random oracle model.

Fault-based attacks are very powerful to recover the secret key for cryptographic implementations. In this work, we consider the faulty output value under a certain fault injection intensity as a new type of leakage called faulty behavior. We examine the data-dependency of the faulty behavior and propose a related side-channel attack called fault behavior analysis (FBA). To verify the validity of the proposed attack, we first show that our attack can work effectively on AES-COMP of SASEBO-R. Then we show how to apply the similar attack on two AES implementations with masking countermeasures, i.e., AES-MAO and AES-TI. Finally we compare the proposed FBA attack with the DFA attack and the FSA attack, trying to complete the research map for the fault-based attack based on setup-time violations.

Recent idea visualization programs still lack automatic idea summarization capabilities. This paper presents a knowledge-based method for automatically providing a short piece of English text about a topic to each idea group in idea charts. This automatic topic identification makes used Yet Another General Ontology (YAGO) and Wordnet as its knowledge bases. We propose a novel topic selection method and we compared its performance with three existing methods using two experimental datasets constructed using two idea visualization programs, i.e., the KJ Method (Kawakita Jiro Method) and mind-mapping programs. Our proposed topic identification method outperformed the baseline method in terms of both performance and consistency.

Product return is a critical but controversial issue. To deal with such a vague return problem, businesses must improve their information transparency in order to administrate the product return behaviour of their end users. This study proposes an intelligent return administration expert system (iRAES) to provide product return forecasting and decision support for returned product administration. The iRAES consists of two intelligent agents that adopt a hybrid data mining algorithm. The return diagnosis agent generates different alarms for certain types of product return, based on forecasts of the return possibility. The return recommender agent is implemented on the basis of case-based reasoning, and provides the return centre clerk with a recommendation for returned product administration. We present a 3C-iShop scenario to demonstrate the feasibility and efficiency of the iRAES architecture. Our experiments identify a particularly interesting return, for which iRAES generates a recommendation for returned product administration. On average, iRAES decreases the effort required to generate a recommendation by 70% compared to previous return administration systems, and improves performance via return decision support by 37%. iRAES is designed to accelerate product return administration, and improve the performance of product return knowledge management.

Recently, Shao et al. [M. Shao and Y. Chin, A privacy-preserving dynamic id-based remote user authentication scheme with access control for multi-server environment, IEICE Transactions on Information and Systems, vol.E95-D, no.1, pp.161–168, 2012] proposed a dynamic ID-based remote user authentication scheme with access control for multi-server environments. They claimed that their scheme could withstand various attacks and provide anonymity. However, in this letter, we will point out that Shao et al.'s scheme has practical pitfalls and is not feasible for real-life implementation. We identify that their scheme is vulnerable to two kinds of attacks and cannot provide anonymity.

Many learning machines such as normal mixtures and layered neural networks are not regular but singular statistical models, because the map from a parameter to a probability distribution is not one-to-one. The conventional statistical asymptotic theory can not be applied to such learning machines because the likelihood function can not be approximated by any normal distribution. Recently, new statistical theory has been established based on algebraic geometry and it was clarified that the generalization and training errors are determined by two birational invariants, the real log canonical threshold and the singular fluctuation. However, their concrete values are left unknown. In the present paper, we propose a new concept, a quasi-regular case in statistical learning theory. A quasi-regular case is not a regular case but a singular case, however, it has the same property as a regular case. In fact, we prove that, in a quasi-regular case, two birational invariants are equal to each other, resulting that the symmetry of the generalization and training errors holds. Moreover, the concrete values of two birational invariants are explicitly obtained, hence the quasi-regular case is useful to study statistical learning theory.

In this paper, we propose an adaptive chunk scheduling for mesh-based peer-to-peer live streaming system, a hybrid class of push and pull chunk delivery approach. The proposed rule-based push-pull scheduler simultaneously pull video chunk from lower latency peers to fill up missing chunks and push video chunk adaptively for rapid chunk delivery. We performed comparative simulation study against rarest first push-pull and status-wise push-pull to prove the efficiency of our proposed algorithm. Mesh-push is made possible by effectively exploiting the information through buffer map exchange. The findings of performance evaluation have suggested a better video continuity and achieved lower source to end delay.

Finding rare cases with medical data is important when hospitals or research institutes want to identify rare diseases. To extract meaningful information from a large amount of sensitive medical data, privacy-preserving data mining techniques can be used. A privacy-preserving t-repetition protocol can be used to find rare cases with distributed medical data. A privacy-preserving t-repetition protocol is to find elements which exactly t parties out of n parties have in common in their datasets without revealing their private datasets. A privacy-preserving t-repetition protocol can be used to find not only common cases with a high t but also rare cases with a low t. In 2011, Chun et al. suggested the generic set operation protocol which can be used to find t-repeated elements. In the paper, we first show that the Chun et al.'s protocol becomes infeasible for calculating t-repeated elements if the number of users is getting bigger. That is, the computational and communicational complexities of the Chun et al.'s protocol in calculating t-repeated elements grow exponentially as the number of users grows. Then, we suggest a polynomial-time protocol with respect to the number of users, which calculates t-repeated elements between users.

In this paper, a complete SSTA scheme is proposed to calculate the output waveform of a logic cell on any random selected point in the process variational space, or the mean value and variance of the output signal with very high accuracy and acceptable CPU cost. At first, Miller capacitances between the input nodes and internal nodes of a logic cell are introduced to construct the improved MCSM model so as to improve the modeling accuracy. Secondly, the stochastic collocation method jointed with the Modified Nested Sparse Grid technique is adopted for SSTA procedure to avoid the exponential increase of the collocation points number caused by tensor product. Thirdly, a Nominal waveform based Fast Simulation Method is developed to speedup the simulation on each collocation point. At last, Automatic Waveform Construction Technique is developed to construct the output waveform with the approximation points as little as possible to decrease the computational cost while guaranteeing high accuracy. Numerical results are also given to demonstrate the efficiency of the proposed algorithm.

A reliable detection of atrial fibrillation (AF) in Electrocardiogram (ECG) monitoring systems is significant for early treatment and health risk reduction. Various ECG mining and analysis studies have addressed a wide variety of clinical and technical issues. However, there is still room for improvement mostly in two areas. First, the morphological descriptors not only between different patients or patient clusters but also within the same patient are potentially changing. As a result, the model constructed using an old training data no longer needs to be adjusted in order to identify new concepts. Second, the number and types of ECG parameters necessary for detecting AF arrhythmia with high quality encounter a massive number of challenges in relation to computational effort and time consumption. We proposed a mixture technique that caters to these limitations. It includes an active learning method in conjunction with an ECG parameter customization technique to achieve a better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed a sensitivity of 95.2%, a specificity of 99.6%, and an overall accuracy of 99.2%.

A fractional-N phase-locked loop (PLL) is designed for the DigRF interface. The digital part of the PLL mainly consists of a dual-mode phase frequency detector (PFD), a digital counter, and a digital delta-sigma modulator (DSM). The PFD can operate on either 52 MHz or 26 MHz reference frequencies, depending on its use of only the rising edge or both the rising and the falling edges of the reference clock. The interface between the counter and the DSM is designed to give enough timing margin in terms of the signal round-trip delay. The circuitry is implemented using a 90-nm CMOS process technology with a 1.2-V supply, draining 1 mA.

In this paper, using the Linear Time Variant (LTV) phase noise model and considering higher order harmonics generated by the oscillator output signal, a more general formula for transformation of the excess phase to the output signal is presented. Despite the basic LTV model which assumes that the total carrier power is within the fundamental harmonic, in the proposed model, the total carrier power is assumed to be distributed among all output harmonics. For the first harmonic, the developed expressions reduce to the basic LTV formulas. Simulation and experimental results are used to ensure the validity of the model.

In recent years, the demand for low-power design has remained undiminished. In this paper, a pseudo power gating (SPG) structure using a normal logic cell is proposed to extend the power gating to an ultrafine grained region at the gate level. In the proposed method, the controlling value of a logic element is used to control the switching activity of modules computing other inputs of the element. For each element, there exists a submodule controlled by an input to the element. Power reduction is maximized by controlling the order of the submodule selection. A basic algorithm and a switching activity first algorithm have been developed to optimize the power. In this application, a steady maximum depth constraint is added to prevent the depth increase caused by the insertion of the control signal. In this work, various factors affecting the power consumption of library level circuits with the SPG are determined. In such factors, the occurrence of glitches increases the power consumption and a method to reduce the occurrence of glitches is proposed by considering the parity of inverters. The proposed SPG method was evaluated through the simulation of the netlist extracted from the layout using the VDEC Rohm 0.18 µm process. Experiments on ISCAS'85 benchmarks show that the reduction in total power consumption achieved is 13% on average with a 2.5% circuit delay degradation. Finally, the effectiveness of the proposed method under different primary input statistics is considered.

Scan-based side channel attack on hardware implementations of cryptographic algorithms has shown its great security threat. Unlike existing scan-based attacks, in our work we observed that instead of the secret-related-registers, some non-secret registers also carry the potential of being misused to help a hacker to retrieve secret keys. In this paper, we first present a scan-based side channel attack method on AES by making use of the round counter registers, which are not paid attention to in previous works, to show the potential security threat in designs with scan chains. And then we discussed the issues of secure DFT requirements and proposed a secure scan scheme to preserve all the advantages and simplicities of traditional scan test, while significantly improve the security with ignorable design overhead, for crypto hardware implementations.

In this paper, we propose a method for designing genetically optimized Linguistic Models (LM) with the aid of fuzzy granulation. The fundamental idea of LM introduced by Pedrycz is followed and their design framework based on Genetic Algorithm (GA) is enhanced. A LM is designed by the use of information granulation realized via Context-based Fuzzy C-Means (CFCM) clustering. This clustering technique builds information granules represented as a fuzzy set. However, it is difficult to optimize the number of linguistic contexts, the number of clusters generated by each context, and the weighting exponent. Thus, we perform simultaneous optimization of design parameters linking information granules in the input and output spaces based on GA. Experiments on the coagulant dosing process in a water purification plant reveal that the proposed method shows better performance than the previous works and LM itself.